rnaseq_Cgigas_ArredondoEspinoza2023: MultiQC Report

General Statistics

Showing ⁰/₁₀₈ rows and ²³/₄₀ columns.

Sample Name	dupInt	Duplication	5'-3' bias	M Aligned	Proper Pairs	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Reads	Reads mapped	% Reads mapped	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Dups	GC	Avg len	Median len	Failed	Seqs	% Duplication	% > Q30	Mb Q30 bases	Reads After Filtering	GC content	% PF	% Adapter	Dups	GC	Avg len	Median len	Failed	Seqs
SRX5644304	0.0	17.0%	1.35	11.7M	61.3%	1.36%	6.1M	23.3M	100.0%	100.0%	2.3%	23.3M	29.5M	29.5M	100.0%	15.6M	11.7M	75.0%	10.8M	69.6%	0.8M							11.0%	94.4%	2631.5Mb	31.1M	41.8%	100.0%	14.1%
SRX5644304_1																						55.3%	41.0%	101bp	101bp	18%	15.6M								56.5%	41.0%	89bp	90bp	9%	15.6M
SRX5644304_2																						53.9%	41.0%	100bp	101bp	18%	15.6M								53.9%	41.0%	89bp	90bp	9%	15.6M
SRX5644305	0.0	9.9%	1.32	6.4M	67.6%	1.35%	3.2M	12.9M	100.0%	100.0%	2.1%	12.9M	16.0M	16.0M	100.0%	8.0M	6.4M	80.5%	6.0M	74.9%	0.4M							6.0%	94.1%	1353.4Mb	16.0M	41.7%	100.0%	6.1%
SRX5644305_1																						43.6%	41.0%	101bp	101bp	9%	8.0M								44.7%	41.0%	90bp	90bp	0%	8.0M
SRX5644305_2																						43.2%	41.0%	100bp	101bp	9%	8.0M								43.2%	41.0%	89bp	90bp	0%	8.0M
SRX5644306	0.0	13.1%	1.82	10.5M	67.5%	1.32%	4.2M	21.0M	100.0%	100.0%	1.5%	21.0M	25.1M	25.1M	100.0%	13.3M	10.5M	78.6%	9.7M	73.1%	0.7M							7.7%	94.3%	2256.7Mb	26.7M	43.8%	100.0%	11.9%
SRX5644306_1																						50.1%	43.0%	101bp	101bp	18%	13.3M								52.3%	43.0%	89bp	90bp	9%	13.3M
SRX5644306_2																						49.3%	43.0%	100bp	101bp	0%	13.3M								49.3%	43.0%	89bp	90bp	0%	13.3M
SRX5644307	0.0	10.4%	1.58	5.6M	70.3%	1.32%	2.2M	11.1M	100.0%	100.0%	1.5%	11.1M	13.3M	13.3M	100.0%	7.5M	5.6M	73.9%	5.2M	69.1%	0.4M							7.5%	94.5%	1274.5Mb	15.1M	42.9%	100.0%	15.0%
SRX5644307_1																						45.4%	42.0%	101bp	101bp	9%	7.5M								47.2%	42.0%	89bp	90bp	0%	7.5M
SRX5644307_2																						43.9%	42.0%	100bp	101bp	0%	7.5M								43.9%	42.0%	89bp	90bp	0%	7.5M
SRX5644308	0.0	12.2%	1.42	9.2M	64.0%	1.38%	5.1M	18.4M	100.0%	100.0%	2.4%	18.4M	23.5M	23.5M	100.0%	12.4M	9.2M	74.4%	8.5M	68.8%	0.7M							8.8%	94.1%	2086.7Mb	24.8M	41.7%	100.0%	13.0%
SRX5644308_1																						50.6%	41.0%	101bp	101bp	18%	12.4M								51.9%	41.0%	89bp	90bp	9%	12.4M
SRX5644308_2																						49.5%	41.0%	100bp	101bp	9%	12.4M								49.4%	41.0%	89bp	90bp	0%	12.4M
SRX5644309	0.0	15.8%	1.46	14.7M	61.2%	1.36%	8.2M	29.3M	100.0%	100.0%	2.3%	29.3M	37.5M	37.5M	100.0%	20.1M	14.7M	72.8%	13.5M	67.1%	1.2M							10.4%	94.1%	3380.4Mb	40.2M	42.6%	100.0%	17.3%
SRX5644309_1																						56.1%	42.0%	101bp	101bp	18%	20.1M								57.5%	42.0%	89bp	90bp	9%	20.1M
SRX5644309_2																						55.4%	42.0%	100bp	101bp	18%	20.1M								55.2%	42.0%	89bp	90bp	9%	20.1M
SRX5644310	0.0	18.0%	2.18	11.0M	58.0%	1.23%	7.0M	22.0M	100.0%	100.0%	2.8%	22.0M	28.9M	28.9M	100.0%	14.4M	11.0M	76.4%	10.1M	70.4%	0.9M							10.1%	94.3%	2426.9Mb	28.7M	45.2%	100.0%	16.4%
SRX5644310_1																						55.7%	45.0%	101bp	101bp	18%	14.4M								58.2%	45.0%	89bp	90bp	9%	14.4M
SRX5644310_2																						55.9%	45.0%	100bp	101bp	18%	14.4M								55.7%	45.0%	89bp	90bp	9%	14.4M
SRX5644311	0.0	15.8%	2.14	11.4M	62.8%	1.26%	5.6M	22.8M	100.0%	100.0%	2.0%	22.8M	28.5M	28.5M	100.0%	15.5M	11.4M	73.6%	10.6M	68.0%	0.9M							9.6%	94.4%	2610.4Mb	31.0M	45.2%	100.0%	19.1%
SRX5644311_1																						54.9%	45.0%	101bp	101bp	18%	15.5M								57.4%	45.0%	89bp	90bp	9%	15.5M
SRX5644311_2																						55.1%	45.0%	100bp	101bp	18%	15.5M								54.9%	45.0%	89bp	90bp	9%	15.5M
SRX5644312	0.0	14.2%	1.58	9.8M	67.6%	1.38%	3.7M	19.7M	100.0%	100.0%	1.4%	19.7M	23.3M	23.3M	100.0%	12.8M	9.8M	76.7%	9.2M	71.8%	0.6M							9.0%	94.7%	2183.2Mb	25.6M	42.2%	100.0%	11.9%
SRX5644312_1																						52.1%	42.0%	101bp	101bp	18%	12.8M								54.0%	42.0%	90bp	90bp	9%	12.8M
SRX5644312_2																						49.5%	42.0%	100bp	101bp	9%	12.8M								49.4%	42.0%	89bp	90bp	0%	12.8M
SRX5644313	0.0	15.6%	1.78	14.2M	65.4%	1.30%	5.7M	28.3M	100.0%	100.0%	1.6%	28.3M	34.0M	34.0M	100.0%	18.3M	14.2M	77.5%	13.2M	72.1%	1.0M							9.5%	94.5%	3102.2Mb	36.6M	43.4%	100.0%	12.9%
SRX5644313_1																						54.6%	43.0%	101bp	101bp	18%	18.3M								56.9%	43.0%	89bp	90bp	9%	18.3M
SRX5644313_2																						53.4%	43.0%	100bp	101bp	18%	18.3M								53.4%	43.0%	89bp	90bp	9%	18.3M
SRX5644314	0.0	12.3%	1.67	7.4M	64.5%	1.28%	3.9M	14.7M	100.0%	100.0%	2.3%	14.7M	18.6M	18.6M	100.0%	9.3M	7.4M	79.4%	6.8M	73.5%	0.5M							7.9%	94.4%	1573.4Mb	18.5M	43.3%	100.0%	9.7%
SRX5644314_1																						48.4%	43.0%	101bp	101bp	9%	9.3M								50.5%	43.0%	90bp	90bp	9%	9.3M
SRX5644314_2																						47.4%	43.0%	100bp	101bp	9%	9.3M								47.4%	43.0%	89bp	90bp	0%	9.3M
SRX5644315	0.0	13.2%	1.76	8.2M	66.4%	1.29%	3.6M	16.4M	100.0%	100.0%	1.7%	16.4M	20.0M	20.0M	100.0%	10.8M	8.2M	75.8%	7.6M	70.5%	0.6M							8.2%	94.4%	1822.9Mb	21.6M	43.7%	100.0%	14.9%
SRX5644315_1																						49.8%	43.0%	101bp	101bp	9%	10.8M								52.0%	43.0%	89bp	90bp	9%	10.8M
SRX5644315_2																						49.1%	43.0%	100bp	101bp	9%	10.8M								49.1%	43.0%	89bp	90bp	0%	10.8M
SRX5644316	0.0	16.7%	1.79	11.3M	62.7%	1.27%	5.4M	22.6M	100.0%	100.0%	2.0%	22.6M	28.0M	28.0M	100.0%	13.9M	11.3M	80.9%	10.5M	75.0%	0.8M							9.5%	94.2%	2366.5Mb	27.9M	44.6%	100.0%	10.8%
SRX5644316_1																						53.9%	44.0%	101bp	101bp	18%	13.9M								56.4%	44.0%	90bp	90bp	9%	13.9M
SRX5644316_2																						53.7%	44.0%	100bp	101bp	9%	13.9M								53.8%	44.0%	89bp	90bp	9%	13.9M
SRX5644317	0.0	16.1%	1.81	12.2M	65.4%	1.27%	4.8M	24.4M	100.0%	100.0%	1.5%	24.4M	29.2M	29.2M	100.0%	15.9M	12.2M	76.6%	11.3M	71.2%	0.9M							9.5%	94.4%	2693.0Mb	31.8M	44.7%	100.0%	15.4%
SRX5644317_1																						54.3%	44.0%	101bp	101bp	18%	15.9M								56.8%	44.0%	89bp	90bp	9%	15.9M
SRX5644317_2																						54.0%	44.0%	100bp	101bp	9%	15.9M								54.0%	44.0%	89bp	90bp	9%	15.9M
SRX5644318	0.0	12.2%	1.70	11.8M	68.0%	1.31%	4.8M	23.6M	100.0%	100.0%	1.6%	23.6M	28.4M	28.4M	100.0%	14.8M	11.8M	79.6%	11.0M	74.2%	0.8M							7.6%	94.3%	2512.4Mb	29.6M	43.3%	100.0%	9.2%
SRX5644318_1																						50.8%	43.0%	101bp	101bp	18%	14.8M								53.1%	43.0%	90bp	90bp	9%	14.8M
SRX5644318_2																						49.9%	43.0%	100bp	101bp	0%	14.8M								49.9%	43.0%	89bp	90bp	0%	14.8M
SRX5644319	0.0	14.3%	1.93	8.5M	62.1%	1.28%	4.8M	16.9M	100.0%	100.0%	2.5%	16.9M	21.7M	21.7M	100.0%	10.5M	8.5M	80.6%	7.8M	74.5%	0.6M							8.5%	94.1%	1777.1Mb	21.0M	44.0%	100.0%	8.9%
SRX5644319_1																						52.1%	43.0%	101bp	101bp	18%	10.5M								54.4%	43.0%	90bp	90bp	9%	10.5M
SRX5644319_2																						51.7%	44.0%	100bp	101bp	9%	10.5M								51.6%	44.0%	89bp	90bp	9%	10.5M
SRX5644320	0.0	11.2%	1.41	8.3M	67.1%	1.39%	3.9M	16.7M	100.0%	100.0%	1.9%	16.7M	20.5M	20.5M	100.0%	11.5M	8.3M	72.6%	7.7M	67.3%	0.6M							9.0%	94.3%	1941.8Mb	23.0M	41.6%	100.0%	14.2%
SRX5644320_1																						49.1%	41.0%	101bp	101bp	10%	11.5M								50.4%	41.0%	89bp	90bp	10%	11.5M
SRX5644320_2																						47.0%	41.0%	100bp	101bp	10%	11.5M								46.9%	41.0%	89bp	90bp	0%	11.5M
SRX5644321	0.0	9.3%	1.37	6.7M	68.3%	1.43%	3.1M	13.3M	100.0%	100.0%	1.9%	13.3M	16.4M	16.4M	100.0%	9.6M	6.7M	69.4%	6.2M	64.3%	0.5M							7.6%	94.2%	1610.7Mb	19.2M	41.5%	100.0%	19.4%
SRX5644321_1																						45.5%	41.0%	101bp	101bp	9%	9.6M								46.9%	41.0%	89bp	90bp	0%	9.6M
SRX5644321_2																						43.7%	41.0%	100bp	101bp	9%	9.6M								43.7%	41.0%	89bp	90bp	0%	9.6M
SRX5644322	0.0	12.0%	1.40	6.6M	67.1%	1.37%	2.9M	13.1M	100.0%	100.0%	1.8%	13.1M	16.1M	16.1M	100.0%	9.2M	6.6M	71.6%	6.1M	66.5%	0.5M							11.1%	94.3%	1556.6Mb	18.4M	41.2%	100.0%	12.0%
SRX5644322_1																						51.8%	41.0%	101bp	101bp	18%	9.2M								53.5%	41.0%	90bp	90bp	9%	9.2M
SRX5644322_2																						50.7%	41.0%	100bp	101bp	18%	9.2M								50.7%	41.0%	89bp	90bp	9%	9.2M
SRX5644323	0.0	10.4%	1.43	8.0M	66.5%	1.39%	4.0M	16.0M	100.0%	100.0%	2.0%	16.0M	20.0M	20.0M	100.0%	11.2M	8.0M	71.3%	7.4M	65.8%	0.6M							7.7%	94.2%	1883.1Mb	22.4M	42.0%	100.0%	18.3%
SRX5644323_1																						47.5%	42.0%	101bp	101bp	9%	11.2M								48.9%	41.0%	89bp	90bp	0%	11.2M
SRX5644323_2																						46.3%	42.0%	100bp	101bp	9%	11.2M								46.2%	42.0%	89bp	90bp	0%	11.2M
SRX5644324	0.0	12.8%	1.48	9.9M	61.5%	1.31%	6.2M	19.8M	100.0%	100.0%	2.7%	19.8M	26.1M	26.1M	100.0%	13.3M	9.9M	74.9%	9.1M	68.5%	0.8M							8.3%	94.3%	2238.9Mb	26.5M	43.1%	100.0%	14.6%
SRX5644324_1																						50.1%	43.0%	101bp	101bp	20%	13.3M								51.2%	43.0%	89bp	90bp	10%	13.3M
SRX5644324_2																						50.7%	43.0%	100bp	101bp	20%	13.3M								50.6%	43.0%	89bp	90bp	10%	13.3M
SRX5644325	0.0	10.2%	1.47	8.6M	68.5%	1.36%	3.8M	17.2M	100.0%	100.0%	1.7%	17.2M	21.0M	21.0M	100.0%	12.2M	8.6M	70.3%	8.0M	65.2%	0.6M							7.7%	94.4%	2053.0Mb	24.4M	42.6%	100.0%	18.3%
SRX5644325_1																						48.1%	42.0%	101bp	101bp	10%	12.2M								49.1%	42.0%	89bp	90bp	0%	12.2M
SRX5644325_2																						48.9%	42.0%	100bp	101bp	0%	12.2M								48.7%	42.0%	89bp	90bp	0%	12.2M
SRX5644326	0.0	10.4%	1.43	8.4M	69.8%	1.31%	3.4M	16.9M	100.0%	100.0%	1.6%	16.9M	20.3M	20.3M	100.0%	11.4M	8.4M	73.8%	7.9M	68.9%	0.6M							7.1%	94.2%	1926.2Mb	22.9M	43.2%	100.0%	15.4%
SRX5644326_1																						46.3%	43.0%	101bp	101bp	10%	11.4M								47.2%	43.0%	89bp	90bp	0%	11.4M
SRX5644326_2																						47.2%	43.0%	100bp	101bp	0%	11.4M								47.1%	43.0%	89bp	90bp	0%	11.4M
SRX5644327	0.0	13.2%	1.50	9.7M	68.1%	1.31%	3.9M	19.5M	100.0%	100.0%	1.7%	19.5M	23.4M	23.4M	100.0%	12.3M	9.7M	79.3%	9.2M	74.6%	0.6M							7.8%	94.2%	2082.2Mb	24.6M	42.6%	100.0%	8.3%
SRX5644327_1																						52.5%	42.0%	101bp	101bp	20%	12.3M								53.5%	42.0%	90bp	90bp	10%	12.3M
SRX5644327_2																						53.3%	42.0%	100bp	101bp	10%	12.3M								53.3%	42.0%	89bp	90bp	10%	12.3M
SRX5644328	0.0	11.3%	1.46	9.8M	62.2%	1.34%	6.3M	19.6M	100.0%	100.0%	2.8%	19.6M	25.9M	25.9M	100.0%	13.1M	9.8M	74.7%	9.0M	68.4%	0.8M							7.4%	94.1%	2209.6Mb	26.2M	42.9%	100.0%	14.8%
SRX5644328_1																						48.6%	42.0%	101bp	101bp	10%	13.1M								49.8%	42.0%	89bp	90bp	0%	13.1M
SRX5644328_2																						49.4%	43.0%	100bp	101bp	10%	13.1M								49.3%	42.0%	89bp	90bp	0%	13.1M
SRX5644329	1.9	82.6%	1.27	13.1M	12.5%	1.39%	7.0M	26.2M	100.0%	100.0%	2.3%	26.2M	33.2M	33.2M	100.0%	17.6M	13.1M	74.5%	12.1M	68.9%	1.0M							59.2%	94.4%	2994.1Mb	35.2M	41.1%	100.0%	11.3%
SRX5644329_1																						84.3%	41.0%	101bp	101bp	20%	17.6M								84.5%	41.0%	90bp	90bp	10%	17.6M
SRX5644329_2																						84.8%	41.0%	100bp	101bp	20%	17.6M								84.8%	41.0%	89bp	90bp	10%	17.6M
SRX5644330	0.0	13.5%	1.45	9.8M	65.9%	1.35%	4.3M	19.6M	100.0%	100.0%	1.7%	19.6M	23.9M	23.9M	100.0%	13.5M	9.8M	72.7%	9.1M	67.4%	0.7M							9.5%	94.5%	2274.0Mb	26.9M	42.3%	100.0%	16.4%
SRX5644330_1																						51.6%	42.0%	101bp	101bp	20%	13.5M								52.5%	42.0%	89bp	90bp	10%	13.5M
SRX5644330_2																						51.2%	42.0%	100bp	101bp	20%	13.5M								51.1%	42.0%	89bp	90bp	10%	13.5M
SRX5644331	0.0	13.5%	1.40	10.6M	67.0%	1.41%	4.3M	21.1M	100.0%	100.0%	1.6%	21.1M	25.5M	25.5M	100.0%	13.9M	10.6M	76.0%	9.8M	70.8%	0.7M							9.8%	94.7%	2374.4Mb	27.8M	41.0%	100.0%	10.5%
SRX5644331_1																						52.4%	41.0%	101bp	101bp	20%	13.9M								53.4%	40.0%	90bp	90bp	10%	13.9M
SRX5644331_2																						51.5%	41.0%	100bp	101bp	20%	13.9M								51.4%	41.0%	90bp	90bp	10%	13.9M
SRX5644332	0.0	11.4%	1.38	7.7M	65.1%	1.38%	4.1M	15.4M	100.0%	100.0%	2.3%	15.4M	19.4M	19.4M	100.0%	10.5M	7.7M	73.6%	7.1M	68.2%	0.6M							8.5%	94.4%	1769.1Mb	20.9M	41.2%	100.0%	13.8%
SRX5644332_1																						49.7%	41.0%	101bp	101bp	10%	10.5M								50.8%	41.0%	89bp	90bp	10%	10.5M
SRX5644332_2																						50.2%	41.0%	100bp	101bp	20%	10.5M								50.1%	41.0%	89bp	90bp	10%	10.5M
SRX5644333	0.0	11.0%	1.43	9.9M	67.3%	1.39%	4.5M	19.8M	100.0%	100.0%	1.9%	19.8M	24.3M	24.3M	100.0%	13.5M	9.9M	73.4%	9.2M	68.0%	0.7M							8.1%	94.3%	2279.4Mb	27.0M	41.7%	100.0%	13.9%
SRX5644333_1																						49.0%	41.0%	101bp	101bp	10%	13.5M								50.2%	41.0%	89bp	90bp	10%	13.5M
SRX5644333_2																						49.3%	41.0%	100bp	101bp	10%	13.5M								49.2%	41.0%	89bp	90bp	0%	13.5M
SRX5644334	0.0	12.9%	1.32	7.9M	61.8%	1.36%	4.8M	15.8M	100.0%	100.0%	2.7%	15.8M	20.6M	20.6M	100.0%	11.1M	7.9M	71.1%	7.3M	65.5%	0.6M							10.2%	94.5%	1880.2Mb	22.2M	41.0%	100.0%	15.9%
SRX5644334_1																						52.7%	41.0%	101bp	101bp	20%	11.1M								53.8%	40.0%	89bp	90bp	10%	11.1M
SRX5644334_2																						53.2%	41.0%	100bp	101bp	20%	11.1M								53.1%	41.0%	89bp	90bp	10%	11.1M
SRX5644335	0.0	11.8%	1.39	7.7M	64.0%	1.37%	4.3M	15.3M	100.0%	100.0%	2.4%	15.3M	19.6M	19.6M	100.0%	10.4M	7.7M	73.5%	7.1M	67.8%	0.6M							8.6%	94.7%	1773.3Mb	20.9M	41.8%	100.0%	14.9%
SRX5644335_1																						48.3%	41.0%	101bp	101bp	10%	10.4M								49.3%	41.0%	89bp	90bp	0%	10.4M
SRX5644335_2																						48.3%	42.0%	100bp	101bp	10%	10.4M								48.2%	41.0%	89bp	90bp	0%	10.4M
SRX5644336	0.0	12.5%	1.40	9.5M	64.8%	1.35%	4.8M	19.0M	100.0%	100.0%	2.1%	19.0M	23.8M	23.8M	100.0%	13.0M	9.5M	72.7%	8.8M	67.2%	0.7M							8.5%	94.2%	2197.2Mb	26.1M	42.2%	100.0%	17.3%
SRX5644336_1																						49.8%	42.0%	101bp	101bp	9%	13.0M								51.1%	42.0%	89bp	90bp	9%	13.0M
SRX5644336_2																						49.3%	42.0%	100bp	101bp	9%	13.0M								49.2%	42.0%	89bp	90bp	0%	13.0M
SRX5644337	0.0	14.0%	1.39	12.9M	66.2%	1.31%	5.6M	25.7M	100.0%	100.0%	1.8%	25.7M	31.3M	31.3M	100.0%	17.7M	12.9M	72.7%	12.0M	67.9%	0.8M							9.2%	94.1%	2974.9Mb	35.4M	42.1%	100.0%	18.2%
SRX5644337_1																						53.2%	42.0%	101bp	101bp	18%	17.7M								54.3%	42.0%	89bp	90bp	9%	17.7M
SRX5644337_2																						52.5%	42.0%	100bp	101bp	9%	17.7M								52.2%	42.0%	89bp	90bp	9%	17.7M
SRX5644338	0.0	14.7%	1.37	7.9M	64.7%	1.34%	3.7M	15.8M	100.0%	100.0%	2.0%	15.8M	19.5M	19.5M	100.0%	11.2M	7.9M	70.2%	7.4M	65.5%	0.5M							9.9%	94.5%	1892.1Mb	22.5M	42.0%	100.0%	20.8%
SRX5644338_1																						52.9%	42.0%	101bp	101bp	18%	11.2M								54.1%	41.0%	89bp	90bp	9%	11.2M
SRX5644338_2																						52.1%	42.0%	100bp	101bp	18%	11.2M								51.9%	42.0%	89bp	90bp	9%	11.2M
SRX5644339	0.0	10.6%	1.38	9.8M	66.6%	1.40%	4.9M	19.6M	100.0%	100.0%	2.0%	19.6M	24.5M	24.5M	100.0%	13.5M	9.8M	72.7%	9.1M	67.2%	0.7M							6.8%	94.2%	2271.0Mb	27.0M	42.1%	100.0%	16.3%
SRX5644339_1																						46.9%	42.0%	101bp	101bp	9%	13.5M								48.1%	42.0%	89bp	90bp	0%	13.5M
SRX5644339_2																						46.3%	42.0%	100bp	101bp	9%	13.5M								46.2%	42.0%	89bp	90bp	0%	13.5M

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: DupRadar	dupInt	Intercept value from DupRadar	`custom_content_dupradar-dupRadar_intercept`
\|\|	Picard: Mark Duplicates	Duplication	Mark Duplicates - Percent Duplication	`picard_mark_duplicates-PERCENT_DUPLICATION`
\|\|	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
\|\|	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: 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`
\|\|	fastp	% Duplication	Duplication rate before filtering	`fastp-pct_duplication`
\|\|	fastp	% > Q30	Percentage of reads > Q30 after filtering	`fastp-after_filtering_q30_rate`
\|\|	fastp	Mb Q30 bases	Bases > Q30 after filtering (millions)	`fastp-after_filtering_q30_bases`	base_count
\|\|	fastp	Reads After Filtering	Total reads after filtering (millions)	`fastp-filtering_result_passed_filter_reads`	read_count
\|\|	fastp	GC content	GC content after filtering	`fastp-after_filtering_gc_content`
\|\|	fastp	% PF	Percent reads passing filter	`fastp-pct_surviving`
\|\|	fastp	% Adapter	Percentage adapter-trimmed reads	`fastp-pct_adapter`
\|\|	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`

Sample status checks

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

Strandedness Checks

Showing ⁰/₇₂ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
SRX5644304	pass	RSeQC	auto	reverse	2.0	95.7	2.3
SRX5644304	pass	Salmon	auto	reverse	12.4	87.6	0.0
SRX5644305	pass	RSeQC	auto	reverse	2.9	92.9	4.2
SRX5644305	pass	Salmon	auto	reverse	7.9	92.1	0.0
SRX5644306	pass	RSeQC	auto	reverse	2.3	95.7	1.9
SRX5644306	pass	Salmon	auto	reverse	10.8	89.2	0.0
SRX5644307	pass	RSeQC	auto	reverse	2.3	94.4	3.2
SRX5644307	pass	Salmon	auto	reverse	12.9	87.1	0.0
SRX5644308	pass	RSeQC	auto	reverse	3.3	90.3	6.4
SRX5644308	pass	Salmon	auto	reverse	11.8	88.2	0.0
SRX5644309	pass	RSeQC	auto	reverse	2.4	95.8	1.8
SRX5644309	pass	Salmon	auto	reverse	14.5	85.5	0.0
SRX5644310	pass	RSeQC	auto	reverse	2.2	94.2	3.6
SRX5644310	pass	Salmon	auto	reverse	13.8	86.2	0.0
SRX5644311	pass	RSeQC	auto	reverse	2.4	94.3	3.3
SRX5644311	pass	Salmon	auto	reverse	15.7	84.3	0.0
SRX5644312	pass	RSeQC	auto	reverse	2.4	94.8	2.8
SRX5644312	pass	Salmon	auto	reverse	11.2	88.8	0.0
SRX5644313	pass	RSeQC	auto	reverse	2.6	94.3	3.1
SRX5644313	pass	Salmon	auto	reverse	11.5	88.5	0.0
SRX5644314	pass	RSeQC	auto	reverse	2.0	93.8	4.2
SRX5644314	pass	Salmon	auto	reverse	9.4	90.6	0.0
SRX5644315	pass	RSeQC	auto	reverse	2.6	92.3	5.0
SRX5644315	pass	Salmon	auto	reverse	12.5	87.5	0.0
SRX5644316	pass	RSeQC	auto	reverse	1.8	95.7	2.5
SRX5644316	pass	Salmon	auto	reverse	10.4	89.6	0.0
SRX5644317	pass	RSeQC	auto	reverse	2.3	95.7	1.9
SRX5644317	pass	Salmon	auto	reverse	13.3	86.7	0.0
SRX5644318	pass	RSeQC	auto	reverse	1.8	96.1	2.0
SRX5644318	pass	Salmon	auto	reverse	9.1	90.9	0.0
SRX5644319	pass	RSeQC	auto	reverse	2.8	93.7	3.6
SRX5644319	pass	Salmon	auto	reverse	9.0	91.0	0.0
SRX5644320	pass	RSeQC	auto	reverse	2.9	92.1	5.0
SRX5644320	pass	Salmon	auto	reverse	12.5	87.5	0.0
SRX5644321	pass	RSeQC	auto	reverse	3.8	91.1	5.1
SRX5644321	pass	Salmon	auto	reverse	15.9	84.1	0.0
SRX5644322	pass	RSeQC	auto	reverse	3.2	92.0	4.7
SRX5644322	pass	Salmon	auto	reverse	11.1	88.9	0.0
SRX5644323	pass	RSeQC	auto	reverse	3.5	91.0	5.5
SRX5644323	pass	Salmon	auto	reverse	15.1	84.9	0.0
SRX5644324	pass	RSeQC	auto	reverse	3.3	92.7	4.0
SRX5644324	pass	Salmon	auto	reverse	12.5	87.5	0.0
SRX5644325	pass	RSeQC	auto	reverse	2.1	93.9	4.0
SRX5644325	pass	Salmon	auto	reverse	15.3	84.7	0.0
SRX5644326	pass	RSeQC	auto	reverse	2.3	91.5	6.2
SRX5644326	pass	Salmon	auto	reverse	13.4	86.6	0.0
SRX5644327	pass	RSeQC	auto	reverse	2.1	89.6	8.2
SRX5644327	pass	Salmon	auto	reverse	8.9	91.1	0.0
SRX5644328	pass	RSeQC	auto	reverse	3.0	93.5	3.5
SRX5644328	pass	Salmon	auto	reverse	12.7	87.3	0.0
SRX5644329	pass	RSeQC	auto	reverse	2.2	92.1	5.7
SRX5644329	pass	Salmon	auto	reverse	11.2	88.8	0.0
SRX5644330	pass	RSeQC	auto	reverse	2.8	92.2	4.9
SRX5644330	pass	Salmon	auto	reverse	13.9	86.1	0.0
SRX5644331	pass	RSeQC	auto	reverse	2.5	94.8	2.7
SRX5644331	pass	Salmon	auto	reverse	10.5	89.5	0.0
SRX5644332	pass	RSeQC	auto	reverse	3.7	91.2	5.1
SRX5644332	pass	Salmon	auto	reverse	12.5	87.5	0.0
SRX5644333	pass	RSeQC	auto	reverse	3.1	92.3	4.6
SRX5644333	pass	Salmon	auto	reverse	12.2	87.8	0.0
SRX5644334	pass	RSeQC	auto	reverse	4.0	90.2	5.8
SRX5644334	pass	Salmon	auto	reverse	13.7	86.3	0.0
SRX5644335	pass	RSeQC	auto	reverse	2.7	92.1	5.2
SRX5644335	pass	Salmon	auto	reverse	12.9	87.1	0.0
SRX5644336	pass	RSeQC	auto	reverse	2.5	93.1	4.4
SRX5644336	pass	Salmon	auto	reverse	14.5	85.5	0.0
SRX5644337	pass	RSeQC	auto	reverse	1.7	95.6	2.7
SRX5644337	pass	Salmon	auto	reverse	15.2	84.8	0.0
SRX5644338	pass	RSeQC	auto	reverse	3.4	90.6	5.9
SRX5644338	pass	Salmon	auto	reverse	16.9	83.1	0.0
SRX5644339	pass	RSeQC	auto	reverse	2.3	94.2	3.5
SRX5644339	pass	Salmon	auto	reverse	14.4	85.6	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

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.

72 samples had less than 1% of reads made up of overrepresented sequences

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 ⁰/₉ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
CTTGAATAAACTTTGCTAAGGATGTGATATGCAATTTCGAGTTTTAAGAT	11	204169	0.0221%
CCCACATTAAAGCTGCACTTAAAAGCATTTTTCGAACAACCAGCTATCTT	9	159719	0.0173%
GGGGACTACTAGCAATAAATAGCAGTTTAAAAACTTTTTCTAAATGAAGT	8	137511	0.0149%
GTTCGATGTAAGAATTCTTGAATAAACTTTGCTAAGGATGTGATATGCAA	6	79089	0.0085%
CTTTAACACACTACGCCCCACATTAAAGCTGCACTTAAAAGCATTTTTCG	4	96087	0.0104%
CCCGCATTAAAGCTGCACTTAAAAGCATTTTTCGAACAACCAGCTATCTT	3	54280	0.0059%
CTCTATTATTCTGCTTTCCTAAAAATCTTTTTATTTCGGGAGGTGCTAAT	1	11084	0.0012%
GTCCTTTCGTACATGCCGAGATAAGTTTATGTAAAGCAGATAGAAACCAA	1	10564	0.0011%
CAGCTATCTTAAAACTCGAAATTGCATATCACATCCTTAGCAAAGTTTAT	1	10106	0.0011%

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.

Min:

Max:

Created with MultiQC

fastp

All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

Filtered Reads

Filtering statistics of sampled reads.

Created with MultiQC

Insert Sizes

Insert size estimation of sampled reads.

Created with MultiQC

Sequence Quality

Average sequencing quality over each base of all reads.

Created with MultiQC

GC Content

Average GC content over each base of all reads.

Created with MultiQC

N content

Average N content over each base of all reads.

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.

72 samples had less than 1% of reads made up of overrepresented sequences

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 ⁰/₁₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
AGCTGCACTTAAAAGCATTTTTCGAACAACCAGCTATCTTAAAACTCGAA	19	345542	0.0373%
CTTTGCTAAGGATGTGATATGCAATTTCGAGTTTTAAGATAGCTGGTTGT	11	204389	0.0221%
AGCAATAAATAGCAGTTTAAAAACTTTTTCTAAATGAAGTATAGGAGCAT	8	138063	0.0149%
AGAATTCTTGAATAAACTTTGCTAAGGATGTGATATGCAATTTCGAGTTT	6	79350	0.0086%
CTACGCCCCACATTAAAGCTGCACTTAAAAGCATTTTTCGAACAACCAGC	4	96786	0.0105%
CTGCTTTCCTAAAAATCTTTTTATTTCGGGAGGTGCTAATACAGCAAAAT	1	11178	0.0012%
AAAACTCGAAATTGCATATCACATCCTTAGCAAAGTTTATTCAAGAATTC	1	10840	0.0012%
ACATGCCGAGATAAGTTTATGTAAAGCAGATAGAAACCAACCTGGCTTAC	1	10665	0.0012%
CATCGAGGTGCCAATCCCTTTAGCCAATACGAACTCTACTAAAGGATTAG	1	9470	0.0010%
CTTATAACATCCTGGTCTAGTGAGTATCTTTAACACACTACGCCCCACAT	1	11170	0.0012%

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.

No samples found with any adapter contamination > 0.1%

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.

Min:

Max:

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

Picard

Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

Mark Duplicates

Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
READS_UNMAPPED = UNMAPPED_READS

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 ⁰/₃₆ 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
SRX5644304	29.5M	0.0M	4.0M	6.1M	0.0M	18.1M	9.0M	9.0M	9.0M	9.0M	12.8M	5.3M	18.1M	0.0M
SRX5644305	16.0M	0.0M	1.3M	3.2M	0.0M	10.8M	5.4M	5.4M	5.4M	5.4M	7.6M	3.2M	10.8M	0.0M
SRX5644306	25.1M	0.0M	2.7M	4.2M	0.0M	17.0M	8.5M	8.5M	8.5M	8.5M	11.3M	5.7M	17.0M	0.0M
SRX5644307	13.3M	0.0M	1.2M	2.2M	0.0M	9.3M	4.7M	4.7M	4.7M	4.7M	6.3M	3.1M	9.3M	0.0M
SRX5644308	23.5M	0.0M	2.2M	5.1M	0.0M	15.0M	7.5M	7.5M	7.5M	7.5M	10.7M	4.3M	15.0M	0.0M
SRX5644309	37.5M	0.0M	4.6M	8.2M	0.0M	22.9M	11.5M	11.5M	11.5M	11.5M	15.8M	7.1M	22.9M	0.0M
SRX5644310	28.9M	0.0M	3.9M	7.0M	0.0M	16.8M	8.4M	8.4M	8.4M	8.4M	10.7M	6.1M	16.8M	0.0M
SRX5644311	28.5M	0.0M	3.6M	5.6M	0.0M	17.9M	8.9M	8.9M	8.9M	8.9M	11.3M	6.6M	17.9M	0.0M
SRX5644312	23.3M	0.0M	2.8M	3.7M	0.0M	15.8M	7.9M	7.9M	7.9M	7.9M	10.9M	4.9M	15.8M	0.0M
SRX5644313	34.0M	0.0M	4.4M	5.7M	0.0M	22.3M	11.1M	11.1M	11.1M	11.1M	14.9M	7.4M	22.3M	0.0M
SRX5644314	18.6M	0.0M	1.8M	3.9M	0.0M	12.0M	6.0M	6.0M	6.0M	6.0M	8.1M	3.9M	12.0M	0.0M
SRX5644315	20.0M	0.0M	2.2M	3.6M	0.0M	13.3M	6.6M	6.6M	6.6M	6.6M	8.8M	4.5M	13.3M	0.0M
SRX5644316	28.0M	0.0M	3.8M	5.4M	0.0M	17.6M	8.8M	8.8M	8.8M	8.8M	11.2M	6.4M	17.6M	0.0M
SRX5644317	29.2M	0.0M	3.9M	4.8M	0.0M	19.1M	9.5M	9.5M	9.5M	9.5M	12.2M	6.9M	19.1M	0.0M
SRX5644318	28.4M	0.0M	2.9M	4.8M	0.0M	19.3M	9.7M	9.7M	9.7M	9.7M	13.1M	6.2M	19.3M	0.0M
SRX5644319	21.7M	0.0M	2.4M	4.8M	0.0M	13.5M	6.7M	6.7M	6.7M	6.7M	9.1M	4.4M	13.5M	0.0M
SRX5644320	20.5M	0.0M	1.9M	3.9M	0.0M	13.8M	6.9M	6.9M	6.9M	6.9M	9.8M	4.0M	13.8M	0.0M
SRX5644321	16.4M	0.0M	1.2M	3.1M	0.0M	11.2M	5.6M	5.6M	5.6M	5.6M	8.1M	3.1M	11.2M	0.0M
SRX5644322	16.1M	0.0M	1.6M	2.9M	0.0M	10.8M	5.4M	5.4M	5.4M	5.4M	7.9M	2.9M	10.8M	0.0M
SRX5644323	20.0M	0.0M	1.7M	4.0M	0.0M	13.3M	6.6M	6.6M	6.6M	6.6M	9.4M	3.8M	13.3M	0.0M
SRX5644324	26.1M	0.0M	2.5M	6.2M	0.0M	16.0M	8.0M	8.0M	8.0M	8.0M	11.2M	4.9M	16.0M	0.0M
SRX5644325	21.0M	0.0M	1.7M	3.8M	0.0M	14.3M	7.2M	7.2M	7.2M	7.2M	10.1M	4.2M	14.3M	0.0M
SRX5644326	20.3M	0.0M	1.8M	3.4M	0.0M	14.2M	7.1M	7.1M	7.1M	7.1M	9.6M	4.5M	14.2M	0.0M
SRX5644327	23.4M	0.0M	2.6M	3.9M	0.0M	15.9M	8.0M	8.0M	8.0M	8.0M	11.3M	4.7M	15.9M	0.0M
SRX5644328	25.9M	0.0M	2.2M	6.3M	0.0M	16.1M	8.0M	8.0M	8.0M	8.0M	11.2M	4.9M	16.1M	0.0M
SRX5644329	33.2M	0.0M	21.7M	7.0M	0.0M	4.2M	2.1M	2.1M	2.1M	2.1M	3.0M	1.2M	4.2M	0.0M
SRX5644330	23.9M	0.0M	2.6M	4.3M	0.0M	15.8M	7.9M	7.9M	7.9M	7.9M	11.0M	4.7M	15.8M	0.0M
SRX5644331	25.5M	0.0M	2.8M	4.3M	0.0M	17.1M	8.5M	8.5M	8.5M	8.5M	12.4M	4.7M	17.1M	0.0M
SRX5644332	19.4M	0.0M	1.8M	4.1M	0.0M	12.7M	6.3M	6.3M	6.3M	6.3M	9.2M	3.4M	12.7M	0.0M
SRX5644333	24.3M	0.0M	2.2M	4.5M	0.0M	16.4M	8.2M	8.2M	8.2M	8.2M	11.6M	4.8M	16.4M	0.0M
SRX5644334	20.6M	0.0M	2.0M	4.8M	0.0M	12.8M	6.4M	6.4M	6.4M	6.4M	9.4M	3.4M	12.8M	0.0M
SRX5644335	19.6M	0.0M	1.8M	4.3M	0.0M	12.6M	6.3M	6.3M	6.3M	6.3M	8.9M	3.7M	12.6M	0.0M
SRX5644336	23.8M	0.0M	2.4M	4.8M	0.0M	15.4M	7.7M	7.7M	7.7M	7.7M	10.9M	4.6M	15.4M	0.0M
SRX5644337	31.3M	0.0M	3.6M	5.6M	0.0M	20.7M	10.4M	10.4M	10.4M	10.4M	14.5M	6.3M	20.7M	0.0M
SRX5644338	19.5M	0.0M	2.3M	3.7M	0.0M	12.6M	6.3M	6.3M	6.3M	6.3M	8.9M	3.7M	12.6M	0.0M
SRX5644339	24.5M	0.0M	2.1M	4.9M	0.0M	16.3M	8.2M	8.2M	8.2M	8.2M	11.4M	4.9M	16.3M	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-1

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 ⁰/₃₆ 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
SRX5644304	23.3M	23.3M	23.3M	4.0M	0.0M	0.5M	2080.8Mb	0.0Mb	356.8Mb	0.0M	0.0M	11.5M	0.2M
SRX5644305	12.9M	12.9M	12.9M	1.3M	0.0M	0.3M	1148.3Mb	0.0Mb	114.6Mb	0.0M	0.0M	6.4M	0.0M
SRX5644306	21.0M	21.0M	21.0M	0.0M	0.0M	0.3M	1872.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	10.4M	0.1M
SRX5644307	11.1M	11.1M	11.1M	0.0M	0.0M	0.2M	994.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	5.5M	0.1M
SRX5644308	18.4M	18.4M	18.4M	2.2M	0.0M	0.4M	1642.6Mb	0.0Mb	201.6Mb	0.0M	0.0M	9.1M	0.1M
SRX5644309	29.3M	29.3M	29.3M	0.0M	0.0M	0.7M	2610.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	14.4M	0.2M
SRX5644310	22.0M	22.0M	22.0M	3.9M	0.0M	0.6M	1961.7Mb	0.0Mb	355.3Mb	0.0M	0.0M	10.8M	0.2M
SRX5644311	22.8M	22.8M	22.8M	0.0M	0.0M	0.5M	2037.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	11.2M	0.2M
SRX5644312	19.7M	19.7M	19.7M	0.0M	0.0M	0.3M	1756.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	9.7M	0.1M
SRX5644313	28.3M	28.3M	28.3M	4.4M	0.0M	0.4M	2530.5Mb	0.0Mb	398.9Mb	0.0M	0.0M	14.0M	0.2M
SRX5644314	14.7M	14.7M	14.7M	0.0M	0.0M	0.3M	1315.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	7.3M	0.1M
SRX5644315	16.4M	16.4M	16.4M	2.2M	0.0M	0.3M	1462.8Mb	0.0Mb	194.5Mb	0.0M	0.0M	8.1M	0.1M
SRX5644316	22.6M	22.6M	22.6M	0.0M	0.0M	0.4M	2019.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	11.2M	0.1M
SRX5644317	24.4M	24.4M	24.4M	3.9M	0.0M	0.4M	2177.2Mb	0.0Mb	353.4Mb	0.0M	0.0M	12.0M	0.2M
SRX5644318	23.6M	23.6M	23.6M	0.0M	0.0M	0.4M	2108.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	11.7M	0.1M
SRX5644319	16.9M	16.9M	16.9M	2.4M	0.0M	0.4M	1512.1Mb	0.0Mb	217.8Mb	0.0M	0.0M	8.4M	0.1M
SRX5644320	16.7M	16.7M	16.7M	1.9M	0.0M	0.3M	1487.0Mb	0.0Mb	168.3Mb	0.0M	0.0M	8.2M	0.1M
SRX5644321	13.3M	13.3M	13.3M	1.2M	0.0M	0.3M	1183.9Mb	0.0Mb	111.9Mb	0.0M	0.0M	6.5M	0.1M
SRX5644322	13.1M	13.1M	13.1M	1.6M	0.0M	0.2M	1172.8Mb	0.0Mb	141.5Mb	0.0M	0.0M	6.5M	0.1M
SRX5644323	16.0M	16.0M	16.0M	0.0M	0.0M	0.3M	1421.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	7.9M	0.1M
SRX5644324	19.8M	19.8M	19.8M	2.5M	0.0M	0.5M	1770.0Mb	0.0Mb	227.7Mb	0.0M	0.0M	9.8M	0.1M
SRX5644325	17.2M	17.2M	17.2M	0.0M	0.0M	0.3M	1527.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	8.4M	0.1M
SRX5644326	16.9M	16.9M	16.9M	1.8M	0.0M	0.3M	1505.2Mb	0.0Mb	157.8Mb	0.0M	0.0M	8.3M	0.1M
SRX5644327	19.5M	19.5M	19.5M	0.0M	0.0M	0.3M	1742.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	9.7M	0.1M
SRX5644328	19.6M	19.6M	19.6M	2.2M	0.0M	0.5M	1746.9Mb	0.0Mb	198.4Mb	0.0M	0.0M	9.7M	0.1M
SRX5644329	26.2M	26.2M	26.2M	21.7M	0.0M	0.6M	2340.7Mb	0.0Mb	1956.8Mb	0.0M	0.0M	13.0M	0.2M
SRX5644330	19.6M	19.6M	19.6M	2.6M	0.0M	0.3M	1744.1Mb	0.0Mb	237.3Mb	0.0M	0.0M	9.6M	0.1M
SRX5644331	21.1M	21.1M	21.1M	2.8M	0.0M	0.3M	1887.0Mb	0.0Mb	256.8Mb	0.0M	0.0M	10.4M	0.1M
SRX5644332	15.4M	15.4M	15.4M	1.8M	0.0M	0.3M	1371.5Mb	0.0Mb	158.6Mb	0.0M	0.0M	7.6M	0.1M
SRX5644333	19.8M	19.8M	19.8M	2.2M	0.0M	0.4M	1763.7Mb	0.0Mb	196.5Mb	0.0M	0.0M	9.8M	0.1M
SRX5644334	15.8M	15.8M	15.8M	0.0M	0.0M	0.4M	1409.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	7.8M	0.1M
SRX5644335	15.3M	15.3M	15.3M	1.8M	0.0M	0.4M	1368.3Mb	0.0Mb	163.5Mb	0.0M	0.0M	7.6M	0.1M
SRX5644336	19.0M	19.0M	19.0M	2.4M	0.0M	0.4M	1690.2Mb	0.0Mb	214.0Mb	0.0M	0.0M	9.3M	0.1M
SRX5644337	25.7M	25.7M	25.7M	3.6M	0.0M	0.5M	2294.8Mb	0.0Mb	324.0Mb	0.0M	0.0M	12.7M	0.2M
SRX5644338	15.8M	15.8M	15.8M	2.3M	0.0M	0.3M	1407.0Mb	0.0Mb	209.7Mb	0.0M	0.0M	7.8M	0.1M
SRX5644339	19.6M	19.6M	19.6M	0.0M	0.0M	0.4M	1746.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	9.7M	0.1M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table-1

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 ⁰/₃₆ 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)
SRX5644304	29.5M	29.5M	29.5M	6.1M	0.0M	23.3M	23.3M	23.3M	0.0M	0.0M	0.0M
SRX5644305	16.0M	16.0M	16.0M	3.2M	1.3M	12.9M	12.9M	12.9M	0.0M	0.0M	0.0M
SRX5644306	25.1M	25.1M	25.1M	4.2M	2.7M	21.0M	21.0M	21.0M	0.0M	0.0M	0.0M
SRX5644307	13.3M	13.3M	13.3M	2.2M	0.0M	11.1M	11.1M	11.1M	0.0M	0.0M	0.0M
SRX5644308	23.5M	23.5M	23.5M	5.1M	2.2M	18.4M	18.4M	18.4M	0.0M	0.0M	0.0M
SRX5644309	37.5M	37.5M	37.5M	8.2M	0.0M	29.3M	29.3M	29.3M	0.0M	0.0M	0.0M
SRX5644310	28.9M	28.9M	28.9M	7.0M	3.9M	22.0M	22.0M	22.0M	0.0M	0.0M	0.0M
SRX5644311	28.5M	28.5M	28.5M	5.6M	0.0M	22.8M	22.8M	22.8M	0.0M	0.0M	0.0M
SRX5644312	23.3M	23.3M	23.3M	3.7M	2.8M	19.7M	19.7M	19.7M	0.0M	0.0M	0.0M
SRX5644313	34.0M	34.0M	34.0M	5.7M	0.0M	28.3M	28.3M	28.3M	0.0M	0.0M	0.0M
SRX5644314	18.6M	18.6M	18.6M	3.9M	0.0M	14.7M	14.7M	14.7M	0.0M	0.0M	0.0M
SRX5644315	20.0M	20.0M	20.0M	3.6M	2.2M	16.4M	16.4M	16.4M	0.0M	0.0M	0.0M
SRX5644316	28.0M	28.0M	28.0M	5.4M	3.8M	22.6M	22.6M	22.6M	0.0M	0.0M	0.0M
SRX5644317	29.2M	29.2M	29.2M	4.8M	0.0M	24.4M	24.4M	24.4M	0.0M	0.0M	0.0M
SRX5644318	28.4M	28.4M	28.4M	4.8M	0.0M	23.6M	23.6M	23.6M	0.0M	0.0M	0.0M
SRX5644319	21.7M	21.7M	21.7M	4.8M	0.0M	16.9M	16.9M	16.9M	0.0M	0.0M	0.0M
SRX5644320	20.5M	20.5M	20.5M	3.9M	0.0M	16.7M	16.7M	16.7M	0.0M	0.0M	0.0M
SRX5644321	16.4M	16.4M	16.4M	3.1M	0.0M	13.3M	13.3M	13.3M	0.0M	0.0M	0.0M
SRX5644322	16.1M	16.1M	16.1M	2.9M	0.0M	13.1M	13.1M	13.1M	0.0M	0.0M	0.0M
SRX5644323	20.0M	20.0M	20.0M	4.0M	0.0M	16.0M	16.0M	16.0M	0.0M	0.0M	0.0M
SRX5644324	26.1M	26.1M	26.1M	6.2M	2.5M	19.8M	19.8M	19.8M	0.0M	0.0M	0.0M
SRX5644325	21.0M	21.0M	21.0M	3.8M	1.7M	17.2M	17.2M	17.2M	0.0M	0.0M	0.0M
SRX5644326	20.3M	20.3M	20.3M	3.4M	0.0M	16.9M	16.9M	16.9M	0.0M	0.0M	0.0M
SRX5644327	23.4M	23.4M	23.4M	3.9M	2.6M	19.5M	19.5M	19.5M	0.0M	0.0M	0.0M
SRX5644328	25.9M	25.9M	25.9M	6.3M	2.2M	19.6M	19.6M	19.6M	0.0M	0.0M	0.0M
SRX5644329	33.2M	33.2M	33.2M	7.0M	0.0M	26.2M	26.2M	26.2M	0.0M	0.0M	0.0M
SRX5644330	23.9M	23.9M	23.9M	4.3M	2.6M	19.6M	19.6M	19.6M	0.0M	0.0M	0.0M
SRX5644331	25.5M	25.5M	25.5M	4.3M	0.0M	21.1M	21.1M	21.1M	0.0M	0.0M	0.0M
SRX5644332	19.4M	19.4M	19.4M	4.1M	0.0M	15.4M	15.4M	15.4M	0.0M	0.0M	0.0M
SRX5644333	24.3M	24.3M	24.3M	4.5M	2.2M	19.8M	19.8M	19.8M	0.0M	0.0M	0.0M
SRX5644334	20.6M	20.6M	20.6M	4.8M	0.0M	15.8M	15.8M	15.8M	0.0M	0.0M	0.0M
SRX5644335	19.6M	19.6M	19.6M	4.3M	1.8M	15.3M	15.3M	15.3M	0.0M	0.0M	0.0M
SRX5644336	23.8M	23.8M	23.8M	4.8M	2.4M	19.0M	19.0M	19.0M	0.0M	0.0M	0.0M
SRX5644337	31.3M	31.3M	31.3M	5.6M	3.6M	25.7M	25.7M	25.7M	0.0M	0.0M	0.0M
SRX5644338	19.5M	19.5M	19.5M	3.7M	0.0M	15.8M	15.8M	15.8M	0.0M	0.0M	0.0M
SRX5644339	24.5M	24.5M	24.5M	4.9M	0.0M	19.6M	19.6M	19.6M	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-dp_table-1

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

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 ⁰/₃₆ 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
SRX5644304	15.6M	11.7M	75.0%	10.8M	69.6%	0.8M	179.0bp	178.5bp	6.9M	6.9M	6.8M	0.1M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.3bp
SRX5644305	8.0M	6.4M	80.5%	6.0M	74.9%	0.4M	180.0bp	178.8bp	3.8M	3.8M	3.8M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.4bp
SRX5644306	13.3M	10.5M	78.6%	9.7M	73.1%	0.7M	179.0bp	178.8bp	7.0M	7.0M	7.0M	0.1M	0.0M	0.0M	1.2%	0.0%	2.6bp	0.0%	2.5bp
SRX5644307	7.5M	5.6M	73.9%	5.2M	69.1%	0.4M	179.0bp	178.7bp	3.7M	3.7M	3.7M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.4bp
SRX5644308	12.4M	9.2M	74.4%	8.5M	68.8%	0.7M	179.0bp	178.4bp	5.3M	5.3M	5.3M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.4bp
SRX5644309	20.1M	14.7M	72.8%	13.5M	67.1%	1.2M	178.0bp	178.3bp	9.1M	9.1M	9.0M	0.1M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.3bp
SRX5644310	14.4M	11.0M	76.4%	10.1M	70.4%	0.9M	179.0bp	178.8bp	8.0M	8.0M	7.9M	0.1M	0.0M	0.0M	1.2%	0.0%	2.5bp	0.0%	2.5bp
SRX5644311	15.5M	11.4M	73.6%	10.6M	68.0%	0.9M	178.0bp	178.5bp	8.4M	8.4M	8.3M	0.1M	0.0M	0.0M	1.2%	0.0%	2.6bp	0.0%	2.5bp
SRX5644312	12.8M	9.8M	76.7%	9.2M	71.8%	0.6M	179.0bp	178.7bp	6.2M	6.1M	6.1M	0.1M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.4bp
SRX5644313	18.3M	14.2M	77.5%	13.2M	72.1%	1.0M	179.0bp	178.8bp	9.4M	9.4M	9.3M	0.1M	0.0M	0.0M	1.2%	0.0%	2.6bp	0.0%	2.4bp
SRX5644314	9.3M	7.4M	79.4%	6.8M	73.5%	0.5M	179.0bp	178.9bp	4.7M	4.7M	4.7M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.5bp
SRX5644315	10.8M	8.2M	75.8%	7.6M	70.5%	0.6M	178.0bp	178.7bp	5.5M	5.5M	5.4M	0.0M	0.0M	0.0M	1.2%	0.0%	2.5bp	0.0%	2.5bp
SRX5644316	13.9M	11.3M	80.9%	10.5M	75.0%	0.8M	180.0bp	179.0bp	8.3M	8.2M	8.1M	0.1M	0.0M	0.0M	1.2%	0.0%	2.5bp	0.0%	2.4bp
SRX5644317	15.9M	12.2M	76.6%	11.3M	71.2%	0.9M	179.0bp	178.8bp	8.7M	8.7M	8.6M	0.1M	0.0M	0.0M	1.2%	0.0%	2.6bp	0.0%	2.5bp
SRX5644318	14.8M	11.8M	79.6%	11.0M	74.2%	0.8M	179.0bp	178.9bp	7.6M	7.5M	7.5M	0.1M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.5bp
SRX5644319	10.5M	8.5M	80.6%	7.8M	74.5%	0.6M	179.0bp	178.9bp	5.5M	5.5M	5.4M	0.0M	0.0M	0.0M	1.2%	0.0%	2.5bp	0.0%	2.5bp
SRX5644320	11.5M	8.3M	72.6%	7.7M	67.3%	0.6M	179.0bp	178.4bp	4.8M	4.8M	4.7M	0.0M	0.0M	0.0M	1.3%	0.1%	2.5bp	0.1%	2.3bp
SRX5644321	9.6M	6.7M	69.4%	6.2M	64.3%	0.5M	178.0bp	178.1bp	3.7M	3.7M	3.6M	0.0M	0.0M	0.0M	1.3%	0.1%	2.5bp	0.1%	2.3bp
SRX5644322	9.2M	6.6M	71.6%	6.1M	66.5%	0.5M	179.0bp	178.5bp	3.5M	3.5M	3.5M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.3bp
SRX5644323	11.2M	8.0M	71.3%	7.4M	65.8%	0.6M	178.0bp	178.2bp	4.6M	4.6M	4.5M	0.0M	0.0M	0.0M	1.3%	0.1%	2.5bp	0.1%	2.3bp
SRX5644324	13.3M	9.9M	74.9%	9.1M	68.5%	0.8M	179.0bp	178.5bp	5.9M	5.9M	5.8M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.4bp
SRX5644325	12.2M	8.6M	70.3%	8.0M	65.2%	0.6M	178.0bp	178.2bp	5.0M	5.0M	5.0M	0.0M	0.0M	0.0M	1.2%	0.1%	2.6bp	0.0%	2.4bp
SRX5644326	11.4M	8.4M	73.8%	7.9M	68.9%	0.6M	178.0bp	178.5bp	5.4M	5.4M	5.4M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.4bp
SRX5644327	12.3M	9.7M	79.3%	9.2M	74.6%	0.6M	179.0bp	178.8bp	5.8M	5.7M	5.7M	0.1M	0.0M	0.0M	1.2%	0.1%	2.6bp	0.1%	2.5bp
SRX5644328	13.1M	9.8M	74.7%	9.0M	68.4%	0.8M	179.0bp	178.4bp	5.8M	5.8M	5.7M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.3bp
SRX5644329	17.6M	13.1M	74.5%	12.1M	68.9%	1.0M	180.0bp	178.5bp	7.5M	7.4M	7.4M	0.1M	0.0M	0.0M	1.3%	0.1%	2.5bp	0.1%	2.3bp
SRX5644330	13.5M	9.8M	72.7%	9.1M	67.4%	0.7M	178.0bp	178.4bp	5.9M	5.9M	5.8M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.3bp
SRX5644331	13.9M	10.6M	76.0%	9.8M	70.8%	0.7M	180.0bp	178.6bp	5.8M	5.8M	5.7M	0.1M	0.0M	0.0M	1.3%	0.1%	2.5bp	0.1%	2.3bp
SRX5644332	10.5M	7.7M	73.6%	7.1M	68.2%	0.6M	179.0bp	178.4bp	4.2M	4.2M	4.2M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.4bp
SRX5644333	13.5M	9.9M	73.4%	9.2M	68.0%	0.7M	179.0bp	178.3bp	5.8M	5.8M	5.7M	0.1M	0.0M	0.0M	1.3%	0.1%	2.6bp	0.1%	2.4bp
SRX5644334	11.1M	7.9M	71.1%	7.3M	65.5%	0.6M	179.0bp	178.3bp	4.2M	4.2M	4.2M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.4bp
SRX5644335	10.4M	7.7M	73.5%	7.1M	67.8%	0.6M	179.0bp	178.4bp	4.5M	4.5M	4.5M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.1%	2.4bp
SRX5644336	13.0M	9.5M	72.7%	8.8M	67.2%	0.7M	178.0bp	178.3bp	5.6M	5.6M	5.6M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.3bp
SRX5644337	17.7M	12.9M	72.7%	12.0M	67.9%	0.8M	178.0bp	178.3bp	8.0M	8.0M	7.9M	0.1M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.4bp
SRX5644338	11.2M	7.9M	70.2%	7.4M	65.5%	0.5M	178.0bp	178.3bp	4.8M	4.7M	4.7M	0.0M	0.0M	0.0M	1.2%	0.1%	2.5bp	0.0%	2.3bp
SRX5644339	13.5M	9.8M	72.7%	9.1M	67.2%	0.7M	178.0bp	178.2bp	5.9M	5.8M	5.8M	0.1M	0.0M	0.0M	1.3%	0.1%	2.5bp	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 sample similarity

Min:

Max:

Created with MultiQC

STAR_SALMON DESeq2 PCA plot

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	`1.2`
CUSTOM_TX2GENE	python	`3.9.5`
DESEQ2_QC_STAR_SALMON	bioconductor-deseq2	`1.28.0`
	r-base	`4.0.3`
DupRadar	bioconductor-dupradar	`1.32.0`
FASTQC_RAW	fastqc	`0.12.1`
FASTQC_TRIM	fastqc	`0.12.1`
FQ_SUBSAMPLE	fq	`0.9.1 (2022-02-22)`
GTF2BED	perl	`5.26.2`
GTF_FILTER	python	`3.9.5`
GUNZIP_FASTA	gunzip	`1.1`
GUNZIP_GTF	gunzip	`1.1`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
PICARD_MARKDUPLICATES	picard	`3.1.1`
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	`1.10.1`
SALMON_QUANT	salmon	`1.10.1`
SAMTOOLS_FLAGSTAT	samtools	`1.2`
SAMTOOLS_IDXSTATS	samtools	`1.2`
SAMTOOLS_INDEX	samtools	`1.2`
SAMTOOLS_SORT	samtools	`1.2`
SAMTOOLS_STATS	samtools	`1.2`
SE_GENE	bioconductor-summarizedexperiment	`1.32.0`
STAR_ALIGN	gawk	`5.1.0`
	samtools	`1.18`
	star	`2.7.10a`
STAR_GENOMEGENERATE	gawk	`5.1.0`
	samtools	`1.18`
	star	`2.7.10a`
STRINGTIE_STRINGTIE	stringtie	`2.2.1`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
UCSC_BEDCLIP	ucsc	`377`
UCSC_BEDGRAPHTOBIGWIG	ucsc	`445`
Workflow	Nextflow	`24.04.4`
	nf-core/rnaseq	`v3.16.1-g1f3f64d`
fastp	fastp	`0.23.4`

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 v3.16.1 (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 v24.04.4 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq -resume -profile singularity --input /mmfs1/gscratch/scrubbed/elstrand/Cgigas_ArredondoEspinoza2023/samplesheet_rnaseq_dataset1.csv --outdir /mmfs1/gscratch/scrubbed/elstrand/Cgigas_ArredondoEspinoza2023/ --gtf /mmfs1/gscratch/scrubbed/elstrand/genomes/C_gigas/GCF_963853765.1_xbMagGiga1.1_genomic.gtf.gz --gff /mmfs1/gscratch/scrubbed/elstrand/genomes/C_gigas/GCF_963853765.1_xbMagGiga1.1_genomic.gff.gz --fasta /mmfs1/gscratch/scrubbed/elstrand/genomes/C_gigas/GCF_963853765.1_xbMagGiga1.1_genomic.fna.gz --trimmer fastp --extra_fastp_args '--cut_mean_quality 30 --trim_front1 10 --trim_front2 10' --aligner star_salmon --skip_pseudo_alignment --multiqc_title rnaseq_Cgigas_ArredondoEspinoza2023 --deseq2_vst

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

input: /mmfs1/gscratch/scrubbed/elstrand/Cgigas_ArredondoEspinoza2023/samplesheet_rnaseq_dataset1.csv
multiqc_title: rnaseq_Cgigas_ArredondoEspinoza2023
outdir: /mmfs1/gscratch/scrubbed/elstrand/Cgigas_ArredondoEspinoza2023/

Reference genome options

fasta: /mmfs1/gscratch/scrubbed/elstrand/genomes/C_gigas/GCF_963853765.1_xbMagGiga1.1_genomic.fna.gz
gff: /mmfs1/gscratch/scrubbed/elstrand/genomes/C_gigas/GCF_963853765.1_xbMagGiga1.1_genomic.gff.gz
gtf: /mmfs1/gscratch/scrubbed/elstrand/genomes/C_gigas/GCF_963853765.1_xbMagGiga1.1_genomic.gtf.gz

Read trimming options

extra_fastp_args: --cut_mean_quality 30 --trim_front1 10 --trim_front2 10
trimmer: fastp

Alignment options

min_mapped_reads: 5

Process skipping options

skip_pseudo_alignment: true

Core Nextflow options

configFiles: N/A
containerEngine: singularity
launchDir: /mmfs1/gscratch/scrubbed/elstrand/Cgigas_ArredondoEspinoza2023/scripts
profile: singularity
projectDir: /mmfs1/home/elstrand/.nextflow/assets/nf-core/rnaseq
revision: master
runName: cheesy_woese
userName: elstrand
workDir: /mmfs1/gscratch/scrubbed/elstrand/Cgigas_ArredondoEspinoza2023/scripts/work

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rnaseq_Cgigas_ArredondoEspinoza2023

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rnaseq_Cgigas_ArredondoEspinoza2023

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General Statistics: Columns

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Strandedness Checks

Samples strandedness check: Columns

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RSeQC

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Inner Distance

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Infer experiment

Bam Stat

RSeQC: Bam Stat: Columns

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Alignment stats

Samtools: stats: Alignment Stats: Columns

Flagstat

Samtools: flagstat: read count: Columns

Mapped reads per contig

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STAR: Summary Statistics: Columns

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