MultiQC Report

General Statistics

Showing ¹⁷⁷/₁₇₇ rows and ²³/₂₈ columns.

Sample Name	M Reads	M Reads Mapped	% rRNA	dupInt	% Dups	5'-3' bias	M Aligned	% Proper Pairs	Error rate	M Non-Primary	M Reads Mapped	% Mapped	% Proper Pairs	% MapQ 0 Reads	M Total seqs	% Aligned	M Aligned	% Dups	% GC	Length	% Failed	M Seqs	% BP Trimmed	% Dups	% GC	Length	% Failed	M Seqs
HGVMYDRXY_CIVRCoV_P1_A01_C001_R1	109.7	109.7	0.00%	0.04%	62.0%	1.23	28.9	17.9%	0.32%	51.9	57.8	100.0%	100.0%	5.7%	57.8	61.1%	18.3							73.5%	56%	133 bp	18%	29.9
HGVMYDRXY_CIVRCoV_P1_A01_C001_R1_1																		75.1%	56%	151 bp	27%	30.0	11.7%	74.4%	56%	133 bp	18%	29.9
HGVMYDRXY_CIVRCoV_P1_A01_C001_R1_2																		74.1%	56%	151 bp	27%	30.0	11.7%
HGVMYDRXY_CIVRCoV_P1_A02_U277_R1	121.8	121.8	0.00%	0.04%	52.6%	1.13	30.9	21.9%	0.34%	60.0	61.8	100.0%	100.0%	6.1%	61.8	59.5%	19.0							67.6%	57%	140 bp	18%	32.0
HGVMYDRXY_CIVRCoV_P1_A02_U277_R1_1																		69.6%	57%	151 bp	27%	32.0	7.2%	69.4%	57%	140 bp	18%	32.0
HGVMYDRXY_CIVRCoV_P1_A02_U277_R1_2																		67.8%	57%	151 bp	27%	32.0	7.2%
HGVMYDRXY_CIVRCoV_P1_A03_C028_R1	148.2	148.2	0.00%	0.04%	85.3%	1.26	31.3	5.3%	0.27%	85.5	62.6	100.0%	100.0%	7.3%	62.6	40.9%	13.5							88.8%	60%	135 bp	27%	32.9
HGVMYDRXY_CIVRCoV_P1_A03_C028_R1_1																		90.3%	59%	151 bp	36%	32.9	10.9%	89.9%	60%	135 bp	27%	32.9
HGVMYDRXY_CIVRCoV_P1_A03_C028_R1_2																		89.1%	59%	151 bp	36%	32.9	10.8%
HGVMYDRXY_CIVRCoV_P1_A04_U514_R1	122.1	122.1	0.00%	0.03%	67.7%	1.20	29.1	13.8%	0.32%	63.8	58.3	100.0%	100.0%	7.1%	58.3	53.7%	16.3							75.9%	58%	138 bp	18%	30.3
HGVMYDRXY_CIVRCoV_P1_A04_U514_R1_1																		77.6%	57%	151 bp	27%	30.3	8.7%	77.4%	58%	138 bp	18%	30.3
HGVMYDRXY_CIVRCoV_P1_A04_U514_R1_2																		76.1%	57%	151 bp	27%	30.3	8.7%
HGVMYDRXY_CIVRCoV_P1_B01_C005_R1	114.5	114.5	0.00%	0.04%	77.7%	1.20	24.6	8.3%	0.31%	65.2	49.3	100.0%	100.0%	7.8%	49.3	44.7%	11.6							82.5%	60%	132 bp	27%	25.9
HGVMYDRXY_CIVRCoV_P1_B01_C005_R1_1																		84.9%	59%	151 bp	36%	26.0	12.4%	84.2%	60%	132 bp	27%	25.9
HGVMYDRXY_CIVRCoV_P1_B01_C005_R1_2																		83.0%	59%	151 bp	36%	26.0	12.3%
HGVMYDRXY_CIVRCoV_P1_B02_C014_R1	91.8	91.8	0.00%	0.03%	73.7%	1.19	20.3	10.2%	0.33%	51.2	40.6	100.0%	100.0%	6.6%	40.6	46.5%	9.8							80.2%	60%	138 bp	27%	21.1
HGVMYDRXY_CIVRCoV_P1_B02_C014_R1_1																		82.0%	59%	151 bp	36%	21.1	8.9%	81.8%	60%	138 bp	27%	21.1
HGVMYDRXY_CIVRCoV_P1_B02_C014_R1_2																		80.4%	59%	151 bp	36%	21.1	9.0%
HGVMYDRXY_CIVRCoV_P1_B03_U409_R1	425.3	425.3	0.00%	0.05%	68.7%	1.16	98.4	13.2%	0.31%	228.4	196.8	100.0%	100.0%	5.8%	196.8	48.3%	50.4							79.7%	59%	134 bp	27%	104.4
HGVMYDRXY_CIVRCoV_P1_B03_U409_R1_1																		82.0%	59%	151 bp	36%	104.5	11.6%	81.4%	59%	134 bp	27%	104.4
HGVMYDRXY_CIVRCoV_P1_B03_U409_R1_2																		80.2%	58%	151 bp	36%	104.5	11.6%
HGVMYDRXY_CIVRCoV_P1_B04_C023_R1	128.9	128.9	0.00%	0.03%	75.3%	1.22	29.0	9.7%	0.31%	70.9	58.0	100.0%	100.0%	6.9%	58.0	47.4%	14.3							81.9%	59%	136 bp	18%	30.1
HGVMYDRXY_CIVRCoV_P1_B04_C023_R1_1																		83.4%	58%	151 bp	27%	30.1	9.9%	83.2%	59%	136 bp	18%	30.1
HGVMYDRXY_CIVRCoV_P1_B04_C023_R1_2																		82.1%	58%	151 bp	27%	30.1	9.9%
HGVMYDRXY_CIVRCoV_P1_C01_U051_R1	133.4	133.4	0.00%	0.04%	69.0%	1.17	29.5	12.3%	0.31%	74.3	59.0	100.0%	100.0%	6.8%	59.0	48.3%	14.9							77.1%	59%	134 bp	27%	30.9
HGVMYDRXY_CIVRCoV_P1_C01_U051_R1_1																		79.8%	58%	151 bp	36%	30.9	11.7%	79.0%	59%	133 bp	27%	30.9
HGVMYDRXY_CIVRCoV_P1_C01_U051_R1_2																		77.7%	58%	151 bp	36%	30.9	11.6%
HGVMYDRXY_CIVRCoV_P1_C02_U288_R1	113.6	113.6	0.00%	0.04%	66.6%	1.18	26.7	13.9%	0.32%	60.2	53.4	100.0%	100.0%	8.0%	53.4	53.6%	14.8							75.9%	58%	138 bp	18%	27.7
HGVMYDRXY_CIVRCoV_P1_C02_U288_R1_1																		77.7%	58%	151 bp	27%	27.7	8.5%	77.4%	58%	138 bp	18%	27.7
HGVMYDRXY_CIVRCoV_P1_C02_U288_R1_2																		76.1%	58%	151 bp	27%	27.7	8.5%
HGVMYDRXY_CIVRCoV_P1_C03_C011_R1	66.8	66.8	0.00%	0.03%	38.6%	1.20	21.5	35.7%	0.34%	23.9	43.0	100.0%	100.0%	3.4%	43.0	74.2%	16.4							55.5%	54%	130 bp	18%	22.1
HGVMYDRXY_CIVRCoV_P1_C03_C011_R1_1																		57.5%	54%	151 bp	18%	22.1	13.8%	56.0%	54%	130 bp	18%	22.1
HGVMYDRXY_CIVRCoV_P1_C03_C011_R1_2																		56.9%	54%	151 bp	18%	22.1	13.8%
HGVMYDRXY_CIVRCoV_P1_C04_U506_R1	96.5	96.5	0.00%	0.03%	49.9%	1.16	25.5	23.9%	0.30%	45.5	50.9	100.0%	100.0%	5.6%	50.9	62.6%	16.5							65.5%	57%	134 bp	18%	26.3
HGVMYDRXY_CIVRCoV_P1_C04_U506_R1_1																		67.0%	56%	151 bp	27%	26.3	11.2%	66.4%	57%	134 bp	18%	26.3
HGVMYDRXY_CIVRCoV_P1_C04_U506_R1_2																		66.0%	56%	151 bp	27%	26.3	11.2%
HGVMYDRXY_CIVRCoV_P1_D01_C007_R1	122.9	122.9	0.00%	0.04%	81.7%	1.21	26.3	6.7%	0.29%	70.3	52.7	100.0%	100.0%	7.4%	52.7	43.9%	12.2							85.3%	60%	131 bp	27%	27.7
HGVMYDRXY_CIVRCoV_P1_D01_C007_R1_1																		87.4%	59%	151 bp	36%	27.8	13.4%	86.7%	60%	131 bp	27%	27.7
HGVMYDRXY_CIVRCoV_P1_D01_C007_R1_2																		86.0%	59%	151 bp	36%	27.8	13.3%
HGVMYDRXY_CIVRCoV_P1_D02_U062_R1	73.9	73.9	0.00%	0.04%	56.0%	1.18	19.1	20.4%	0.31%	35.6	38.3	100.0%	100.0%	5.7%	38.3	60.1%	11.9							68.2%	57%	138 bp	18%	19.7
HGVMYDRXY_CIVRCoV_P1_D02_U062_R1_1																		70.1%	57%	151 bp	27%	19.8	8.6%	69.8%	57%	138 bp	18%	19.7
HGVMYDRXY_CIVRCoV_P1_D02_U062_R1_2																		68.5%	57%	151 bp	27%	19.8	8.6%
HGVMYDRXY_CIVRCoV_P1_D03_U396_R1	91.9	91.9	0.00%	0.04%	41.6%	1.13	27.2	31.4%	0.32%	37.5	54.4	100.0%	100.0%	4.5%	54.4	69.2%	19.4							61.1%	56%	131 bp	18%	28.1
HGVMYDRXY_CIVRCoV_P1_D03_U396_R1_1																		63.8%	55%	151 bp	27%	28.1	13.4%	62.1%	56%	131 bp	18%	28.1
HGVMYDRXY_CIVRCoV_P1_D03_U396_R1_2																		62.6%	55%	151 bp	27%	28.1	13.3%
HGVMYDRXY_CIVRCoV_P1_D04_C019_R1	99.0	99.0	0.00%	0.03%	47.0%	1.18	28.4	27.5%	0.31%	42.2	56.8	100.0%	100.0%	4.5%	56.8	67.6%	19.8							65.0%	55%	134 bp	18%	29.3
HGVMYDRXY_CIVRCoV_P1_D04_C019_R1_1																		66.7%	55%	151 bp	27%	29.3	11.5%	65.9%	55%	134 bp	18%	29.3
HGVMYDRXY_CIVRCoV_P1_D04_C019_R1_2																		65.7%	55%	151 bp	27%	29.3	11.5%
HGVMYDRXY_CIVRCoV_P1_E01_U175_R1	84.2	84.2	0.00%	0.04%	46.1%	1.18	23.4	27.1%	0.33%	37.3	46.9	100.0%	100.0%	4.6%	46.9	65.5%	15.9							61.6%	56%	132 bp	18%	24.4
HGVMYDRXY_CIVRCoV_P1_E01_U175_R1_1																		64.1%	56%	151 bp	27%	24.4	12.6%	62.7%	56%	132 bp	18%	24.4
HGVMYDRXY_CIVRCoV_P1_E01_U175_R1_2																		62.9%	56%	151 bp	27%	24.4	12.7%
HGVMYDRXY_CIVRCoV_P1_E02_U327_R1	97.8	97.8	0.00%	0.02%	45.8%	1.16	27.3	27.6%	0.35%	43.2	54.7	100.0%	100.0%	5.0%	54.7	66.0%	18.6							63.3%	56%	140 bp	18%	28.2
HGVMYDRXY_CIVRCoV_P1_E02_U327_R1_1																		65.6%	56%	151 bp	27%	28.2	7.1%	65.4%	56%	140 bp	18%	28.2
HGVMYDRXY_CIVRCoV_P1_E02_U327_R1_2																		63.5%	56%	151 bp	27%	28.2	7.1%
HGVMYDRXY_CIVRCoV_P1_E03_C026_R1	53.5	53.5	0.00%	0.03%	54.6%	1.20	13.5	20.3%	0.31%	26.5	27.0	100.0%	100.0%	5.4%	27.0	58.3%	8.2							67.1%	58%	132 bp	18%	14.0
HGVMYDRXY_CIVRCoV_P1_E03_C026_R1_1																		69.3%	57%	151 bp	27%	14.0	12.5%	68.3%	58%	132 bp	18%	14.0
HGVMYDRXY_CIVRCoV_P1_E03_C026_R1_2																		68.0%	57%	151 bp	27%	14.0	12.5%
HGVMYDRXY_CIVRCoV_P1_E04_Neg1_R1	0.0	0.0	0.00%	0.09%	6.1%		0.0	67.2%	0.53%	0.0	0.0	100.0%	100.0%	2.8%	0.0	15.8%	0.0							24.7%	46%	111 bp	36%	0.0
HGVMYDRXY_CIVRCoV_P1_E04_Neg1_R1_1																		53.6%	62%	151 bp	55%	0.0	59.0%	25.9%	45%	109 bp	27%	0.0
HGVMYDRXY_CIVRCoV_P1_E04_Neg1_R1_2																		26.7%	63%	151 bp	45%	0.0	25.7%
HGVMYDRXY_CIVRCoV_P1_F01_C004_R1	162.1	162.1	0.00%	0.04%	83.2%	1.21	33.2	5.9%	0.26%	95.7	66.5	100.0%	100.0%	7.8%	66.5	39.9%	14.0							86.8%	60%	133 bp	27%	35.2
HGVMYDRXY_CIVRCoV_P1_F01_C004_R1_1																		88.7%	59%	151 bp	36%	35.2	12.0%	88.1%	60%	133 bp	27%	35.2
HGVMYDRXY_CIVRCoV_P1_F01_C004_R1_2																		87.3%	59%	151 bp	36%	35.2	12.0%
HGVMYDRXY_CIVRCoV_P1_F02_C025_R1	138.5	138.5	0.00%	0.04%	77.3%	1.23	29.4	8.4%	0.31%	79.7	58.8	100.0%	100.0%	8.7%	58.8	45.1%	13.8							82.8%	60%	139 bp	27%	30.5
HGVMYDRXY_CIVRCoV_P1_F02_C025_R1_1																		84.4%	59%	151 bp	36%	30.6	7.7%	84.2%	60%	139 bp	27%	30.5
HGVMYDRXY_CIVRCoV_P1_F02_C025_R1_2																		82.9%	59%	151 bp	36%	30.6	7.7%
HGVMYDRXY_CIVRCoV_P1_F03_U470_R1	69.7	69.7	0.00%	0.04%	61.5%	1.26	16.8	16.3%	0.31%	36.1	33.6	100.0%	100.0%	5.9%	33.6	53.4%	9.4							71.4%	59%	132 bp	18%	17.6
HGVMYDRXY_CIVRCoV_P1_F03_U470_R1_1																		73.9%	58%	151 bp	27%	17.6	12.8%	73.0%	59%	132 bp	18%	17.6
HGVMYDRXY_CIVRCoV_P1_F03_U470_R1_2																		72.2%	58%	151 bp	27%	17.6	12.8%
HGVMYDRXY_CIVRCoV_P1_G01_C008_R1	57.0	57.0	0.00%	0.04%	53.6%	1.18	15.8	23.0%	0.35%	25.4	31.6	100.0%	100.0%	4.7%	31.6	64.9%	10.6							64.6%	56%	132 bp	18%	16.3
HGVMYDRXY_CIVRCoV_P1_G01_C008_R1_1																		67.0%	55%	151 bp	27%	16.3	12.7%	66.0%	56%	132 bp	18%	16.3
HGVMYDRXY_CIVRCoV_P1_G01_C008_R1_2																		65.5%	55%	151 bp	27%	16.3	12.6%
HGVMYDRXY_CIVRCoV_P1_G02_U264_R1	91.6	91.6	0.00%	0.03%	51.2%	1.23	25.3	24.1%	0.31%	40.9	50.7	100.0%	100.0%	5.0%	50.7	65.8%	17.2							68.8%	57%	137 bp	18%	26.1
HGVMYDRXY_CIVRCoV_P1_G02_U264_R1_1																		70.5%	56%	151 bp	27%	26.1	9.2%	70.1%	57%	137 bp	18%	26.1
HGVMYDRXY_CIVRCoV_P1_G02_U264_R1_2																		69.2%	56%	151 bp	27%	26.1	9.2%
HGVMYDRXY_CIVRCoV_P1_G03_C015_R1	141.8	141.8	0.00%	0.03%	80.2%	1.22	29.3	7.1%	0.28%	83.1	58.7	100.0%	100.0%	7.5%	58.7	40.1%	12.4							84.2%	60%	134 bp	27%	30.9
HGVMYDRXY_CIVRCoV_P1_G03_C015_R1_1																		86.5%	59%	151 bp	36%	30.9	11.2%	86.0%	60%	134 bp	27%	30.9
HGVMYDRXY_CIVRCoV_P1_G03_C015_R1_2																		84.6%	59%	151 bp	36%	30.9	11.2%
HGVMYDRXY_CIVRCoV_P1_H01_U055_R1	121.2	121.2	0.00%	0.04%	62.3%	1.19	29.3	16.3%	0.32%	62.7	58.6	100.0%	100.0%	6.9%	58.6	56.1%	17.1							72.7%	58%	134 bp	18%	30.5
HGVMYDRXY_CIVRCoV_P1_H01_U055_R1_1																		74.8%	57%	151 bp	27%	30.5	11.3%	74.1%	58%	134 bp	18%	30.5
HGVMYDRXY_CIVRCoV_P1_H01_U055_R1_2																		73.3%	57%	151 bp	27%	30.5	11.3%
HGVMYDRXY_CIVRCoV_P1_H02_C020_R1	122.7	122.7	0.00%	0.03%	66.6%	1.20	29.4	14.2%	0.30%	64.0	58.7	100.0%	100.0%	6.4%	58.7	54.0%	16.5							75.9%	58%	139 bp	18%	30.5
HGVMYDRXY_CIVRCoV_P1_H02_C020_R1_1																		77.7%	58%	151 bp	27%	30.5	7.8%	77.5%	58%	139 bp	18%	30.5
HGVMYDRXY_CIVRCoV_P1_H02_C020_R1_2																		76.0%	58%	151 bp	27%	30.5	7.8%
HGVMYDRXY_CIVRCoV_P1_H03_C021_R1	95.3	95.3	0.00%	0.04%	54.0%	1.21	24.4	21.1%	0.32%	46.6	48.8	100.0%	100.0%	5.8%	48.8	60.0%	15.2							68.5%	58%	136 bp	18%	25.3
HGVMYDRXY_CIVRCoV_P1_H03_C021_R1_1																		70.5%	57%	151 bp	27%	25.3	10.2%	70.0%	58%	136 bp	18%	25.3
HGVMYDRXY_CIVRCoV_P1_H03_C021_R1_2																		68.9%	57%	151 bp	27%	25.3	10.3%
HGVMYDRXY_CIVRCoV_P2_A05_U420_R1	100.2	100.2	0.00%	0.04%	42.3%	1.17	29.1	30.4%	0.35%	42.0	58.3	100.0%	100.0%	5.1%	58.3	69.7%	21.0							58.5%	55%	130 bp	18%	30.1
HGVMYDRXY_CIVRCoV_P2_A05_U420_R1_1																		61.8%	55%	151 bp	27%	30.2	14.2%	60.1%	56%	130 bp	18%	30.1
HGVMYDRXY_CIVRCoV_P2_A05_U420_R1_2																		60.1%	55%	151 bp	27%	30.2	14.2%
HGVMYDRXY_CIVRCoV_P2_A06_C012_R1	116.7	116.7	0.00%	0.03%	71.8%	1.21	27.7	11.8%	0.32%	61.4	55.3	100.0%	100.0%	6.9%	55.3	52.3%	15.2							78.8%	58%	134 bp	18%	29.0
HGVMYDRXY_CIVRCoV_P2_A06_C012_R1_1																		80.8%	57%	151 bp	27%	29.0	11.6%	80.2%	58%	134 bp	18%	29.0
HGVMYDRXY_CIVRCoV_P2_A06_C012_R1_2																		79.3%	57%	151 bp	27%	29.0	11.6%
HGVMYDRXY_CIVRCoV_P2_A07_C027_R1	115.0	115.0	0.00%	0.03%	67.8%	1.21	26.1	12.9%	0.29%	62.8	52.2	100.0%	100.0%	7.3%	52.2	49.3%	13.6							76.1%	59%	131 bp	18%	27.7
HGVMYDRXY_CIVRCoV_P2_A07_C027_R1_1																		78.4%	58%	151 bp	27%	27.7	13.6%	77.5%	59%	131 bp	18%	27.7
HGVMYDRXY_CIVRCoV_P2_A07_C027_R1_2																		76.9%	58%	151 bp	27%	27.7	13.6%
HGVMYDRXY_CIVRCoV_P2_A08_U340_R1	89.7	89.7	0.00%	0.03%	28.3%	1.23	32.1	46.6%	0.34%	25.4	64.3	100.0%	100.0%	3.2%	64.3	82.6%	27.2							53.6%	52%	133 bp	18%	32.9
HGVMYDRXY_CIVRCoV_P2_A08_U340_R1_1																		55.9%	52%	151 bp	27%	32.9	11.9%	55.0%	52%	133 bp	18%	32.9
HGVMYDRXY_CIVRCoV_P2_A08_U340_R1_2																		54.4%	52%	151 bp	27%	32.9	11.9%
HGVMYDRXY_CIVRCoV_P2_B05_C010_R1	110.4	110.4	0.00%	0.03%	62.2%	1.16	26.9	16.3%	0.30%	56.6	53.8	100.0%	100.0%	6.7%	53.8	56.1%	15.8							72.2%	58%	130 bp	18%	28.2
HGVMYDRXY_CIVRCoV_P2_B05_C010_R1_1																		74.5%	57%	151 bp	27%	28.2	14.0%	73.4%	58%	130 bp	18%	28.2
HGVMYDRXY_CIVRCoV_P2_B05_C010_R1_2																		73.1%	57%	151 bp	27%	28.2	14.0%
HGVMYDRXY_CIVRCoV_P2_B06_U467_R1	115.7	115.7	0.00%	0.04%	49.9%	1.20	29.6	22.8%	0.34%	56.5	59.2	100.0%	100.0%	6.4%	59.2	61.6%	19.0							65.1%	57%	133 bp	18%	30.8
HGVMYDRXY_CIVRCoV_P2_B06_U467_R1_1																		67.3%	57%	151 bp	27%	30.9	12.3%	66.4%	57%	133 bp	18%	30.8
HGVMYDRXY_CIVRCoV_P2_B06_U467_R1_2																		65.8%	56%	151 bp	27%	30.9	12.3%
HGVMYDRXY_CIVRCoV_P2_B07_U477_R1	89.1	89.1	0.00%	0.04%	39.7%	1.18	27.7	33.8%	0.37%	33.7	55.4	100.0%	100.0%	3.9%	55.4	72.9%	20.8							59.8%	56%	136 bp	18%	28.6
HGVMYDRXY_CIVRCoV_P2_B07_U477_R1_1																		61.8%	56%	151 bp	27%	28.6	9.8%	61.1%	56%	136 bp	18%	28.6
HGVMYDRXY_CIVRCoV_P2_B07_U477_R1_2																		60.4%	56%	151 bp	27%	28.6	9.9%
HGVMYDRXY_CIVRCoV_P2_B08_Neg2_R1	0.0	0.0	0.65%	0.06%	8.3%	0.70	0.0	63.8%	0.57%	0.0	0.0	100.0%	99.9%	3.0%	0.0	10.0%	0.0							10.0%	47%	129 bp	27%	0.0
HGVMYDRXY_CIVRCoV_P2_B08_Neg2_R1_1																		29.9%	55%	151 bp	36%	0.0	35.4%	10.2%	47%	129 bp	27%	0.0
HGVMYDRXY_CIVRCoV_P2_B08_Neg2_R1_2																		13.8%	56%	151 bp	45%	0.0	16.8%
HGVMYDRXY_CIVRCoV_P2_C05_U008_R1	135.3	135.3	0.00%	0.05%	66.8%	1.24	33.2	14.3%	0.31%	68.8	66.5	100.0%	100.0%	6.9%	66.5	55.5%	19.5							76.2%	58%	130 bp	18%	35.0
HGVMYDRXY_CIVRCoV_P2_C05_U008_R1_1																		78.7%	57%	151 bp	27%	35.1	14.2%	77.4%	58%	130 bp	18%	35.0
HGVMYDRXY_CIVRCoV_P2_C05_U008_R1_2																		77.3%	57%	151 bp	27%	35.1	14.1%
HGVMYDRXY_CIVRCoV_P2_C06_C024_R1	122.0	122.0	0.00%	0.03%	54.7%	1.18	30.3	20.3%	0.31%	61.4	60.6	100.0%	100.0%	6.5%	60.6	57.8%	18.3							69.8%	58%	134 bp	18%	31.7
HGVMYDRXY_CIVRCoV_P2_C06_C024_R1_1																		71.5%	58%	151 bp	27%	31.7	11.3%	70.9%	58%	134 bp	18%	31.7
HGVMYDRXY_CIVRCoV_P2_C06_C024_R1_2																		70.4%	58%	151 bp	27%	31.7	11.3%
HGVMYDRXY_CIVRCoV_P2_C07_C006_R1	116.8	116.8	0.00%	0.07%	44.7%	1.20	35.9	30.8%	0.34%	45.1	71.7	100.0%	100.0%	4.4%	71.7	73.3%	27.1							62.3%	54%	132 bp	18%	36.9
HGVMYDRXY_CIVRCoV_P2_C07_C006_R1_1																		64.7%	54%	151 bp	18%	36.9	12.8%	63.5%	54%	132 bp	18%	36.9
HGVMYDRXY_CIVRCoV_P2_C07_C006_R1_2																		63.4%	54%	151 bp	18%	36.9	12.8%
HGVMYDRXY_CIVRCoV_P2_D05_U194_R1	128.6	128.6	0.00%	0.04%	58.3%	1.15	31.6	18.5%	0.32%	65.3	63.3	100.0%	100.0%	6.0%	63.3	55.5%	18.5							69.6%	58%	134 bp	18%	33.3
HGVMYDRXY_CIVRCoV_P2_D05_U194_R1_1																		72.6%	58%	151 bp	27%	33.3	11.2%	71.9%	58%	134 bp	18%	33.3
HGVMYDRXY_CIVRCoV_P2_D05_U194_R1_2																		70.3%	58%	151 bp	27%	33.3	11.2%
HGVMYDRXY_CIVRCoV_P2_D06_U487_R1	75.9	75.9	0.00%	0.03%	22.6%	1.14	27.0	50.5%	0.40%	21.8	54.1	100.0%	100.0%	2.9%	54.1	81.8%	22.8							49.1%	53%	135 bp	18%	27.9
HGVMYDRXY_CIVRCoV_P2_D06_U487_R1_1																		51.4%	53%	151 bp	27%	27.9	10.8%	50.7%	53%	135 bp	18%	27.9
HGVMYDRXY_CIVRCoV_P2_D06_U487_R1_2																		49.8%	53%	151 bp	27%	27.9	10.8%
HGVMYDRXY_CIVRCoV_P2_D07_U439_R1	106.8	106.8	0.00%	0.04%	51.4%	1.18	29.0	23.5%	0.33%	48.8	58.0	100.0%	100.0%	5.8%	58.0	65.7%	19.7							64.7%	55%	132 bp	18%	30.0
HGVMYDRXY_CIVRCoV_P2_D07_U439_R1_1																		66.6%	55%	151 bp	27%	30.0	12.9%	65.8%	55%	132 bp	18%	30.0
HGVMYDRXY_CIVRCoV_P2_D07_U439_R1_2																		65.5%	55%	151 bp	27%	30.0	12.9%
HGVMYDRXY_CIVRCoV_P2_E05_C002_R1	129.0	129.0	0.00%	0.04%	68.9%	1.18	30.8	13.1%	0.31%	67.3	61.6	100.0%	100.0%	6.4%	61.6	51.6%	16.9							76.7%	58%	132 bp	18%	32.7
HGVMYDRXY_CIVRCoV_P2_E05_C002_R1_1																		79.2%	58%	151 bp	27%	32.7	12.6%	78.4%	58%	132 bp	18%	32.7
HGVMYDRXY_CIVRCoV_P2_E05_C002_R1_2																		77.5%	57%	151 bp	27%	32.7	12.7%
HGVMYDRXY_CIVRCoV_P2_E06_U356_R1	103.6	103.6	0.00%	0.03%	59.8%	1.22	25.5	17.4%	0.33%	52.5	51.1	100.0%	100.0%	6.7%	51.1	56.8%	15.2							71.5%	58%	132 bp	18%	26.7
HGVMYDRXY_CIVRCoV_P2_E06_U356_R1_1																		74.1%	57%	151 bp	27%	26.7	12.4%	73.3%	58%	132 bp	18%	26.7
HGVMYDRXY_CIVRCoV_P2_E06_U356_R1_2																		72.3%	57%	151 bp	27%	26.7	12.4%
HGVMYDRXY_CIVRCoV_P2_E07_U244_R1	167.2	167.2	0.00%	0.04%	59.1%	1.23	41.9	18.2%	0.30%	83.4	83.8	100.0%	100.0%	5.9%	83.8	57.1%	25.0							73.2%	59%	131 bp	18%	43.8
HGVMYDRXY_CIVRCoV_P2_E07_U244_R1_1																		75.0%	58%	151 bp	27%	43.9	13.4%	74.1%	59%	131 bp	18%	43.8
HGVMYDRXY_CIVRCoV_P2_E07_U244_R1_2																		73.9%	58%	151 bp	27%	43.9	13.3%
HGVMYDRXY_CIVRCoV_P2_F04_C003_R1	136.4	136.4	0.00%	0.03%	70.2%	1.17	31.4	12.2%	0.31%	73.6	62.8	100.0%	100.0%	7.1%	62.8	50.6%	16.8							77.5%	59%	133 bp	18%	33.1
HGVMYDRXY_CIVRCoV_P2_F04_C003_R1_1																		79.4%	58%	151 bp	27%	33.1	11.7%	78.9%	59%	133 bp	18%	33.1
HGVMYDRXY_CIVRCoV_P2_F04_C003_R1_2																		77.9%	58%	151 bp	27%	33.1	11.7%
HGVMYDRXY_CIVRCoV_P2_F05_U488_R1	95.5	95.5	0.00%	0.04%	34.0%	1.18	30.2	37.9%	0.37%	35.1	60.4	100.0%	100.0%	4.0%	60.4	74.8%	23.4							54.0%	54%	133 bp	18%	31.3
HGVMYDRXY_CIVRCoV_P2_F05_U488_R1_1																		55.6%	54%	151 bp	27%	31.3	11.8%	54.9%	54%	133 bp	18%	31.3
HGVMYDRXY_CIVRCoV_P2_F05_U488_R1_2																		54.6%	54%	151 bp	27%	31.3	11.8%
HGVMYDRXY_CIVRCoV_P2_F06_C009_R1	131.1	131.1	0.00%	0.04%	56.9%	1.20	34.8	20.5%	0.34%	61.4	69.7	100.0%	100.0%	6.0%	69.7	62.4%	22.6							69.2%	56%	132 bp	18%	36.2
HGVMYDRXY_CIVRCoV_P2_F06_C009_R1_1																		71.3%	56%	151 bp	27%	36.2	12.8%	70.4%	56%	132 bp	18%	36.2
HGVMYDRXY_CIVRCoV_P2_F06_C009_R1_2																		70.1%	55%	151 bp	27%	36.2	12.9%
HGVMYDRXY_CIVRCoV_P2_F07_U030_R1	136.0	136.0	0.00%	0.03%	49.9%	1.19	36.4	24.0%	0.32%	63.2	72.8	100.0%	100.0%	5.9%	72.8	64.2%	24.2							67.5%	57%	139 bp	18%	37.7
HGVMYDRXY_CIVRCoV_P2_F07_U030_R1_1																		68.9%	57%	151 bp	27%	37.7	8.0%	68.7%	57%	139 bp	18%	37.7
HGVMYDRXY_CIVRCoV_P2_F07_U030_R1_2																		67.6%	57%	151 bp	27%	37.7	8.0%
HGVMYDRXY_CIVRCoV_P2_G04_C018_R1	116.4	116.4	0.00%	0.03%	66.9%	1.18	29.1	14.5%	0.31%	58.3	58.1	100.0%	100.0%	6.4%	58.1	55.5%	16.9							75.7%	57%	135 bp	18%	30.4
HGVMYDRXY_CIVRCoV_P2_G04_C018_R1_1																		77.7%	57%	151 bp	27%	30.4	10.9%	77.1%	57%	135 bp	18%	30.4
HGVMYDRXY_CIVRCoV_P2_G04_C018_R1_2																		76.3%	57%	151 bp	27%	30.4	10.9%
HGVMYDRXY_CIVRCoV_P2_G05_U128_R1	142.3	142.3	0.00%	0.03%	54.4%	1.21	37.0	21.5%	0.33%	68.2	74.1	100.0%	100.0%	6.0%	74.1	61.4%	23.7							69.1%	57%	134 bp	18%	38.6
HGVMYDRXY_CIVRCoV_P2_G05_U128_R1_1																		71.0%	56%	151 bp	27%	38.6	11.4%	70.4%	57%	134 bp	18%	38.6
HGVMYDRXY_CIVRCoV_P2_G05_U128_R1_2																		69.6%	56%	151 bp	27%	38.6	11.4%
HGVMYDRXY_CIVRCoV_P2_G06_C022_R1	114.0	114.0	0.00%	0.03%	66.3%	1.20	29.2	15.3%	0.31%	55.5	58.5	100.0%	100.0%	6.1%	58.5	58.8%	17.9							74.9%	57%	134 bp	18%	30.4
HGVMYDRXY_CIVRCoV_P2_G06_C022_R1_1																		77.1%	56%	151 bp	27%	30.4	11.5%	76.4%	57%	134 bp	18%	30.4
HGVMYDRXY_CIVRCoV_P2_G06_C022_R1_2																		75.5%	56%	151 bp	27%	30.4	11.5%
HGVMYDRXY_CIVRCoV_P2_G07_C017_R1	164.9	164.9	0.00%	0.03%	71.0%	1.20	36.3	11.3%	0.30%	92.3	72.6	100.0%	100.0%	7.4%	72.6	46.5%	17.8							78.9%	60%	137 bp	18%	38.2
HGVMYDRXY_CIVRCoV_P2_G07_C017_R1_1																		80.6%	59%	151 bp	27%	38.2	9.0%	80.4%	60%	137 bp	18%	38.2
HGVMYDRXY_CIVRCoV_P2_G07_C017_R1_2																		79.1%	59%	151 bp	27%	38.2	9.0%
HGVMYDRXY_CIVRCoV_P2_H04_U236_R1	119.8	119.8	0.00%	0.04%	56.4%	1.16	29.6	19.4%	0.31%	60.5	59.3	100.0%	100.0%	6.2%	59.3	56.6%	17.7							67.8%	58%	130 bp	18%	31.3
HGVMYDRXY_CIVRCoV_P2_H04_U236_R1_1																		70.6%	57%	151 bp	27%	31.3	14.3%	69.1%	58%	130 bp	18%	31.3
HGVMYDRXY_CIVRCoV_P2_H04_U236_R1_2																		69.1%	57%	151 bp	27%	31.3	14.3%
HGVMYDRXY_CIVRCoV_P2_H05_C013_R1	120.1	120.1	0.00%	0.04%	53.7%	1.21	33.0	22.7%	0.34%	54.2	65.9	100.0%	100.0%	5.4%	65.9	63.9%	22.0							68.0%	57%	130 bp	18%	34.4
HGVMYDRXY_CIVRCoV_P2_H05_C013_R1_1																		70.2%	56%	151 bp	27%	34.4	13.7%	69.2%	57%	130 bp	18%	34.4
HGVMYDRXY_CIVRCoV_P2_H05_C013_R1_2																		69.0%	56%	151 bp	27%	34.4	13.7%
HGVMYDRXY_CIVRCoV_P2_H06_U482_R1	110.1	110.1	0.00%	0.03%	66.3%	1.22	26.1	14.1%	0.31%	57.9	52.2	100.0%	100.0%	6.8%	52.2	51.8%	14.3							74.8%	59%	129 bp	18%	27.6
HGVMYDRXY_CIVRCoV_P2_H06_U482_R1_1																		77.4%	58%	151 bp	27%	27.6	14.6%	76.4%	59%	129 bp	18%	27.6
HGVMYDRXY_CIVRCoV_P2_H06_U482_R1_2																		75.8%	58%	151 bp	27%	27.6	14.6%
HGVMYDRXY_CIVRCoV_P2_H07_C016_R1	103.3	103.3	0.00%	0.03%	54.3%	1.19	28.1	22.3%	0.33%	47.1	56.1	100.0%	100.0%	5.3%	56.1	63.0%	18.3							67.3%	57%	134 bp	18%	29.1
HGVMYDRXY_CIVRCoV_P2_H07_C016_R1_1																		69.5%	56%	151 bp	27%	29.2	11.1%	69.0%	57%	134 bp	18%	29.1
HGVMYDRXY_CIVRCoV_P2_H07_C016_R1_2																		67.7%	56%	151 bp	27%	29.2	11.1%
HGVMYDRXY_undetermined_R1	308.0	308.0	0.00%	0.00%	61.6%	1.18	75.3	16.8%	0.76%	157.3	150.7	100.0%	100.0%	5.7%	150.7	32.3%	42.3							52.2%	53%	141 bp	18%	130.9
HGVMYDRXY_undetermined_R1_1																		62.0%	52%	151 bp	36%	131.0	6.2%	61.6%	52%	142 bp	27%	130.9
HGVMYDRXY_undetermined_R1_2																		52.5%	53%	151 bp	27%	131.0	6.5%

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Sort	Group	Column	Description	ID	Scale
\|\|	Samtools	M Reads	Total reads in the bam file (millions)	`flagstat_total`	read_count
\|\|	Samtools	M Reads Mapped	Reads Mapped in the bam file (millions)	`mapped_passed`	read_count
\|\|	Biotype Counts	% rRNA	% reads overlapping rRNA features	`percent_rRNA`	None
\|\|	DupRadar	dupInt	Intercept value from DupRadar	`dupRadar_intercept`	None
\|\|	Picard	% Dups	Mark Duplicates - Percent Duplication	`PERCENT_DUPLICATION`	None
\|\|	QualiMap	5'-3' bias	5'-3' bias	`5_3_bias`	None
\|\|	QualiMap	M Aligned	Reads Aligned (millions)	`reads_aligned`	read_count
\|\|	RSeQC	% Proper Pairs	% Reads mapped in proper pairs	`proper_pairs_percent`	None
\|\|	Samtools	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`error_rate`	None
\|\|	Samtools	M Non-Primary	Non-primary alignments (millions)	`non-primary_alignments`	read_count
\|\|	Samtools	M Reads Mapped	Reads Mapped in the bam file (millions)	`reads_mapped`	read_count
\|\|	Samtools	% Mapped	% Mapped Reads	`reads_mapped_percent`	None
\|\|	Samtools	% Proper Pairs	% Properly Paired Reads	`reads_properly_paired_percent`	None
\|\|	Samtools	% MapQ 0 Reads	% of Reads that are Ambiguously Placed (MapQ=0)	`reads_MQ0_percent`	None
\|\|	Samtools	M Total seqs	Total sequences in the bam file (millions)	`raw_total_sequences`	read_count
\|\|	STAR	% Aligned	% Uniquely mapped reads	`uniquely_mapped_percent`	None
\|\|	STAR	M Aligned	Uniquely mapped reads (millions)	`uniquely_mapped`	read_count
\|\|	FastQC (raw)	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC (raw)	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC (raw)	Length	Average Sequence Length (bp)	`avg_sequence_length`	None
\|\|	FastQC (raw)	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC (raw)	M Seqs	Total Sequences (millions)	`total_sequences`	read_count
\|\|	Cutadapt	% BP Trimmed	% Total Base Pairs trimmed	`percent_trimmed`	None
\|\|	FastQC (trimmed)	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC (trimmed)	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC (trimmed)	Length	Average Sequence Length (bp)	`avg_sequence_length`	None
\|\|	FastQC (trimmed)	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC (trimmed)	M Seqs	Total Sequences (millions)	`total_sequences`	read_count

Biotype Counts

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

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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.

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Picard

Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

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

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Preseq

Preseq estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count. A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.

Complexity curve

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.

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QualiMap

QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

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).

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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).

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RSeQC

RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.

Read Distribution

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

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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.

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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.

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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.

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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).

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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).

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Bam Stat

All numbers reported in millions.

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Samtools

Samtools is a suite of programs for interacting with high-throughput sequencing data.

Percent Mapped

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

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).

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Alignment metrics

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

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Samtools Flagstat

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

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XY counts

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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.

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STAR

STAR is an ultrafast universal RNA-seq aligner.

Alignment Scores

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FastQC (raw)

FastQC (raw) This section of the report shows FastQC results before adapter trimming.

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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 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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Sequence Length Distribution

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

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 (eg 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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Overrepresented sequences

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 over represented.

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 of 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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

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Cutadapt

Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.

Filtered Reads

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

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Trimmed Sequence Lengths

This plot shows the number of reads with certain lengths of adapter trimmed.

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

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

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

FastQC (trimmed)

FastQC (trimmed) This section of the report shows FastQC results after adapter trimming.

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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 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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Sequence Length Distribution

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

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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 (eg 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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Overrepresented sequences

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 over represented.

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 of 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.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

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.

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nf-core/rnaseq Software Versions

are collected at run time from the software output.

bedtools: 2.29.2
bioconductor-summarizedexperiment: 1.20.0
bioconductor-tximeta: 1.8.0
deseq2: 1.28.0
dupradar: 1.18.0
fastqc: 0.11.9
nextflow: 20.11.0-edge
nf-core/rnaseq: 3.0
picard: 2.23.9
preseq: 2.0.3
qualimap: 2.2.2-dev
rseqc: 3.0.1
salmon: 1.4.0
samtools: 1.10
star: 2.6.1d
stringtie: 2.1.4
subread: 2.0.1
trimgalore: 0.6.6
ucsc: 377

nf-core/rnaseq Workflow Summary

- this information is collected when the pipeline is started.

Core Nextflow options

runName: evil_poincare
containerEngine: singularity
launchDir: /scratch/cgsb/gresham/mk5636
workDir: /scratch/mk5636/nextflow_work_dir
projectDir: /scratch/cgsb/gresham/mk5636/.nextflow/assets/nf-core/rnaseq
userName: mk5636
profile: singularity
configFiles: /scratch/cgsb/gresham/mk5636/.nextflow/assets/nf-core/rnaseq/nextflow.config, /scratch/cgsb/gresham/mk5636/HGVMYDRXY_merged.config

Input/output options

input: /scratch/cgsb/gresham/mk5636/samplesheets/HGVMYDRXY_merged.csv
outdir: /scratch/cgsb/gresham/mk5636/HGVMYDRXY_merged/results
email: mk5636@nyu.edu

Reference genome options

fasta: /scratch/work/cgsb/genomes/Public/Vertebrate_mammalian/Homo_sapiens/Ensembl/GRCh38.p10/Homo_sapiens.GRCh38.dna.toplevel.fa
gtf: /scratch/work/cgsb/genomes/Public/Vertebrate_mammalian/Homo_sapiens/Ensembl/GRCh38.p10/Homo_sapiens.GRCh38.88.gtf
star_index: /scratch/cgsb/gresham/mk5636/HKVNYDRXX_1/results/genome/index/star

Generic options

tracedir: ./results/pipeline_info

Toggle navigation v1.9

MultiQC Toolbox

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About MultiQC

General Statistics

General Statistics: Columns

Biotype Counts

DupRadar

Picard

Mark Duplicates Help

Preseq

Complexity curve

QualiMap

Genomic origin of reads Help

Gene Coverage Profile Help

RSeQC

Read Distribution

Inner Distance

Read Duplication

Junction Annotation

Junction Saturation

Infer experiment

Bam Stat

Samtools

Percent Mapped Help

Alignment metrics

Samtools Flagstat

XY counts

Mapped reads per contig

STAR

Alignment Scores

FastQC (raw)

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences Help

Adapter Content Help

Status Checks Help

Cutadapt

Filtered Reads

Trimmed Sequence Lengths Help

FastQC (trimmed)

Sequence Counts Help

Sequence Quality Histograms Help

Per Sequence Quality Scores Help

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help

Per Base N Content Help

Sequence Length Distribution

Sequence Duplication Levels Help

Overrepresented sequences Help

Adapter Content Help

Status Checks Help

nf-core/rnaseq Software Versions

nf-core/rnaseq Workflow Summary

v1.9

Highlight Samples

Rename Samples

Show / Hide Samples

Mark Duplicates

Genomic origin of reads

Gene Coverage Profile

Percent Mapped

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences

Adapter Content

Status Checks

Trimmed Sequence Lengths

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences

Adapter Content

Status Checks