MultiQC Report

General Statistics

Showing ¹⁸⁶/₁₈₆ rows and ¹⁹/₃₉ columns.

Sample Name	% rRNA	dupInt	5'-3' bias	M Aligned	Exonic	Intronic	Intergenic	Overlapping Exon	Proper Pairs	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Mean insert	Reads	Reads mapped	% Reads mapped	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Dups	GC	Avg len	Median len	Failed	Seqs	Trimmed bases	Dups	GC	Avg len	Median len	Failed	Seqs
brain_GLAD_164	0.8	0.0	1.21	20.8M	34.5M	2.7M	0.7M	1.2M	85.3%	0.21%	3.8M	41.7M	100.0%	100.0%	0.2%	41.7M	1366.3bp	45.5M	45.5M	100.0%	24.3M	20.8M	85.6%	19.4M	79.7%	1.4M
brain_GLAD_164_1																											56.3%	49.0%	150bp	150bp	27%	24.4M	1.8%	56.3%	49.0%	147bp	150bp	27%	24.3M
brain_GLAD_164_2																											55.1%	49.0%	150bp	150bp	27%	24.4M	1.9%	55.2%	49.0%	147bp	150bp	27%	24.3M
brain_II2_403	0.5	0.0	1.26	28.0M	45.4M	4.5M	1.1M	1.9M	85.0%	0.18%	5.1M	56.1M	100.0%	99.9%	0.2%	56.1M	1308.7bp	61.3M	61.3M	100.0%	28.9M	28.1M	97.2%	26.1M	90.2%	2.0M
brain_II2_403_1																											50.9%	47.0%	150bp	150bp	18%	28.9M	0.9%	50.9%	48.0%	149bp	150bp	18%	28.9M
brain_II2_403_2																											50.5%	48.0%	150bp	150bp	27%	28.9M	1.0%	50.6%	48.0%	148bp	150bp	27%	28.9M
brain_II2_419	0.5	0.0	1.27	23.7M	38.5M	3.5M	1.0M	1.5M	84.7%	0.19%	4.4M	47.4M	100.0%	99.9%	0.2%	47.4M	1322.7bp	51.9M	51.9M	100.0%	24.3M	23.7M	97.5%	22.0M	90.3%	1.7M
brain_II2_419_1																											48.5%	47.0%	150bp	150bp	0%	24.4M	0.9%	48.5%	47.0%	149bp	150bp	9%	24.3M
brain_II2_419_2																											48.1%	47.0%	150bp	150bp	9%	24.4M	1.0%	48.2%	47.0%	148bp	150bp	18%	24.3M
brain_II2_431	0.6	0.0	1.26	41.3M	67.8M	5.9M	1.6M	2.6M	85.6%	0.16%	7.2M	82.6M	100.0%	100.0%	0.2%	82.6M	1346.1bp	89.8M	89.8M	100.0%	42.4M	41.3M	97.5%	38.5M	90.8%	2.8M
brain_II2_431_1																											55.7%	48.0%	150bp	150bp	9%	42.4M	0.8%	55.7%	48.0%	149bp	150bp	18%	42.4M
brain_II2_431_2																											55.3%	48.0%	150bp	150bp	9%	42.4M	0.9%	55.4%	48.0%	149bp	150bp	18%	42.4M
brain_JJ3_283	0.5	0.0	1.26	26.6M	43.5M	3.4M	1.0M	1.6M	83.9%	0.20%	5.2M	53.2M	100.0%	100.0%	0.2%	53.2M	1192.8bp	58.4M	58.4M	100.0%	27.3M	26.6M	97.4%	24.5M	89.7%	2.1M
brain_JJ3_283_1																											53.4%	47.0%	150bp	150bp	18%	27.4M	0.9%	53.4%	47.0%	148bp	150bp	18%	27.3M
brain_JJ3_283_2																											52.8%	48.0%	150bp	150bp	9%	27.4M	1.1%	52.8%	48.0%	148bp	150bp	18%	27.3M
brain_JJ3_308	0.6	0.0	1.29	25.8M	42.4M	3.6M	1.0M	1.6M	85.2%	0.20%	4.6M	51.7M	100.0%	99.9%	0.2%	51.7M	1317.9bp	56.3M	56.3M	100.0%	26.6M	25.8M	97.2%	24.0M	90.3%	1.8M
brain_JJ3_308_1																											50.9%	48.0%	150bp	150bp	18%	26.6M	0.9%	50.9%	48.0%	148bp	150bp	18%	26.6M
brain_JJ3_308_2																											50.5%	48.0%	150bp	150bp	18%	26.6M	1.1%	50.6%	48.0%	148bp	150bp	18%	26.6M
brain_JJ3_316	0.6	0.0	1.28	26.1M	43.1M	3.3M	0.9M	1.6M	84.8%	0.19%	4.8M	52.2M	100.0%	100.0%	0.2%	52.2M	1319.6bp	57.0M	57.0M	100.0%	26.9M	26.1M	97.1%	24.2M	90.0%	1.9M
brain_JJ3_316_1																											51.4%	48.0%	150bp	150bp	9%	26.9M	0.9%	51.4%	48.0%	148bp	150bp	18%	26.9M
brain_JJ3_316_2																											51.0%	48.0%	150bp	150bp	9%	26.9M	1.0%	51.1%	48.0%	148bp	150bp	18%	26.9M
brain_KK4_403	0.4	0.0	1.25	26.3M	43.2M	3.6M	1.0M	1.7M	84.8%	0.19%	4.9M	52.7M	100.0%	99.9%	0.2%	52.7M	1386.4bp	57.5M	57.5M	100.0%	27.1M	26.3M	97.3%	24.4M	90.2%	1.9M
brain_KK4_403_1																											50.7%	48.0%	150bp	150bp	9%	27.1M	0.8%	50.7%	48.0%	149bp	150bp	18%	27.1M
brain_KK4_403_2																											50.5%	48.0%	150bp	150bp	18%	27.1M	1.0%	50.6%	48.0%	148bp	150bp	27%	27.1M
brain_KK4_410	0.6	0.0	1.28	29.7M	47.0M	5.7M	1.2M	2.1M	84.7%	0.20%	5.5M	59.5M	100.0%	99.9%	0.2%	59.5M	1284.2bp	65.0M	65.0M	100.0%	30.7M	29.8M	96.8%	27.6M	89.7%	2.2M
brain_KK4_410_1																											51.7%	47.0%	150bp	150bp	18%	30.8M	0.8%	51.7%	47.0%	149bp	150bp	18%	30.7M
brain_KK4_410_2																											51.4%	47.0%	150bp	150bp	27%	30.8M	1.0%	51.6%	47.0%	149bp	150bp	27%	30.7M
brain_KK4_428	0.4	0.0	1.29	19.4M	31.1M	3.4M	0.8M	1.2M	84.6%	0.20%	3.6M	38.9M	100.0%	99.9%	0.2%	38.9M	1276.1bp	42.5M	42.5M	100.0%	20.1M	19.5M	97.0%	18.0M	89.7%	1.5M
brain_KK4_428_1																											46.9%	47.0%	150bp	150bp	0%	20.1M	0.9%	47.0%	47.0%	148bp	150bp	9%	20.1M
brain_KK4_428_2																											46.5%	47.0%	150bp	150bp	0%	20.1M	1.1%	46.6%	47.0%	148bp	150bp	9%	20.1M
brain_MAD_261	0.9	0.0	1.21	19.4M	31.3M	3.2M	0.7M	1.2M	85.1%	0.20%	3.6M	38.8M	100.0%	100.0%	0.2%	38.8M	1310.1bp	42.3M	42.3M	100.0%	22.5M	19.4M	86.1%	18.0M	80.1%	1.4M
brain_MAD_261_1																											54.6%	49.0%	150bp	150bp	27%	22.5M	1.6%	54.6%	49.0%	147bp	150bp	27%	22.5M
brain_MAD_261_2																											53.6%	49.0%	150bp	150bp	27%	22.5M	1.8%	53.7%	49.0%	147bp	150bp	27%	22.5M
brain_MAD_271	0.5	0.0	1.17	18.3M	30.6M	2.0M	0.6M	1.0M	84.9%	0.20%	3.4M	36.6M	100.0%	100.0%	0.2%	36.6M	1392.8bp	40.0M	40.0M	100.0%	20.9M	18.3M	87.5%	17.0M	81.3%	1.3M
brain_MAD_271.2	0.3	0.0	1.19	29.8M	48.6M	4.4M	1.1M	2.0M	84.7%	0.19%	5.6M	59.6M	100.0%	99.9%	0.2%	59.6M	1447.2bp	65.2M	65.2M	100.0%	30.6M	29.8M	97.4%	27.7M	90.3%	2.2M
brain_MAD_271.2_1																											50.8%	48.0%	150bp	150bp	9%	30.7M	0.9%	50.8%	48.0%	149bp	150bp	18%	30.6M
brain_MAD_271.2_2																											50.2%	48.0%	150bp	150bp	18%	30.7M	1.0%	50.3%	48.0%	148bp	150bp	27%	30.6M
brain_MAD_271_1																											54.0%	49.0%	150bp	150bp	27%	20.9M	1.5%	54.0%	49.0%	148bp	150bp	27%	20.9M
brain_MAD_271_2																											52.9%	49.0%	150bp	150bp	27%	20.9M	1.7%	53.0%	49.0%	147bp	150bp	27%	20.9M
brain_MAD_274	0.4	0.0	1.22	19.5M	32.9M	2.0M	0.6M	1.1M	86.3%	0.16%	3.2M	39.0M	100.0%	100.0%	0.2%	39.0M	1445.3bp	42.2M	42.2M	100.0%	22.0M	19.5M	88.7%	18.2M	83.0%	1.3M
brain_MAD_274_1																											58.5%	49.0%	150bp	150bp	18%	22.0M	1.8%	58.5%	49.0%	147bp	150bp	18%	22.0M
brain_MAD_274_2																											57.1%	49.0%	150bp	150bp	18%	22.0M	1.9%	57.2%	49.0%	147bp	150bp	18%	22.0M
brain_MAD_404	0.3	0.0	1.21	25.4M	41.8M	3.9M	1.0M	1.8M	86.5%	0.20%	4.2M	50.9M	100.0%	99.9%	0.2%	50.9M	1453.6bp	55.1M	55.1M	100.0%	26.3M	25.5M	96.9%	23.8M	90.7%	1.6M
brain_MAD_404_1																											49.6%	48.0%	150bp	150bp	0%	26.3M	0.9%	49.7%	48.0%	148bp	150bp	9%	26.3M
brain_MAD_404_2																											49.1%	48.0%	150bp	150bp	0%	26.3M	1.1%	49.2%	48.0%	148bp	150bp	9%	26.3M
brain_MAD_413	0.8	0.0	1.18	21.3M	36.0M	2.5M	0.7M	1.3M	87.2%	0.21%	3.4M	42.6M	100.0%	100.0%	0.2%	42.6M	1484.2bp	46.0M	46.0M	100.0%	25.1M	21.3M	85.0%	20.1M	80.0%	1.3M
brain_MAD_413_1																											55.0%	49.0%	150bp	150bp	27%	25.1M	1.4%	55.0%	49.0%	148bp	150bp	27%	25.1M
brain_MAD_413_2																											53.3%	49.0%	150bp	150bp	27%	25.1M	1.6%	53.4%	49.0%	148bp	150bp	27%	25.1M
brain_MAD_416	1.1	0.0	1.20	29.8M	46.6M	6.6M	1.2M	2.1M	85.5%	0.20%	5.2M	59.7M	100.0%	99.9%	0.2%	59.7M	1359.6bp	64.9M	64.9M	100.0%	30.9M	29.9M	96.6%	27.8M	89.9%	2.1M
brain_MAD_416_1																											49.5%	48.0%	150bp	150bp	0%	31.0M	0.8%	49.6%	48.0%	149bp	150bp	9%	30.9M
brain_MAD_416_2																											49.1%	48.0%	150bp	150bp	9%	31.0M	1.0%	49.2%	48.0%	148bp	150bp	18%	30.9M
brain_MAD_419	1.4	0.0	1.21	23.3M	37.9M	3.9M	0.9M	1.5M	86.3%	0.21%	3.9M	46.6M	100.0%	99.9%	0.2%	46.6M	1465.7bp	50.4M	50.4M	100.0%	24.1M	23.3M	96.6%	21.8M	90.4%	1.5M
brain_MAD_419_1																											46.4%	48.0%	150bp	150bp	0%	24.1M	0.9%	46.4%	48.0%	149bp	150bp	9%	24.1M
brain_MAD_419_2																											45.9%	48.0%	150bp	150bp	0%	24.1M	1.1%	46.0%	49.0%	148bp	150bp	9%	24.1M
brain_MAD_461	1.9	0.0	1.20	22.5M	36.0M	4.2M	0.9M	1.5M	85.9%	0.23%	3.8M	44.9M	100.0%	99.9%	0.2%	44.9M	1430.8bp	48.8M	48.8M	100.0%	23.4M	22.5M	96.2%	21.0M	89.7%	1.5M
brain_MAD_461_1																											46.7%	49.0%	150bp	150bp	0%	23.4M	0.9%	46.7%	49.0%	149bp	150bp	9%	23.4M
brain_MAD_461_2																											46.3%	49.0%	150bp	150bp	0%	23.4M	1.0%	46.4%	49.0%	148bp	150bp	9%	23.4M
brain_MAD_462	1.3	0.0	1.24	20.6M	31.9M	4.7M	0.8M	1.4M	85.1%	0.22%	3.7M	41.2M	100.0%	99.9%	0.2%	41.2M	1297.2bp	44.9M	44.9M	100.0%	21.5M	20.6M	96.1%	19.1M	89.2%	1.5M
brain_MAD_462_1																											45.5%	48.0%	150bp	150bp	0%	21.5M	1.0%	45.6%	48.0%	148bp	150bp	9%	21.5M
brain_MAD_462_2																											45.3%	48.0%	150bp	150bp	0%	21.5M	1.2%	45.3%	48.0%	148bp	150bp	9%	21.5M
brain_MAD_463	0.7	0.0	1.19	27.7M	46.0M	3.8M	0.9M	1.7M	86.5%	0.20%	4.6M	55.5M	100.0%	100.0%	0.2%	55.5M	1371.6bp	60.1M	60.1M	100.0%	31.4M	27.7M	88.5%	26.0M	82.9%	1.7M
brain_MAD_463_1																											58.5%	49.0%	150bp	150bp	27%	31.4M	1.5%	58.5%	49.0%	148bp	150bp	27%	31.4M
brain_MAD_463_2																											57.3%	49.0%	150bp	150bp	27%	31.4M	1.7%	57.4%	49.0%	147bp	150bp	27%	31.4M
brain_MAD_562	1.8	0.0	1.23	26.9M	42.7M	5.3M	1.2M	1.7M	85.4%	0.21%	4.7M	53.9M	100.0%	99.9%	0.2%	53.9M	1371.3bp	58.6M	58.6M	100.0%	28.0M	27.0M	96.4%	25.1M	89.6%	1.9M
brain_MAD_562_1																											49.6%	48.0%	150bp	150bp	0%	28.0M	0.8%	49.6%	48.0%	149bp	150bp	9%	28.0M
brain_MAD_562_2																											49.2%	48.0%	150bp	150bp	0%	28.0M	1.0%	49.3%	48.0%	148bp	150bp	9%	28.0M
brain_SAD_326	0.6	0.0	1.19	20.5M	34.9M	2.1M	0.6M	1.3M	87.5%	0.17%	3.1M	41.0M	100.0%	100.0%	0.2%	41.0M	1543.4bp	44.1M	44.1M	100.0%	24.2M	20.5M	84.7%	19.3M	79.7%	1.2M
brain_SAD_326.2	1.4	0.0	1.18	41.7M	66.4M	7.3M	1.6M	2.6M	83.9%	0.20%	8.2M	83.4M	100.0%	99.9%	0.2%	83.4M	1412.0bp	91.7M	91.7M	100.0%	43.2M	41.7M	96.6%	38.5M	89.1%	3.3M
brain_SAD_326.2_1																											54.9%	48.0%	150bp	150bp	9%	43.3M	0.9%	54.9%	48.0%	149bp	150bp	18%	43.2M
brain_SAD_326.2_2																											54.7%	49.0%	150bp	150bp	18%	43.3M	1.0%	54.8%	49.0%	148bp	150bp	27%	43.2M
brain_SAD_326_1																											56.9%	49.0%	150bp	150bp	18%	24.2M	1.1%	56.9%	49.0%	148bp	150bp	18%	24.2M
brain_SAD_326_2																											55.6%	49.0%	150bp	150bp	18%	24.2M	1.2%	55.7%	49.0%	148bp	150bp	18%	24.2M
brain_SAD_532	0.3	0.0	1.23	24.2M	39.7M	3.5M	0.9M	1.7M	85.7%	0.18%	4.2M	48.4M	100.0%	99.9%	0.2%	48.4M	1445.5bp	52.6M	52.6M	100.0%	24.9M	24.2M	97.3%	22.5M	90.7%	1.6M
brain_SAD_532_1																											46.9%	48.0%	150bp	150bp	0%	24.9M	0.9%	47.0%	48.0%	149bp	150bp	9%	24.9M
brain_SAD_532_2																											46.7%	48.0%	150bp	150bp	9%	24.9M	1.1%	46.8%	48.0%	148bp	150bp	18%	24.9M
brain_SAD_535	0.6	0.0	1.18	38.4M	64.5M	4.3M	1.3M	2.2M	86.1%	0.19%	6.6M	76.8M	100.0%	100.0%	0.2%	76.8M	1456.5bp	83.4M	83.4M	100.0%	44.2M	38.4M	86.8%	35.9M	81.3%	2.5M
brain_SAD_535_1																											65.2%	49.0%	150bp	150bp	27%	44.3M	1.5%	65.2%	49.0%	148bp	150bp	27%	44.2M
brain_SAD_535_2																											64.1%	49.0%	150bp	150bp	27%	44.3M	1.7%	64.2%	49.0%	147bp	150bp	27%	44.2M
brain_SAD_587	1.2	0.0	1.20	22.5M	37.4M	3.0M	0.7M	1.3M	86.4%	0.22%	3.8M	45.1M	100.0%	100.0%	0.2%	45.1M	1418.7bp	48.8M	48.8M	100.0%	26.8M	22.5M	84.2%	21.1M	78.8%	1.4M
brain_SAD_587_1																											56.6%	49.0%	150bp	150bp	27%	26.8M	1.5%	56.6%	49.0%	148bp	150bp	27%	26.8M
brain_SAD_587_2																											55.6%	49.0%	150bp	150bp	27%	26.8M	1.6%	55.7%	49.0%	148bp	150bp	27%	26.8M
brain_SAD_626	1.3	0.0	1.22	29.7M	46.6M	6.2M	1.2M	1.9M	84.9%	0.20%	5.5M	59.4M	100.0%	99.9%	0.2%	59.4M	1351.4bp	64.9M	64.9M	100.0%	30.8M	29.7M	96.5%	27.6M	89.6%	2.1M
brain_SAD_626_1																											50.2%	48.0%	150bp	150bp	9%	30.8M	0.9%	50.2%	48.0%	148bp	150bp	18%	30.8M
brain_SAD_626_2																											49.8%	48.0%	150bp	150bp	9%	30.8M	1.1%	49.9%	48.0%	148bp	150bp	18%	30.8M
brain_SAD_628	1.4	0.0	1.20	19.8M	31.8M	3.6M	0.7M	1.4M	86.3%	0.23%	3.3M	39.6M	100.0%	99.9%	0.2%	39.6M	1457.0bp	42.9M	42.9M	100.0%	20.6M	19.8M	96.1%	18.5M	89.9%	1.3M
brain_SAD_628_1																											45.0%	49.0%	150bp	150bp	0%	20.6M	1.0%	45.0%	49.0%	148bp	150bp	9%	20.6M
brain_SAD_628_2																											44.5%	49.0%	150bp	150bp	0%	20.6M	1.2%	44.6%	49.0%	148bp	150bp	9%	20.6M
brain_SAD_672	1.6	0.0	1.19	23.7M	37.5M	4.7M	1.0M	1.6M	85.6%	0.23%	4.1M	47.4M	100.0%	99.9%	0.2%	47.4M	1373.6bp	51.5M	51.5M	100.0%	24.7M	23.7M	96.0%	22.1M	89.4%	1.6M
brain_SAD_672_1																											47.1%	48.0%	150bp	150bp	0%	24.7M	0.9%	47.1%	48.0%	148bp	150bp	9%	24.7M
brain_SAD_672_2																											46.8%	48.0%	150bp	150bp	0%	24.7M	1.1%	46.9%	48.0%	148bp	150bp	9%	24.7M
brain_SAD_678	1.7	0.0	1.23	47.4M	77.0M	7.6M	1.8M	3.1M	85.6%	0.19%	8.3M	94.9M	100.0%	99.9%	0.2%	94.9M	1444.4bp	103.1M	103.1M	100.0%	49.4M	47.5M	96.1%	44.2M	89.5%	3.3M
brain_SAD_678_1																											57.7%	49.0%	150bp	150bp	9%	49.4M	0.8%	57.7%	49.0%	149bp	150bp	18%	49.4M
brain_SAD_678_2																											57.5%	49.0%	150bp	150bp	18%	49.4M	0.9%	57.6%	49.0%	149bp	150bp	27%	49.4M
liver_GLAD_164	0.7	0.0	1.16	17.3M	29.1M	1.6M	0.3M	1.1M	80.7%	0.18%	4.8M	34.6M	100.0%	100.0%	0.7%	34.6M	1806.6bp	39.4M	39.4M	100.0%	23.7M	17.3M	72.9%	15.9M	67.1%	1.4M
liver_GLAD_164_1																											73.1%	48.0%	150bp	150bp	27%	23.7M	1.3%	73.1%	48.0%	148bp	150bp	27%	23.7M
liver_GLAD_164_2																											72.4%	48.0%	150bp	150bp	27%	23.7M	1.4%	72.4%	48.0%	148bp	150bp	27%	23.7M
liver_II2_403	0.4	0.0	1.12	21.5M	33.9M	4.0M	0.6M	1.8M	80.6%	0.21%	5.8M	43.1M	100.0%	100.0%	0.5%	43.1M	1693.6bp	48.9M	48.9M	100.0%	23.3M	21.6M	92.4%	19.7M	84.5%	1.9M
liver_II2_403_1																											64.4%	48.0%	150bp	150bp	9%	23.4M	0.9%	64.5%	49.0%	149bp	150bp	18%	23.3M
liver_II2_403_2																											63.8%	49.0%	150bp	150bp	9%	23.4M	1.0%	63.9%	49.0%	148bp	150bp	18%	23.3M
liver_II2_419	0.4	0.0	1.17	19.4M	31.1M	3.4M	0.5M	1.5M	82.7%	0.21%	4.5M	38.8M	100.0%	100.0%	0.5%	38.8M	1714.0bp	43.3M	43.3M	100.0%	20.3M	19.4M	95.7%	17.9M	88.3%	1.5M
liver_II2_419_1																											62.2%	49.0%	150bp	150bp	9%	20.3M	0.8%	62.2%	49.0%	149bp	150bp	18%	20.3M
liver_II2_419_2																											61.8%	49.0%	150bp	150bp	9%	20.3M	1.0%	61.9%	49.0%	149bp	150bp	18%	20.3M
liver_II2_431	1.0	0.0	1.19	24.5M	39.1M	4.2M	0.5M	1.7M	81.5%	0.23%	6.1M	49.0M	100.0%	100.0%	0.4%	49.0M	1678.9bp	55.1M	55.1M	100.0%	26.4M	24.5M	92.7%	22.5M	85.0%	2.0M
liver_II2_431_1																											67.0%	49.0%	150bp	150bp	9%	26.5M	0.8%	67.1%	49.0%	149bp	150bp	18%	26.4M
liver_II2_431_2																											66.4%	49.0%	150bp	150bp	9%	26.5M	1.0%	66.5%	49.0%	149bp	150bp	18%	26.4M
liver_JJ3_283	1.2	0.0	1.15	24.5M	38.8M	4.1M	0.6M	1.8M	80.1%	0.22%	6.6M	49.0M	100.0%	100.0%	0.5%	49.0M	1754.4bp	55.6M	55.6M	100.0%	27.8M	24.5M	88.2%	22.3M	80.3%	2.2M
liver_JJ3_283_1																											69.1%	49.0%	150bp	150bp	9%	27.8M	0.8%	69.1%	49.0%	149bp	150bp	18%	27.8M
liver_JJ3_283_2																											68.6%	49.0%	150bp	150bp	9%	27.8M	0.9%	68.6%	49.0%	149bp	150bp	18%	27.8M
liver_JJ3_308	1.2	0.0	1.15	24.9M	38.5M	5.0M	0.7M	1.8M	78.7%	0.25%	7.6M	49.9M	100.0%	100.0%	0.6%	49.9M	1713.3bp	57.5M	57.5M	100.0%	28.1M	25.0M	88.8%	22.6M	80.5%	2.3M
liver_JJ3_308_1																											70.0%	49.0%	150bp	150bp	9%	28.1M	0.8%	70.1%	49.0%	149bp	150bp	18%	28.1M
liver_JJ3_308_2																											69.7%	49.0%	150bp	150bp	18%	28.1M	0.9%	69.8%	49.0%	149bp	150bp	27%	28.1M
liver_JJ3_316	0.3	0.0	1.18	26.2M	42.5M	3.7M	0.6M	1.8M	81.1%	0.18%	6.6M	52.4M	100.0%	100.0%	0.4%	52.4M	1725.3bp	59.0M	59.0M	100.0%	28.2M	26.2M	93.0%	23.9M	84.9%	2.3M
liver_JJ3_316_1																											68.5%	48.0%	150bp	150bp	9%	28.2M	0.8%	68.6%	48.0%	149bp	150bp	18%	28.2M
liver_JJ3_316_2																											68.3%	48.0%	150bp	150bp	9%	28.2M	0.9%	68.4%	48.0%	149bp	150bp	18%	28.2M
liver_KK4_403	0.7	0.0	1.14	18.3M	28.7M	3.0M	0.5M	1.3M	77.8%	0.24%	5.7M	36.7M	100.0%	100.0%	0.6%	36.7M	1599.9bp	42.3M	42.3M	100.0%	20.6M	18.3M	89.0%	16.5M	79.9%	1.9M
liver_KK4_403_1																											69.1%	48.0%	150bp	150bp	9%	20.6M	0.9%	69.1%	48.0%	149bp	150bp	18%	20.6M
liver_KK4_403_2																											68.5%	48.0%	150bp	150bp	9%	20.6M	1.0%	68.6%	48.0%	148bp	150bp	18%	20.6M
liver_KK4_410	0.4	0.0	1.13	21.5M	33.1M	4.2M	0.6M	1.7M	77.9%	0.22%	6.6M	43.1M	100.0%	100.0%	0.6%	43.1M	1689.5bp	49.7M	49.7M	100.0%	23.8M	21.6M	90.7%	19.4M	81.6%	2.2M
liver_KK4_410_1																											66.3%	48.0%	150bp	150bp	9%	23.8M	0.8%	66.3%	48.0%	149bp	150bp	18%	23.8M
liver_KK4_410_2																											65.9%	48.0%	150bp	150bp	9%	23.8M	1.0%	66.0%	48.0%	148bp	150bp	18%	23.8M
liver_KK4_428	1.3	0.0	1.14	23.0M	36.4M	4.1M	0.5M	1.7M	80.1%	0.26%	6.4M	45.9M	100.0%	100.0%	0.6%	45.9M	1774.8bp	52.3M	52.3M	100.0%	26.4M	23.0M	87.1%	21.0M	79.5%	2.0M
liver_KK4_428_1																											68.6%	49.0%	150bp	150bp	9%	26.4M	0.8%	68.7%	49.0%	149bp	150bp	18%	26.4M
liver_KK4_428_2																											68.2%	49.0%	150bp	150bp	18%	26.4M	0.9%	68.3%	49.0%	149bp	150bp	27%	26.4M
liver_MAD_261	0.7	0.0	1.15	16.7M	28.0M	1.7M	0.4M	1.1M	81.3%	0.18%	4.5M	33.4M	100.0%	100.0%	0.6%	33.4M	1792.4bp	37.8M	37.8M	100.0%	21.9M	16.7M	76.1%	15.4M	70.1%	1.3M
liver_MAD_261_1																											71.1%	48.0%	150bp	150bp	27%	22.0M	1.4%	71.1%	48.0%	148bp	150bp	27%	21.9M
liver_MAD_261_2																											70.1%	48.0%	150bp	150bp	27%	22.0M	1.5%	70.2%	48.0%	148bp	150bp	27%	21.9M
liver_MAD_271	0.8	0.0	1.16	18.4M	31.0M	1.9M	0.4M	1.3M	83.2%	0.19%	4.2M	36.8M	100.0%	100.0%	0.5%	36.8M	1719.5bp	41.1M	41.1M	100.0%	22.9M	18.4M	80.3%	17.1M	74.6%	1.3M
liver_MAD_271.2	1.6	0.0	1.18	28.1M	43.2M	5.7M	0.9M	2.1M	80.4%	0.25%	7.4M	56.2M	100.0%	99.9%	0.5%	56.2M	1652.1bp	63.5M	63.5M	100.0%	30.9M	28.1M	90.9%	25.6M	82.7%	2.5M
liver_MAD_271.2_1																											68.0%	49.0%	150bp	150bp	9%	31.0M	0.9%	68.1%	49.0%	149bp	150bp	18%	30.9M
liver_MAD_271.2_2																											67.4%	49.0%	150bp	150bp	18%	31.0M	1.0%	67.5%	49.0%	148bp	150bp	27%	30.9M
liver_MAD_271_1																											71.4%	49.0%	150bp	150bp	27%	22.9M	1.4%	71.4%	49.0%	148bp	150bp	27%	22.9M
liver_MAD_271_2																											70.1%	49.0%	150bp	150bp	27%	22.9M	1.6%	70.2%	49.0%	148bp	150bp	27%	22.9M
liver_MAD_274	0.5	0.0	1.16	19.4M	33.0M	2.1M	0.4M	1.3M	85.0%	0.18%	4.0M	38.8M	100.0%	100.0%	0.4%	38.8M	1823.2bp	42.8M	42.8M	100.0%	24.5M	19.4M	79.2%	18.2M	74.2%	1.2M
liver_MAD_274_1																											70.0%	49.0%	150bp	150bp	27%	24.5M	1.4%	70.0%	49.0%	148bp	150bp	27%	24.5M
liver_MAD_274_2																											68.9%	49.0%	150bp	150bp	27%	24.5M	1.5%	68.9%	49.0%	148bp	150bp	27%	24.5M
liver_MAD_404	1.4	0.0	1.14	20.8M	32.0M	4.1M	0.6M	1.4M	79.2%	0.26%	5.9M	41.6M	100.0%	100.0%	0.5%	41.6M	1621.1bp	47.5M	47.5M	100.0%	23.2M	20.8M	89.6%	18.8M	81.0%	2.0M
liver_MAD_404_1																											68.4%	49.0%	150bp	150bp	9%	23.3M	0.9%	68.4%	49.0%	148bp	150bp	18%	23.2M
liver_MAD_404_2																											67.9%	49.0%	150bp	150bp	9%	23.3M	1.1%	68.0%	49.0%	148bp	150bp	18%	23.2M
liver_MAD_413	0.6	0.0	1.16	14.9M	24.0M	2.3M	0.4M	1.0M	80.8%	0.18%	4.0M	29.9M	100.0%	100.0%	0.6%	29.9M	1570.5bp	33.9M	33.9M	100.0%	19.6M	14.9M	76.3%	13.7M	70.0%	1.2M
liver_MAD_413_1																											68.2%	48.0%	150bp	150bp	27%	19.6M	1.9%	68.2%	48.0%	147bp	150bp	27%	19.6M
liver_MAD_413_2																											67.2%	48.0%	150bp	150bp	27%	19.6M	2.0%	67.3%	48.0%	147bp	150bp	27%	19.6M
liver_MAD_416	0.5	0.0	1.12	24.7M	39.5M	4.3M	0.6M	2.1M	82.5%	0.19%	5.7M	49.4M	100.0%	100.0%	0.4%	49.4M	1771.2bp	55.1M	55.1M	100.0%	25.9M	24.7M	95.4%	22.8M	87.8%	2.0M
liver_MAD_416_1																											65.7%	49.0%	150bp	150bp	9%	25.9M	0.8%	65.7%	49.0%	149bp	150bp	18%	25.9M
liver_MAD_416_2																											65.4%	49.0%	150bp	150bp	18%	25.9M	1.0%	65.5%	49.0%	149bp	150bp	27%	25.9M
liver_MAD_419	1.0	0.0	1.21	41.9M	65.3M	6.6M	1.1M	2.9M	76.3%	0.20%	14.1M	83.9M	100.0%	100.0%	0.6%	83.9M	1706.0bp	97.9M	97.9M	100.0%	50.5M	42.0M	83.1%	37.4M	74.1%	4.5M
liver_MAD_419_1																											76.1%	48.0%	150bp	150bp	9%	50.6M	0.7%	76.2%	48.0%	149bp	150bp	18%	50.5M
liver_MAD_419_2																											75.7%	48.0%	150bp	150bp	18%	50.6M	0.8%	75.8%	48.0%	149bp	150bp	27%	50.5M
liver_MAD_461	1.6	0.0	1.18	20.4M	30.6M	4.4M	0.7M	1.6M	76.9%	0.26%	6.7M	40.8M	100.0%	99.9%	0.7%	40.8M	1584.3bp	47.4M	47.4M	100.0%	25.1M	20.4M	81.3%	18.3M	72.8%	2.1M
liver_MAD_461_1																											70.3%	48.0%	150bp	150bp	9%	25.1M	0.9%	70.3%	48.0%	148bp	150bp	18%	25.1M
liver_MAD_461_2																											69.7%	48.0%	150bp	150bp	18%	25.1M	1.1%	69.8%	48.0%	148bp	150bp	27%	25.1M
liver_MAD_462	1.1	0.0	1.15	24.9M	39.1M	4.3M	0.7M	1.9M	80.0%	0.19%	6.7M	49.8M	100.0%	100.0%	0.5%	49.8M	1678.9bp	56.5M	56.5M	100.0%	26.5M	24.9M	94.1%	22.6M	85.4%	2.3M
liver_MAD_462_1																											67.1%	48.0%	150bp	150bp	9%	26.5M	0.8%	67.1%	48.0%	149bp	150bp	18%	26.5M
liver_MAD_462_2																											66.6%	48.0%	150bp	150bp	18%	26.5M	1.0%	66.8%	48.0%	148bp	150bp	27%	26.5M
liver_MAD_463	0.6	0.0	1.17	22.7M	36.6M	3.8M	0.6M	1.8M	83.6%	0.20%	5.0M	45.4M	100.0%	100.0%	0.4%	45.4M	1645.6bp	50.3M	50.3M	100.0%	27.7M	22.7M	81.8%	21.1M	75.9%	1.6M
liver_MAD_463_1																											73.5%	49.0%	150bp	150bp	27%	27.8M	1.4%	73.5%	49.0%	148bp	150bp	27%	27.7M
liver_MAD_463_2																											72.3%	49.0%	150bp	150bp	27%	27.8M	1.5%	72.4%	49.0%	148bp	150bp	27%	27.7M
liver_MAD_562	1.3	0.0	1.16	79.0M	128.2M	11.5M	2.0M	6.1M	81.4%	0.15%	19.8M	158.2M	100.0%	99.9%	0.5%	158.2M	1786.8bp	178.0M	178.0M	100.0%	83.3M	79.1M	95.0%	72.5M	87.1%	6.6M
liver_MAD_562_1																											76.1%	48.0%	150bp	150bp	9%	83.3M	0.7%	76.2%	48.0%	149bp	150bp	18%	83.3M
liver_MAD_562_2																											75.5%	48.0%	150bp	150bp	18%	83.3M	0.8%	75.6%	48.0%	149bp	150bp	27%	83.3M
liver_SAD_326	1.2	0.0	1.20	23.3M	39.3M	2.3M	0.5M	1.6M	81.1%	0.20%	6.5M	46.6M	100.0%	100.0%	0.7%	46.6M	1732.7bp	53.1M	53.1M	100.0%	30.1M	23.3M	77.6%	21.6M	71.7%	1.8M
liver_SAD_326.2	0.6	0.0	1.14	22.0M	34.9M	3.6M	0.6M	1.6M	79.8%	0.20%	6.2M	43.9M	100.0%	100.0%	0.6%	43.9M	1687.5bp	50.1M	50.1M	100.0%	25.1M	22.0M	87.4%	20.0M	79.6%	2.0M
liver_SAD_326.2_1																											67.7%	48.0%	150bp	150bp	9%	25.2M	0.7%	67.7%	48.0%	149bp	150bp	18%	25.1M
liver_SAD_326.2_2																											67.2%	48.0%	150bp	150bp	18%	25.2M	0.8%	67.3%	48.0%	149bp	150bp	27%	25.1M
liver_SAD_326_1																											77.2%	49.0%	150bp	150bp	27%	30.1M	1.6%	77.2%	49.0%	147bp	150bp	27%	30.1M
liver_SAD_326_2																											75.9%	49.0%	150bp	150bp	27%	30.1M	1.7%	76.0%	49.0%	147bp	150bp	27%	30.1M
liver_SAD_532	0.6	0.0	1.13	24.3M	38.9M	4.0M	0.6M	1.9M	80.8%	0.19%	6.5M	48.6M	100.0%	100.0%	0.5%	48.6M	1706.0bp	55.1M	55.1M	100.0%	27.4M	24.3M	88.8%	22.3M	81.4%	2.0M
liver_SAD_532_1																											66.4%	48.0%	150bp	150bp	9%	27.4M	0.9%	66.5%	48.0%	149bp	150bp	18%	27.4M
liver_SAD_532_2																											65.9%	48.0%	150bp	150bp	18%	27.4M	1.0%	66.0%	48.0%	148bp	150bp	27%	27.4M
liver_SAD_535	0.5	0.0	1.15	20.8M	34.7M	2.4M	0.5M	1.5M	83.3%	0.19%	4.6M	41.6M	100.0%	100.0%	0.5%	41.6M	1726.2bp	46.3M	46.3M	100.0%	26.1M	20.8M	79.8%	19.3M	73.9%	1.5M
liver_SAD_535_1																											72.7%	49.0%	150bp	150bp	27%	26.1M	1.4%	72.7%	49.0%	148bp	150bp	27%	26.1M
liver_SAD_535_2																											71.4%	49.0%	150bp	150bp	27%	26.1M	1.5%	71.4%	49.0%	148bp	150bp	27%	26.1M
liver_SAD_587	0.6	0.0	1.16	17.6M	28.1M	2.8M	0.5M	1.3M	79.6%	0.18%	5.2M	35.2M	100.0%	100.0%	0.7%	35.2M	1654.8bp	40.4M	40.4M	100.0%	23.6M	17.6M	74.6%	16.1M	68.2%	1.5M
liver_SAD_587_1																											71.0%	48.0%	150bp	150bp	27%	23.6M	1.7%	71.1%	48.0%	147bp	150bp	27%	23.6M
liver_SAD_587_2																											70.2%	48.0%	150bp	150bp	27%	23.6M	1.9%	70.2%	48.0%	147bp	150bp	27%	23.6M
liver_SAD_626	1.1	0.0	1.16	70.0M	111.5M	10.1M	1.8M	5.0M	78.9%	0.15%	20.2M	140.0M	100.0%	100.0%	0.5%	140.0M	1732.6bp	160.2M	160.2M	100.0%	75.2M	70.0M	93.1%	63.2M	84.1%	6.8M
liver_SAD_626_1																											76.1%	48.0%	150bp	150bp	9%	75.3M	0.7%	76.1%	48.0%	149bp	150bp	18%	75.2M
liver_SAD_626_2																											75.6%	48.0%	150bp	150bp	18%	75.3M	0.8%	75.7%	48.0%	149bp	150bp	27%	75.2M
liver_SAD_628	1.0	0.0	1.18	26.9M	41.5M	5.0M	0.7M	2.1M	78.0%	0.21%	8.1M	53.9M	100.0%	99.9%	0.5%	53.9M	1640.0bp	62.0M	62.0M	100.0%	30.4M	27.0M	88.6%	24.2M	79.5%	2.8M
liver_SAD_628_1																											69.9%	48.0%	150bp	150bp	9%	30.5M	0.9%	69.9%	48.0%	149bp	150bp	18%	30.4M
liver_SAD_628_2																											69.7%	48.0%	150bp	150bp	18%	30.5M	1.0%	69.8%	48.0%	148bp	150bp	27%	30.4M
liver_SAD_672	0.9	0.0	1.15	19.5M	29.4M	3.8M	0.5M	1.5M	75.3%	0.22%	6.8M	39.1M	100.0%	99.9%	0.6%	39.1M	1617.6bp	45.9M	45.9M	100.0%	24.5M	19.5M	79.7%	17.3M	70.5%	2.3M
liver_SAD_672_1																											70.9%	47.0%	150bp	150bp	9%	24.6M	0.9%	70.9%	47.0%	149bp	150bp	18%	24.5M
liver_SAD_672_2																											70.6%	47.0%	150bp	150bp	9%	24.6M	1.0%	70.7%	47.0%	148bp	150bp	18%	24.5M
liver_SAD_678	1.0	0.0	1.16	22.6M	36.2M	3.7M	0.5M	1.8M	81.6%	0.20%	5.5M	45.2M	100.0%	100.0%	0.5%	45.2M	1710.8bp	50.7M	50.7M	100.0%	23.9M	22.6M	94.5%	20.7M	86.5%	1.9M
liver_SAD_678_1																											67.0%	49.0%	150bp	150bp	9%	24.0M	0.9%	67.0%	49.0%	149bp	150bp	18%	23.9M
liver_SAD_678_2																											66.7%	49.0%	150bp	150bp	9%	24.0M	1.0%	66.8%	49.0%	148bp	150bp	18%	23.9M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	Custom content: Biotype Counts	% rRNA	% reads overlapping rRNA features	`custom_content_biotype_counts-percent_rRNA`
\|\|	Custom content: DupRadar	dupInt	Intercept value from DupRadar	`custom_content_dupradar-dupRadar_intercept`
\|\|	QualiMap: RNASeq	5'-3' bias	5'-3' bias	`qualimap_rnaseq-5_3_bias`
\|\|	QualiMap: RNASeq	M Aligned	Reads Aligned (millions)	`qualimap_rnaseq-reads_aligned`	read_count
\|\|	QualiMap: RNASeq	Exonic	Reads aligned to exonic regions	`qualimap_rnaseq-reads_aligned_exonic`	read_count
\|\|	QualiMap: RNASeq	Intronic	Reads aligned to intronic regions	`qualimap_rnaseq-reads_aligned_intronic`	read_count
\|\|	QualiMap: RNASeq	Intergenic	Reads aligned to intergenic regions	`qualimap_rnaseq-reads_aligned_intergenic`	read_count
\|\|	QualiMap: RNASeq	Overlapping Exon	Reads aligned to overlapping exon regions	`qualimap_rnaseq-reads_aligned_overlapping_exon`	read_count
\|\|	RSeQC: Bam Stat	Proper Pairs	% Reads mapped in proper pairs	`rseqc_bam_stat-proper_pairs_percent`
\|\|	Samtools: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_stats-error_rate`
\|\|	Samtools: stats	Non-primary	Non-primary alignments (millions)	`samtools_stats-non_primary_alignments`	read_count
\|\|	Samtools: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_stats-reads_mapped`	read_count
\|\|	Samtools: stats	% Mapped	% Mapped reads	`samtools_stats-reads_mapped_percent`
\|\|	Samtools: stats	% Proper pairs	% Properly paired reads	`samtools_stats-reads_properly_paired_percent`
\|\|	Samtools: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_stats-reads_MQ0_percent`
\|\|	Samtools: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_stats-raw_total_sequences`	read_count
\|\|	Samtools: stats	Mean insert	Average insert size	`samtools_stats-insert_size_average`
\|\|	Samtools: flagstat	Reads	Total reads in the bam file (millions)	`samtools_flagstat-flagstat_total`	read_count
\|\|	Samtools: flagstat	Reads mapped	Reads mapped in the bam file (millions)	`samtools_flagstat-mapped_passed`	read_count
\|\|	Samtools: flagstat	% Reads mapped	% Reads mapped in the bam file	`samtools_flagstat-mapped_passed_pct`
\|\|	STAR	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	STAR	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	STAR	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	STAR	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	STAR	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	STAR	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	FastQC (raw)	Dups	% duplicate reads	`fastqc_raw-percent_duplicates`
\|\|	FastQC (raw)	GC	Average % GC content	`fastqc_raw-percent_gc`
\|\|	FastQC (raw)	Avg len	Average read length	`fastqc_raw-avg_sequence_length`
\|\|	FastQC (raw)	Median len	Median read length	`fastqc_raw-median_sequence_length`
\|\|	FastQC (raw)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw-percent_fails`
\|\|	FastQC (raw)	Seqs	Total sequences (millions)	`fastqc_raw-total_sequences`	read_count
\|\|	Cutadapt	Trimmed bases	% total base pairs trimmed	`cutadapt-percent_trimmed`
\|\|	FastQC (trimmed)	Dups	% duplicate reads	`fastqc_trimmed-percent_duplicates`
\|\|	FastQC (trimmed)	GC	Average % GC content	`fastqc_trimmed-percent_gc`
\|\|	FastQC (trimmed)	Avg len	Average read length	`fastqc_trimmed-avg_sequence_length`
\|\|	FastQC (trimmed)	Median len	Median read length	`fastqc_trimmed-median_sequence_length`
\|\|	FastQC (trimmed)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_trimmed-percent_fails`
\|\|	FastQC (trimmed)	Seqs	Total sequences (millions)	`fastqc_trimmed-total_sequences`	read_count

Sample status checks

Reports on sample strandedness status, and any failures in trimming or mapping.

Strandedness Checks

Showing ¹²⁴/₁₂₄ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
brain_GLAD_164	pass	RSeQC	auto	unstranded	39.7	37.9	22.4
brain_GLAD_164	pass	Salmon	auto	unstranded	50.6	49.4	0.0
brain_II2_403	pass	RSeQC	auto	unstranded	37.7	37.9	24.4
brain_II2_403	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_II2_419	pass	RSeQC	auto	unstranded	39.3	39.7	21.0
brain_II2_419	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_II2_431	pass	RSeQC	auto	unstranded	41.0	41.4	17.7
brain_II2_431	pass	Salmon	auto	unstranded	49.5	50.5	0.0
brain_JJ3_283	pass	RSeQC	auto	unstranded	38.0	38.6	23.4
brain_JJ3_283	pass	Salmon	auto	unstranded	49.3	50.7	0.0
brain_JJ3_308	pass	RSeQC	auto	unstranded	38.0	38.4	23.5
brain_JJ3_308	pass	Salmon	auto	unstranded	49.3	50.7	0.0
brain_JJ3_316	pass	RSeQC	auto	unstranded	38.6	39.4	22.0
brain_JJ3_316	pass	Salmon	auto	unstranded	49.5	50.5	0.0
brain_KK4_403	pass	RSeQC	auto	unstranded	38.1	38.4	23.5
brain_KK4_403	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_KK4_410	pass	RSeQC	auto	unstranded	37.0	37.8	25.1
brain_KK4_410	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_KK4_428	pass	RSeQC	auto	unstranded	39.9	40.0	20.1
brain_KK4_428	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_MAD_261	pass	RSeQC	auto	unstranded	40.2	39.5	20.3
brain_MAD_261	pass	Salmon	auto	unstranded	50.6	49.4	0.0
brain_MAD_271	pass	RSeQC	auto	unstranded	40.3	39.1	20.6
brain_MAD_271	pass	Salmon	auto	unstranded	50.5	49.5	0.0
brain_MAD_271.2	pass	RSeQC	auto	unstranded	38.1	38.4	23.5
brain_MAD_271.2	pass	Salmon	auto	unstranded	49.5	50.5	0.0
brain_MAD_274	pass	RSeQC	auto	unstranded	39.9	38.5	21.6
brain_MAD_274	pass	Salmon	auto	unstranded	50.7	49.3	0.0
brain_MAD_404	pass	RSeQC	auto	unstranded	37.8	38.2	24.0
brain_MAD_404	pass	Salmon	auto	unstranded	49.6	50.4	0.0
brain_MAD_413	pass	RSeQC	auto	unstranded	40.3	37.9	21.8
brain_MAD_413	pass	Salmon	auto	unstranded	50.5	49.5	0.0
brain_MAD_416	pass	RSeQC	auto	unstranded	37.7	37.8	24.5
brain_MAD_416	pass	Salmon	auto	unstranded	49.5	50.5	0.0
brain_MAD_419	pass	RSeQC	auto	unstranded	38.7	39.4	21.8
brain_MAD_419	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_MAD_461	pass	RSeQC	auto	unstranded	39.1	39.0	22.0
brain_MAD_461	pass	Salmon	auto	unstranded	49.6	50.4	0.0
brain_MAD_462	pass	RSeQC	auto	unstranded	38.8	39.5	21.7
brain_MAD_462	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_MAD_463	pass	RSeQC	auto	unstranded	38.4	37.2	24.4
brain_MAD_463	pass	Salmon	auto	unstranded	50.7	49.3	0.0
brain_MAD_562	pass	RSeQC	auto	unstranded	38.1	38.9	23.0
brain_MAD_562	pass	Salmon	auto	unstranded	49.5	50.5	0.0
brain_SAD_326	pass	RSeQC	auto	unstranded	40.2	39.0	20.8
brain_SAD_326	pass	Salmon	auto	unstranded	50.4	49.6	0.0
brain_SAD_326.2	pass	RSeQC	auto	unstranded	41.3	41.7	16.9
brain_SAD_326.2	pass	Salmon	auto	unstranded	49.6	50.4	0.0
brain_SAD_532	pass	RSeQC	auto	unstranded	38.5	38.7	22.8
brain_SAD_532	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_SAD_535	pass	RSeQC	auto	unstranded	37.9	36.2	25.9
brain_SAD_535	pass	Salmon	auto	unstranded	50.6	49.4	0.0
brain_SAD_587	pass	RSeQC	auto	unstranded	39.0	38.0	23.0
brain_SAD_587	pass	Salmon	auto	unstranded	50.5	49.5	0.0
brain_SAD_626	pass	RSeQC	auto	unstranded	37.3	37.8	24.9
brain_SAD_626	pass	Salmon	auto	unstranded	49.3	50.7	0.0
brain_SAD_628	pass	RSeQC	auto	unstranded	39.9	40.5	19.6
brain_SAD_628	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_SAD_672	pass	RSeQC	auto	unstranded	39.3	40.1	20.7
brain_SAD_672	pass	Salmon	auto	unstranded	49.4	50.6	0.0
brain_SAD_678	pass	RSeQC	auto	unstranded	39.5	39.6	20.9
brain_SAD_678	pass	Salmon	auto	unstranded	49.5	50.5	0.0
liver_GLAD_164	pass	RSeQC	auto	unstranded	30.8	30.0	39.2
liver_GLAD_164	pass	Salmon	auto	unstranded	50.6	49.4	0.0
liver_II2_403	pass	RSeQC	auto	unstranded	24.0	24.7	51.3
liver_II2_403	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_II2_419	pass	RSeQC	auto	unstranded	29.6	29.7	40.7
liver_II2_419	pass	Salmon	auto	unstranded	49.5	50.5	0.0
liver_II2_431	pass	RSeQC	auto	unstranded	23.1	23.3	53.6
liver_II2_431	pass	Salmon	auto	unstranded	49.4	50.6	0.0
liver_JJ3_283	pass	RSeQC	auto	unstranded	21.1	21.2	57.7
liver_JJ3_283	pass	Salmon	auto	unstranded	49.4	50.6	0.0
liver_JJ3_308	pass	RSeQC	auto	unstranded	23.8	24.0	52.2
liver_JJ3_308	pass	Salmon	auto	unstranded	49.2	50.8	0.0
liver_JJ3_316	pass	RSeQC	auto	unstranded	25.2	24.9	49.9
liver_JJ3_316	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_KK4_403	pass	RSeQC	auto	unstranded	34.6	35.1	30.3
liver_KK4_403	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_KK4_410	pass	RSeQC	auto	unstranded	21.7	22.3	55.9
liver_KK4_410	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_KK4_428	pass	RSeQC	auto	unstranded	21.9	21.9	56.1
liver_KK4_428	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_MAD_261	pass	RSeQC	auto	unstranded	38.3	36.8	24.9
liver_MAD_261	pass	Salmon	auto	unstranded	50.5	49.5	0.0
liver_MAD_271	pass	RSeQC	auto	unstranded	33.6	32.2	34.2
liver_MAD_271	pass	Salmon	auto	unstranded	50.7	49.3	0.0
liver_MAD_271.2	pass	RSeQC	auto	unstranded	25.9	26.3	47.8
liver_MAD_271.2	pass	Salmon	auto	unstranded	49.1	50.9	0.0
liver_MAD_274	pass	RSeQC	auto	unstranded	36.6	35.6	27.7
liver_MAD_274	pass	Salmon	auto	unstranded	50.7	49.3	0.0
liver_MAD_404	pass	RSeQC	auto	unstranded	28.8	28.5	42.7
liver_MAD_404	pass	Salmon	auto	unstranded	49.4	50.6	0.0
liver_MAD_413	pass	RSeQC	auto	unstranded	39.3	37.9	22.8
liver_MAD_413	pass	Salmon	auto	unstranded	50.7	49.3	0.0
liver_MAD_416	pass	RSeQC	auto	unstranded	31.2	31.3	37.4
liver_MAD_416	pass	Salmon	auto	unstranded	49.5	50.5	0.0
liver_MAD_419	pass	RSeQC	auto	unstranded	39.1	39.7	21.2
liver_MAD_419	pass	Salmon	auto	unstranded	49.2	50.8	0.0
liver_MAD_461	pass	RSeQC	auto	unstranded	37.9	38.3	23.8
liver_MAD_461	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_MAD_462	pass	RSeQC	auto	unstranded	31.0	31.1	38.0
liver_MAD_462	pass	Salmon	auto	unstranded	49.5	50.5	0.0
liver_MAD_463	pass	RSeQC	auto	unstranded	31.0	29.3	39.7
liver_MAD_463	pass	Salmon	auto	unstranded	50.7	49.3	0.0
liver_MAD_562	pass	RSeQC	auto	unstranded	45.4	45.4	9.2
liver_MAD_562	pass	Salmon	auto	unstranded	49.6	50.4	0.0
liver_SAD_326	pass	RSeQC	auto	unstranded	32.7	31.8	35.5
liver_SAD_326	pass	Salmon	auto	unstranded	50.8	49.2	0.0
liver_SAD_326.2	pass	RSeQC	auto	unstranded	33.3	33.5	33.1
liver_SAD_326.2	pass	Salmon	auto	unstranded	49.6	50.4	0.0
liver_SAD_532	pass	RSeQC	auto	unstranded	30.3	30.3	39.4
liver_SAD_532	pass	Salmon	auto	unstranded	49.6	50.4	0.0
liver_SAD_535	pass	RSeQC	auto	unstranded	37.2	36.0	26.8
liver_SAD_535	pass	Salmon	auto	unstranded	50.6	49.4	0.0
liver_SAD_587	pass	RSeQC	auto	unstranded	37.8	36.7	25.5
liver_SAD_587	pass	Salmon	auto	unstranded	50.5	49.5	0.0
liver_SAD_626	pass	RSeQC	auto	unstranded	44.3	44.7	11.0
liver_SAD_626	pass	Salmon	auto	unstranded	49.8	50.2	0.0
liver_SAD_628	pass	RSeQC	auto	unstranded	25.9	26.2	47.9
liver_SAD_628	pass	Salmon	auto	unstranded	49.3	50.7	0.0
liver_SAD_672	pass	RSeQC	auto	unstranded	34.3	34.9	30.8
liver_SAD_672	pass	Salmon	auto	unstranded	49.1	50.9	0.0
liver_SAD_678	pass	RSeQC	auto	unstranded	33.2	33.8	33.0
liver_SAD_678	pass	Salmon	auto	unstranded	49.5	50.5	0.0

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fail_strand_check_table_table

Sort	Column	Description	ID
\|\|	Status	Pass if the strandedness inferred by RSeQC matches that provided by the user or predicted by Salmon. Fail otherwise.	`samples_strandedness_check-Status`
\|\|	Strand inference method	Tool used to detect strandedness	`samples_strandedness_check-Strand_inference_method`
\|\|	Provided strandedness	User-provided strandedness	`samples_strandedness_check-Provided_strandedness`
\|\|	Inferred strandedness	Interred strandedness	`samples_strandedness_check-Inferred_strandedness`
\|\|	Sense (%)	Sense (%)	`samples_strandedness_check-Sense`
\|\|	Antisense (%)	Antisense (%)	`samples_strandedness_check-Antisense`
\|\|	Unstranded (%)	Unstranded (%)	`samples_strandedness_check-Unstranded`

FastQC (raw)

This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

All samples have sequences of a single length (150bp)

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ¹³/₁₃ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	38	1271332	0.0354%
GAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTACCAAAATGAA	13	558499	0.0156%
CAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTACCAAAAT	6	286743	0.0080%
AAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTACCAAAATG	6	270971	0.0076%
GTCTGTCTTAGGTATAGTAAATGTATTGAATCCATCATACGTCACAGAAT	6	240078	0.0067%
CGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTAC	4	173125	0.0048%
CCACGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCC	4	118576	0.0033%
CACGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCT	3	109750	0.0031%
CTTTAATGTAGAAAAGATTAATGGGGAATGGCATACTATTATCCTGGCCT	3	86982	0.0024%
GGAGTCTAACGCGTGCGCGAGTCAGGGGCTCGTCCGAAAGCCGCCGTGGC	2	57662	0.0016%
GCTAGTTCTACGGGAAGGAACTTTAATGTAGAAAAGATTAATGGGGAATG	2	55180	0.0015%
CTGGAATTGGACTTCTCAGTGAGACAGGATGGAATGCAGATCACAGAATT	1	26609	0.0007%
GCCACGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCC	1	25187	0.0007%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_raw_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_raw-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_raw-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_raw-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

Cutadapt

Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

Created with MultiQC

Trimmed Sequence Lengths (3')

This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

See the cutadapt documentation for more information on how these numbers are generated.

Created with MultiQC

FastQC (trimmed)

This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ¹³/₁₃ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	35	1149865	0.0321%
GAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTACCAAAATGAA	13	558379	0.0156%
CAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTACCAAAAT	6	286676	0.0080%
AAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTACCAAAATG	6	270918	0.0076%
GTCTGTCTTAGGTATAGTAAATGTATTGAATCCATCATACGTCACAGAAT	6	240004	0.0067%
CGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCTAC	4	173093	0.0048%
CCACGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCC	4	118538	0.0033%
CACGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCCCT	3	109742	0.0031%
CTTTAATGTAGAAAAGATTAATGGGGAATGGCATACTATTATCCTGGCCT	3	86955	0.0024%
GGAGTCTAACGCGTGCGCGAGTCAGGGGCTCGTCCGAAAGCCGCCGTGGC	2	57630	0.0016%
GCTAGTTCTACGGGAAGGAACTTTAATGTAGAAAAGATTAATGGGGAATG	2	55171	0.0015%
CTGGAATTGGACTTCTCAGTGAGACAGGATGGAATGCAGATCACAGAATT	1	26591	0.0007%
GCCACGATCACAAGAAAGACGTGGTCCTGACAGACAGACAATCCTATTCC	1	25177	0.0007%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_trimmed_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_trimmed-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_trimmed-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_trimmed-total_percent`

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

DupRadar

DupRadar provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.URL: bioconductor.org/packages/release/bioc/html/dupRadar.html

Created with MultiQC

QualiMap

Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

Genomic origin of reads

Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

Created with MultiQC

Gene Coverage Profile

Mean distribution of coverage depth across the length of all mapped transcripts.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

Created with MultiQC

RSeQC

Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

Read Distribution

Read Distribution calculates how mapped reads are distributed over genome features.

Created with MultiQC

Inner Distance

Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

Created with MultiQC

Read Duplication

read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

Created with MultiQC

Junction Annotation

Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

Created with MultiQC

Junction Saturation

Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

Click a line to see the data side by side (as in the original RSeQC plot).

Created with MultiQC

Infer experiment

Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

Created with MultiQC

Bam Stat

All numbers reported in millions.

Created with MultiQC

Showing ⁶²/₆₂ rows and ¹⁴/₁₄ columns.

Sample Name	Total records	QC failed	Duplicates	Non primary hit	Unmapped	Unique	Read-1	Read-2	+ve strand	-ve strand	Non-splice reads	Splice reads	Proper pairs	Different chrom
brain_GLAD_164	45.5M	0.0M	0.0M	3.8M	0.0M	38.8M	19.4M	19.4M	19.4M	19.4M	26.1M	12.7M	38.8M	0.0M
brain_II2_403	61.3M	0.0M	0.0M	5.1M	0.0M	52.1M	26.0M	26.0M	26.0M	26.0M	35.8M	16.3M	52.1M	0.0M
brain_II2_419	51.9M	0.0M	0.0M	4.4M	0.0M	43.9M	22.0M	22.0M	22.0M	22.0M	30.1M	13.8M	43.9M	0.0M
brain_II2_431	89.8M	0.0M	0.0M	7.2M	0.0M	76.9M	38.5M	38.4M	38.5M	38.5M	52.5M	24.4M	76.9M	0.0M
brain_JJ3_283	58.4M	0.0M	0.0M	5.2M	0.0M	49.0M	24.5M	24.5M	24.5M	24.5M	34.2M	14.8M	49.0M	0.0M
brain_JJ3_308	56.3M	0.0M	0.0M	4.6M	0.0M	48.0M	24.0M	24.0M	24.0M	24.0M	32.9M	15.1M	48.0M	0.0M
brain_JJ3_316	57.0M	0.0M	0.0M	4.8M	0.0M	48.4M	24.2M	24.2M	24.2M	24.2M	32.8M	15.6M	48.4M	0.0M
brain_KK4_403	57.5M	0.0M	0.0M	4.9M	0.0M	48.8M	24.4M	24.4M	24.4M	24.4M	32.8M	16.0M	48.8M	0.0M
brain_KK4_410	65.0M	0.0M	0.0M	5.5M	0.0M	55.1M	27.5M	27.5M	27.5M	27.5M	38.7M	16.4M	55.0M	0.0M
brain_KK4_428	42.5M	0.0M	0.0M	3.6M	0.0M	36.0M	18.0M	18.0M	18.0M	18.0M	25.2M	10.8M	36.0M	0.0M
brain_MAD_261	42.3M	0.0M	0.0M	3.6M	0.0M	36.0M	18.0M	18.0M	18.0M	18.0M	24.5M	11.6M	36.0M	0.0M
brain_MAD_271	40.0M	0.0M	0.0M	3.4M	0.0M	34.0M	17.0M	17.0M	17.0M	17.0M	22.3M	11.7M	34.0M	0.0M
brain_MAD_271.2	65.2M	0.0M	0.0M	5.6M	0.0M	55.3M	27.6M	27.6M	27.6M	27.6M	36.7M	18.6M	55.3M	0.0M
brain_MAD_274	42.2M	0.0M	0.0M	3.2M	0.0M	36.5M	18.2M	18.2M	18.2M	18.2M	24.1M	12.4M	36.4M	0.0M
brain_MAD_404	55.1M	0.0M	0.0M	4.2M	0.0M	47.7M	23.8M	23.8M	23.8M	23.8M	31.6M	16.0M	47.6M	0.0M
brain_MAD_413	46.0M	0.0M	0.0M	3.4M	0.0M	40.1M	20.1M	20.1M	20.1M	20.1M	26.2M	13.9M	40.1M	0.0M
brain_MAD_416	64.9M	0.0M	0.0M	5.2M	0.0M	55.6M	27.8M	27.8M	27.8M	27.8M	38.7M	16.8M	55.5M	0.0M
brain_MAD_419	50.4M	0.0M	0.0M	3.9M	0.0M	43.5M	21.8M	21.8M	21.8M	21.8M	29.0M	14.5M	43.5M	0.0M
brain_MAD_461	48.8M	0.0M	0.0M	3.8M	0.0M	41.9M	21.0M	20.9M	21.0M	21.0M	28.1M	13.8M	41.9M	0.0M
brain_MAD_462	44.9M	0.0M	0.0M	3.7M	0.0M	38.3M	19.1M	19.1M	19.1M	19.1M	26.6M	11.6M	38.2M	0.0M
brain_MAD_463	60.1M	0.0M	0.0M	4.6M	0.0M	52.0M	26.0M	26.0M	26.0M	26.0M	34.7M	17.3M	52.0M	0.0M
brain_MAD_562	58.6M	0.0M	0.0M	4.7M	0.0M	50.1M	25.1M	25.1M	25.1M	25.1M	34.6M	15.5M	50.1M	0.0M
brain_SAD_326	44.1M	0.0M	0.0M	3.1M	0.0M	38.6M	19.3M	19.3M	19.3M	19.3M	25.0M	13.5M	38.6M	0.0M
brain_SAD_326.2	91.7M	0.0M	0.0M	8.2M	0.0M	76.9M	38.5M	38.5M	38.5M	38.5M	51.7M	25.3M	76.9M	0.0M
brain_SAD_532	52.6M	0.0M	0.0M	4.2M	0.0M	45.1M	22.5M	22.5M	22.5M	22.5M	29.9M	15.2M	45.0M	0.0M
brain_SAD_535	83.4M	0.0M	0.0M	6.6M	0.0M	71.9M	35.9M	35.9M	35.9M	35.9M	47.2M	24.7M	71.8M	0.0M
brain_SAD_587	48.8M	0.0M	0.0M	3.8M	0.0M	42.2M	21.1M	21.1M	21.1M	21.1M	28.1M	14.1M	42.2M	0.0M
brain_SAD_626	64.9M	0.0M	0.0M	5.5M	0.0M	55.1M	27.6M	27.5M	27.6M	27.6M	37.6M	17.5M	55.1M	0.0M
brain_SAD_628	42.9M	0.0M	0.0M	3.3M	0.0M	37.0M	18.5M	18.5M	18.5M	18.5M	24.6M	12.4M	37.0M	0.0M
brain_SAD_672	51.5M	0.0M	0.0M	4.1M	0.0M	44.2M	22.1M	22.1M	22.1M	22.1M	30.4M	13.8M	44.1M	0.0M
brain_SAD_678	103.1M	0.0M	0.0M	8.3M	0.0M	88.4M	44.2M	44.2M	44.2M	44.2M	59.0M	29.3M	88.3M	0.0M
liver_GLAD_164	39.4M	0.0M	0.0M	4.8M	0.0M	31.8M	15.9M	15.9M	15.9M	15.9M	15.6M	16.2M	31.8M	0.0M
liver_II2_403	48.9M	0.0M	0.0M	5.8M	0.0M	39.4M	19.7M	19.7M	19.7M	19.7M	20.0M	19.4M	39.4M	0.0M
liver_II2_419	43.3M	0.0M	0.0M	4.5M	0.0M	35.8M	17.9M	17.9M	17.9M	17.9M	18.1M	17.7M	35.8M	0.0M
liver_II2_431	55.1M	0.0M	0.0M	6.1M	0.0M	44.9M	22.4M	22.4M	22.4M	22.4M	23.2M	21.7M	44.9M	0.0M
liver_JJ3_283	55.6M	0.0M	0.0M	6.6M	0.0M	44.6M	22.3M	22.3M	22.3M	22.3M	22.6M	22.0M	44.5M	0.0M
liver_JJ3_308	57.5M	0.0M	0.0M	7.6M	0.0M	45.2M	22.6M	22.6M	22.6M	22.6M	23.6M	21.6M	45.2M	0.0M
liver_JJ3_316	59.0M	0.0M	0.0M	6.6M	0.0M	47.8M	23.9M	23.9M	23.9M	23.9M	24.3M	23.5M	47.8M	0.0M
liver_KK4_403	42.3M	0.0M	0.0M	5.7M	0.0M	32.9M	16.5M	16.5M	16.5M	16.5M	17.1M	15.9M	32.9M	0.0M
liver_KK4_410	49.7M	0.0M	0.0M	6.6M	0.0M	38.8M	19.4M	19.4M	19.4M	19.4M	20.1M	18.7M	38.7M	0.0M
liver_KK4_428	52.3M	0.0M	0.0M	6.4M	0.0M	41.9M	21.0M	21.0M	21.0M	21.0M	21.4M	20.5M	41.9M	0.0M
liver_MAD_261	37.8M	0.0M	0.0M	4.5M	0.0M	30.8M	15.4M	15.4M	15.4M	15.4M	15.6M	15.2M	30.8M	0.0M
liver_MAD_271	41.1M	0.0M	0.0M	4.2M	0.0M	34.2M	17.1M	17.1M	17.1M	17.1M	17.6M	16.6M	34.2M	0.0M
liver_MAD_271.2	63.5M	0.0M	0.0M	7.4M	0.0M	51.1M	25.6M	25.6M	25.6M	25.6M	28.0M	23.1M	51.1M	0.0M
liver_MAD_274	42.8M	0.0M	0.0M	4.0M	0.0M	36.4M	18.2M	18.2M	18.2M	18.2M	18.3M	18.1M	36.4M	0.0M
liver_MAD_404	47.5M	0.0M	0.0M	5.9M	0.0M	37.6M	18.8M	18.8M	18.8M	18.8M	20.3M	17.4M	37.6M	0.0M
liver_MAD_413	33.9M	0.0M	0.0M	4.0M	0.0M	27.4M	13.7M	13.7M	13.7M	13.7M	14.9M	12.5M	27.4M	0.0M
liver_MAD_416	55.1M	0.0M	0.0M	5.7M	0.0M	45.5M	22.7M	22.7M	22.7M	22.7M	22.2M	23.3M	45.5M	0.0M
liver_MAD_419	97.9M	0.0M	0.0M	14.1M	0.0M	74.8M	37.4M	37.4M	37.4M	37.4M	39.6M	35.2M	74.8M	0.0M
liver_MAD_461	47.4M	0.0M	0.0M	6.7M	0.0M	36.5M	18.3M	18.3M	18.3M	18.3M	19.9M	16.6M	36.5M	0.0M
liver_MAD_462	56.5M	0.0M	0.0M	6.7M	0.0M	45.2M	22.6M	22.6M	22.6M	22.6M	23.7M	21.5M	45.2M	0.0M
liver_MAD_463	50.3M	0.0M	0.0M	5.0M	0.0M	42.1M	21.1M	21.0M	21.0M	21.0M	23.1M	19.0M	42.1M	0.0M
liver_MAD_562	178.0M	0.0M	0.0M	19.8M	0.0M	144.9M	72.5M	72.4M	72.5M	72.5M	73.5M	71.4M	144.8M	0.0M
liver_SAD_326	53.1M	0.0M	0.0M	6.5M	0.0M	43.1M	21.6M	21.6M	21.6M	21.6M	21.7M	21.4M	43.1M	0.0M
liver_SAD_326.2	50.1M	0.0M	0.0M	6.2M	0.0M	40.0M	20.0M	20.0M	20.0M	20.0M	20.3M	19.7M	40.0M	0.0M
liver_SAD_532	55.1M	0.0M	0.0M	6.5M	0.0M	44.6M	22.3M	22.3M	22.3M	22.3M	22.2M	22.4M	44.5M	0.0M
liver_SAD_535	46.3M	0.0M	0.0M	4.6M	0.0M	38.6M	19.3M	19.3M	19.3M	19.3M	20.2M	18.4M	38.6M	0.0M
liver_SAD_587	40.4M	0.0M	0.0M	5.2M	0.0M	32.2M	16.1M	16.1M	16.1M	16.1M	17.5M	14.7M	32.2M	0.0M
liver_SAD_626	160.2M	0.0M	0.0M	20.2M	0.0M	126.4M	63.2M	63.2M	63.2M	63.2M	65.4M	61.0M	126.3M	0.0M
liver_SAD_628	62.0M	0.0M	0.0M	8.1M	0.0M	48.4M	24.2M	24.2M	24.2M	24.2M	25.7M	22.6M	48.4M	0.0M
liver_SAD_672	45.9M	0.0M	0.0M	6.8M	0.0M	34.6M	17.3M	17.3M	17.3M	17.3M	18.4M	16.1M	34.5M	0.0M
liver_SAD_678	50.7M	0.0M	0.0M	5.5M	0.0M	41.4M	20.7M	20.7M	20.7M	20.7M	20.7M	20.7M	41.4M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: rseqc_bam_stat_table

Sort	Column	Description	ID	Scale
\|\|	Total records	Total records	`total_records`	read_count
\|\|	QC failed	QC failed	`qc_failed`	read_count
\|\|	Duplicates	Optical/PCR duplicate	`optical_pcr_duplicate`	read_count
\|\|	Non primary hit	Non primary hit	`non_primary_hits`	read_count
\|\|	Unmapped	Unmapped reads	`unmapped_reads`	read_count
\|\|	Unique	mapq >= mapq_cut (unique)	`mapq_gte_mapq_cut_unique`	read_count
\|\|	Read-1	Read-1	`read_1`	read_count
\|\|	Read-2	Read-2	`read_2`	read_count
\|\|	+ve strand	Reads map to '+'	`reads_map_to_sense`	read_count
\|\|	-ve strand	Reads map to '-'	`reads_map_to_antisense`	read_count
\|\|	Non-splice reads	Non-splice reads	`non_splice_reads`	read_count
\|\|	Splice reads	Splice reads	`splice_reads`	read_count
\|\|	Proper pairs	Reads mapped in proper pairs	`reads_mapped_in_proper_pairs`	read_count
\|\|	Different chrom	Proper-paired reads map to different chrom	`proper_paired_reads_map_to_different_chrom`	read_count

Samtools

Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

Percent mapped

Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

Created with MultiQC

Alignment stats

This module parses the output from samtools stats. All numbers in millions.

Created with MultiQC

Showing ⁶²/₆₂ rows and ¹³/₁₃ columns.

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
brain_GLAD_164	41.7M	41.7M	41.7M	0.0M	0.0M	0.1M	6022.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.0M	0.8M
brain_II2_403	56.1M	56.1M	56.1M	0.0M	0.0M	0.1M	8316.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	27.7M	0.4M
brain_II2_419	47.4M	47.4M	47.4M	0.0M	0.0M	0.1M	7029.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	23.4M	0.3M
brain_II2_431	82.6M	82.5M	82.5M	0.0M	0.0M	0.2M	12252.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	40.8M	0.5M
brain_JJ3_283	53.2M	53.2M	53.2M	0.0M	0.0M	0.1M	7883.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	26.2M	0.4M
brain_JJ3_308	51.7M	51.6M	51.6M	0.0M	0.0M	0.1M	7653.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	25.4M	0.4M
brain_JJ3_316	52.2M	52.2M	52.2M	0.0M	0.0M	0.1M	7738.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	25.7M	0.4M
brain_KK4_403	52.7M	52.6M	52.6M	0.0M	0.0M	0.1M	7806.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	26.0M	0.3M
brain_KK4_410	59.5M	59.4M	59.4M	0.0M	0.0M	0.1M	8815.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	29.4M	0.4M
brain_KK4_428	38.9M	38.9M	38.9M	0.0M	0.0M	0.1M	5763.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	19.2M	0.3M
brain_MAD_261	38.8M	38.7M	38.7M	0.0M	0.0M	0.1M	5612.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.7M	0.7M
brain_MAD_271	36.6M	36.5M	36.5M	0.0M	0.0M	0.1M	5304.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	17.6M	0.6M
brain_MAD_271.2	59.6M	59.6M	59.6M	0.0M	0.0M	0.1M	8839.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	29.4M	0.4M
brain_MAD_274	39.0M	39.0M	39.0M	0.0M	0.0M	0.1M	5657.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.8M	0.7M
brain_MAD_404	50.9M	50.9M	50.9M	0.0M	0.0M	0.1M	7538.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	25.1M	0.4M
brain_MAD_413	42.6M	42.6M	42.6M	0.0M	0.0M	0.1M	6185.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.6M	0.7M
brain_MAD_416	59.7M	59.7M	59.7M	0.0M	0.0M	0.1M	8847.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	29.5M	0.3M
brain_MAD_419	46.6M	46.5M	46.5M	0.0M	0.0M	0.1M	6893.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	23.0M	0.3M
brain_MAD_461	44.9M	44.9M	44.9M	0.0M	0.0M	0.1M	6651.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	22.2M	0.3M
brain_MAD_462	41.2M	41.2M	41.2M	0.0M	0.0M	0.1M	6093.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.3M	0.3M
brain_MAD_463	55.5M	55.5M	55.5M	0.0M	0.0M	0.1M	8067.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	26.8M	0.9M
brain_MAD_562	53.9M	53.9M	53.9M	0.0M	0.0M	0.1M	7985.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	26.6M	0.3M
brain_SAD_326	41.0M	40.9M	40.9M	0.0M	0.0M	0.1M	5950.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.0M	0.4M
brain_SAD_326.2	83.4M	83.4M	83.4M	0.0M	0.0M	0.2M	12358.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	41.1M	0.6M
brain_SAD_532	48.4M	48.3M	48.3M	0.0M	0.0M	0.1M	7166.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	23.8M	0.3M
brain_SAD_535	76.8M	76.8M	76.8M	0.0M	0.0M	0.2M	11153.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	37.2M	1.2M
brain_SAD_587	45.1M	45.0M	45.0M	0.0M	0.0M	0.1M	6527.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	21.8M	0.7M
brain_SAD_626	59.4M	59.4M	59.4M	0.0M	0.0M	0.1M	8791.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	29.3M	0.4M
brain_SAD_628	39.6M	39.5M	39.5M	0.0M	0.0M	0.1M	5847.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	19.5M	0.3M
brain_SAD_672	47.4M	47.4M	47.4M	0.0M	0.0M	0.1M	7013.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	23.4M	0.3M
brain_SAD_678	94.9M	94.8M	94.8M	0.0M	0.0M	0.2M	14055.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	46.9M	0.5M
liver_GLAD_164	34.6M	34.6M	34.6M	0.0M	0.0M	0.2M	5012.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	16.7M	0.5M
liver_II2_403	43.1M	43.1M	43.1M	0.0M	0.0M	0.2M	6381.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	21.2M	0.3M
liver_II2_419	38.8M	38.8M	38.8M	0.0M	0.0M	0.2M	5752.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	19.1M	0.3M
liver_II2_431	49.0M	49.0M	49.0M	0.0M	0.0M	0.2M	7247.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	24.1M	0.3M
liver_JJ3_283	49.0M	48.9M	48.9M	0.0M	0.0M	0.2M	7246.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	24.2M	0.3M
liver_JJ3_308	49.9M	49.9M	49.9M	0.0M	0.0M	0.3M	7382.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	24.6M	0.3M
liver_JJ3_316	52.4M	52.4M	52.4M	0.0M	0.0M	0.2M	7769.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	25.9M	0.3M
liver_KK4_403	36.7M	36.6M	36.6M	0.0M	0.0M	0.2M	5425.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.0M	0.3M
liver_KK4_410	43.1M	43.1M	43.1M	0.0M	0.0M	0.3M	6380.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	21.2M	0.3M
liver_KK4_428	45.9M	45.9M	45.9M	0.0M	0.0M	0.3M	6799.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	22.7M	0.3M
liver_MAD_261	33.4M	33.4M	33.4M	0.0M	0.0M	0.2M	4837.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	16.2M	0.5M
liver_MAD_271	36.8M	36.8M	36.8M	0.0M	0.0M	0.2M	5320.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	17.8M	0.6M
liver_MAD_271.2	56.2M	56.2M	56.2M	0.0M	0.0M	0.3M	8306.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	27.7M	0.4M
liver_MAD_274	38.8M	38.8M	38.8M	0.0M	0.0M	0.2M	5614.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	18.8M	0.6M
liver_MAD_404	41.6M	41.6M	41.6M	0.0M	0.0M	0.2M	6150.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.5M	0.3M
liver_MAD_413	29.9M	29.9M	29.9M	0.0M	0.0M	0.2M	4303.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.3M	0.6M
liver_MAD_416	49.4M	49.4M	49.4M	0.0M	0.0M	0.2M	7321.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	24.3M	0.3M
liver_MAD_419	83.9M	83.8M	83.8M	0.0M	0.0M	0.5M	12432.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	41.5M	0.4M
liver_MAD_461	40.8M	40.7M	40.7M	0.0M	0.0M	0.3M	6020.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.1M	0.3M
liver_MAD_462	49.8M	49.8M	49.8M	0.0M	0.0M	0.3M	7379.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	24.5M	0.3M
liver_MAD_463	45.4M	45.4M	45.4M	0.0M	0.0M	0.2M	6557.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	22.0M	0.7M
liver_MAD_562	158.2M	158.1M	158.1M	0.0M	0.0M	0.8M	23483.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	78.3M	0.7M
liver_SAD_326	46.6M	46.6M	46.6M	0.0M	0.0M	0.3M	6746.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	22.5M	0.8M
liver_SAD_326.2	43.9M	43.9M	43.9M	0.0M	0.0M	0.3M	6514.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	21.7M	0.3M
liver_SAD_532	48.6M	48.6M	48.6M	0.0M	0.0M	0.3M	7200.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	23.9M	0.4M
liver_SAD_535	41.6M	41.6M	41.6M	0.0M	0.0M	0.2M	6019.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	20.2M	0.6M
liver_SAD_587	35.2M	35.2M	35.2M	0.0M	0.0M	0.3M	5074.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	16.9M	0.7M
liver_SAD_626	140.0M	140.0M	140.0M	0.0M	0.0M	0.8M	20779.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	69.2M	0.7M
liver_SAD_628	53.9M	53.9M	53.9M	0.0M	0.0M	0.3M	7979.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	26.6M	0.4M
liver_SAD_672	39.1M	39.1M	39.1M	0.0M	0.0M	0.2M	5780.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	19.2M	0.3M
liver_SAD_678	45.2M	45.2M	45.2M	0.0M	0.0M	0.2M	6696.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	22.3M	0.3M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table

Sort	Column	Description	ID	Scale
\|\|	Total sequences	Total sequences	`raw_total_sequences`	read_count
\|\|	Mapped & paired	Paired-end technology bit set + both mates mapped	`reads_mapped_and_paired`	read_count
\|\|	Properly paired	Proper-pair bit set	`reads_properly_paired`	read_count
\|\|	Duplicated	PCR or optical duplicate bit set	`reads_duplicated`	read_count
\|\|	QC Failed	QC Failed	`reads_QC_failed`	read_count
\|\|	Reads MQ0	Reads mapped and MQ=0	`reads_MQ0`	read_count
\|\|	Mapped bases (CIGAR)	Mapped bases (CIGAR)	`bases_mapped__cigar`	base_count
\|\|	Bases Trimmed	Bases Trimmed	`bases_trimmed`	base_count
\|\|	Duplicated bases	Duplicated bases	`bases_duplicated`	base_count
\|\|	Diff chromosomes	Pairs on different chromosomes	`pairs_on_different_chromosomes`	read_count
\|\|	Other orientation	Pairs with other orientation	`pairs_with_other_orientation`	read_count
\|\|	Inward pairs	Inward oriented pairs	`inward_oriented_pairs`	read_count
\|\|	Outward pairs	Outward oriented pairs	`outward_oriented_pairs`	read_count

Flagstat

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁶²/₆₂ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
brain_GLAD_164	45.5M	45.5M	45.5M	3.8M	0.0M	41.7M	41.7M	41.7M	0.0M	0.0M	0.0M
brain_II2_403	61.3M	61.3M	61.3M	5.1M	0.0M	56.1M	56.1M	56.1M	0.0M	0.0M	0.0M
brain_II2_419	51.9M	51.9M	51.9M	4.4M	0.0M	47.4M	47.4M	47.4M	0.0M	0.0M	0.0M
brain_II2_431	89.8M	89.8M	89.8M	7.2M	0.0M	82.6M	82.5M	82.5M	0.0M	0.0M	0.0M
brain_JJ3_283	58.4M	58.4M	58.4M	5.2M	0.0M	53.2M	53.2M	53.2M	0.0M	0.0M	0.0M
brain_JJ3_308	56.3M	56.3M	56.3M	4.6M	0.0M	51.7M	51.6M	51.6M	0.0M	0.0M	0.0M
brain_JJ3_316	57.0M	57.0M	57.0M	4.8M	0.0M	52.2M	52.2M	52.2M	0.0M	0.0M	0.0M
brain_KK4_403	57.5M	57.5M	57.5M	4.9M	0.0M	52.7M	52.6M	52.6M	0.0M	0.0M	0.0M
brain_KK4_410	65.0M	65.0M	65.0M	5.5M	0.0M	59.5M	59.4M	59.4M	0.0M	0.0M	0.0M
brain_KK4_428	42.5M	42.5M	42.5M	3.6M	0.0M	38.9M	38.9M	38.9M	0.0M	0.0M	0.0M
brain_MAD_261	42.3M	42.3M	42.3M	3.6M	0.0M	38.8M	38.7M	38.7M	0.0M	0.0M	0.0M
brain_MAD_271	40.0M	40.0M	40.0M	3.4M	0.0M	36.6M	36.5M	36.5M	0.0M	0.0M	0.0M
brain_MAD_271.2	65.2M	65.2M	65.2M	5.6M	0.0M	59.6M	59.6M	59.6M	0.0M	0.0M	0.0M
brain_MAD_274	42.2M	42.2M	42.2M	3.2M	0.0M	39.0M	39.0M	39.0M	0.0M	0.0M	0.0M
brain_MAD_404	55.1M	55.1M	55.1M	4.2M	0.0M	50.9M	50.9M	50.9M	0.0M	0.0M	0.0M
brain_MAD_413	46.0M	46.0M	46.0M	3.4M	0.0M	42.6M	42.6M	42.6M	0.0M	0.0M	0.0M
brain_MAD_416	64.9M	64.9M	64.9M	5.2M	0.0M	59.7M	59.7M	59.7M	0.0M	0.0M	0.0M
brain_MAD_419	50.4M	50.4M	50.4M	3.9M	0.0M	46.6M	46.5M	46.5M	0.0M	0.0M	0.0M
brain_MAD_461	48.8M	48.8M	48.8M	3.8M	0.0M	44.9M	44.9M	44.9M	0.0M	0.0M	0.0M
brain_MAD_462	44.9M	44.9M	44.9M	3.7M	0.0M	41.2M	41.2M	41.2M	0.0M	0.0M	0.0M
brain_MAD_463	60.1M	60.1M	60.1M	4.6M	0.0M	55.5M	55.5M	55.5M	0.0M	0.0M	0.0M
brain_MAD_562	58.6M	58.6M	58.6M	4.7M	0.0M	53.9M	53.9M	53.9M	0.0M	0.0M	0.0M
brain_SAD_326	44.1M	44.1M	44.1M	3.1M	0.0M	41.0M	40.9M	40.9M	0.0M	0.0M	0.0M
brain_SAD_326.2	91.7M	91.7M	91.7M	8.2M	0.0M	83.4M	83.4M	83.4M	0.0M	0.0M	0.0M
brain_SAD_532	52.6M	52.6M	52.6M	4.2M	0.0M	48.4M	48.3M	48.3M	0.0M	0.0M	0.0M
brain_SAD_535	83.4M	83.4M	83.4M	6.6M	0.0M	76.8M	76.8M	76.8M	0.0M	0.0M	0.0M
brain_SAD_587	48.8M	48.8M	48.8M	3.8M	0.0M	45.1M	45.0M	45.0M	0.0M	0.0M	0.0M
brain_SAD_626	64.9M	64.9M	64.9M	5.5M	0.0M	59.4M	59.4M	59.4M	0.0M	0.0M	0.0M
brain_SAD_628	42.9M	42.9M	42.9M	3.3M	0.0M	39.6M	39.5M	39.5M	0.0M	0.0M	0.0M
brain_SAD_672	51.5M	51.5M	51.5M	4.1M	0.0M	47.4M	47.4M	47.4M	0.0M	0.0M	0.0M
brain_SAD_678	103.1M	103.1M	103.1M	8.3M	0.0M	94.9M	94.8M	94.8M	0.1M	0.0M	0.0M
liver_GLAD_164	39.4M	39.4M	39.4M	4.8M	0.0M	34.6M	34.6M	34.6M	0.0M	0.0M	0.0M
liver_II2_403	48.9M	48.9M	48.9M	5.8M	0.0M	43.1M	43.1M	43.1M	0.0M	0.0M	0.0M
liver_II2_419	43.3M	43.3M	43.3M	4.5M	0.0M	38.8M	38.8M	38.8M	0.0M	0.0M	0.0M
liver_II2_431	55.1M	55.1M	55.1M	6.1M	0.0M	49.0M	49.0M	49.0M	0.0M	0.0M	0.0M
liver_JJ3_283	55.6M	55.6M	55.6M	6.6M	0.0M	49.0M	48.9M	48.9M	0.0M	0.0M	0.0M
liver_JJ3_308	57.5M	57.5M	57.5M	7.6M	0.0M	49.9M	49.9M	49.9M	0.0M	0.0M	0.0M
liver_JJ3_316	59.0M	59.0M	59.0M	6.6M	0.0M	52.4M	52.4M	52.4M	0.0M	0.0M	0.0M
liver_KK4_403	42.3M	42.3M	42.3M	5.7M	0.0M	36.7M	36.6M	36.6M	0.0M	0.0M	0.0M
liver_KK4_410	49.7M	49.7M	49.7M	6.6M	0.0M	43.1M	43.1M	43.1M	0.0M	0.0M	0.0M
liver_KK4_428	52.3M	52.3M	52.3M	6.4M	0.0M	45.9M	45.9M	45.9M	0.0M	0.0M	0.0M
liver_MAD_261	37.8M	37.8M	37.8M	4.5M	0.0M	33.4M	33.4M	33.4M	0.0M	0.0M	0.0M
liver_MAD_271	41.1M	41.1M	41.1M	4.2M	0.0M	36.8M	36.8M	36.8M	0.0M	0.0M	0.0M
liver_MAD_271.2	63.5M	63.5M	63.5M	7.4M	0.0M	56.2M	56.2M	56.2M	0.0M	0.0M	0.0M
liver_MAD_274	42.8M	42.8M	42.8M	4.0M	0.0M	38.8M	38.8M	38.8M	0.0M	0.0M	0.0M
liver_MAD_404	47.5M	47.5M	47.5M	5.9M	0.0M	41.6M	41.6M	41.6M	0.0M	0.0M	0.0M
liver_MAD_413	33.9M	33.9M	33.9M	4.0M	0.0M	29.9M	29.9M	29.9M	0.0M	0.0M	0.0M
liver_MAD_416	55.1M	55.1M	55.1M	5.7M	0.0M	49.4M	49.4M	49.4M	0.0M	0.0M	0.0M
liver_MAD_419	97.9M	97.9M	97.9M	14.1M	0.0M	83.9M	83.8M	83.8M	0.0M	0.0M	0.0M
liver_MAD_461	47.4M	47.4M	47.4M	6.7M	0.0M	40.8M	40.7M	40.7M	0.0M	0.0M	0.0M
liver_MAD_462	56.5M	56.5M	56.5M	6.7M	0.0M	49.8M	49.8M	49.8M	0.0M	0.0M	0.0M
liver_MAD_463	50.3M	50.3M	50.3M	5.0M	0.0M	45.4M	45.4M	45.4M	0.0M	0.0M	0.0M
liver_MAD_562	178.0M	178.0M	178.0M	19.8M	0.0M	158.2M	158.1M	158.1M	0.1M	0.0M	0.0M
liver_SAD_326	53.1M	53.1M	53.1M	6.5M	0.0M	46.6M	46.6M	46.6M	0.0M	0.0M	0.0M
liver_SAD_326.2	50.1M	50.1M	50.1M	6.2M	0.0M	43.9M	43.9M	43.9M	0.0M	0.0M	0.0M
liver_SAD_532	55.1M	55.1M	55.1M	6.5M	0.0M	48.6M	48.6M	48.6M	0.0M	0.0M	0.0M
liver_SAD_535	46.3M	46.3M	46.3M	4.6M	0.0M	41.6M	41.6M	41.6M	0.0M	0.0M	0.0M
liver_SAD_587	40.4M	40.4M	40.4M	5.2M	0.0M	35.2M	35.2M	35.2M	0.0M	0.0M	0.0M
liver_SAD_626	160.2M	160.2M	160.2M	20.2M	0.0M	140.0M	140.0M	140.0M	0.1M	0.0M	0.0M
liver_SAD_628	62.0M	62.0M	62.0M	8.1M	0.0M	53.9M	53.9M	53.9M	0.0M	0.0M	0.0M
liver_SAD_672	45.9M	45.9M	45.9M	6.8M	0.0M	39.1M	39.1M	39.1M	0.0M	0.0M	0.0M
liver_SAD_678	50.7M	50.7M	50.7M	5.5M	0.0M	45.2M	45.2M	45.2M	0.0M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-flagstat-table_table

Sort	Column	Description	ID	Scale
\|\|	Total Reads	Total Reads	`flagstat_total`	read_count
\|\|	Total Passed QC	Total Passed QC	`total_passed`	read_count
\|\|	Mapped	Mapped	`mapped_passed`	read_count
\|\|	Secondary Alignments	Secondary Alignments	`secondary_passed`	read_count
\|\|	Duplicates	Duplicates	`duplicates_passed`	read_count
\|\|	Paired in Sequencing	Paired in Sequencing	`paired_in_sequencing_passed`	read_count
\|\|	Properly Paired	Properly Paired	`properly_paired_passed`	read_count
\|\|	Self and mate mapped	Reads with itself and mate mapped	`with_itself_and_mate_mapped_passed`	read_count
\|\|	Singletons	Singletons	`singletons_passed`	read_count
\|\|	Mate mapped to diff chr	Mate mapped to different chromosome	`with_mate_mapped_to_a_different_chr_passed`	read_count
\|\|	Diff chr (mapQ >= 5)	Mate mapped to different chromosome (mapQ >= 5)	`with_mate_mapped_to_a_different_chr_mapQ_5__passed`	read_count

Flagstat: Percentage of total

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁶²/₆₂ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
brain_GLAD_164	100.0%	100.0%	100.0%	8.4%	0.0%	91.6%	91.6%	91.6%	0.0%	0.0%	0.0%
brain_II2_403	100.0%	100.0%	100.0%	8.4%	0.0%	91.6%	91.6%	91.6%	0.1%	0.0%	0.0%
brain_II2_419	100.0%	100.0%	100.0%	8.5%	0.0%	91.5%	91.4%	91.4%	0.0%	0.0%	0.0%
brain_II2_431	100.0%	100.0%	100.0%	8.0%	0.0%	92.0%	92.0%	92.0%	0.0%	0.0%	0.0%
brain_JJ3_283	100.0%	100.0%	100.0%	8.9%	0.0%	91.1%	91.1%	91.1%	0.0%	0.0%	0.0%
brain_JJ3_308	100.0%	100.0%	100.0%	8.2%	0.0%	91.8%	91.8%	91.8%	0.0%	0.0%	0.0%
brain_JJ3_316	100.0%	100.0%	100.0%	8.4%	0.0%	91.6%	91.5%	91.5%	0.0%	0.0%	0.0%
brain_KK4_403	100.0%	100.0%	100.0%	8.5%	0.0%	91.5%	91.5%	91.5%	0.1%	0.0%	0.0%
brain_KK4_410	100.0%	100.0%	100.0%	8.5%	0.0%	91.5%	91.5%	91.5%	0.1%	0.0%	0.0%
brain_KK4_428	100.0%	100.0%	100.0%	8.5%	0.0%	91.5%	91.5%	91.5%	0.0%	0.0%	0.0%
brain_MAD_261	100.0%	100.0%	100.0%	8.4%	0.0%	91.6%	91.5%	91.5%	0.0%	0.0%	0.0%
brain_MAD_271	100.0%	100.0%	100.0%	8.6%	0.0%	91.4%	91.4%	91.4%	0.0%	0.0%	0.0%
brain_MAD_271.2	100.0%	100.0%	100.0%	8.6%	0.0%	91.4%	91.4%	91.4%	0.1%	0.0%	0.0%
brain_MAD_274	100.0%	100.0%	100.0%	7.7%	0.0%	92.3%	92.3%	92.3%	0.0%	0.0%	0.0%
brain_MAD_404	100.0%	100.0%	100.0%	7.6%	0.0%	92.4%	92.4%	92.4%	0.1%	0.0%	0.0%
brain_MAD_413	100.0%	100.0%	100.0%	7.3%	0.0%	92.7%	92.6%	92.6%	0.0%	0.0%	0.0%
brain_MAD_416	100.0%	100.0%	100.0%	8.0%	0.0%	92.0%	91.9%	91.9%	0.1%	0.0%	0.0%
brain_MAD_419	100.0%	100.0%	100.0%	7.7%	0.0%	92.3%	92.3%	92.3%	0.1%	0.0%	0.0%
brain_MAD_461	100.0%	100.0%	100.0%	7.9%	0.0%	92.1%	92.1%	92.1%	0.0%	0.0%	0.0%
brain_MAD_462	100.0%	100.0%	100.0%	8.2%	0.0%	91.8%	91.7%	91.7%	0.1%	0.0%	0.0%
brain_MAD_463	100.0%	100.0%	100.0%	7.7%	0.0%	92.3%	92.2%	92.2%	0.0%	0.0%	0.0%
brain_MAD_562	100.0%	100.0%	100.0%	8.0%	0.0%	92.0%	91.9%	91.9%	0.1%	0.0%	0.0%
brain_SAD_326	100.0%	100.0%	100.0%	7.1%	0.0%	92.9%	92.9%	92.9%	0.0%	0.0%	0.0%
brain_SAD_326.2	100.0%	100.0%	100.0%	9.0%	0.0%	91.0%	91.0%	91.0%	0.0%	0.0%	0.0%
brain_SAD_532	100.0%	100.0%	100.0%	8.0%	0.0%	92.0%	92.0%	92.0%	0.1%	0.0%	0.0%
brain_SAD_535	100.0%	100.0%	100.0%	7.9%	0.0%	92.1%	92.0%	92.0%	0.0%	0.0%	0.0%
brain_SAD_587	100.0%	100.0%	100.0%	7.7%	0.0%	92.3%	92.3%	92.3%	0.0%	0.0%	0.0%
brain_SAD_626	100.0%	100.0%	100.0%	8.4%	0.0%	91.6%	91.5%	91.5%	0.1%	0.0%	0.0%
brain_SAD_628	100.0%	100.0%	100.0%	7.7%	0.0%	92.3%	92.2%	92.2%	0.1%	0.0%	0.0%
brain_SAD_672	100.0%	100.0%	100.0%	8.0%	0.0%	92.0%	91.9%	91.9%	0.1%	0.0%	0.0%
brain_SAD_678	100.0%	100.0%	100.0%	8.0%	0.0%	92.0%	91.9%	91.9%	0.1%	0.0%	0.0%
liver_GLAD_164	100.0%	100.0%	100.0%	12.3%	0.0%	87.7%	87.7%	87.7%	0.0%	0.0%	0.0%
liver_II2_403	100.0%	100.0%	100.0%	11.8%	0.0%	88.2%	88.2%	88.2%	0.0%	0.0%	0.0%
liver_II2_419	100.0%	100.0%	100.0%	10.4%	0.0%	89.6%	89.6%	89.6%	0.0%	0.0%	0.0%
liver_II2_431	100.0%	100.0%	100.0%	11.0%	0.0%	89.0%	88.9%	88.9%	0.0%	0.0%	0.0%
liver_JJ3_283	100.0%	100.0%	100.0%	11.9%	0.0%	88.1%	88.0%	88.0%	0.0%	0.0%	0.0%
liver_JJ3_308	100.0%	100.0%	100.0%	13.2%	0.0%	86.8%	86.8%	86.8%	0.0%	0.0%	0.0%
liver_JJ3_316	100.0%	100.0%	100.0%	11.1%	0.0%	88.9%	88.8%	88.8%	0.0%	0.0%	0.0%
liver_KK4_403	100.0%	100.0%	100.0%	13.4%	0.0%	86.6%	86.6%	86.6%	0.0%	0.0%	0.0%
liver_KK4_410	100.0%	100.0%	100.0%	13.3%	0.0%	86.7%	86.6%	86.6%	0.0%	0.0%	0.0%
liver_KK4_428	100.0%	100.0%	100.0%	12.2%	0.0%	87.8%	87.8%	87.8%	0.0%	0.0%	0.0%
liver_MAD_261	100.0%	100.0%	100.0%	11.8%	0.0%	88.2%	88.2%	88.2%	0.0%	0.0%	0.0%
liver_MAD_271	100.0%	100.0%	100.0%	10.3%	0.0%	89.7%	89.6%	89.6%	0.0%	0.0%	0.0%
liver_MAD_271.2	100.0%	100.0%	100.0%	11.6%	0.0%	88.4%	88.4%	88.4%	0.1%	0.0%	0.0%
liver_MAD_274	100.0%	100.0%	100.0%	9.3%	0.0%	90.7%	90.7%	90.7%	0.0%	0.0%	0.0%
liver_MAD_404	100.0%	100.0%	100.0%	12.4%	0.0%	87.6%	87.6%	87.6%	0.0%	0.0%	0.0%
liver_MAD_413	100.0%	100.0%	100.0%	11.9%	0.0%	88.1%	88.0%	88.0%	0.0%	0.0%	0.0%
liver_MAD_416	100.0%	100.0%	100.0%	10.4%	0.0%	89.6%	89.6%	89.6%	0.0%	0.0%	0.0%
liver_MAD_419	100.0%	100.0%	100.0%	14.4%	0.0%	85.6%	85.6%	85.6%	0.0%	0.0%	0.0%
liver_MAD_461	100.0%	100.0%	100.0%	14.1%	0.0%	85.9%	85.9%	85.9%	0.0%	0.0%	0.0%
liver_MAD_462	100.0%	100.0%	100.0%	11.8%	0.0%	88.2%	88.2%	88.2%	0.0%	0.0%	0.0%
liver_MAD_463	100.0%	100.0%	100.0%	9.8%	0.0%	90.2%	90.1%	90.1%	0.0%	0.0%	0.0%
liver_MAD_562	100.0%	100.0%	100.0%	11.1%	0.0%	88.9%	88.8%	88.8%	0.0%	0.0%	0.0%
liver_SAD_326	100.0%	100.0%	100.0%	12.2%	0.0%	87.8%	87.8%	87.8%	0.0%	0.0%	0.0%
liver_SAD_326.2	100.0%	100.0%	100.0%	12.3%	0.0%	87.7%	87.6%	87.6%	0.0%	0.0%	0.0%
liver_SAD_532	100.0%	100.0%	100.0%	11.8%	0.0%	88.2%	88.2%	88.2%	0.0%	0.0%	0.0%
liver_SAD_535	100.0%	100.0%	100.0%	10.0%	0.0%	90.0%	89.9%	89.9%	0.0%	0.0%	0.0%
liver_SAD_587	100.0%	100.0%	100.0%	12.9%	0.0%	87.1%	87.1%	87.1%	0.0%	0.0%	0.0%
liver_SAD_626	100.0%	100.0%	100.0%	12.6%	0.0%	87.4%	87.4%	87.4%	0.0%	0.0%	0.0%
liver_SAD_628	100.0%	100.0%	100.0%	13.1%	0.0%	86.9%	86.9%	86.9%	0.0%	0.0%	0.0%
liver_SAD_672	100.0%	100.0%	100.0%	14.8%	0.0%	85.2%	85.2%	85.2%	0.0%	0.0%	0.0%
liver_SAD_678	100.0%	100.0%	100.0%	10.9%	0.0%	89.1%	89.1%	89.1%	0.0%	0.0%	0.0%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-flagstat-pct-table_table

Sort	Column	Description	ID
\|\|	Total Reads	Total Reads	`flagstat_total_pct`
\|\|	Total Passed QC	Total Passed QC	`total_passed_pct`
\|\|	Mapped	Mapped	`mapped_passed_pct`
\|\|	Secondary Alignments	Secondary Alignments	`secondary_passed_pct`
\|\|	Duplicates	Duplicates	`duplicates_passed_pct`
\|\|	Paired in Sequencing	Paired in Sequencing	`paired_in_sequencing_passed_pct`
\|\|	Properly Paired	Properly Paired	`properly_paired_passed_pct`
\|\|	Self and mate mapped	Self and mate mapped	`with_itself_and_mate_mapped_passed_pct`
\|\|	Singletons	Singletons	`singletons_passed_pct`
\|\|	Mate mapped to diff chr	Mate mapped to diff chr	`with_mate_mapped_to_a_different_chr_passed_pct`
\|\|	Diff chr (mapQ >= 5)	Diff chr (mapQ >= 5)	`with_mate_mapped_to_a_different_chr_mapQ_5__passed_pct`

XY counts

Created with MultiQC

Mapped reads per contig

The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

Created with MultiQC

STAR

Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

Summary Statistics

Summary statistics from the STAR alignment

Showing ⁶²/₆₂ rows and ¹⁰/₁₉ columns.

Sample Name	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Avg. read len	Avg. mapped len	Splices	Annotated splices	GT/AG splices	GC/AG splices	AT/AC splices	Non-canonical splices	Mismatch rate	Del rate	Del len	Ins rate	Ins len
brain_GLAD_164	24.3M	20.8M	85.6%	19.4M	79.7%	1.4M	294.0bp	289.0bp	16.0M	16.0M	15.9M	0.1M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.6bp
brain_II2_403	28.9M	28.1M	97.2%	26.1M	90.2%	2.0M	297.0bp	296.2bp	20.7M	20.7M	20.5M	0.2M	0.0M	0.0M	0.1%	0.0%	2.1bp	0.0%	1.9bp
brain_II2_419	24.3M	23.7M	97.5%	22.0M	90.3%	1.7M	297.0bp	296.3bp	17.6M	17.6M	17.4M	0.2M	0.0M	0.0M	0.2%	0.0%	2.1bp	0.0%	1.9bp
brain_II2_431	42.4M	41.3M	97.5%	38.5M	90.8%	2.8M	297.0bp	296.6bp	31.1M	31.1M	30.7M	0.3M	0.0M	0.0M	0.1%	0.0%	2.2bp	0.0%	1.9bp
brain_JJ3_283	27.3M	26.6M	97.4%	24.5M	89.7%	2.1M	297.0bp	296.1bp	18.8M	18.7M	18.5M	0.2M	0.0M	0.0M	0.2%	0.0%	2.1bp	0.0%	1.8bp
brain_JJ3_308	26.6M	25.8M	97.2%	24.0M	90.3%	1.8M	297.0bp	296.1bp	19.2M	19.2M	19.0M	0.2M	0.0M	0.0M	0.2%	0.0%	2.2bp	0.0%	1.9bp
brain_JJ3_316	26.9M	26.1M	97.1%	24.2M	90.0%	1.9M	297.0bp	296.1bp	19.8M	19.8M	19.6M	0.2M	0.0M	0.0M	0.2%	0.0%	2.3bp	0.0%	2.0bp
brain_KK4_403	27.1M	26.3M	97.3%	24.4M	90.2%	1.9M	297.0bp	296.4bp	20.4M	20.3M	20.1M	0.2M	0.0M	0.0M	0.2%	0.0%	2.1bp	0.0%	1.9bp
brain_KK4_410	30.7M	29.8M	96.8%	27.6M	89.7%	2.2M	297.0bp	296.3bp	20.8M	20.8M	20.6M	0.2M	0.0M	0.0M	0.2%	0.0%	2.3bp	0.0%	1.9bp
brain_KK4_428	20.1M	19.5M	97.0%	18.0M	89.7%	1.5M	297.0bp	296.1bp	13.7M	13.7M	13.5M	0.1M	0.0M	0.0M	0.2%	0.0%	2.1bp	0.0%	2.0bp
brain_MAD_261	22.5M	19.4M	86.1%	18.0M	80.1%	1.4M	295.0bp	289.6bp	14.6M	14.6M	14.4M	0.1M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.6bp
brain_MAD_271	20.9M	18.3M	87.5%	17.0M	81.3%	1.3M	295.0bp	290.1bp	14.7M	14.7M	14.6M	0.1M	0.0M	0.0M	0.1%	0.0%	1.9bp	0.0%	1.6bp
brain_MAD_271.2	30.6M	29.8M	97.4%	27.7M	90.3%	2.2M	297.0bp	296.3bp	23.7M	23.7M	23.4M	0.2M	0.0M	0.0M	0.2%	0.0%	2.0bp	0.0%	2.0bp
brain_MAD_274	22.0M	19.5M	88.7%	18.2M	83.0%	1.3M	294.0bp	290.3bp	15.7M	15.6M	15.5M	0.1M	0.0M	0.0M	0.1%	0.0%	1.9bp	0.0%	1.5bp
brain_MAD_404	26.3M	25.5M	96.9%	23.8M	90.7%	1.6M	297.0bp	296.0bp	20.4M	20.4M	20.1M	0.2M	0.0M	0.0M	0.2%	0.0%	2.2bp	0.0%	2.1bp
brain_MAD_413	25.1M	21.3M	85.0%	20.1M	80.0%	1.3M	295.0bp	290.1bp	17.6M	17.6M	17.4M	0.2M	0.0M	0.0M	0.2%	0.0%	2.0bp	0.0%	1.5bp
brain_MAD_416	30.9M	29.9M	96.6%	27.8M	89.9%	2.1M	297.0bp	296.2bp	21.4M	21.4M	21.1M	0.2M	0.0M	0.0M	0.2%	0.0%	2.5bp	0.0%	2.0bp
brain_MAD_419	24.1M	23.3M	96.6%	21.8M	90.4%	1.5M	297.0bp	295.9bp	18.4M	18.4M	18.2M	0.2M	0.0M	0.0M	0.2%	0.0%	2.6bp	0.0%	2.2bp
brain_MAD_461	23.4M	22.5M	96.2%	21.0M	89.7%	1.5M	297.0bp	295.9bp	17.5M	17.5M	17.3M	0.2M	0.0M	0.0M	0.2%	0.0%	2.9bp	0.0%	2.2bp
brain_MAD_462	21.5M	20.6M	96.1%	19.1M	89.2%	1.5M	297.0bp	295.5bp	14.8M	14.8M	14.6M	0.1M	0.0M	0.0M	0.2%	0.0%	2.5bp	0.0%	2.1bp
brain_MAD_463	31.4M	27.7M	88.5%	26.0M	82.9%	1.7M	295.0bp	290.8bp	21.9M	21.9M	21.7M	0.2M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.5bp
brain_MAD_562	28.0M	27.0M	96.4%	25.1M	89.6%	1.9M	297.0bp	296.0bp	19.7M	19.7M	19.5M	0.2M	0.0M	0.0M	0.2%	0.0%	2.7bp	0.0%	1.8bp
brain_SAD_326	24.2M	20.5M	84.7%	19.3M	79.7%	1.2M	296.0bp	290.5bp	17.1M	17.1M	16.9M	0.1M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.6bp
brain_SAD_326.2	43.2M	41.7M	96.6%	38.5M	89.1%	3.3M	297.0bp	296.0bp	32.2M	32.2M	31.8M	0.3M	0.0M	0.0M	0.2%	0.0%	2.6bp	0.0%	2.3bp
brain_SAD_532	24.9M	24.2M	97.3%	22.5M	90.7%	1.6M	297.0bp	296.2bp	19.3M	19.3M	19.1M	0.2M	0.0M	0.0M	0.2%	0.0%	2.0bp	0.0%	1.9bp
brain_SAD_535	44.2M	38.4M	86.8%	35.9M	81.3%	2.5M	295.0bp	290.5bp	31.2M	31.2M	30.9M	0.3M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.6bp
brain_SAD_587	26.8M	22.5M	84.2%	21.1M	78.8%	1.4M	295.0bp	289.7bp	17.8M	17.8M	17.6M	0.2M	0.0M	0.0M	0.2%	0.0%	2.1bp	0.0%	1.7bp
brain_SAD_626	30.8M	29.7M	96.5%	27.6M	89.6%	2.1M	297.0bp	295.9bp	22.3M	22.2M	22.0M	0.2M	0.0M	0.0M	0.2%	0.0%	2.6bp	0.0%	2.1bp
brain_SAD_628	20.6M	19.8M	96.1%	18.5M	89.9%	1.3M	296.0bp	295.5bp	15.8M	15.8M	15.7M	0.2M	0.0M	0.0M	0.2%	0.0%	2.7bp	0.0%	2.2bp
brain_SAD_672	24.7M	23.7M	96.0%	22.1M	89.4%	1.6M	297.0bp	295.7bp	17.5M	17.5M	17.3M	0.2M	0.0M	0.0M	0.2%	0.0%	2.8bp	0.0%	2.2bp
brain_SAD_678	49.4M	47.5M	96.1%	44.2M	89.5%	3.3M	297.0bp	296.2bp	37.3M	37.3M	36.8M	0.4M	0.0M	0.0M	0.1%	0.0%	2.8bp	0.0%	2.1bp
liver_GLAD_164	23.7M	17.3M	72.9%	15.9M	67.1%	1.4M	296.0bp	290.2bp	19.9M	19.9M	19.8M	0.1M	0.0M	0.0M	0.1%	0.0%	2.1bp	0.0%	1.6bp
liver_II2_403	23.3M	21.6M	92.4%	19.7M	84.5%	1.9M	297.0bp	296.0bp	24.0M	24.0M	23.8M	0.2M	0.0M	0.0M	0.2%	0.0%	2.5bp	0.0%	2.4bp
liver_II2_419	20.3M	19.4M	95.7%	17.9M	88.3%	1.5M	297.0bp	296.2bp	22.0M	22.0M	21.8M	0.2M	0.0M	0.0M	0.2%	0.0%	2.4bp	0.0%	2.7bp
liver_II2_431	26.4M	24.5M	92.7%	22.5M	85.0%	2.0M	297.0bp	295.9bp	26.9M	26.9M	26.7M	0.2M	0.0M	0.0M	0.2%	0.0%	3.0bp	0.0%	2.6bp
liver_JJ3_283	27.8M	24.5M	88.2%	22.3M	80.3%	2.2M	297.0bp	295.9bp	27.2M	27.2M	27.0M	0.2M	0.0M	0.0M	0.2%	0.0%	3.1bp	0.0%	2.8bp
liver_JJ3_308	28.1M	25.0M	88.8%	22.6M	80.5%	2.3M	297.0bp	295.7bp	26.8M	26.8M	26.5M	0.2M	0.0M	0.0M	0.2%	0.0%	3.3bp	0.0%	3.1bp
liver_JJ3_316	28.2M	26.2M	93.0%	23.9M	84.9%	2.3M	297.0bp	296.4bp	29.1M	29.1M	28.8M	0.3M	0.0M	0.0M	0.2%	0.0%	2.5bp	0.0%	2.5bp
liver_KK4_403	20.6M	18.3M	89.0%	16.5M	79.9%	1.9M	297.0bp	295.8bp	19.7M	19.7M	19.5M	0.2M	0.0M	0.0M	0.2%	0.0%	2.8bp	0.0%	2.7bp
liver_KK4_410	23.8M	21.6M	90.7%	19.4M	81.6%	2.2M	297.0bp	295.9bp	23.2M	23.2M	23.0M	0.2M	0.0M	0.0M	0.2%	0.0%	2.6bp	0.0%	2.8bp
liver_KK4_428	26.4M	23.0M	87.1%	21.0M	79.5%	2.0M	297.0bp	295.9bp	25.3M	25.3M	25.1M	0.2M	0.0M	0.0M	0.2%	0.0%	3.2bp	0.0%	2.9bp
liver_MAD_261	21.9M	16.7M	76.1%	15.4M	70.1%	1.3M	295.0bp	289.9bp	18.7M	18.6M	18.5M	0.1M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.7bp
liver_MAD_271	22.9M	18.4M	80.3%	17.1M	74.6%	1.3M	295.0bp	289.1bp	20.4M	20.3M	20.2M	0.2M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.7bp
liver_MAD_271.2	30.9M	28.1M	90.9%	25.6M	82.7%	2.5M	297.0bp	295.4bp	28.6M	28.6M	28.3M	0.3M	0.0M	0.0M	0.2%	0.0%	3.1bp	0.0%	2.8bp
liver_MAD_274	24.5M	19.4M	79.2%	18.2M	74.2%	1.2M	295.0bp	289.1bp	22.2M	22.2M	22.0M	0.2M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.6bp
liver_MAD_404	23.2M	20.8M	89.6%	18.8M	81.0%	2.0M	297.0bp	295.2bp	21.5M	21.5M	21.3M	0.2M	0.0M	0.0M	0.2%	0.0%	3.3bp	0.0%	2.9bp
liver_MAD_413	19.6M	14.9M	76.3%	13.7M	70.0%	1.2M	294.0bp	288.5bp	15.4M	15.4M	15.3M	0.1M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.7bp
liver_MAD_416	25.9M	24.7M	95.4%	22.8M	87.8%	2.0M	297.0bp	296.4bp	28.9M	28.9M	28.7M	0.2M	0.0M	0.0M	0.2%	0.0%	2.6bp	0.0%	2.3bp
liver_MAD_419	50.5M	42.0M	83.1%	37.4M	74.1%	4.5M	297.0bp	296.4bp	43.6M	43.6M	43.1M	0.4M	0.0M	0.0M	0.2%	0.0%	3.1bp	0.0%	3.0bp
liver_MAD_461	25.1M	20.4M	81.3%	18.3M	72.8%	2.1M	297.0bp	295.3bp	20.5M	20.5M	20.3M	0.2M	0.0M	0.0M	0.2%	0.0%	3.5bp	0.0%	2.3bp
liver_MAD_462	26.5M	24.9M	94.1%	22.6M	85.4%	2.3M	297.0bp	296.2bp	26.8M	26.8M	26.5M	0.3M	0.0M	0.0M	0.2%	0.0%	2.9bp	0.0%	2.4bp
liver_MAD_463	27.7M	22.7M	81.8%	21.1M	75.9%	1.6M	295.0bp	289.2bp	23.4M	23.4M	23.2M	0.2M	0.0M	0.0M	0.1%	0.0%	2.1bp	0.0%	1.7bp
liver_MAD_562	83.3M	79.1M	95.0%	72.5M	87.1%	6.6M	297.0bp	296.8bp	88.9M	88.8M	87.9M	0.8M	0.1M	0.1M	0.1%	0.0%	2.8bp	0.0%	2.0bp
liver_SAD_326	30.1M	23.3M	77.6%	21.6M	71.7%	1.8M	295.0bp	289.4bp	26.3M	26.2M	26.1M	0.2M	0.0M	0.0M	0.1%	0.0%	2.3bp	0.0%	1.7bp
liver_SAD_326.2	25.1M	22.0M	87.4%	20.0M	79.6%	2.0M	297.0bp	296.5bp	24.4M	24.4M	24.2M	0.2M	0.0M	0.0M	0.2%	0.0%	2.6bp	0.0%	2.8bp
liver_SAD_532	27.4M	24.3M	88.8%	22.3M	81.4%	2.0M	297.0bp	296.1bp	27.7M	27.7M	27.4M	0.2M	0.0M	0.0M	0.2%	0.0%	2.7bp	0.0%	2.5bp
liver_SAD_535	26.1M	20.8M	79.8%	19.3M	73.9%	1.5M	295.0bp	289.2bp	22.6M	22.6M	22.4M	0.2M	0.0M	0.0M	0.1%	0.0%	2.2bp	0.0%	1.7bp
liver_SAD_587	23.6M	17.6M	74.6%	16.1M	68.2%	1.5M	294.0bp	288.4bp	18.0M	18.0M	17.9M	0.1M	0.0M	0.0M	0.1%	0.0%	2.0bp	0.0%	1.8bp
liver_SAD_626	75.2M	70.0M	93.1%	63.2M	84.1%	6.8M	297.0bp	296.7bp	75.6M	75.6M	74.7M	0.8M	0.0M	0.0M	0.1%	0.0%	2.7bp	0.0%	2.4bp
liver_SAD_628	30.4M	27.0M	88.6%	24.2M	79.5%	2.8M	297.0bp	295.9bp	28.2M	28.2M	27.9M	0.3M	0.0M	0.0M	0.2%	0.0%	3.0bp	0.0%	2.4bp
liver_SAD_672	24.5M	19.5M	79.7%	17.3M	70.5%	2.3M	297.0bp	295.7bp	20.0M	20.0M	19.8M	0.2M	0.0M	0.0M	0.2%	0.0%	2.9bp	0.0%	2.5bp
liver_SAD_678	23.9M	22.6M	94.5%	20.7M	86.5%	1.9M	297.0bp	296.1bp	25.7M	25.7M	25.5M	0.2M	0.0M	0.0M	0.2%	0.0%	3.0bp	0.0%	2.3bp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: star_summary_table_table

Sort	Column	Description	ID	Scale
\|\|	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	Avg. read len	Average input read length	`star-avg_input_read_length`
\|\|	Avg. mapped len	Average mapped length	`star-avg_mapped_read_length`
\|\|	Splices	Number of splices: Total	`star-num_splices`	read_count
\|\|	Annotated splices	Number of splices: Annotated (sjdb)	`star-num_annotated_splices`	read_count
\|\|	GT/AG splices	Number of splices: GT/AG	`star-num_GTAG_splices`	read_count
\|\|	GC/AG splices	Number of splices: GC/AG	`star-num_GCAG_splices`	read_count
\|\|	AT/AC splices	Number of splices: AT/AC	`star-num_ATAC_splices`	read_count
\|\|	Non-canonical splices	Number of splices: Non-canonical	`star-num_noncanonical_splices`	read_count
\|\|	Mismatch rate	Mismatch rate per base	`star-mismatch_rate`
\|\|	Del rate	Deletion rate per base	`star-deletion_rate`
\|\|	Del len	Deletion average length	`star-deletion_length`
\|\|	Ins rate	Insertion rate per base	`star-insertion_rate`
\|\|	Ins len	Insertion average length	`star-insertion_length`

Alignment Scores

Created with MultiQC

Sample relationships

Plots interrogating sample relationships, based on final count matrices.

STAR_SALMON DESeq2 PCA plot

Created with MultiQC

STAR_SALMON DESeq2 sample similarity

Created with MultiQC

Biotype Counts

Biotype Counts shows reads overlapping genomic features of different biotypes, counted by featureCounts.

Created with MultiQC

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
BEDTOOLS_GENOMECOV_FW	bedtools	`2.31.1`
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DESEQ2_QC_STAR_SALMON	bioconductor-deseq2	`1.28.0`
	r-base	`4.0.3`
DupRadar	bioconductor-dupradar	`1.32.0`
FASTQC	fastqc	`0.12.1`
FQ_LINT	fq	`0.12.0 (2024-07-08)`
FQ_SUBSAMPLE	fq	`0.12.0 (2024-07-08)`
GTF2BED	perl	`5.26.2`
GTF_FILTER	python	`3.9.5`
GUNZIP_FASTA	gunzip	`1.13`
GUNZIP_GTF	gunzip	`1.13`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
MULTIQC_CUSTOM_BIOTYPE	python	`3.9.5`
QUALIMAP_RNASEQ	qualimap	`2.3`
RSEQC_BAMSTAT	rseqc	`5.0.2`
RSEQC_INFEREXPERIMENT	rseqc	`5.0.2`
RSEQC_INNERDISTANCE	rseqc	`5.0.2`
RSEQC_JUNCTIONANNOTATION	rseqc	`5.0.2`
RSEQC_JUNCTIONSATURATION	rseqc	`5.0.2`
RSEQC_READDISTRIBUTION	rseqc	`5.0.2`
RSEQC_READDUPLICATION	rseqc	`5.0.2`
SALMON_INDEX	salmon	`1.10.3`
SALMON_QUANT	salmon	`1.10.3`
SAMTOOLS_FLAGSTAT	samtools	`1.21`
SAMTOOLS_IDXSTATS	samtools	`1.21`
SAMTOOLS_INDEX	samtools	`1.21`
SAMTOOLS_SORT	samtools	`1.21`
SAMTOOLS_STATS	samtools	`1.21`
SE_GENE_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
SE_TRANSCRIPT_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
STAR_ALIGN	gawk	`5.1.0`
	samtools	`1.21`
	star	`2.7.11b`
STAR_GENOMEGENERATE	gawk	`5.1.0`
	samtools	`1.21`
	star	`2.7.11b`
STRINGTIE_STRINGTIE	stringtie	`2.2.3`
SUBREAD_FEATURECOUNTS	subread	`2.0.6`
TRIMGALORE	cutadapt	`4.9`
	pigz	`2.8`
	trimgalore	`0.6.10`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
UCSC_BEDCLIP	ucsc	`377`
UCSC_BEDGRAPHTOBIGWIG	ucsc	`469`
Workflow	Nextflow	`24.10.4`
	nf-core/rnaseq	`v3.19.0-g0bb032c`

nf-core/rnaseq Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

Methods

Data was processed using nf-core/rnaseq v3.19.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v24.10.4 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq --input /scratch/jar301/steve2/sample_sheet_batch1_batch2.csv --outdir /scratch/jar301/steve2/results_mouse --aligner star_salmon --fasta /home/jar301/reference_genome/Mus_musculus.GRCm39.dna.primary_assembly.fa.gz --gtf /home/jar301/reference_genome/Mus_musculus.GRCm39.114.gtf.gz --skip_markduplicates --igenomes_ignore --genome null --email jm.arbones@uky.edu -profile singularity -c /home/jar301/nextflow.config

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/rnaseq Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

Input/output options

email: jm.arbones@uky.edu
input: /scratch/jar301/steve2/sample_sheet_batch1_batch2.csv
outdir: /scratch/jar301/steve2/results_mouse

Reference genome options

fasta: /home/jar301/reference_genome/Mus_musculus.GRCm39.dna.primary_assembly.fa.gz
genome: null
gtf: /home/jar301/reference_genome/Mus_musculus.GRCm39.114.gtf.gz
igenomes_ignore: true

Process skipping options

skip_markduplicates: true

Generic options

trace_report_suffix: 2025-10-12_09-54-44

Core Nextflow options

configFiles: /home/jar301/.nextflow/assets/nf-core/rnaseq/nextflow.config, /home/jar301/nextflow.config
containerEngine: singularity
launchDir: /scratch/jar301/steve2
profile: singularity
projectDir: /home/jar301/.nextflow/assets/nf-core/rnaseq
revision: master
runName: loquacious_kirch
userName: jar301
workDir: /scratch/jar301/steve2/work

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