MultiQC Report

General Statistics

Showing ⁶/₈ rows and ⁴²/₅₈ columns.

Sample Name	InsertSizeMean	InsertSizeMedian	InsertSizeMode	Insert Size	≥ 5X	Mean Insert Size	≥ 10X	Median	M Reads	% Mapped	Contamination (S)	InsertSizeStandardDeviation	DuplicateReadsPct	ChimericReadPairPct	M Reads	% Aligned	≥ 1X	≥ 30X	≥ 50X	Mean Cov.	% Dups	% GC	Average Read Length	Median Read Length	% Failed	M Seqs	Family ID	Ancestry	P(Ancestry)	Sex / Het Ratio	Correct Sex	Sex	% GC	Ins. size	≥ 1X	≥ 5X	≥ 10X	≥ 30X	≥ 50X	Median cov	Mean cov	% Aligned	M Aligned	M Total reads	Error rate	Vars	SNP	Indel	Ts/Tv	MNP	M Non-Primary	% Proper Pairs	% MapQ 0 Reads	M Total seqs	M Reads Mapped	% Reads Mapped	Error rate	M Reads Mapped
HG002.novaseq.pcr-free.30x.R1																					12.2%	41%	151 bp	151 bp	0%	355.8
HG002.novaseq.pcr-free.30x.R2																					11.7%	41%	151 bp	151 bp	0%	355.8
RIH0_ANA0-HG002-19-10x	413.8	377.0	283.0	379 bp	94.0%	415 bp	64.0%	11.0X	236.5	99.6%	0.552%	179.5	3.8	2.7	2.0	100%	97.0%			10.8X							RIH0_ANA0-HG002-19-10x_DBC0_0	EUR	1.0	0.1	True	male	42%	375	92.2%	88.4%	60.9%	0.3%	0.1%	11.0X	11.2X	99.6%	233.8	234.8	0.74%	8,447,878	7,466,122	985,626	0.67	0	0.0	97.2%	3.8%	234.8	235.5	99.6%	0.74%	233.8
RIH0_ANA0-HG002-19-15x	414.2	377.0	283.0	379 bp	92.0%	415 bp	87.0%	16.0X	358.3	99.6%	0.562%	179.7	5.5	2.7	2.0	100%	92.0%	1.0%		16.6X							RIH0_ANA0-HG002-19-15x_DBC0_0	EUR	1.0	0.1	True	male	42%	375	92.3%	91.5%	85.1%	2.1%	0.3%	17.0X	17.0X	99.6%	354.3	355.8	0.74%	9,160,754	8,022,998	1,142,556	0.62	0	0.0	97.2%	3.8%	355.8	356.8	99.6%	0.74%	354.3
RIH0_ANA0-HG002-19-20x	414.6	377.0	283.0	378 bp	97.0%	414 bp	96.0%	21.0X	480.2	99.6%	0.561%	179.9	7.1	2.7	2.0	100%	97.0%	8.0%		21.2X							RIH0_ANA0-HG002-19-20x_DBC0_0	EUR	1.0	0.1	True	male	42%	375	92.3%	92.0%	89.9%	15.3%	0.5%	22.0X	22.8X	99.6%	474.7	476.8	0.74%	8,696,778	7,453,936	1,248,012	0.69	0	0.0	97.2%	3.8%	476.8	478.1	99.6%	0.74%	474.7
RIH0_ANA0-HG002-19-25x	414.9	377.0	283.0	378 bp	92.0%	414 bp	92.0%	27.0X	602.0	99.6%	0.568%	180.1	8.7	2.7	2.0	100%	92.0%	24.0%	1.0%	21.8X							RIH0_ANA0-HG002-19-25x_DBC0_0	EUR	1.0	0.1	True	male	42%	375	92.3%	92.2%	91.3%	43.3%	1.2%	28.0X	28.6X	99.6%	595.1	597.7	0.74%	8,355,246	7,048,514	1,312,102	0.75	0	0.0	97.2%	3.8%	597.7	599.4	99.6%	0.74%	595.1
RIH0_ANA0-HG002-19-30x	415.1	378.0	283.0	378 bp	92.0%	413 bp	92.0%	31.0X	716.7	99.6%	0.584%	180.3	10.1	2.8	2.0	100%	92.0%	64.0%	1.0%	23.4X							RIH0_ANA0-HG002-19-30x_DBC0_0	EUR	1.0	0.0	True	male	42%	375	92.3%	92.2%	91.8%	67.0%	4.7%	34.0X	34.0X	99.6%	708.5	711.6	0.74%	8,115,213	6,790,487	1,330,271	0.80	0	0.0	97.2%	3.8%	711.6	713.6	99.6%	0.74%	708.5
RIH0_ANA0-HG002-19-5x	413.4	376.0	283.0	379 bp	68.0%	415 bp	12.0%	6.0X	121.8	99.6%	0.490%	179.4	2.1	2.7	2.0	100%	93.0%			5.7X							RIH0_ANA0-HG002-19-5x_DBC0_0	EUR	1.0	0.2	True	male	42%	375	91.4%	61.0%	9.9%	0.1%	0.1%	5.0X	5.8X	99.6%	120.4	121.0	0.74%	5,459,824	4,763,751	697,972	1.07	0	0.0	97.2%	3.8%	121.0	121.3	99.6%	0.74%	120.4

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Sort	Group	Column	Description	ID	Scale
\|\|	alignstats_b	InsertSizeMean	InsertSizeMean	`InsertSizeMean`	None
\|\|	alignstats_b	InsertSizeMedian	InsertSizeMedian	`InsertSizeMedian`	None
\|\|	alignstats_b	InsertSizeMode	InsertSizeMode	`InsertSizeMode`	None
\|\|	Picard	Insert Size	Median Insert Size, all read orientations (bp)	`summed_median`	None
\|\|	mosdepth	≥ 5X	Fraction of genome with at least 5X coverage	`5_x_pc`	None
\|\|	Picard	Mean Insert Size	Mean Insert Size, all read orientations (bp)	`summed_mean`	None
\|\|	mosdepth	≥ 10X	Fraction of genome with at least 10X coverage	`10_x_pc`	None
\|\|	mosdepth	Median	Median coverage	`median_coverage`	None
\|\|	Samtools	M Reads	Total reads in the bam file (millions)	`flagstat_total`	read_count
\|\|	Samtools	% Mapped	% Mapped Reads	`reads_mapped_percent`	None
\|\|	VerifyBAMID	Contamination (S)	VerifyBamID: FREEMIX - Sequence-only estimate of contamination.	`FREEMIX`	None
\|\|	alignstats_b	InsertSizeStandardDeviation	InsertSizeStandardDeviation	`InsertSizeStandardDeviation`	None
\|\|	alignstats_b	DuplicateReadsPct	DuplicateReadsPct	`DuplicateReadsPct`	None
\|\|	alignstats_b	ChimericReadPairPct	ChimericReadPairPct	`ChimericReadPairPct`	None
\|\|	Picard	M Reads	The total number of reads in the input file (millions)	`TOTAL_READS`	read_count
\|\|	Picard	% Aligned	Percent of aligned reads	`PCT_PF_READS_ALIGNED`	None
\|\|	mosdepth	≥ 1X	Fraction of genome with at least 1X coverage	`1_x_pc`	None
\|\|	mosdepth	≥ 30X	Fraction of genome with at least 30X coverage	`30_x_pc`	None
\|\|	mosdepth	≥ 50X	Fraction of genome with at least 50X coverage	`50_x_pc`	None
\|\|	mosdepth	Mean Cov.	Mean coverage	`mean_coverage`	None
\|\|	FastQC	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC	Average Read Length	Average Read Length (bp)	`avg_sequence_length`	None
\|\|	FastQC	Median Read Length	Median Read Length (bp)	`median_sequence_length`	None
\|\|	FastQC	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC	M Seqs	Total Sequences (millions)	`total_sequences`	read_count
\|\|	Peddy	Family ID	Family ID	`family_id`	None
\|\|	Peddy	Ancestry	Ancestry Prediction	`ancestry-prediction`	None
\|\|	Peddy	P(Ancestry)	Probability predicted ancestry is correct.	`ancestry-prob_het_check`	None
\|\|	Peddy	Sex / Het Ratio	Sex / Het Ratio	`sex_het_ratio`	None
\|\|	Peddy	Correct Sex	Displays False if error in sample sex prediction	`error_sex_check`	None
\|\|	Peddy	Sex	Predicted sex	`predicted_sex_sex_check`	None
\|\|	QualiMap	% GC	Mean GC content	`avg_gc`	None
\|\|	QualiMap	Ins. size	Median insert size	`median_insert_size`	None
\|\|	QualiMap	≥ 1X	Fraction of genome with at least 1X coverage	`1_x_pc`	None
\|\|	QualiMap	≥ 5X	Fraction of genome with at least 5X coverage	`5_x_pc`	None
\|\|	QualiMap	≥ 10X	Fraction of genome with at least 10X coverage	`10_x_pc`	None
\|\|	QualiMap	≥ 30X	Fraction of genome with at least 30X coverage	`30_x_pc`	None
\|\|	QualiMap	≥ 50X	Fraction of genome with at least 50X coverage	`50_x_pc`	None
\|\|	QualiMap	Median cov	Median coverage	`median_coverage`	None
\|\|	QualiMap	Mean cov	Mean coverage	`mean_coverage`	None
\|\|	QualiMap	% Aligned	% mapped reads	`percentage_aligned`	None
\|\|	QualiMap	M Aligned	Number of mapped reads (millions)	`mapped_reads`	read_count
\|\|	QualiMap	M Total reads	Number of reads (millions)	`total_reads`	read_count
\|\|	QualiMap	Error rate	Alignment error rate. Total edit distance (SAM NM field) over the number of mapped bases	`general_error_rate`	None
\|\|	Bcftools Stats	Vars	Variations total	`number_of_records`	None
\|\|	Bcftools Stats	SNP	Variation SNPs	`number_of_SNPs`	None
\|\|	Bcftools Stats	Indel	Variation Insertions/Deletions	`number_of_indels`	None
\|\|	Bcftools Stats	Ts/Tv	Variant SNP transition / transversion ratio	`tstv`	None
\|\|	Bcftools Stats	MNP	Variation multinucleotide polymorphisms	`number_of_MNPs`	None
\|\|	Samtools	M Non-Primary	Non-primary alignments (millions)	`non-primary_alignments`	read_count
\|\|	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
\|\|	Samtools	M Reads Mapped	Reads Mapped in the bam file (millions)	`mapped_passed`	read_count
\|\|	Samtools	% Reads Mapped	% Reads Mapped in the bam file	`mapped_passed_pct`	None
\|\|	Samtools	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`error_rate`	None
\|\|	Samtools	M Reads Mapped	Reads Mapped in the bam file (millions)	`reads_mapped`	read_count

Norm Cov Evenness

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Giabhcr Concordance

Showing ⁶/₆ rows and ¹⁸/₁₈ columns.

Sample	SNPClass	TgtRegionSize	TN	FN	TP	FP	Fscore	Sensitivity-Recall	Specificity	PPV	Precision	AltId	CmpFootprint	AllVarMeanDP	CovBin	Aligner	SNVCaller
RIH0_ANA0-HG002-19-10x_DBC0_0	All	2,525,727,871.0	2,521,853,171.0	138,015.0	3,736,685.0	17,977.0	1.0	1.0	1.0	1.0	1.0	HG002	wgsHC	-1.0	-2.0	bwa2b	deep
RIH0_ANA0-HG002-19-15x_DBC0_0	All	2,525,727,871.0	2,521,853,214.0	44,414.0	3,830,243.0	12,818.0	1.0	1.0	1.0	1.0	1.0	HG002	wgsHC	-1.0	-2.0	bwa2b	deep
RIH0_ANA0-HG002-19-20x_DBC0_0	All	2,525,727,871.0	2,521,853,297.0	26,707.0	3,847,867.0	8,517.0	1.0	1.0	1.0	1.0	1.0	HG002	wgsHC	-1.0	-2.0	bwa2b	deep
RIH0_ANA0-HG002-19-25x_DBC0_0	All	2,525,727,871.0	2,521,853,356.0	21,743.0	3,852,772.0	5,775.0	1.0	1.0	1.0	1.0	1.0	HG002	wgsHC	-1.0	-2.0	bwa2b	deep
RIH0_ANA0-HG002-19-30x_DBC0_0	All	2,525,727,871.0	2,521,853,400.0	19,944.0	3,854,527.0	4,159.0	1.0	1.0	1.0	1.0	1.0	HG002	wgsHC	-1.0	-2.0	bwa2b	deep
RIH0_ANA0-HG002-19-5x_DBC0_0	All	2,525,727,871.0	2,521,853,088.0	693,693.0	3,181,090.0	20,452.0	0.9	0.8	1.0	1.0	1.0	HG002	wgsHC	-1.0	-2.0	bwa2b	deep

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

Sort	Column	Description	ID	Scale
\|\|	SNPClass	SNPClass	`SNPClass`	None
\|\|	TgtRegionSize	TgtRegionSize	`TgtRegionSize`	None
\|\|	TN	TN	`TN`	None
\|\|	FN	FN	`FN`	None
\|\|	TP	TP	`TP`	None
\|\|	FP	FP	`FP`	None
\|\|	Fscore	Fscore	`Fscore`	None
\|\|	Sensitivity-Recall	Sensitivity-Recall	`Sensitivity-Recall`	None
\|\|	Specificity	Specificity	`Specificity`	None
\|\|	FDR	FDR	`FDR`	None
\|\|	PPV	PPV	`PPV`	None
\|\|	Precision	Precision	`Precision`	None
\|\|	AltId	AltId	`AltId`	None
\|\|	CmpFootprint	CmpFootprint	`CmpFootprint`	None
\|\|	AllVarMeanDP	AllVarMeanDP	`AllVarMeanDP`	None
\|\|	CovBin	CovBin	`CovBin`	None
\|\|	Aligner	Aligner	`Aligner`	None
\|\|	SNVCaller	SNVCaller	`SNVCaller`	None

Alignstats

Showing ⁶/₆ rows and ¹⁶⁶/₁₆₆ columns.

sample	aligner	AlignedBases	AlignedBasesPct	AlignedReadLengthMean	AlignedReadLengthMedian	AlignedReadLengthMode	AlignedReadLengthStandardDeviation	ChimericReadPairPct	DeletedBases	DuplicateBases	DuplicateBasesPct	DuplicateReads	DuplicateReadsPct	InputFileName	InputFileSize	InsertSizeMean	InsertSizeMedian	InsertSizeMode	InsertSizeStandardDeviation	InsertedBases	MappedBases	MappedBasesPct	MappedReads	MappedReadsPct	MatchedBases	MatchedBasesPct	MismatchedBases	MismatchedBasesPct	PerfectBases	PerfectBasesPct	PerfectReads	PerfectReadsPct	Q20Bases	Q20BasesPct	R1AlignedBases	R1AlignedBasesPct	R1AlignedReadLengthMean	R1AlignedReadLengthMedian	R1AlignedReadLengthMode	R1AlignedReadLengthStandardDeviation	R1DeletedBases	R1InsertedBases	R1MappedBases	R1MappedBasesPct	R1MappedReads	R1MappedReadsPct	R1MatchedBases	R1MatchedBasesPct	R1MismatchedBases	R1MismatchedBasesPct	R1PerfectBases	R1PerfectBasesPct	R1PerfectReads	R1PerfectReadsPct	R1Q20Bases	R1Q20BasesPct	R1SoftClippedBases	R1SoftClippedBasesPct	R1SoftClippedReads	R1SoftClippedReadsPct	R1UnmappedBases	R1UnmappedBasesPct	R1UnmappedReads	R1UnmappedReadsPct	R1UnpairedReads	R1UnpairedReadsPct	R1YieldBases	R1YieldReads	R2AlignedBases	R2AlignedBasesPct	R2AlignedReadLengthMean	R2AlignedReadLengthMedian	R2AlignedReadLengthMode	R2AlignedReadLengthStandardDeviation	R2DeletedBases	R2InsertedBases	R2MappedBases	R2MappedBasesPct	R2MappedReads	R2MappedReadsPct	R2MatchedBases	R2MatchedBasesPct	R2MismatchedBases	R2MismatchedBasesPct	R2PerfectBases	R2PerfectBasesPct	R2PerfectReads	R2PerfectReadsPct	R2Q20Bases	R2Q20BasesPct	R2SoftClippedBases	R2SoftClippedBasesPct	R2SoftClippedReads	R2SoftClippedReadsPct	R2UnmappedBases	R2UnmappedBasesPct	R2UnmappedReads	R2UnmappedReadsPct	R2UnpairedReads	R2UnpairedReadsPct	R2YieldBases	R2YieldReads	SoftClippedBases	SoftClippedBasesPct	SoftClippedReads	SoftClippedReadsPct	TotalPairs	TotalRecords	TotalSameChrPairs	TotalSameChrPairsPct	UnfilteredRecords	UnfilteredRecordsPct	UnmappedBases	UnmappedBasesPct	UnmappedReads	UnmappedReadsPct	UnpairedReads	UnpairedReadsPct	WgsAlignedReads	WgsAlignedReadsPct	WgsCalculatedAlignedReads	WgsCovDuplicateReads	WgsCovDuplicateReadsPct	WgsCoverageBases1	WgsCoverageBases10	WgsCoverageBases100	WgsCoverageBases1000	WgsCoverageBases100Pct	WgsCoverageBases10Pct	WgsCoverageBases15	WgsCoverageBases15Pct	WgsCoverageBases1Pct	WgsCoverageBases20	WgsCoverageBases20Pct	WgsCoverageBases30	WgsCoverageBases30Pct	WgsCoverageBases40	WgsCoverageBases40Pct	WgsCoverageBases50	WgsCoverageBases500	WgsCoverageBases50Pct	WgsCoverageBases60	WgsCoverageBases60Pct	WgsCoverageBases70	WgsCoverageBases70Pct	WgsCoverageMean	WgsCoverageMedian	WgsCoverageStandardDeviation	WgsExpectedAlignedReads	WgsReadsPaired	WgsReadsPairedWithMates	WgsTotalReads	YieldBases	YieldReads
RIH0_ANA0-HG002-19-10x	bwa2b	34,817,018,247.0	97.5	147.9	151.0	151.0	15.6	2.7	14,478,827.0	1,340,785,625.0	3.8	8,879,375.0	3.8	results/day/b37/RIH0_ANA0-HG002-19-10x/align/bwa2b/RIH0_ANA0-HG002-19-10x.bwa2b.mrkdup.sort.bam	15,394,126,878.0	413.8	377.0	283.0	179.5	15,289,510.0	35,557,743,495.0	99.6	235,481,745.0	99.6	34,557,870,934.0	99.3	243,857,803.0	0.7	24,057,666,328.0	67.7	160,884,305.0	68.3	33,811,634,699.0	97.1	17,476,482,978.0	98.0	148.4	151.0	151.0	14.1	7,324,611.0	7,687,495.0	17,787,619,404.0	99.7	117,798,804.0	99.7	17,360,627,304.0	99.3	108,168,179.0	0.6	12,506,292,613.0	70.3	83,465,601.0	70.9	17,043,988,740.0	97.5	311,136,426.0	1.7	6,659,987.0	5.7	51,070,767.0	0.3	338,217.0	0.3	400,387.0	0.3	17,838,690,171.0	118,137,021.0	17,340,535,269.0	97.0	147.3	151.0	151.0	16.9	7,154,216.0	7,602,015.0	17,770,124,091.0	99.4	117,682,941.0	99.4	17,197,243,630.0	99.2	135,689,624.0	0.8	11,551,373,715.0	65.0	77,418,704.0	65.8	16,767,645,959.0	96.7	429,588,822.0	2.4	8,603,633.0	7.3	102,829,339.0	0.6	680,989.0	0.6	70,715.0	0.1	17,872,953,430.0	118,363,930.0	740,725,248.0	2.1	15,263,620.0	6.5	117,612,226.0	236,500,951.0	115,124,903.0	97.3	236,500,951.0	100.0	153,900,106.0	0.4	1,019,206.0	0.4	280,602.0	0.2	235,481,745.0	99.6	226,602,370.0	8,879,375.0	3.8	2,860,529,749.0	1,813,212,190.0	1,586,172.0	331,692.0	0.1	58.5	538,868,963.0	17.4	92.2	80,662,036.0	2.6	9,867,059.0	0.3	5,882,476.0	0.2	4,077,436.0	520,570.0	0.1	3,067,396.0	0.1	2,437,175.0	0.1	10.8	10.0	88.0	235,481,745.0	235,481,745.0	234,988,586.0	236,500,951.0	35,711,643,601.0	236,500,951.0
RIH0_ANA0-HG002-19-15x	bwa2b	52,754,471,245.0	97.5	147.9	151.0	151.0	15.6	2.7	21,936,874.0	2,947,219,812.0	5.5	19,518,012.0	5.5	results/day/b37/RIH0_ANA0-HG002-19-15x/align/bwa2b/RIH0_ANA0-HG002-19-15x.bwa2b.mrkdup.sort.bam	22,212,096,197.0	414.2	377.0	283.0	179.7	23,169,620.0	53,877,096,564.0	99.6	356,801,964.0	99.6	52,361,826,707.0	99.3	369,474,918.0	0.7	36,453,373,116.0	67.7	243,780,205.0	68.3	51,231,283,159.0	97.1	26,480,171,927.0	98.0	148.4	151.0	151.0	14.1	11,090,488.0	11,646,850.0	26,951,559,348.0	99.7	178,487,148.0	99.7	26,304,639,099.0	99.3	163,885,978.0	0.6	18,950,321,178.0	70.3	126,471,507.0	70.9	25,824,949,260.0	97.5	471,387,421.0	1.7	10,091,063.0	5.7	77,350,958.0	0.3	512,258.0	0.3	605,697.0	0.3	27,028,910,306.0	178,999,406.0	26,274,299,318.0	97.0	147.3	151.0	151.0	16.9	10,846,386.0	11,522,770.0	26,925,537,216.0	99.4	178,314,816.0	99.4	26,057,187,608.0	99.2	205,588,940.0	0.8	17,503,051,938.0	65.0	117,308,698.0	65.8	25,406,333,899.0	96.7	651,237,898.0	2.4	13,039,561.0	7.3	155,611,691.0	0.6	1,030,541.0	0.6	107,374.0	0.1	27,081,148,907.0	179,345,357.0	1,122,625,319.0	2.1	23,130,624.0	6.5	178,207,442.0	358,344,763.0	174,439,891.0	97.3	358,344,763.0	100.0	232,962,649.0	0.4	1,542,799.0	0.4	424,844.0	0.2	356,801,964.0	99.6	337,283,952.0	19,518,012.0	5.4	2,862,435,320.0	2,610,066,017.0	2,566,337.0	432,519.0	0.1	84.1	1,835,504,440.0	59.2	92.3	757,211,644.0	24.4	39,888,083.0	1.3	11,682,144.0	0.4	7,786,007.0	665,983.0	0.3	5,717,785.0	0.2	4,455,555.0	0.1	16.1	16.0	120.7	356,801,964.0	356,801,964.0	356,055,662.0	358,344,763.0	54,110,059,213.0	358,344,763.0
RIH0_ANA0-HG002-19-20x	bwa2b	70,691,818,722.0	97.5	147.9	151.0	151.0	15.6	2.7	29,388,324.0	5,125,351,626.0	7.1	33,942,726.0	7.1	results/day/b37/RIH0_ANA0-HG002-19-20x/align/bwa2b/RIH0_ANA0-HG002-19-20x.bwa2b.mrkdup.sort.bam	29,001,567,674.0	414.6	377.0	283.0	179.9	31,042,170.0	72,195,780,552.0	99.6	478,117,752.0	99.6	70,165,679,878.0	99.3	495,096,674.0	0.7	48,846,358,199.0	67.7	326,656,032.0	68.3	68,650,722,208.0	97.1	35,483,765,853.0	98.0	148.4	151.0	151.0	14.1	14,860,719.0	15,609,041.0	36,115,298,613.0	99.7	239,174,163.0	99.7	35,248,544,627.0	99.3	219,612,185.0	0.6	25,392,782,256.0	70.3	169,466,978.0	70.9	34,605,761,428.0	97.5	631,532,760.0	1.7	13,521,995.0	5.7	103,599,137.0	0.3	686,087.0	0.3	811,706.0	0.3	36,218,897,750.0	239,860,250.0	35,208,052,869.0	97.0	147.3	151.0	151.0	16.9	14,527,605.0	15,433,129.0	36,080,481,939.0	99.4	238,943,589.0	99.4	34,917,135,251.0	99.2	275,484,489.0	0.8	23,453,575,943.0	65.0	157,189,054.0	65.8	34,044,960,780.0	96.7	872,429,070.0	2.4	17,473,969.0	7.3	208,490,381.0	0.6	1,380,731.0	0.6	143,615.0	0.1	36,288,972,320.0	240,324,320.0	1,503,961,830.0	2.1	30,995,964.0	6.5	238,799,974.0	480,184,570.0	233,751,564.0	97.3	480,184,570.0	100.0	312,089,518.0	0.4	2,066,818.0	0.4	569,025.0	0.2	478,117,752.0	99.6	444,175,026.0	33,942,726.0	7.1	2,862,888,002.0	2,774,338,246.0	3,910,819.0	517,032.0	0.1	89.4	2,518,027,421.0	81.2	92.3	1,829,311,242.0	59.0	287,418,569.0	9.3	26,152,741.0	0.8	12,639,646.0	789,408.0	0.4	9,106,406.0	0.3	6,963,835.0	0.2	21.2	21.0	150.3	478,117,752.0	478,117,752.0	477,117,942.0	480,184,570.0	72,507,870,070.0	480,184,570.0
RIH0_ANA0-HG002-19-25x	bwa2b	88,623,101,573.0	97.5	147.9	151.0	151.0	15.6	2.7	36,842,245.0	7,845,079,066.0	8.7	51,954,166.0	8.7	results/day/b37/RIH0_ANA0-HG002-19-25x/align/bwa2b/RIH0_ANA0-HG002-19-25x.bwa2b.mrkdup.sort.bam	35,778,523,537.0	414.9	377.0	283.0	180.1	38,930,557.0	90,508,331,675.0	99.6	599,392,925.0	99.6	87,963,459,643.0	99.3	620,711,373.0	0.7	61,235,515,723.0	67.7	409,506,511.0	68.3	86,064,122,062.0	97.1	44,484,418,448.0	98.0	148.4	151.0	151.0	14.1	18,636,376.0	19,580,473.0	45,276,027,391.0	99.7	299,841,241.0	99.7	44,189,508,660.0	99.3	275,329,315.0	0.6	31,833,668,052.0	70.3	212,451,760.0	70.9	43,383,647,690.0	97.5	791,608,943.0	1.7	16,950,091.0	5.7	129,845,504.0	0.3	859,904.0	0.3	1,017,368.0	0.3	45,405,872,895.0	300,701,145.0	44,138,683,125.0	97.0	147.3	151.0	151.0	16.9	18,205,869.0	19,350,084.0	45,232,304,284.0	99.4	299,551,684.0	99.4	43,773,950,983.0	99.2	345,382,058.0	0.8	29,401,847,671.0	65.0	197,054,751.0	65.8	42,680,474,372.0	96.7	1,093,621,159.0	2.4	21,905,716.0	7.3	261,353,216.0	0.6	1,730,816.0	0.6	179,618.0	0.1	45,493,657,500.0	301,282,500.0	1,885,230,102.0	2.1	38,855,807.0	6.5	299,372,066.0	601,983,645.0	293,042,768.0	97.3	601,983,645.0	100.0	391,198,720.0	0.4	2,590,720.0	0.4	713,448.0	0.2	599,392,925.0	99.6	547,438,759.0	51,954,166.0	8.6	2,863,112,361.0	2,823,287,253.0	5,444,291.0	590,174.0	0.2	91.0	2,709,860,246.0	87.4	92.3	2,441,185,433.0	78.7	965,691,167.0	31.1	115,085,950.0	3.7	21,043,683.0	904,811.0	0.7	13,156,876.0	0.4	10,003,189.0	0.3	26.1	26.0	177.6	599,392,925.0	599,392,925.0	598,140,251.0	601,983,645.0	90,899,530,395.0	601,983,645.0
RIH0_ANA0-HG002-19-30x	bwa2b	105,507,381,170.0	97.5	147.9	151.0	151.0	15.6	2.8	43,859,437.0	10,880,162,607.0	10.1	72,054,057.0	10.1	results/day/b37/RIH0_ANA0-HG002-19-30x/align/bwa2b/RIH0_ANA0-HG002-19-30x.bwa2b.mrkdup.sort.bam	42,138,253,023.0	415.1	378.0	283.0	180.3	46,349,174.0	107,751,491,285.0	99.6	713,586,035.0	99.6	104,722,120,970.0	99.3	738,911,026.0	0.7	72,903,186,675.0	67.7	487,533,937.0	68.3	102,460,629,933.0	97.1	52,959,480,412.0	98.0	148.4	151.0	151.0	14.1	22,181,414.0	23,309,619.0	53,901,894,388.0	99.7	356,966,188.0	99.7	52,608,414,955.0	99.3	327,755,838.0	0.6	37,899,470,814.0	70.3	252,934,768.0	70.9	51,648,862,309.0	97.5	942,413,976.0	1.7	20,177,603.0	5.7	154,554,389.0	0.3	1,023,539.0	0.3	1,211,153.0	0.3	54,056,448,777.0	357,989,727.0	52,547,900,758.0	97.0	147.3	151.0	151.0	16.9	21,678,023.0	23,039,555.0	53,849,596,897.0	99.4	356,619,847.0	99.4	52,113,706,015.0	99.2	411,155,188.0	0.8	35,003,715,861.0	65.0	234,599,169.0	65.8	50,811,767,624.0	96.7	1,301,696,139.0	2.4	26,074,990.0	7.3	311,148,335.0	0.6	2,060,585.0	0.6	213,497.0	0.1	54,160,745,232.0	358,680,432.0	2,244,110,115.0	2.1	46,252,593.0	6.5	356,406,350.0	716,670,159.0	348,872,233.0	97.3	716,670,159.0	100.0	465,702,724.0	0.4	3,084,124.0	0.4	849,432.0	0.2	713,586,035.0	99.6	641,531,978.0	72,054,057.0	10.1	2,863,245,843.0	2,843,359,811.0	7,084,561.0	652,098.0	0.2	91.7	2,773,612,008.0	89.4	92.3	2,645,688,357.0	85.3	1,719,936,983.0	55.4	404,526,659.0	13.0	51,140,672.0	1,006,261.0	1.6	18,421,848.0	0.6	13,205,799.0	0.4	30.6	31.0	201.6	713,586,035.0	713,586,035.0	712,095,239.0	716,670,159.0	108,217,194,009.0	716,670,159.0
RIH0_ANA0-HG002-19-5x	bwa2b	17,935,223,273.0	97.5	147.9	151.0	151.0	15.6	2.7	7,454,138.0	380,136,762.0	2.1	2,517,462.0	2.1	results/day/b37/RIH0_ANA0-HG002-19-5x/align/bwa2b/RIH0_ANA0-HG002-19-5x.bwa2b.mrkdup.sort.bam	8,789,067,503.0	413.4	376.0	283.0	179.4	7,875,106.0	18,316,859,002.0	99.6	121,303,702.0	99.6	17,801,707,307.0	99.3	125,640,860.0	0.7	12,392,731,179.0	67.7	82,875,400.0	68.3	17,417,370,184.0	97.1	9,002,494,706.0	98.0	148.4	151.0	151.0	14.1	3,769,647.0	3,956,854.0	9,162,899,705.0	99.7	60,681,455.0	99.7	8,942,817,440.0	99.3	55,720,412.0	0.6	6,442,700,662.0	70.3	42,997,764.0	70.9	8,779,758,443.0	97.5	160,404,999.0	1.8	3,432,111.0	5.7	26,327,907.0	0.3	174,357.0	0.3	205,964.0	0.3	9,189,227,612.0	60,855,812.0	8,932,728,567.0	97.0	147.4	151.0	151.0	16.9	3,684,491.0	3,918,252.0	9,153,959,297.0	99.4	60,622,247.0	99.4	8,858,889,867.0	99.2	69,920,448.0	0.8	5,950,030,517.0	65.0	39,877,636.0	65.8	8,637,611,741.0	96.7	221,230,730.0	2.4	4,430,699.0	7.3	52,918,403.0	0.6	350,453.0	0.6	36,741.0	0.1	9,206,877,700.0	60,972,700.0	381,635,729.0	2.1	7,862,810.0	6.5	60,585,506.0	121,828,512.0	59,304,387.0	97.3	121,828,512.0	100.0	79,246,310.0	0.4	524,810.0	0.4	144,489.0	0.2	121,303,702.0	99.6	118,786,240.0	2,517,462.0	2.1	2,835,693,935.0	277,407,135.0	941,991.0	189,420.0	0.0	8.9	19,924,388.0	0.6	91.4	6,866,217.0	0.2	3,402,678.0	0.1	2,213,673.0	0.1	1,684,709.0	342,282.0	0.1	1,395,068.0	0.0	1,214,370.0	0.0	5.7	5.0	52.1	121,303,702.0	121,303,702.0	121,049,557.0	121,828,512.0	18,396,105,312.0	121,828,512.0

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Sort	Column	Description	ID	Scale
\|\|	aligner	aligner	`aligner`	None
\|\|	AlignedBases	AlignedBases	`AlignedBases`	None
\|\|	AlignedBasesPct	AlignedBasesPct	`AlignedBasesPct`	None
\|\|	AlignedReadLengthMean	AlignedReadLengthMean	`AlignedReadLengthMean`	None
\|\|	AlignedReadLengthMedian	AlignedReadLengthMedian	`AlignedReadLengthMedian`	None
\|\|	AlignedReadLengthMode	AlignedReadLengthMode	`AlignedReadLengthMode`	None
\|\|	AlignedReadLengthStandardDeviation	AlignedReadLengthStandardDeviation	`AlignedReadLengthStandardDeviation`	None
\|\|	ChimericReadPairPct	ChimericReadPairPct	`ChimericReadPairPct`	None
\|\|	DeletedBases	DeletedBases	`DeletedBases`	None
\|\|	DeletedBasesPct	DeletedBasesPct	`DeletedBasesPct`	None
\|\|	DuplicateBases	DuplicateBases	`DuplicateBases`	None
\|\|	DuplicateBasesPct	DuplicateBasesPct	`DuplicateBasesPct`	None
\|\|	DuplicateReads	DuplicateReads	`DuplicateReads`	None
\|\|	DuplicateReadsPct	DuplicateReadsPct	`DuplicateReadsPct`	None
\|\|	FilteredRecords	FilteredRecords	`FilteredRecords`	None
\|\|	FilteredRecordsPct	FilteredRecordsPct	`FilteredRecordsPct`	None
\|\|	InputFileName	InputFileName	`InputFileName`	None
\|\|	InputFileSize	InputFileSize	`InputFileSize`	None
\|\|	InsertSizeMean	InsertSizeMean	`InsertSizeMean`	None
\|\|	InsertSizeMedian	InsertSizeMedian	`InsertSizeMedian`	None
\|\|	InsertSizeMode	InsertSizeMode	`InsertSizeMode`	None
\|\|	InsertSizeStandardDeviation	InsertSizeStandardDeviation	`InsertSizeStandardDeviation`	None
\|\|	InsertedBases	InsertedBases	`InsertedBases`	None
\|\|	InsertedBasesPct	InsertedBasesPct	`InsertedBasesPct`	None
\|\|	MappedBases	MappedBases	`MappedBases`	None
\|\|	MappedBasesPct	MappedBasesPct	`MappedBasesPct`	None
\|\|	MappedReads	MappedReads	`MappedReads`	None
\|\|	MappedReadsPct	MappedReadsPct	`MappedReadsPct`	None
\|\|	MatchedBases	MatchedBases	`MatchedBases`	None
\|\|	MatchedBasesPct	MatchedBasesPct	`MatchedBasesPct`	None
\|\|	MismatchedBases	MismatchedBases	`MismatchedBases`	None
\|\|	MismatchedBasesPct	MismatchedBasesPct	`MismatchedBasesPct`	None
\|\|	PerfectBases	PerfectBases	`PerfectBases`	None
\|\|	PerfectBasesPct	PerfectBasesPct	`PerfectBasesPct`	None
\|\|	PerfectReads	PerfectReads	`PerfectReads`	None
\|\|	PerfectReadsPct	PerfectReadsPct	`PerfectReadsPct`	None
\|\|	Q20Bases	Q20Bases	`Q20Bases`	None
\|\|	Q20BasesPct	Q20BasesPct	`Q20BasesPct`	None
\|\|	R1AlignedBases	R1AlignedBases	`R1AlignedBases`	None
\|\|	R1AlignedBasesPct	R1AlignedBasesPct	`R1AlignedBasesPct`	None
\|\|	R1AlignedReadLengthMean	R1AlignedReadLengthMean	`R1AlignedReadLengthMean`	None
\|\|	R1AlignedReadLengthMedian	R1AlignedReadLengthMedian	`R1AlignedReadLengthMedian`	None
\|\|	R1AlignedReadLengthMode	R1AlignedReadLengthMode	`R1AlignedReadLengthMode`	None
\|\|	R1AlignedReadLengthStandardDeviation	R1AlignedReadLengthStandardDeviation	`R1AlignedReadLengthStandardDeviation`	None
\|\|	R1DeletedBases	R1DeletedBases	`R1DeletedBases`	None
\|\|	R1DeletedBasesPct	R1DeletedBasesPct	`R1DeletedBasesPct`	None
\|\|	R1InsertedBases	R1InsertedBases	`R1InsertedBases`	None
\|\|	R1InsertedBasesPct	R1InsertedBasesPct	`R1InsertedBasesPct`	None
\|\|	R1MappedBases	R1MappedBases	`R1MappedBases`	None
\|\|	R1MappedBasesPct	R1MappedBasesPct	`R1MappedBasesPct`	None
\|\|	R1MappedReads	R1MappedReads	`R1MappedReads`	None
\|\|	R1MappedReadsPct	R1MappedReadsPct	`R1MappedReadsPct`	None
\|\|	R1MatchedBases	R1MatchedBases	`R1MatchedBases`	None
\|\|	R1MatchedBasesPct	R1MatchedBasesPct	`R1MatchedBasesPct`	None
\|\|	R1MismatchedBases	R1MismatchedBases	`R1MismatchedBases`	None
\|\|	R1MismatchedBasesPct	R1MismatchedBasesPct	`R1MismatchedBasesPct`	None
\|\|	R1PerfectBases	R1PerfectBases	`R1PerfectBases`	None
\|\|	R1PerfectBasesPct	R1PerfectBasesPct	`R1PerfectBasesPct`	None
\|\|	R1PerfectReads	R1PerfectReads	`R1PerfectReads`	None
\|\|	R1PerfectReadsPct	R1PerfectReadsPct	`R1PerfectReadsPct`	None
\|\|	R1Q20Bases	R1Q20Bases	`R1Q20Bases`	None
\|\|	R1Q20BasesPct	R1Q20BasesPct	`R1Q20BasesPct`	None
\|\|	R1SoftClippedBases	R1SoftClippedBases	`R1SoftClippedBases`	None
\|\|	R1SoftClippedBasesPct	R1SoftClippedBasesPct	`R1SoftClippedBasesPct`	None
\|\|	R1SoftClippedReads	R1SoftClippedReads	`R1SoftClippedReads`	None
\|\|	R1SoftClippedReadsPct	R1SoftClippedReadsPct	`R1SoftClippedReadsPct`	None
\|\|	R1UnmappedBases	R1UnmappedBases	`R1UnmappedBases`	None
\|\|	R1UnmappedBasesPct	R1UnmappedBasesPct	`R1UnmappedBasesPct`	None
\|\|	R1UnmappedReads	R1UnmappedReads	`R1UnmappedReads`	None
\|\|	R1UnmappedReadsPct	R1UnmappedReadsPct	`R1UnmappedReadsPct`	None
\|\|	R1UnpairedReads	R1UnpairedReads	`R1UnpairedReads`	None
\|\|	R1UnpairedReadsPct	R1UnpairedReadsPct	`R1UnpairedReadsPct`	None
\|\|	R1YieldBases	R1YieldBases	`R1YieldBases`	None
\|\|	R1YieldReads	R1YieldReads	`R1YieldReads`	None
\|\|	R2AlignedBases	R2AlignedBases	`R2AlignedBases`	None
\|\|	R2AlignedBasesPct	R2AlignedBasesPct	`R2AlignedBasesPct`	None
\|\|	R2AlignedReadLengthMean	R2AlignedReadLengthMean	`R2AlignedReadLengthMean`	None
\|\|	R2AlignedReadLengthMedian	R2AlignedReadLengthMedian	`R2AlignedReadLengthMedian`	None
\|\|	R2AlignedReadLengthMode	R2AlignedReadLengthMode	`R2AlignedReadLengthMode`	None
\|\|	R2AlignedReadLengthStandardDeviation	R2AlignedReadLengthStandardDeviation	`R2AlignedReadLengthStandardDeviation`	None
\|\|	R2DeletedBases	R2DeletedBases	`R2DeletedBases`	None
\|\|	R2DeletedBasesPct	R2DeletedBasesPct	`R2DeletedBasesPct`	None
\|\|	R2InsertedBases	R2InsertedBases	`R2InsertedBases`	None
\|\|	R2InsertedBasesPct	R2InsertedBasesPct	`R2InsertedBasesPct`	None
\|\|	R2MappedBases	R2MappedBases	`R2MappedBases`	None
\|\|	R2MappedBasesPct	R2MappedBasesPct	`R2MappedBasesPct`	None
\|\|	R2MappedReads	R2MappedReads	`R2MappedReads`	None
\|\|	R2MappedReadsPct	R2MappedReadsPct	`R2MappedReadsPct`	None
\|\|	R2MatchedBases	R2MatchedBases	`R2MatchedBases`	None
\|\|	R2MatchedBasesPct	R2MatchedBasesPct	`R2MatchedBasesPct`	None
\|\|	R2MismatchedBases	R2MismatchedBases	`R2MismatchedBases`	None
\|\|	R2MismatchedBasesPct	R2MismatchedBasesPct	`R2MismatchedBasesPct`	None
\|\|	R2PerfectBases	R2PerfectBases	`R2PerfectBases`	None
\|\|	R2PerfectBasesPct	R2PerfectBasesPct	`R2PerfectBasesPct`	None
\|\|	R2PerfectReads	R2PerfectReads	`R2PerfectReads`	None
\|\|	R2PerfectReadsPct	R2PerfectReadsPct	`R2PerfectReadsPct`	None
\|\|	R2Q20Bases	R2Q20Bases	`R2Q20Bases`	None
\|\|	R2Q20BasesPct	R2Q20BasesPct	`R2Q20BasesPct`	None
\|\|	R2SoftClippedBases	R2SoftClippedBases	`R2SoftClippedBases`	None
\|\|	R2SoftClippedBasesPct	R2SoftClippedBasesPct	`R2SoftClippedBasesPct`	None
\|\|	R2SoftClippedReads	R2SoftClippedReads	`R2SoftClippedReads`	None
\|\|	R2SoftClippedReadsPct	R2SoftClippedReadsPct	`R2SoftClippedReadsPct`	None
\|\|	R2UnmappedBases	R2UnmappedBases	`R2UnmappedBases`	None
\|\|	R2UnmappedBasesPct	R2UnmappedBasesPct	`R2UnmappedBasesPct`	None
\|\|	R2UnmappedReads	R2UnmappedReads	`R2UnmappedReads`	None
\|\|	R2UnmappedReadsPct	R2UnmappedReadsPct	`R2UnmappedReadsPct`	None
\|\|	R2UnpairedReads	R2UnpairedReads	`R2UnpairedReads`	None
\|\|	R2UnpairedReadsPct	R2UnpairedReadsPct	`R2UnpairedReadsPct`	None
\|\|	R2YieldBases	R2YieldBases	`R2YieldBases`	None
\|\|	R2YieldReads	R2YieldReads	`R2YieldReads`	None
\|\|	SoftClippedBases	SoftClippedBases	`SoftClippedBases`	None
\|\|	SoftClippedBasesPct	SoftClippedBasesPct	`SoftClippedBasesPct`	None
\|\|	SoftClippedReads	SoftClippedReads	`SoftClippedReads`	None
\|\|	SoftClippedReadsPct	SoftClippedReadsPct	`SoftClippedReadsPct`	None
\|\|	TotalPairs	TotalPairs	`TotalPairs`	None
\|\|	TotalRecords	TotalRecords	`TotalRecords`	None
\|\|	TotalSameChrPairs	TotalSameChrPairs	`TotalSameChrPairs`	None
\|\|	TotalSameChrPairsPct	TotalSameChrPairsPct	`TotalSameChrPairsPct`	None
\|\|	UnfilteredRecords	UnfilteredRecords	`UnfilteredRecords`	None
\|\|	UnfilteredRecordsPct	UnfilteredRecordsPct	`UnfilteredRecordsPct`	None
\|\|	UnmappedBases	UnmappedBases	`UnmappedBases`	None
\|\|	UnmappedBasesPct	UnmappedBasesPct	`UnmappedBasesPct`	None
\|\|	UnmappedReads	UnmappedReads	`UnmappedReads`	None
\|\|	UnmappedReadsPct	UnmappedReadsPct	`UnmappedReadsPct`	None
\|\|	UnpairedReads	UnpairedReads	`UnpairedReads`	None
\|\|	UnpairedReadsPct	UnpairedReadsPct	`UnpairedReadsPct`	None
\|\|	WgsAlignedReads	WgsAlignedReads	`WgsAlignedReads`	None
\|\|	WgsAlignedReadsPct	WgsAlignedReadsPct	`WgsAlignedReadsPct`	None
\|\|	WgsCalculatedAlignedReads	WgsCalculatedAlignedReads	`WgsCalculatedAlignedReads`	None
\|\|	WgsCovDuplicateReads	WgsCovDuplicateReads	`WgsCovDuplicateReads`	None
\|\|	WgsCovDuplicateReadsPct	WgsCovDuplicateReadsPct	`WgsCovDuplicateReadsPct`	None
\|\|	WgsCoverageBases1	WgsCoverageBases1	`WgsCoverageBases1`	None
\|\|	WgsCoverageBases10	WgsCoverageBases10	`WgsCoverageBases10`	None
\|\|	WgsCoverageBases100	WgsCoverageBases100	`WgsCoverageBases100`	None
\|\|	WgsCoverageBases1000	WgsCoverageBases1000	`WgsCoverageBases1000`	None
\|\|	WgsCoverageBases1000Pct	WgsCoverageBases1000Pct	`WgsCoverageBases1000Pct`	None
\|\|	WgsCoverageBases100Pct	WgsCoverageBases100Pct	`WgsCoverageBases100Pct`	None
\|\|	WgsCoverageBases10Pct	WgsCoverageBases10Pct	`WgsCoverageBases10Pct`	None
\|\|	WgsCoverageBases15	WgsCoverageBases15	`WgsCoverageBases15`	None
\|\|	WgsCoverageBases15Pct	WgsCoverageBases15Pct	`WgsCoverageBases15Pct`	None
\|\|	WgsCoverageBases1Pct	WgsCoverageBases1Pct	`WgsCoverageBases1Pct`	None
\|\|	WgsCoverageBases20	WgsCoverageBases20	`WgsCoverageBases20`	None
\|\|	WgsCoverageBases20Pct	WgsCoverageBases20Pct	`WgsCoverageBases20Pct`	None
\|\|	WgsCoverageBases30	WgsCoverageBases30	`WgsCoverageBases30`	None
\|\|	WgsCoverageBases30Pct	WgsCoverageBases30Pct	`WgsCoverageBases30Pct`	None
\|\|	WgsCoverageBases40	WgsCoverageBases40	`WgsCoverageBases40`	None
\|\|	WgsCoverageBases40Pct	WgsCoverageBases40Pct	`WgsCoverageBases40Pct`	None
\|\|	WgsCoverageBases50	WgsCoverageBases50	`WgsCoverageBases50`	None
\|\|	WgsCoverageBases500	WgsCoverageBases500	`WgsCoverageBases500`	None
\|\|	WgsCoverageBases500Pct	WgsCoverageBases500Pct	`WgsCoverageBases500Pct`	None
\|\|	WgsCoverageBases50Pct	WgsCoverageBases50Pct	`WgsCoverageBases50Pct`	None
\|\|	WgsCoverageBases60	WgsCoverageBases60	`WgsCoverageBases60`	None
\|\|	WgsCoverageBases60Pct	WgsCoverageBases60Pct	`WgsCoverageBases60Pct`	None
\|\|	WgsCoverageBases70	WgsCoverageBases70	`WgsCoverageBases70`	None
\|\|	WgsCoverageBases70Pct	WgsCoverageBases70Pct	`WgsCoverageBases70Pct`	None
\|\|	WgsCoverageMean	WgsCoverageMean	`WgsCoverageMean`	None
\|\|	WgsCoverageMedian	WgsCoverageMedian	`WgsCoverageMedian`	None
\|\|	WgsCoverageStandardDeviation	WgsCoverageStandardDeviation	`WgsCoverageStandardDeviation`	None
\|\|	WgsExpectedAlignedReads	WgsExpectedAlignedReads	`WgsExpectedAlignedReads`	None
\|\|	WgsFilteredLowBaseQualityBases	WgsFilteredLowBaseQualityBases	`WgsFilteredLowBaseQualityBases`	None
\|\|	WgsFilteredOverlapBases	WgsFilteredOverlapBases	`WgsFilteredOverlapBases`	None
\|\|	WgsReadsPaired	WgsReadsPaired	`WgsReadsPaired`	None
\|\|	WgsReadsPairedWithMates	WgsReadsPairedWithMates	`WgsReadsPairedWithMates`	None
\|\|	WgsTotalReads	WgsTotalReads	`WgsTotalReads`	None
\|\|	YieldBases	YieldBases	`YieldBases`	None
\|\|	YieldReads	YieldReads	`YieldReads`	None

Rules Benchmarkdata

Showing ¹⁴/₁₄ rows and ¹³/₁₃ columns.

sample	rule	s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time	rule_prefix	rule_suffix
all.	alignstats_smmary_compile	0.7	0:00:00	9.3	345.9	6.6	6.8	5.1	0.0	0.0	0.0	alignstats_smmary_compile
SEQQC-multiqc_.	raw_fastqc	141.4	0:02:21	179.0	1,078.9	176.6	177.0	199.1	1.8	55.2	78.1	raw_fastqc
RIH0_ANA0-HG002-19-15x	bwa2b.deep.18	324.5	0:05:24	61,594.2	404,361.0	47,431.2	47,522.1	1,983.5	5,370.9	2,337.2	164.9	bwa2b	deep.18
RIH0_ANA0-HG002-19-10x	bwa2b.deep.merge	67.6	0:01:07	94.4	3,550.3	97.9	98.2	483.6	743.2	23.4	3.6	bwa2b	deep.merge
RIH0_ANA0-HG002-19-30x	bwa2b.deep.1	746.8	0:12:26	61,484.8	408,854.4	47,324.0	47,414.9	4,132.8	8,023.8	5,751.6	206.0	bwa2b	deep.1
results/day/b37/RIH0_ANA0-HG002-19-25x_DBC0_0/align/bwa2b/snv/deep/concordance/logs/RIH0_ANA0-HG002-19-25xbwa2b.deep.concordance.bench.tsv	325.1	0:05:25	57,188.4	246,462.2	57,188.8	57,189.3	1,492.3	670.4	133.4	433.8		325.0	605.0
results/day/b37/RIH0_ANA0-HG002-19-15x_DBC0_0/align/bwa2b/snv/deep/concordance/logs/RIH0_ANA0-HG002-19-15xbwa2b.deep.concordance.bench.tsv	323.4	0:05:23	53,319.8	246,526.2	53,319.9	53,320.4	1,241.9	679.9	134.8	436.2		323.0	4,095.0
results/day/b37/RIH0_ANA0-HG002-19-10x_DBC0_0/align/bwa2b/snv/deep/concordance/logs/RIH0_ANA0-HG002-19-10xbwa2b.deep.concordance.bench.tsv	326.6	0:05:26	67,049.8	246,654.2	67,050.1	67,050.6	1,480.3	647.4	131.5	429.6		326.0	6,325.0
RIH0_ANA0-HG002-19-5x	bwa2b.qmap	451.8	0:07:31	21,106.0	161,520.1	21,103.0	21,103.4	2,020.2	0.5	366.6	1,655.4	bwa2b	qmap
RIH0_ANA0-HG002-19-20x	bwa2b.deep.bcfstat	8.2	0:00:08	11.9	3,183.5	10.9	11.1	145.4	0.0	91.4	7.5	bwa2b	deep.bcfstat
results/day/b37/RIH0_ANA0-HG002-19-5x_DBC0_0/align/bwa2b/snv/deep/concordance/logs/RIH0_ANA0-HG002-19-5xbwa2b.deep.concordance.bench.tsv	328.0	0:05:28	41,927.8	246,654.2	41,928.2	41,928.7	1,152.5	427.8	87.7	286.7		328.0	283.0
results/day/b37/RIH0_ANA0-HG002-19-30x_DBC0_0/align/bwa2b/snv/deep/concordance/logs/RIH0_ANA0-HG002-19-30xbwa2b.deep.concordance.bench.tsv	324.6	0:05:24	65,313.2	246,334.2	65,312.5	65,313.0	97.8	464.1	95.7	311.0		324.0	5,566.0
RIH0_ANA0-HG002-19-25x	bwa2b.deep.5	582.4	0:09:42	61,461.5	406,857.5	47,301.2	47,392.1	2,735.4	6,966.1	4,657.4	202.4	bwa2b	deep.5
results/day/b37/RIH0_ANA0-HG002-19-20x_DBC0_0/align/bwa2b/snv/deep/concordance/logs/RIH0_ANA0-HG002-19-20xbwa2b.deep.concordance.bench.tsv	310.5	0:05:10	54,273.0	246,590.2	54,272.7	54,273.2	1,163.8	484.8	98.1	304.5		310.0	4,560.0

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

Sort	Column	Description	ID	Scale
\|\|	rule	rule	`rule`	None
\|\|	s	s	`s`	None
\|\|	h:m:s	h:m:s	`h:m:s`	None
\|\|	max_rss	max_rss	`max_rss`	None
\|\|	max_vms	max_vms	`max_vms`	None
\|\|	max_uss	max_uss	`max_uss`	None
\|\|	max_pss	max_pss	`max_pss`	None
\|\|	io_in	io_in	`io_in`	None
\|\|	io_out	io_out	`io_out`	None
\|\|	mean_load	mean_load	`mean_load`	None
\|\|	cpu_time	cpu_time	`cpu_time`	None
\|\|	rule_prefix	rule_prefix	`rule_prefix`	None
\|\|	rule_suffix	rule_suffix	`rule_suffix`	None

VerifyBAMID

VerifyBAMID detects sample contamination and/or sample swaps.DOI: 10.1016/j.ajhg.2012.09.004.

The following values provide estimates of sample contamination. Click help for more information.

Please note that FREEMIX is named Contamination (Seq) and CHIPMIX is named Contamination (S+A) in this MultiQC report.

VerifyBamID provides a series of information that is informative to determine whether the sample is possibly contaminated or swapped, but there is no single criteria that works for every circumstances. There are a few unmodeled factor in the estimation of [SELF-IBD]/[BEST-IBD] and [%MIX], so please note that the MLE estimation may not always exactly match to the true amount of contamination. Here we provide a guideline to flag potentially contaminated/swapped samples:

Each sample or lane can be checked in this way. When [CHIPMIX] >> 0.02 and/or [FREEMIX] >> 0.02, meaning 2% or more of non-reference bases are observed in reference sites, we recommend to examine the data more carefully for the possibility of contamination.
We recommend to check each lane for the possibility of sample swaps. When [CHIPMIX] ~ 1 AND [FREEMIX] ~ 0, then it is possible that the sample is swapped with another sample. When [CHIPMIX] ~ 0 in .bestSM file, [CHIP_ID] might be actually the swapped sample. Otherwise, the swapped sample may not exist in the genotype data you have compared.
When genotype data is not available but allele-frequency-based estimates of [FREEMIX] >= 0.03 and [FREELK1]-[FREELK0] is large, then it is possible that the sample is contaminated with other sample. We recommend to use per-sample data rather than per-lane data for checking this for low coverage data, because the inference will be more confident when there are large number of bases with depth 2 or higher.

Copied from the VerifyBAMID documentation - see the link for more details.

Showing ⁶/₆ rows and ⁷/₁₂ columns.

Sample Name	Contamination (Seq)	Read Group	SNPS	M Reads	Average Depth	FREEELK1	FREELK0	FREE_RH	FREE_RA	DPREF	RDPHET	RDPALT
RIH0_ANA0-HG002-19-10x	0.552%	NA	100,000	1.0	10.3 X	-313,464	-317,565	NA	NA	NA	NA	NA
RIH0_ANA0-HG002-19-15x	0.562%	NA	100,000	1.5	15.3 X	-433,923	-439,599	NA	NA	NA	NA	NA
RIH0_ANA0-HG002-19-20x	0.561%	NA	100,000	2.0	20.2 X	-549,494	-558,052	NA	NA	NA	NA	NA
RIH0_ANA0-HG002-19-25x	0.568%	NA	100,000	2.5	24.9 X	-660,297	-670,909	NA	NA	NA	NA	NA
RIH0_ANA0-HG002-19-30x	0.584%	NA	100,000	2.9	29.3 X	-762,057	-774,400	NA	NA	NA	NA	NA
RIH0_ANA0-HG002-19-5x	0.490%	NA	100,000	0.5	5.4 X	-188,375	-194,341	NA	NA	NA	NA	NA

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

Sort	Group	Column	Description	ID	Scale
\|\|	VerifyBAMID	Contamination (Seq)	VerifyBamID: FREEMIX - Sequence-only estimate of contamination.	`FREEMIX`	None
\|\|	VerifyBAMID	Read Group	ReadGroup ID of sequenced lane.	`RG`	None
\|\|	VerifyBAMID	SNPS	# SNPs passing the criteria from the VCF file	`#SNPS`	None
\|\|	VerifyBAMID	M Reads	Number of reads loaded from the BAM file (millions)	`#READS`	read_count
\|\|	VerifyBAMID	Average Depth	Average sequencing depth at the sites in the VCF file	`AVG_DP`	None
\|\|	VerifyBAMID	FREEELK1	Maximum log-likelihood of the sequence reads given estimated contamination under sequence-only method	`FREELK1`	None
\|\|	VerifyBAMID	FREELK0	Log-likelihood of the sequence reads given no contamination under sequence-only method	`FREELK0`	None
\|\|	VerifyBAMID	FREE_RH	Estimated reference bias parameter Pr(refBase\|HET) (when --free-refBias or --free-full is used)	`FREE_RH`	None
\|\|	VerifyBAMID	FREE_RA	Estimated reference bias parameter Pr(refBase\|HOMALT) (when --free-refBias or --free-full is used)	`FREE_RA`	None
\|\|	VerifyBAMID	DPREF	Depth (Coverage) of HomRef site (based on the genotypes of (SELF_SM/BEST_SM), passing mapQ, baseQual, maxDepth thresholds.	`DPREF`	None
\|\|	VerifyBAMID	RDPHET	DPHET/DPREF, Relative depth to HomRef site at Heterozygous site.	`RDPHET`	None
\|\|	VerifyBAMID	RDPALT	DPHET/DPREF, Relative depth to HomRef site at HomAlt site.	`RDPALT`	None

Picard

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

Alignment Summary

Please note that Picard's read counts are divided by two for paired-end data. Total bases (including unaligned) is not provided.

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Mean read length

The mean read length of the set of reads examined.

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Base Distribution

Plot shows the distribution of bases by cycle.

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GC Coverage Bias

This plot shows bias in coverage across regions of the genome with varying GC content. A perfect library would be a flat line at y = 1.

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Insert Size

Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

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Mean Base Quality by Cycle

Plot shows the mean base quality by cycle.

This metric gives an overall snapshot of sequencing machine performance. For most types of sequencing data, the output is expected to show a slight reduction in overall base quality scores towards the end of each read.

Spikes in quality within reads are not expected and may indicate that technical problems occurred during sequencing.

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Base Quality Distribution

Plot shows the count of each base quality score.

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mosdepth

mosdepth performs fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.DOI: 10.1093/bioinformatics/btx699.

Cumulative coverage distribution

Proportion of bases in the reference genome with, at least, a given depth of coverage

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

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Coverage distribution

Proportion of bases in the reference genome with a given depth of coverage

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

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Average coverage per contig

Average coverage per contig or chromosome

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FastQC

FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

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.

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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.

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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.

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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.

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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.

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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.

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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.

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

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.

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Status Checks

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

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

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

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

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

Min:

Max:

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goleft indexcov

goleft indexcov quickly estimates coverage from a whole-genome bam index.DOI: 10.1093/gigascience/gix090.

Scaled coverage ROC plot

Coverage (ROC) plot that shows genome coverage at at given (scaled) depth.

Lower coverage samples have shorter curves where the proportion of regions covered drops off more quickly. This indicates a higher fraction of low coverage regions.

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Problem coverage bins

This plot identifies problematic samples using binned coverage distributions.

We expect bins to be around 1, so deviations from this indicate problems. Low coverage bins (< 0.15) on the x-axis have regions with low or missing coverage. Higher values indicate truncated BAM files or missing data. Bins with skewed distributions (<0.85 or >1.15) on the y-axis detect dosage bias. Large values on the y-axis are likely to impact CNV and structural variant calling. See the goleft indexcov bin documentation for more details.

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Peddy

Peddy calculates genotype :: pedigree correspondence checks, ancestry checks and sex checks using VCF files.DOI: 10.1016/j.ajhg.2017.01.017.

PCA Plot

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Het Check

Proportion of sites that were heterozygous against median depth.

A high proportion of heterozygous sites suggests contamination, a low proportion suggests consanguinity.

See the main peddy documentation for more details about the het_check command.

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Sex Check

Predicted sex against heterozygosity ratio

Higher values of Sex Het Ratio suggests the sample is female, low values suggest male.

See the main peddy documentation for more details about the het_check command.

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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.DOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503.

Coverage histogram

Distribution of the number of locations in the reference genome with a given depth of coverage.

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

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Cumulative genome coverage

Percentage of the reference genome with at least the given depth of coverage.

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

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Insert size histogram

Distribution of estimated insert sizes of mapped reads.

To overcome limitations in the length of DNA or RNA sequencing reads, many sequencing instruments can produce two or more shorter reads from one longer fragment in which the relative position of reads is approximately known, such as paired-end or mate-pair reads (Mardis 2013). Such techniques can extend the reach of sequencing technology, allowing for more accurate placement of reads (Reinert et al. 2015) and better resolution of repeat regions (Reinert et al. 2015), as well as detection of structural variation (Alkan et al. 2011) and chimeric transcripts (Maher et al. 2009).

All these methods assume that the approximate size of an insert is known. (Insert size can be defined as the length in bases of a sequenced DNA or RNA fragment, excluding technical sequences such as adapters, which are typically removed before alignment.) This plot allows for that assumption to be assessed. With the set of mapped fragments for a given sample, QualiMap groups the fragments by insert size, then plots the frequency of mapped fragments (y-axis) over a range of insert sizes (x-axis). In an ideal case, the distribution of fragment sizes for a sequencing library would culminate in a single peak indicating average insert size, with a narrow spread indicating highly consistent fragment lengths.

QualiMap calculates insert sizes as follows: for each fragment in which every read mapped successfully to the same reference sequence, it extracts the insert size from the TLEN field of the leftmost read (see the Qualimap 2 documentation), where the TLEN (or 'observed Template LENgth') field contains 'the number of bases from the leftmost mapped base to the rightmost mapped base' (SAM format specification). Note that because it is defined in terms of alignment to a reference sequence, the value of the TLEN field may differ from the insert size due to factors such as alignment clipping, alignment errors, or structural variation or splicing in a gap between reads from the same fragment.

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GC content distribution

Each solid line represents the distribution of GC content of mapped reads for a given sample.

GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).

QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).

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Bcftools

Bcftools contains utilities for variant calling and manipulating VCFs and BCFs.DOI: 10.1093/gigascience/giab008.

Variant Substitution Types

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Variant Quality

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Indel Distribution

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Samtools

Samtools is a suite of programs for interacting with high-throughput sequencing data.DOI: 10.1093/bioinformatics/btp352.

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 < 0.001% of the total aligned reads are omitted from this plot.

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Run Time

Run Time This analysis is about the MultiQC run itself, profiling the time spent on different parts of the MultiQC execution. It is designed to help developers optimise how they run MultiQC, to get the most efficient and fastest configuration possible. For more information, see the MultiQC documentation. .

Files searched

Number of files searched by MultiQC, categorised by what happened to them. Total file searches: 27524.

Note that only files are considered in this plot - skipped directories are not shown.

Some search patterns do not discard files after they match (shared: true), so it is possible that some files may be double-counted in this plot.

Skipped: No match - File was searched, but didn't match any search patterns
Skipped: Ignore pattern - File matched a MultiQC ignore pattern (see -x / --ignore / config.fn_ignore_paths)
Skipped: Filesize limit - File was skipped because it was too large (see config.log_filesize_limit)
Skipped: Symlinks - File was a symlink and skipped (see config.ignore_symlinks)
Skipped: Not a file - File could not be read (eg. was a unix pipe or something)

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Search patterns

Time spent running each search pattern to find files for MultiQC modules. Total file search time: 46.79 seconds.

NOTE: Usually, MultiQC run time is fairly insignificant - in the order of seconds. Unless you are running MultiQC on many thousands of analysis files, optimising this process will have limited practical benefit.

MultiQC works by recursively looking through all files found in the analysis directories. After skipping any that are too big / binary file types etc, it uses the search patterns defined in multiqc/utils/search_patterns.yaml. These work by matching either file names or file contents. Generally speaking, matching filenames is super fast and matching file contents is slower.

Please see the MultiQC Documentation for information on how to optimise MultiQC to speed this process up. The plot below shows which search keys are running and how long each has taken to run in total. This should help to guide you to where optimisation is most worthwhile.

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MultiQC Toolbox

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About MultiQC

General Statistics

General Statistics: Columns

Norm Cov Evenness

Giabhcr Concordance

Giabhcr Concordance-Plot: Columns

Alignstats

Alignstats-Plot: Columns

Rules Benchmarkdata

Rules Benchmarkdata-Plot: Columns

VerifyBAMID

Verifybamid-Results: Columns

Picard

Alignment Summary

Mean read length

Base Distribution

GC Coverage Bias

Insert Size

Mean Base Quality by Cycle Help

Base Quality Distribution

mosdepth

Cumulative coverage distribution Help

Coverage distribution Help

Average coverage per contig

FastQC

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

goleft indexcov

Scaled coverage ROC plot Help

Problem coverage bins Help

Peddy

PCA Plot

Het Check Help

Sex Check Help

QualiMap

Coverage histogram Help

Cumulative genome coverage Help

Insert size histogram Help

GC content distribution Help

Bcftools

Variant Substitution Types

Variant Quality

Indel Distribution

Samtools

Percent Mapped Help

Alignment metrics

Samtools Flagstat

XY counts

Mapped reads per contig

Run Time

Files searched Help

Search patterns Help

v1.14

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Mean Base Quality by Cycle

Cumulative coverage distribution

Coverage distribution

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

Scaled coverage ROC plot

Problem coverage bins

Het Check

Sex Check

Coverage histogram

Cumulative genome coverage

Insert size histogram

GC content distribution

Percent Mapped

Files searched

Search patterns