PTox-WP6-Danio: MultiQC Report

General Statistics

Showing ³⁶/₃₆ rows and ²¹/₄₅ columns.

Sample Name	5'-3' bias	M Aligned	Exonic	Intronic	Intergenic	Overlapping Exon	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Mean insert	Reads	Reads mapped	% Reads mapped	rRNA	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	M Unique Reads	% Pass Dedup	Dups	GC	Avg len	Median len	Failed	Seqs	% > Q30	Mb Q30 bases	Reads After Filtering	GC content	% PF	% Adapter	Mean R1 Length	Dups	GC	Avg len	Median len	Failed	Seqs
RZY000ZA1	0.75	1.4M	0.8M	0.3M	0.1M	0.1M	0.89%	0.6M	1.4M	100.0%	0.0%	3.6%	1.4M	0.0bp	1.9M	1.9M	100.0%	11.8%	19.0M	17.9M	94.1%	16.5M	86.8%	1.4M	1.9M	8.8%	82.7%	39.0%	151bp	151bp	27%	23.7M	89.3%	2243.6Mb	21.5M	37.8%	90.7%	29.9%	141.0bp	89.1%	37.0%	117bp	141bp	18%	21.5M
RZY000ZA2	0.62	2.8M	1.6M	0.8M	0.2M	0.2M	0.97%	1.0M	2.8M	100.0%	0.0%	2.9%	2.8M	0.0bp	3.8M	3.8M	100.0%	9.4%	20.4M	19.4M	95.2%	17.8M	87.0%	1.7M	3.8M	15.9%	74.1%	37.0%	151bp	151bp	36%	24.0M	89.4%	2318.5Mb	22.5M	37.0%	93.7%	32.3%	141.0bp	82.8%	37.0%	115bp	141bp	36%	22.5M
RZY000ZA3	0.61	2.3M	1.3M	0.6M	0.2M	0.2M	0.95%	0.7M	2.3M	100.0%	0.0%	2.8%	2.3M	0.0bp	3.0M	3.0M	100.0%	9.4%	23.6M	22.6M	95.6%	20.7M	87.8%	1.9M	3.0M	11.2%	80.9%	36.0%	151bp	151bp	36%	27.6M	89.9%	2745.1Mb	26.0M	36.9%	94.5%	28.6%	141.0bp	87.4%	36.0%	117bp	141bp	45%	26.0M
RZY000ZA4	0.76	2.6M	1.4M	0.7M	0.2M	0.2M	0.97%	1.0M	2.6M	100.0%	0.0%	3.2%	2.6M	0.0bp	3.6M	3.6M	100.0%	8.3%	25.3M	24.1M	95.2%	22.1M	87.3%	2.0M	3.6M	12.1%	78.5%	38.0%	151bp	151bp	36%	29.8M	89.8%	2851.0Mb	27.6M	37.7%	92.7%	34.8%	141.0bp	85.9%	37.0%	115bp	141bp	36%	27.6M
RZY000ZA5	0.88	1.4M	0.9M	0.3M	0.1M	0.1M	0.85%	0.6M	1.4M	100.0%	0.0%	3.7%	1.4M	0.0bp	2.0M	2.0M	100.0%	11.2%	19.9M	18.7M	93.8%	17.4M	87.3%	1.3M	2.0M	9.0%	81.3%	37.0%	151bp	151bp	27%	25.3M	88.8%	2335.8Mb	22.4M	37.2%	88.5%	28.4%	141.0bp	88.9%	37.0%	117bp	141bp	27%	22.4M
RZY000ZA6	0.80	2.8M	1.5M	0.8M	0.2M	0.2M	0.96%	1.0M	2.8M	100.0%	0.0%	3.2%	2.8M	0.0bp	3.8M	3.8M	100.0%	7.7%	27.3M	26.0M	95.4%	23.8M	87.4%	2.2M	3.8M	11.9%	77.0%	39.0%	151bp	151bp	27%	33.3M	90.5%	3034.9Mb	29.5M	37.9%	88.7%	37.8%	141.0bp	86.2%	37.0%	114bp	141bp	45%	29.5M
RZY019HA1	0.67	2.3M	1.4M	0.5M	0.2M	0.2M	0.89%	0.8M	2.3M	100.0%	0.0%	2.9%	2.3M	0.0bp	3.1M	3.1M	100.0%	10.7%	23.4M	22.4M	95.9%	20.4M	87.2%	2.0M	3.1M	11.5%	78.9%	37.0%	151bp	151bp	36%	28.2M	89.7%	2741.7Mb	26.2M	37.6%	92.8%	29.7%	141.0bp	87.3%	37.0%	117bp	141bp	27%	26.2M
RZY019HA2	0.50	1.5M	0.9M	0.4M	0.1M	0.1M	0.93%	0.5M	1.5M	100.0%	0.0%	2.6%	1.5M	0.0bp	2.0M	2.0M	100.0%	10.8%	14.7M	14.1M	95.9%	12.9M	87.7%	1.2M	2.0M	11.7%	80.7%	36.0%	151bp	151bp	27%	17.4M	89.6%	1738.1Mb	16.5M	36.8%	94.8%	26.8%	141.0bp	86.8%	36.0%	118bp	141bp	36%	16.5M
RZY019HA3	0.70	2.6M	1.4M	0.7M	0.2M	0.2M	0.98%	0.9M	2.6M	100.0%	0.0%	3.1%	2.6M	0.0bp	3.6M	3.6M	100.0%	9.6%	21.2M	20.2M	95.2%	18.3M	86.3%	1.9M	3.6M	14.2%	76.0%	37.0%	151bp	151bp	27%	25.3M	89.4%	2388.3Mb	23.4M	36.9%	92.7%	34.2%	141.0bp	84.1%	36.0%	114bp	141bp	45%	23.4M
RZY019HA4	0.56	1.6M	0.9M	0.4M	0.1M	0.1M	0.95%	0.6M	1.6M	100.0%	0.0%	3.1%	1.6M	0.0bp	2.2M	2.2M	100.0%	9.9%	21.2M	20.3M	95.7%	18.7M	88.3%	1.6M	2.2M	9.2%	83.4%	36.0%	151bp	151bp	36%	25.0M	90.2%	2465.0Mb	23.5M	37.1%	94.0%	30.2%	141.0bp	89.3%	37.0%	116bp	141bp	45%	23.5M
RZY019HA5	0.57	2.4M	1.3M	0.7M	0.2M	0.2M	0.99%	0.8M	2.4M	100.0%	0.0%	2.9%	2.4M	0.0bp	3.3M	3.3M	100.0%	8.9%	25.0M	23.8M	95.2%	21.9M	87.8%	1.9M	3.3M	11.5%	80.7%	36.0%	151bp	151bp	36%	29.1M	90.2%	2906.9Mb	27.5M	36.7%	94.5%	28.9%	141.0bp	86.9%	36.0%	117bp	141bp	45%	27.5M
RZY019HA6	0.63	1.8M	1.0M	0.4M	0.1M	0.1M	0.98%	0.6M	1.8M	100.0%	0.0%	3.0%	1.8M	0.0bp	2.4M	2.4M	100.0%	9.3%	26.0M	24.9M	95.8%	23.1M	88.9%	1.8M	2.4M	8.1%	85.1%	36.0%	151bp	151bp	27%	30.3M	90.5%	3081.6Mb	28.6M	37.1%	94.7%	26.8%	141.0bp	90.3%	37.0%	119bp	141bp	36%	28.6M
RZY019LA1	0.75	0.8M	0.5M	0.1M	0.1M	0.1M	0.81%	0.4M	0.8M	100.0%	0.0%	4.5%	0.8M	0.0bp	1.2M	1.2M	100.0%	11.4%	16.3M	15.2M	93.5%	14.5M	89.0%	0.7M	1.2M	6.5%	83.3%	40.0%	151bp	151bp	36%	21.3M	88.8%	1921.1Mb	18.4M	38.7%	86.1%	28.1%	141.0bp	91.1%	38.0%	118bp	141bp	18%	18.4M
RZY019LA2	0.65	1.4M	0.8M	0.3M	0.1M	0.1M	0.92%	0.5M	1.4M	100.0%	0.0%	2.8%	1.4M	0.0bp	1.8M	1.8M	100.0%	10.1%	18.2M	17.5M	96.0%	16.1M	88.3%	1.4M	1.8M	8.8%	84.4%	36.0%	151bp	151bp	27%	21.5M	90.2%	2147.3Mb	20.2M	37.1%	94.2%	26.9%	141.0bp	89.6%	37.0%	118bp	141bp	27%	20.2M
RZY019LA3	0.67	3.3M	1.8M	0.9M	0.3M	0.2M	0.96%	1.1M	3.3M	100.0%	0.0%	2.9%	3.3M	0.0bp	4.4M	4.4M	100.0%	8.9%	23.9M	22.7M	95.0%	20.8M	87.4%	1.8M	4.4M	15.9%	74.3%	37.0%	151bp	151bp	27%	28.3M	89.6%	2712.6Mb	26.2M	37.2%	92.7%	32.2%	141.0bp	83.0%	37.0%	116bp	141bp	36%	26.2M
RZY019LA4	0.80	1.6M	0.9M	0.4M	0.1M	0.1M	0.96%	0.6M	1.6M	100.0%	0.0%	3.2%	1.6M	0.0bp	2.3M	2.3M	100.0%	9.4%	20.9M	20.0M	95.7%	18.4M	88.0%	1.6M	2.3M	9.4%	82.6%	38.0%	151bp	151bp	36%	24.6M	89.8%	2402.0Mb	23.1M	37.6%	93.9%	31.6%	141.0bp	88.7%	37.0%	116bp	141bp	45%	23.1M
RZY019LA5	0.78	3.0M	1.6M	0.9M	0.2M	0.2M	0.97%	1.0M	3.0M	100.0%	0.0%	2.7%	3.0M	0.0bp	4.0M	4.0M	100.0%	8.3%	24.5M	23.4M	95.7%	21.6M	88.3%	1.8M	4.0M	14.2%	77.2%	36.0%	151bp	151bp	27%	28.3M	90.4%	2838.4Mb	26.7M	37.2%	94.3%	29.1%	141.0bp	84.7%	37.0%	118bp	141bp	36%	26.7M
RZY019LA6	0.80	1.3M	0.8M	0.2M	0.1M	0.1M	0.84%	0.6M	1.3M	100.0%	0.0%	3.9%	1.3M	0.0bp	1.9M	1.9M	100.0%	9.0%	23.6M	22.2M	94.3%	20.8M	88.5%	1.4M	1.9M	7.2%	81.6%	40.0%	151bp	151bp	27%	30.5M	89.5%	2717.8Mb	25.9M	38.4%	85.0%	30.9%	141.0bp	90.5%	38.0%	117bp	141bp	27%	25.9M
RZY996LA1	0.61	1.3M	0.8M	0.3M	0.1M	0.1M	0.93%	0.5M	1.3M	100.0%	0.0%	3.1%	1.3M	0.0bp	1.8M	1.8M	100.0%	9.9%	22.3M	21.5M	96.1%	19.7M	88.1%	1.8M	1.8M	6.9%	86.8%	36.0%	151bp	151bp	27%	26.2M	90.4%	2641.3Mb	24.8M	37.5%	94.6%	27.5%	141.0bp	91.4%	37.0%	118bp	141bp	27%	24.8M
RZY996LA2	0.79	1.4M	0.9M	0.3M	0.1M	0.1M	0.93%	0.5M	1.4M	100.0%	0.0%	2.7%	1.4M	0.0bp	1.9M	1.9M	100.0%	9.9%	16.9M	16.2M	95.8%	14.8M	88.0%	1.3M	1.9M	9.9%	83.1%	37.0%	151bp	151bp	27%	19.8M	90.0%	2004.4Mb	18.7M	37.6%	94.6%	26.5%	141.0bp	88.3%	37.0%	119bp	141bp	27%	18.7M
RZY996LA3	0.65	0.9M	0.5M	0.2M	0.1M	0.1M	0.94%	0.3M	0.9M	100.0%	0.0%	3.0%	0.9M	0.0bp	1.2M	1.2M	100.0%	10.3%	13.6M	13.1M	95.6%	12.1M	88.6%	1.0M	1.2M	7.8%	85.5%	37.0%	151bp	151bp	27%	16.1M	89.9%	1622.7Mb	15.2M	37.4%	94.2%	26.6%	141.0bp	90.0%	37.0%	119bp	141bp	27%	15.2M
RZY996LA4	0.86	1.5M	0.9M	0.3M	0.1M	0.1M	0.91%	0.6M	1.5M	100.0%	0.0%	3.3%	1.5M	0.0bp	2.1M	2.1M	100.0%	8.4%	20.1M	19.1M	94.8%	18.0M	89.6%	1.1M	2.1M	9.2%	81.3%	40.0%	151bp	151bp	36%	24.8M	89.9%	2306.3Mb	22.0M	38.0%	88.5%	32.2%	141.0bp	88.5%	37.0%	117bp	141bp	27%	22.0M
RZY996LA5	0.73	2.0M	1.1M	0.5M	0.2M	0.1M	0.99%	0.7M	2.0M	100.0%	0.0%	2.9%	2.0M	0.0bp	2.6M	2.6M	100.0%	8.6%	23.0M	21.9M	95.3%	20.3M	88.0%	1.7M	2.6M	10.0%	83.2%	36.0%	151bp	151bp	27%	26.8M	90.1%	2702.9Mb	25.2M	37.1%	94.1%	25.2%	141.0bp	88.4%	37.0%	119bp	141bp	27%	25.2M
RZY996LA6	0.73	1.7M	1.0M	0.4M	0.1M	0.1M	0.96%	0.6M	1.7M	100.0%	0.0%	2.8%	1.7M	0.0bp	2.2M	2.2M	100.0%	9.4%	20.7M	19.8M	95.7%	18.4M	88.9%	1.4M	2.2M	9.6%	83.5%	37.0%	151bp	151bp	27%	23.9M	90.3%	2454.9Mb	22.8M	37.6%	95.3%	27.1%	141.0bp	89.0%	37.0%	119bp	141bp	36%	22.8M
RZY996MA1	0.71	2.8M	1.5M	0.8M	0.2M	0.2M	0.98%	1.0M	2.8M	100.0%	0.0%	3.0%	2.8M	0.0bp	3.7M	3.7M	100.0%	8.9%	25.5M	24.4M	95.6%	22.1M	86.8%	2.3M	3.7M	12.4%	78.9%	36.0%	151bp	151bp	27%	29.7M	89.9%	2882.1Mb	28.0M	36.9%	94.2%	33.3%	141.0bp	85.8%	36.0%	115bp	141bp	36%	28.0M
RZY996MA2	0.84	1.5M	0.8M	0.3M	0.1M	0.1M	0.96%	0.6M	1.5M	100.0%	0.0%	3.6%	1.5M	0.0bp	2.1M	2.1M	100.0%	9.7%	19.8M	18.7M	94.3%	17.2M	86.8%	1.5M	2.1M	9.0%	81.6%	38.0%	151bp	151bp	27%	24.8M	89.6%	2274.4Mb	21.9M	37.0%	88.3%	31.0%	141.0bp	88.9%	36.0%	116bp	141bp	27%	21.9M
RZY996MA3	0.68	3.3M	1.8M	1.0M	0.3M	0.2M	0.98%	1.1M	3.3M	100.0%	0.0%	2.8%	3.3M	0.0bp	4.4M	4.4M	100.0%	8.4%	24.8M	23.7M	95.6%	21.8M	87.7%	2.0M	4.4M	15.2%	75.4%	36.0%	151bp	151bp	36%	28.7M	90.0%	2839.3Mb	27.1M	37.0%	94.4%	31.0%	141.0bp	83.7%	36.0%	116bp	141bp	45%	27.1M
RZY996MA4	0.66	2.2M	1.2M	0.6M	0.2M	0.2M	0.97%	0.8M	2.2M	100.0%	0.0%	3.1%	2.2M	0.0bp	3.0M	3.0M	100.0%	9.1%	22.5M	21.4M	95.3%	19.7M	87.6%	1.7M	3.0M	11.6%	79.8%	37.0%	151bp	151bp	36%	26.4M	90.0%	2578.1Mb	24.7M	37.2%	93.6%	32.0%	141.0bp	86.8%	37.0%	116bp	141bp	45%	24.7M
RZY996MA5	0.85	2.8M	1.5M	0.8M	0.2M	0.2M	0.97%	0.9M	2.8M	100.0%	0.0%	2.8%	2.8M	0.0bp	3.7M	3.7M	100.0%	8.8%	21.3M	20.3M	95.5%	18.8M	88.3%	1.5M	3.7M	15.2%	75.2%	37.0%	151bp	151bp	27%	25.2M	90.0%	2444.8Mb	23.3M	37.1%	92.4%	30.1%	141.0bp	83.8%	37.0%	117bp	141bp	27%	23.3M
RZY996MA6	0.67	2.1M	1.2M	0.6M	0.2M	0.1M	0.96%	0.7M	2.1M	100.0%	0.0%	2.7%	2.1M	0.0bp	2.7M	2.7M	100.0%	8.8%	22.7M	21.7M	95.8%	20.1M	88.6%	1.6M	2.7M	10.6%	82.1%	36.0%	151bp	151bp	27%	26.2M	90.5%	2691.0Mb	24.9M	37.3%	94.8%	26.0%	141.0bp	88.0%	37.0%	120bp	141bp	27%	24.9M
RZY999ZA1	1.17	1.0M	0.7M	0.2M	0.1M	0.1M	0.86%	0.4M	1.0M	100.0%	0.0%	3.7%	1.0M	0.0bp	1.5M	1.5M	100.0%	12.1%	16.6M	15.7M	94.7%	14.9M	89.8%	0.8M	1.5M	7.9%	83.4%	38.0%	151bp	151bp	27%	21.3M	89.2%	1960.2Mb	18.8M	37.5%	88.4%	29.0%	141.0bp	90.2%	37.0%	117bp	141bp	18%	18.8M
RZY999ZA2	0.67	1.6M	0.9M	0.4M	0.1M	0.1M	0.95%	0.5M	1.6M	100.0%	0.0%	2.9%	1.6M	0.0bp	2.1M	2.1M	100.0%	9.0%	20.4M	19.5M	95.9%	18.0M	88.4%	1.5M	2.1M	9.1%	84.2%	35.0%	151bp	151bp	27%	23.7M	90.4%	2371.2Mb	22.4M	36.6%	94.5%	28.2%	141.0bp	89.3%	36.0%	117bp	141bp	36%	22.4M
RZY999ZA3	0.65	2.7M	1.6M	0.7M	0.2M	0.2M	0.93%	0.9M	2.7M	100.0%	0.0%	2.7%	2.7M	0.0bp	3.6M	3.6M	100.0%	8.6%	20.7M	19.8M	95.8%	18.2M	87.9%	1.6M	3.6M	15.0%	74.5%	37.0%	151bp	151bp	27%	24.7M	90.1%	2332.8Mb	22.6M	37.1%	91.6%	33.9%	141.0bp	83.9%	37.0%	114bp	141bp	45%	22.6M
RZY999ZA4	0.74	1.1M	0.6M	0.2M	0.1M	0.1M	0.89%	0.5M	1.1M	100.0%	0.0%	4.2%	1.1M	0.0bp	1.6M	1.6M	100.0%	8.0%	17.1M	16.2M	94.4%	15.3M	89.4%	0.9M	1.6M	8.2%	81.2%	40.0%	151bp	151bp	27%	21.5M	90.0%	1928.8Mb	18.6M	38.4%	86.5%	35.5%	141.0bp	89.2%	38.0%	115bp	141bp	27%	18.6M
RZY999ZA5	0.64	1.3M	0.8M	0.3M	0.1M	0.1M	0.96%	0.5M	1.3M	100.0%	0.0%	3.3%	1.3M	0.0bp	1.8M	1.8M	100.0%	9.7%	21.8M	20.8M	95.2%	19.3M	88.2%	1.5M	1.8M	7.4%	85.5%	36.0%	151bp	151bp	27%	25.9M	90.0%	2557.8Mb	24.2M	36.8%	93.3%	28.6%	141.0bp	90.8%	36.0%	117bp	141bp	36%	24.2M
RZY999ZA6	0.70	1.8M	1.0M	0.5M	0.1M	0.1M	0.97%	0.6M	1.8M	100.0%	0.0%	2.8%	1.8M	0.0bp	2.4M	2.4M	100.0%	8.9%	20.9M	20.0M	95.5%	18.6M	89.0%	1.4M	2.4M	10.1%	83.1%	36.0%	151bp	151bp	27%	24.2M	90.1%	2473.0Mb	23.0M	37.3%	95.0%	25.5%	141.0bp	88.3%	37.0%	120bp	141bp	36%	23.0M

Expand table

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
\|\|	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
\|\|	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`
\|\|	SortMeRNA	rRNA	Percentage of reads matched to a SortMeRNA database	`sortmerna-rRNA_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
\|\|	UMI-tools	M Unique Reads	Reads remaining after deduplication (millions)	`umi_tools-dedup_output_reads`	read_count
\|\|	UMI-tools	% Pass Dedup	% processed reads that passed deduplication	`umi_tools-dedup_percent_passing`
\|\|	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
\|\|	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`
\|\|	fastp	Mean R1 Length	Mean read length of R1	`fastp-before_filtering_read1_mean_length`
\|\|	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

The strandedness used for analysis in this workflow can either be provided by the user or automatically inferred by Salmon using a sample of reads. In both cases, strandedness is verified at the end of the workflow using RSeQC's infer_experiment.py on genomic alignments. In this table, a pass indicates a match between the supplied strandedness (or that inferred by Salmon) and RSeQC results. A fail indicates a mismatch or 'undetermined' strandedness. 'Undetermined' strandedness can signal QC issues, including potential genomic DNA contamination.

Note: Rows are duplicated for an 'auto' setting to allow comparison of statistics between inference methods.

Showing ³⁶/₃₆ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
RZY000ZA1	pass	RSeQC	auto	forward	84.2	3.0	12.8
RZY000ZA2	pass	RSeQC	auto	forward	87.5	2.6	9.9
RZY000ZA3	pass	RSeQC	auto	forward	88.2	2.6	9.1
RZY000ZA4	pass	RSeQC	auto	forward	87.9	2.9	9.1
RZY000ZA5	pass	RSeQC	auto	forward	83.0	3.2	13.8
RZY000ZA6	pass	RSeQC	auto	forward	88.1	2.9	9.1
RZY019HA1	pass	RSeQC	auto	forward	86.6	2.7	10.6
RZY019HA2	pass	RSeQC	auto	forward	84.9	3.1	12.0
RZY019HA3	pass	RSeQC	auto	forward	87.4	3.3	9.3
RZY019HA4	pass	RSeQC	auto	forward	85.2	3.3	11.5
RZY019HA5	pass	RSeQC	auto	forward	87.6	3.2	9.2
RZY019HA6	pass	RSeQC	auto	forward	85.8	3.0	11.2
RZY019LA1	pass	RSeQC	auto	forward	82.4	3.6	13.9
RZY019LA2	pass	RSeQC	auto	forward	85.4	2.7	11.9
RZY019LA3	pass	RSeQC	auto	forward	87.0	2.8	10.2
RZY019LA4	pass	RSeQC	auto	forward	86.1	3.0	10.8
RZY019LA5	pass	RSeQC	auto	forward	88.1	2.7	9.2
RZY019LA6	pass	RSeQC	auto	forward	83.3	3.2	13.5
RZY996LA1	pass	RSeQC	auto	forward	86.1	2.8	11.1
RZY996LA2	pass	RSeQC	auto	forward	85.7	2.8	11.5
RZY996LA3	pass	RSeQC	auto	forward	84.2	2.7	13.2
RZY996LA4	pass	RSeQC	auto	forward	84.9	2.9	12.2
RZY996LA5	pass	RSeQC	auto	forward	86.1	2.8	11.1
RZY996LA6	pass	RSeQC	auto	forward	86.6	2.8	10.7
RZY996MA1	pass	RSeQC	auto	forward	87.5	2.9	9.6
RZY996MA2	pass	RSeQC	auto	forward	85.1	3.2	11.8
RZY996MA3	pass	RSeQC	auto	forward	86.9	3.0	10.1
RZY996MA4	pass	RSeQC	auto	forward	88.3	2.9	8.7
RZY996MA5	pass	RSeQC	auto	forward	87.3	2.9	9.8
RZY996MA6	pass	RSeQC	auto	forward	89.1	2.7	8.2
RZY999ZA1	pass	RSeQC	auto	forward	81.7	3.3	15.1
RZY999ZA2	pass	RSeQC	auto	forward	86.6	2.7	10.7
RZY999ZA3	pass	RSeQC	auto	forward	87.6	2.5	9.8
RZY999ZA4	pass	RSeQC	auto	forward	83.3	3.7	13.1
RZY999ZA5	pass	RSeQC	auto	forward	86.4	3.0	10.6
RZY999ZA6	pass	RSeQC	auto	forward	86.3	2.6	11.1

Expand table

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.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

36

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

36

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

2

23

11

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

36

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

36

All samples have sequences of a single length (151bp)

Sequence Duplication Levels

36

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.

36 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 ⁵/₅ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
ATAACCTATAATCAACCTAAAACCACAAACTAAACCATTATATTATCCTA	29	1049566	0.1149%
CAGACCTATCGAACCTGGCAATAGCTGGTTGTCTGGGAGATGAATAAAAG	26	880050	0.0963%
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	12	447890	0.0490%
AACCATTATATTATCCTAGTATGGGAGACAGAAAAGATAAAATACTAAGC	4	121982	0.0134%
TCAACTTAAAAACATCAATAACCTATAATCAACCTAAAACCACAAACTAA	1	30467	0.0033%

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

36

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

fastp

All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).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

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.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

36

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

36

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

12

24

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

36

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

36

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

Created with MultiQC

Sequence Duplication Levels

36

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 ²⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
ATCAACCTAAAACCACAAACTAAACCATTATATTATCCTAGTATGGGAGA	36	9640984	1.1423%
GCTGGTTGTCTGGGAGATGAATAAAAGTTCAGCTTCGTACACCCCAAATT	36	3928175	0.4654%
ATCAACCTAAAACCACAAACTAAACCATTATATTATCCTAGTATGGG	36	3290849	0.3899%
AACATCAATAACCTATAATCAACCTAAAACCACAAACTAAACCATTATAT	36	3237054	0.3835%
CCATCAAGATCAACTTAAAAACATCAATAACCTATAATCAACCTAAAACC	36	2979868	0.3531%
GCTAGCTTAACCATCAAGATCAACTTAAAAACATCAATAACCTATAATCA	36	2541545	0.3011%
ACCATCAAGATCAACTTAAAAACATCAATAACCTATAATCAACCTAAAAC	36	2431406	0.2881%
TTATCCTAGTATGGGAGACAGAAAAGATAAAATACTAAGCAATAGAGAAA	36	2232038	0.2645%
ACCACAAACTAAACCATTATATTATCCTAGTATGGGAGACAGAAAAGATA	36	2030348	0.2406%
ACCTATAATCAACCTAAAACCACAAACTAAACCATTATATTATCCTAGTA	36	1950596	0.2311%
AAACATCAATAACCTATAATCAACCTAAAACCACAAACTAAACCATTATA	36	1941239	0.2300%
GAACCTGGCAATAGCTGGTTGTCTGGGAGATGAATAAAAGTTCAGCTTCG	35	1703749	0.2019%
CGACCTCAGATCAGACGAGACAACCCGCTGAATTTAAGCATATTACTAAG	36	1702103	0.2017%
GCTTAACCATCAAGATCAACTTAAAAACATCAATAACCTATAATCAACCT	36	1653298	0.1959%
AAAACATCAATAACCTATAATCAACCTAAAACCACAAACTAAACCATTAT	36	1543048	0.1828%
CATCAAGATCAACTTAAAAACATCAATAACCTATAATCAACCTAAAACCA	35	1323352	0.1568%
AACCACAAACTAAACCATTATATTATCCTAGTATGGGAGACAGAAAAGAT	30	965432	0.1144%
GATCAACTTAAAAACATCAATAACCTATAATCAACCTAAAACCACAAACT	30	964858	0.1143%
GTCCTACGTGATCTGAGTTCAGACCGGAGTAATCCAGGTCAGTTTCTATC	27	894648	0.1060%
ATCAACCTAAAACCACAAACTAAACCATTATATTATCCTAGTATGGGAG	28	862667	0.1022%

Expand table

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

36

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

Preseq

Estimates library complexity, showing how many additional unique reads are sequenced for increasing total read count.http://smithlabresearch.org/software/preseqDOI: 10.1038/nmeth.2375

A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.

Complexity curve (molecule count)

Note that the x-axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.

Created with MultiQC

QualiMap

Quality control of alignment data and its derivatives like feature counts.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.http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

Read Distribution

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

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.

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 ³⁶/₃₆ rows and ¹⁰/₁₀ columns.

Sample Name	Total records	QC failed	Duplicates	Non primary hit	Unmapped	Unique	+ve strand	-ve strand	Non-splice reads	Splice reads
RZY000ZA1	1.9M	0.0M	0.0M	0.6M	0.0M	1.2M	0.6M	0.6M	1.0M	0.2M
RZY000ZA2	3.8M	0.0M	0.0M	1.0M	0.0M	2.6M	1.4M	1.2M	2.3M	0.3M
RZY000ZA3	3.0M	0.0M	0.0M	0.7M	0.0M	2.1M	1.1M	1.0M	1.8M	0.2M
RZY000ZA4	3.6M	0.0M	0.0M	1.0M	0.0M	2.4M	1.2M	1.1M	2.1M	0.3M
RZY000ZA5	2.0M	0.0M	0.0M	0.6M	0.0M	1.3M	0.7M	0.6M	1.1M	0.2M
RZY000ZA6	3.8M	0.0M	0.0M	1.0M	0.0M	2.5M	1.3M	1.2M	2.2M	0.3M
RZY019HA1	3.1M	0.0M	0.0M	0.8M	0.0M	2.1M	1.1M	1.0M	1.8M	0.3M
RZY019HA2	2.0M	0.0M	0.0M	0.5M	0.0M	1.4M	0.7M	0.6M	1.2M	0.2M
RZY019HA3	3.6M	0.0M	0.0M	0.9M	0.0M	2.4M	1.2M	1.1M	2.1M	0.3M
RZY019HA4	2.2M	0.0M	0.0M	0.6M	0.0M	1.5M	0.8M	0.7M	1.3M	0.2M
RZY019HA5	3.3M	0.0M	0.0M	0.8M	0.0M	2.2M	1.2M	1.1M	2.0M	0.2M
RZY019HA6	2.4M	0.0M	0.0M	0.6M	0.0M	1.6M	0.9M	0.7M	1.4M	0.2M
RZY019LA1	1.2M	0.0M	0.0M	0.4M	0.0M	0.7M	0.4M	0.3M	0.5M	0.1M
RZY019LA2	1.8M	0.0M	0.0M	0.5M	0.0M	1.2M	0.7M	0.6M	1.1M	0.1M
RZY019LA3	4.4M	0.0M	0.0M	1.1M	0.0M	3.0M	1.6M	1.4M	2.7M	0.3M
RZY019LA4	2.3M	0.0M	0.0M	0.6M	0.0M	1.5M	0.8M	0.7M	1.3M	0.2M
RZY019LA5	4.0M	0.0M	0.0M	1.0M	0.0M	2.8M	1.5M	1.3M	2.5M	0.3M
RZY019LA6	1.9M	0.0M	0.0M	0.6M	0.0M	1.1M	0.6M	0.5M	1.0M	0.2M
RZY996LA1	1.8M	0.0M	0.0M	0.5M	0.0M	1.2M	0.6M	0.5M	1.0M	0.1M
RZY996LA2	1.9M	0.0M	0.0M	0.5M	0.0M	1.3M	0.7M	0.6M	1.2M	0.2M
RZY996LA3	1.2M	0.0M	0.0M	0.3M	0.0M	0.8M	0.5M	0.4M	0.7M	0.1M
RZY996LA4	2.1M	0.0M	0.0M	0.6M	0.0M	1.4M	0.7M	0.6M	1.2M	0.2M
RZY996LA5	2.6M	0.0M	0.0M	0.7M	0.0M	1.8M	0.9M	0.8M	1.6M	0.2M
RZY996LA6	2.2M	0.0M	0.0M	0.6M	0.0M	1.5M	0.8M	0.7M	1.4M	0.2M
RZY996MA1	3.7M	0.0M	0.0M	1.0M	0.0M	2.5M	1.3M	1.2M	2.3M	0.3M
RZY996MA2	2.1M	0.0M	0.0M	0.6M	0.0M	1.3M	0.7M	0.6M	1.2M	0.1M
RZY996MA3	4.4M	0.0M	0.0M	1.1M	0.0M	3.0M	1.6M	1.4M	2.7M	0.3M
RZY996MA4	3.0M	0.0M	0.0M	0.8M	0.0M	2.0M	1.1M	0.9M	1.8M	0.2M
RZY996MA5	3.7M	0.0M	0.0M	0.9M	0.0M	2.6M	1.3M	1.2M	2.3M	0.3M
RZY996MA6	2.7M	0.0M	0.0M	0.7M	0.0M	1.9M	1.0M	0.9M	1.7M	0.2M
RZY999ZA1	1.5M	0.0M	0.0M	0.4M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
RZY999ZA2	2.1M	0.0M	0.0M	0.5M	0.0M	1.5M	0.8M	0.7M	1.3M	0.2M
RZY999ZA3	3.6M	0.0M	0.0M	0.9M	0.0M	2.5M	1.3M	1.2M	2.2M	0.3M
RZY999ZA4	1.6M	0.0M	0.0M	0.5M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
RZY999ZA5	1.8M	0.0M	0.0M	0.5M	0.0M	1.2M	0.6M	0.5M	1.1M	0.1M
RZY999ZA6	2.4M	0.0M	0.0M	0.6M	0.0M	1.6M	0.9M	0.8M	1.5M	0.2M

Expand table

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
\|\|	+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

Samtools

Toolkit for interacting with BAM/CRAM files.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 ³⁶/₃₆ 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
RZY000ZA1	1.4M	0.0M	0.0M	0.0M	0.0M	0.0M	128.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY000ZA2	2.8M	0.0M	0.0M	0.0M	0.0M	0.1M	279.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY000ZA3	2.3M	0.0M	0.0M	0.0M	0.0M	0.1M	224.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY000ZA4	2.6M	0.0M	0.0M	0.0M	0.0M	0.1M	254.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY000ZA5	1.4M	0.0M	0.0M	0.0M	0.0M	0.1M	132.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY000ZA6	2.8M	0.0M	0.0M	0.0M	0.0M	0.1M	270.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019HA1	2.3M	0.0M	0.0M	0.0M	0.0M	0.1M	228.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019HA2	1.5M	0.0M	0.0M	0.0M	0.0M	0.0M	149.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019HA3	2.6M	0.0M	0.0M	0.0M	0.0M	0.1M	252.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019HA4	1.6M	0.0M	0.0M	0.0M	0.0M	0.1M	159.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019HA5	2.4M	0.0M	0.0M	0.0M	0.0M	0.1M	240.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019HA6	1.8M	0.0M	0.0M	0.0M	0.0M	0.1M	173.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019LA1	0.8M	0.0M	0.0M	0.0M	0.0M	0.0M	68.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019LA2	1.4M	0.0M	0.0M	0.0M	0.0M	0.0M	133.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019LA3	3.3M	0.0M	0.0M	0.0M	0.0M	0.1M	323.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019LA4	1.6M	0.0M	0.0M	0.0M	0.0M	0.1M	158.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019LA5	3.0M	0.0M	0.0M	0.0M	0.0M	0.1M	302.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY019LA6	1.3M	0.0M	0.0M	0.0M	0.0M	0.1M	119.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996LA1	1.3M	0.0M	0.0M	0.0M	0.0M	0.0M	126.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996LA2	1.4M	0.0M	0.0M	0.0M	0.0M	0.0M	143.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996LA3	0.9M	0.0M	0.0M	0.0M	0.0M	0.0M	87.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996LA4	1.5M	0.0M	0.0M	0.0M	0.0M	0.0M	144.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996LA5	2.0M	0.0M	0.0M	0.0M	0.0M	0.1M	191.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996LA6	1.7M	0.0M	0.0M	0.0M	0.0M	0.0M	166.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996MA1	2.8M	0.0M	0.0M	0.0M	0.0M	0.1M	268.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996MA2	1.5M	0.0M	0.0M	0.0M	0.0M	0.1M	136.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996MA3	3.3M	0.0M	0.0M	0.0M	0.0M	0.1M	325.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996MA4	2.2M	0.0M	0.0M	0.0M	0.0M	0.1M	214.3Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996MA5	2.8M	0.0M	0.0M	0.0M	0.0M	0.1M	277.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY996MA6	2.1M	0.0M	0.0M	0.0M	0.0M	0.1M	206.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY999ZA1	1.0M	0.0M	0.0M	0.0M	0.0M	0.0M	93.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY999ZA2	1.6M	0.0M	0.0M	0.0M	0.0M	0.0M	155.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY999ZA3	2.7M	0.0M	0.0M	0.0M	0.0M	0.1M	267.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY999ZA4	1.1M	0.0M	0.0M	0.0M	0.0M	0.0M	96.9Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY999ZA5	1.3M	0.0M	0.0M	0.0M	0.0M	0.0M	125.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M
RZY999ZA6	1.8M	0.0M	0.0M	0.0M	0.0M	0.1M	178.4Mb	0.0Mb	0.0Mb	0.0M	0.0M	0.0M	0.0M

Expand table

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 ³⁶/₃₆ 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)
RZY000ZA1	1.9M	1.9M	1.9M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY000ZA2	3.8M	3.8M	3.8M	1.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY000ZA3	3.0M	3.0M	3.0M	0.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY000ZA4	3.6M	3.6M	3.6M	1.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY000ZA5	2.0M	2.0M	2.0M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY000ZA6	3.8M	3.8M	3.8M	1.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019HA1	3.1M	3.1M	3.1M	0.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019HA2	2.0M	2.0M	2.0M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019HA3	3.6M	3.6M	3.6M	0.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019HA4	2.2M	2.2M	2.2M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019HA5	3.3M	3.3M	3.3M	0.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019HA6	2.4M	2.4M	2.4M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019LA1	1.2M	1.2M	1.2M	0.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019LA2	1.8M	1.8M	1.8M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019LA3	4.4M	4.4M	4.4M	1.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019LA4	2.3M	2.3M	2.3M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019LA5	4.0M	4.0M	4.0M	1.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY019LA6	1.9M	1.9M	1.9M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996LA1	1.8M	1.8M	1.8M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996LA2	1.9M	1.9M	1.9M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996LA3	1.2M	1.2M	1.2M	0.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996LA4	2.1M	2.1M	2.1M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996LA5	2.6M	2.6M	2.6M	0.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996LA6	2.2M	2.2M	2.2M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996MA1	3.7M	3.7M	3.7M	1.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996MA2	2.1M	2.1M	2.1M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996MA3	4.4M	4.4M	4.4M	1.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996MA4	3.0M	3.0M	3.0M	0.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996MA5	3.7M	3.7M	3.7M	0.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY996MA6	2.7M	2.7M	2.7M	0.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY999ZA1	1.5M	1.5M	1.5M	0.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY999ZA2	2.1M	2.1M	2.1M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY999ZA3	3.6M	3.6M	3.6M	0.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY999ZA4	1.6M	1.6M	1.6M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY999ZA5	1.8M	1.8M	1.8M	0.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
RZY999ZA6	2.4M	2.4M	2.4M	0.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M

Expand table

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 ³⁶/₃₆ 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)
RZY000ZA1	100.0%	100.0%	100.0%	29.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY000ZA2	100.0%	100.0%	100.0%	25.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY000ZA3	100.0%	100.0%	100.0%	24.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY000ZA4	100.0%	100.0%	100.0%	26.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY000ZA5	100.0%	100.0%	100.0%	30.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY000ZA6	100.0%	100.0%	100.0%	27.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019HA1	100.0%	100.0%	100.0%	25.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019HA2	100.0%	100.0%	100.0%	23.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019HA3	100.0%	100.0%	100.0%	26.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019HA4	100.0%	100.0%	100.0%	26.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019HA5	100.0%	100.0%	100.0%	25.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019HA6	100.0%	100.0%	100.0%	25.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019LA1	100.0%	100.0%	100.0%	33.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019LA2	100.0%	100.0%	100.0%	24.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019LA3	100.0%	100.0%	100.0%	25.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019LA4	100.0%	100.0%	100.0%	27.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019LA5	100.0%	100.0%	100.0%	24.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY019LA6	100.0%	100.0%	100.0%	31.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996LA1	100.0%	100.0%	100.0%	26.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996LA2	100.0%	100.0%	100.0%	24.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996LA3	100.0%	100.0%	100.0%	25.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996LA4	100.0%	100.0%	100.0%	27.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996LA5	100.0%	100.0%	100.0%	25.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996LA6	100.0%	100.0%	100.0%	24.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996MA1	100.0%	100.0%	100.0%	25.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996MA2	100.0%	100.0%	100.0%	29.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996MA3	100.0%	100.0%	100.0%	24.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996MA4	100.0%	100.0%	100.0%	26.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996MA5	100.0%	100.0%	100.0%	24.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY996MA6	100.0%	100.0%	100.0%	24.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY999ZA1	100.0%	100.0%	100.0%	29.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY999ZA2	100.0%	100.0%	100.0%	25.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY999ZA3	100.0%	100.0%	100.0%	24.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY999ZA4	100.0%	100.0%	100.0%	32.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY999ZA5	100.0%	100.0%	100.0%	27.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
RZY999ZA6	100.0%	100.0%	100.0%	24.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%

Expand table

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`

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.https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

Summary Statistics

Summary statistics from the STAR alignment

Showing ³⁶/₃₆ 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
RZY000ZA1	19.0M	17.9M	94.1%	16.5M	86.8%	1.4M	103.0bp	107.1bp	2.8M	2.8M	2.8M	0.1M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY000ZA2	20.4M	19.4M	95.2%	17.8M	87.0%	1.7M	100.0bp	103.1bp	2.1M	2.1M	2.1M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY000ZA3	23.6M	22.6M	95.6%	20.7M	87.8%	1.9M	104.0bp	106.0bp	2.7M	2.7M	2.7M	0.1M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY000ZA4	25.3M	24.1M	95.2%	22.1M	87.3%	2.0M	100.0bp	103.2bp	2.5M	2.5M	2.5M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY000ZA5	19.9M	18.7M	93.8%	17.4M	87.3%	1.3M	102.0bp	106.8bp	3.5M	3.5M	3.5M	0.1M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	2.1bp
RZY000ZA6	27.3M	26.0M	95.4%	23.8M	87.4%	2.2M	100.0bp	102.7bp	3.1M	3.1M	3.0M	0.1M	0.0M	0.1M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019HA1	23.4M	22.4M	95.9%	20.4M	87.2%	2.0M	103.0bp	105.5bp	3.3M	3.3M	3.3M	0.1M	0.0M	0.0M	0.6%	0.0%	2.5bp	0.0%	2.1bp
RZY019HA2	14.7M	14.1M	95.9%	12.9M	87.7%	1.2M	105.0bp	106.8bp	1.7M	1.7M	1.7M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019HA3	21.2M	20.2M	95.2%	18.3M	86.3%	1.9M	99.0bp	102.0bp	2.1M	2.1M	2.1M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019HA4	21.2M	20.3M	95.7%	18.7M	88.3%	1.6M	103.0bp	105.3bp	2.2M	2.2M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019HA5	25.0M	23.8M	95.2%	21.9M	87.8%	1.9M	103.0bp	105.8bp	2.4M	2.4M	2.4M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019HA6	26.0M	24.9M	95.8%	23.1M	88.9%	1.8M	106.0bp	107.8bp	2.9M	2.9M	2.8M	0.1M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019LA1	16.3M	15.2M	93.5%	14.5M	89.0%	0.7M	104.0bp	109.2bp	3.9M	3.9M	3.8M	0.1M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	2.1bp
RZY019LA2	18.2M	17.5M	96.0%	16.1M	88.3%	1.4M	105.0bp	107.2bp	2.2M	2.2M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019LA3	23.9M	22.7M	95.0%	20.8M	87.4%	1.8M	101.0bp	103.7bp	2.5M	2.5M	2.5M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019LA4	20.9M	20.0M	95.7%	18.4M	88.0%	1.6M	103.0bp	104.8bp	2.2M	2.2M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY019LA5	24.5M	23.4M	95.7%	21.6M	88.3%	1.8M	105.0bp	106.5bp	2.7M	2.7M	2.6M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.2bp
RZY019LA6	23.6M	22.2M	94.3%	20.8M	88.5%	1.4M	103.0bp	107.4bp	4.7M	4.7M	4.6M	0.1M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	2.0bp
RZY996LA1	22.3M	21.5M	96.1%	19.7M	88.1%	1.8M	106.0bp	107.6bp	2.8M	2.8M	2.8M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996LA2	16.9M	16.2M	95.8%	14.8M	88.0%	1.3M	106.0bp	108.4bp	1.9M	1.9M	1.9M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996LA3	13.6M	13.1M	95.6%	12.1M	88.6%	1.0M	106.0bp	108.0bp	1.6M	1.6M	1.5M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996LA4	20.1M	19.1M	94.8%	18.0M	89.6%	1.1M	104.0bp	106.9bp	3.0M	3.0M	3.0M	0.0M	0.0M	0.0M	0.7%	0.1%	2.4bp	0.0%	2.1bp
RZY996LA5	23.0M	21.9M	95.3%	20.3M	88.0%	1.7M	106.0bp	108.1bp	2.6M	2.6M	2.5M	0.1M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996LA6	20.7M	19.8M	95.7%	18.4M	88.9%	1.4M	106.0bp	108.4bp	2.3M	2.3M	2.3M	0.1M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996MA1	25.5M	24.4M	95.6%	22.1M	86.8%	2.3M	101.0bp	102.8bp	2.4M	2.4M	2.3M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996MA2	19.8M	18.7M	94.3%	17.2M	86.8%	1.5M	102.0bp	105.7bp	2.3M	2.3M	2.3M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996MA3	24.8M	23.7M	95.6%	21.8M	87.7%	2.0M	102.0bp	104.4bp	2.6M	2.6M	2.5M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996MA4	22.5M	21.4M	95.3%	19.7M	87.6%	1.7M	102.0bp	104.3bp	2.3M	2.3M	2.2M	0.1M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996MA5	21.3M	20.3M	95.5%	18.8M	88.3%	1.5M	103.0bp	105.0bp	2.3M	2.3M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY996MA6	22.7M	21.7M	95.8%	20.1M	88.6%	1.6M	107.0bp	108.7bp	2.5M	2.5M	2.4M	0.0M	0.0M	0.0M	0.7%	0.1%	2.6bp	0.0%	2.1bp
RZY999ZA1	16.6M	15.7M	94.7%	14.9M	89.8%	0.8M	103.0bp	106.8bp	2.8M	2.8M	2.7M	0.0M	0.0M	0.0M	0.6%	0.1%	2.4bp	0.0%	2.0bp
RZY999ZA2	20.4M	19.5M	95.9%	18.0M	88.4%	1.5M	104.0bp	106.1bp	2.3M	2.3M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY999ZA3	20.7M	19.8M	95.8%	18.2M	87.9%	1.6M	101.0bp	103.0bp	2.2M	2.2M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY999ZA4	17.1M	16.2M	94.4%	15.3M	89.4%	0.9M	102.0bp	105.9bp	2.8M	2.8M	2.8M	0.0M	0.0M	0.0M	0.7%	0.0%	2.4bp	0.0%	2.1bp
RZY999ZA5	21.8M	20.8M	95.2%	19.3M	88.2%	1.5M	103.0bp	106.2bp	2.2M	2.2M	2.2M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp
RZY999ZA6	20.9M	20.0M	95.5%	18.6M	89.0%	1.4M	106.0bp	108.3bp	2.4M	2.4M	2.4M	0.0M	0.0M	0.0M	0.7%	0.1%	2.5bp	0.0%	2.1bp

Expand table

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

UMI-tools

Tools for dealing with Unique Molecular Identifiers (UMIs)/(RMTs) and scRNA-Seq barcodes.https://github.com/CGATOxford/UMI-toolsDOI: 10.1101/gr.209601.116

Deduplicated Reads

Created with MultiQC

UMI Stats

Statistics from running umi_tools dedup or umi_tools extract

Positions Dedup: Total number of positions deduplicated
Total UMIs: Total UMIs found in sample
Unique UMIs: Unique UMIs found in sample
Mean #UMI: Mean number of unique UMIs per position
Max #UMI: Max number of unique UMIs per position

Created with MultiQC

Showing ³⁶/₃₆ rows and ⁵/₅ columns.

Sample Name	Positions Dedup	Total UMIs	Unique UMIs	Mean #UMI	Max #UMI
RZY000ZA1	1211091	21891764	6859	1.78	3093
RZY000ZA2	2424415	23918897	6860	1.83	4112
RZY000ZA3	1929192	27113171	6988	1.83	4028
RZY000ZA4	2354069	29520767	7050	1.75	4095
RZY000ZA5	1214499	22516801	6820	1.88	3206
RZY000ZA6	2459489	31903740	7074	1.79	4119
RZY019HA1	1769588	27106259	6979	2.12	4143
RZY019HA2	1263966	16833721	6566	1.81	3840
RZY019HA3	2328130	25059248	6915	1.78	4152
RZY019HA4	1463687	24255967	6889	1.78	4005
RZY019HA5	2164598	28580012	6995	1.75	4051
RZY019HA6	1560816	29489319	7100	1.77	4063
RZY019LA1	700875	17671868	6639	1.84	2183
RZY019LA2	1165926	20763488	6689	1.80	3679
RZY019LA3	2794129	27715894	7026	1.82	4137
RZY019LA4	1481214	23987014	6888	1.78	3946
RZY019LA5	2573707	28178679	7015	1.80	4119
RZY019LA6	1132121	26301866	7006	1.87	2745
RZY996LA1	1123983	25615342	6968	1.82	3676
RZY996LA2	1264061	19295637	6680	1.74	3629
RZY996LA3	814889	15446203	6480	1.68	3073
RZY996LA4	1330726	22606794	6840	1.74	3170
RZY996LA5	1760872	26328632	6981	1.69	3733
RZY996LA6	1482135	23379391	6886	1.74	3868
RZY996MA1	2414221	29976402	7055	1.80	4163
RZY996MA2	1359839	22904666	6827	1.71	3306
RZY996MA3	2809939	28685291	7043	1.81	4149
RZY996MA4	1976823	25925925	6919	1.75	4024
RZY996MA5	2390451	24460765	6921	1.80	4100
RZY996MA6	1782372	25746356	7004	1.77	4006
RZY999ZA1	892661	18445063	6711	1.82	2501
RZY999ZA2	1394347	23331116	6852	1.76	3867
RZY999ZA3	2262495	23897336	6905	1.85	4139
RZY999ZA4	1025328	19209243	6730	1.72	2725
RZY999ZA5	1209087	24713903	6919	1.73	3633
RZY999ZA6	1573820	23626472	6868	1.74	3863

Expand table

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

Sort	Column	Description	ID
\|\|	Positions Dedup	Total number of positions deduplicated	`positions_deduplicated`
\|\|	Total UMIs	Total UMIs found in sample	`total_umis`
\|\|	Unique UMIs	Unique UMIs found in sample	`unique_umis`
\|\|	Mean #UMI	Mean number of unique UMIs per position	`mean_umi_per_pos`
\|\|	Max #UMI	Max number of unique UMIs per position	`max_umi_per_pos`

Sample relationships

Plots interrogating sample relationships, based on final count matrices.

STAR_SALMON DESeq2 sample similarity

is generated from clustering by Euclidean distances between DESeq2 rlog values for each sample in the deseq2_qc.r script.

Created with MultiQC

STAR_SALMON DESeq2 PCA plot

PCA plot between samples in the experiment. These values are calculated using DESeq2 in the deseq2_qc.r script.

Created with MultiQC

SortMeRNA

Program for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data.http://bioinfo.lifl.fr/RNA/sortmernaDOI: 10.1093/bioinformatics/bts611

The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data.

Created with MultiQC

Software Versions

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

Group	Software	Version
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DESEQ2_QC_STAR_SALMON	bioconductor-deseq2	`1.46.0`
	r-base	`4.4.2`
FASTQC_RAW	fastqc	`0.12.1`
FASTQC_TRIM	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`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
PRESEQ_LCEXTRAP	preseq	`3.2.0`
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_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_SORT_QUALIMAP	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`
SortMeRNA	sortmerna	`4.3.7`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
UMITOOLS_DEDUP	umitools	`1.1.5`
UMITOOLS_EXTRACT	umitools	`1.1.5`
Workflow	Nextflow	`25.04.7`
	nf-core/rnaseq	`v3.22.2-g3816d48`
fastp	fastp	`0.24.0`

nf-core/rnaseq Methods Description

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

Methods

Data was processed using nf-core/rnaseq v3.22.2 (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 v25.04.7 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq -r 3.22.2 -c ../rnaseq.config -params-file ../params_rnaseq.json -profile crg_slurm

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.https://github.com/nf-core/rnaseq

Input/output options

input: ../samplesheet_Danio_ZY.csv
multiqc_title: PTox-WP6-Danio
outdir: ./rnaseq_output_ZY

Reference genome options

fasta: ../refs/Danio/genome_with_spikes.fa
gtf: ../refs/Danio/genome_annotation_with_spikes.gtf
salmon_index: ./rnaseq_output/genome/index/salmon
star_index: ./rnaseq_output/genome/index/star

Read trimming options

extra_fastp_args: --length_required=20 --trim_poly_g --poly_g_min_len=20 --trim_poly_x --poly_x_min_len=15 --adapter_sequence=AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC --dont_eval_duplication --cut_tail
trimmer: fastp

Read filtering options

remove_ribo_rna: true
sortmerna_index: ./rnaseq_output/genome/sortmerna/idx

UMI options

umi_discard_read: 0
umitools_bc_pattern: ^(?P.{6})(?P.{4}).*
umitools_dedup_stats: true
umitools_extract_method: regex
with_umi: true

Alignment options

extra_salmon_quant_args: --noLengthCorrection
extra_star_align_args: --alignIntronMax 1000000 --alignIntronMin 20 --alignMatesGapMax 1000000 --alignSJoverhangMin 8 --outFilterMismatchNmax 999 --outFilterMultimapNmax 20 --outFilterType BySJout --outFilterMismatchNoverLmax 0.1 --clip3pAdapterSeq AAAAAAAA
stranded_threshold: 0.7

Process skipping options

skip_bigwig: true
skip_biotype_qc: true
skip_dupradar: true
skip_preseq: false
skip_pseudo_alignment: true
skip_stringtie: true

Generic options

monochrome_logs: true
trace_report_suffix: 2026-10-16_12-55-03

Core Nextflow options

configFiles: /users/rg/vntasis/.nextflow/assets/nf-core/rnaseq/nextflow.config, /users/project/precisiontox_008279_no_backup/wp6_data/rnaseq_run-Danio/../rnaseq.config
containerEngine: apptainer
launchDir: /users/project/precisiontox_008279_no_backup/wp6_data/rnaseq_run-Danio
profile: crg_slurm
projectDir: /users/rg/vntasis/.nextflow/assets/nf-core/rnaseq
revision: 3.22.2
runName: goofy_wozniak
userName: vntasis
workDir: /users/project/precisiontox_008279_no_backup/wp6_data/rnaseq_run-Danio/work

PTox-WP6-Danio

MultiQC Toolbox

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PTox-WP6-Danio

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General Statistics

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General Statistics: Columns

Sample status checks

Strandedness Checks

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Samples strandedness check: Columns

FastQC (raw)

Sequence Counts Help

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Per Base Sequence Content Help

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Overrepresented sequences by sample Help

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Top overrepresented sequences

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FastQC: Top overrepresented sequences: Columns

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fastp

Filtered Reads

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Sequence Quality

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GC Content

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N content

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FastQC (trimmed)

Sequence Counts Help

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Per Base Sequence Content Help

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Overrepresented sequences by sample Help

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Top overrepresented sequences

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FastQC: Top overrepresented sequences: Columns

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Preseq

Complexity curve (molecule count)

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QualiMap

Genomic origin of reads Help

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Gene Coverage Profile Help

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RSeQC

Read Distribution

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Sequence Counts

Per Base Sequence Content

Overrepresented sequences by sample

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Sequence Counts

Per Base Sequence Content

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Percent mapped

UMI Stats