PTox-Production-phase2-HepG2-include-dups: MultiQC Report

General Statistics

Showing ⁴⁸/₄₈ rows and ¹⁰/₁₉ columns.

Sample Name	dupInt	Duplication	5'-3' bias	M Aligned	Exonic	Intronic	Intergenic	Overlapping Exon	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Mean insert	Reads	Reads mapped	% Reads mapped
GAG000ZA1	1.6	86.4%	1.66	4.2M	1.9M	1.1M	0.6M	0.3M	0.67%	1.2M	4.2M	100.0%	0.0%	2.1%	4.2M	0.0bp	5.4M	5.4M	100.0%
GAG000ZA2	1.2	83.9%	1.33	7.0M	3.0M	2.0M	0.8M	0.5M	0.62%	2.1M	7.0M	100.0%	0.0%	2.0%	7.0M	0.0bp	9.1M	9.1M	100.0%
GAG000ZA3	1.5	86.1%	1.21	6.7M	2.9M	2.1M	0.6M	0.5M	0.55%	2.2M	6.7M	100.0%	0.0%	2.4%	6.7M	0.0bp	8.9M	8.9M	100.0%
GAG000ZA4	1.3	84.7%	1.59	4.6M	1.9M	1.3M	0.5M	0.3M	0.70%	1.8M	4.6M	100.0%	0.0%	2.5%	4.6M	0.0bp	6.4M	6.4M	100.0%
GAG000ZA5	1.0	80.2%	1.11	5.3M	2.3M	1.5M	0.6M	0.4M	0.65%	1.7M	5.3M	100.0%	0.0%	2.2%	5.3M	0.0bp	7.0M	7.0M	100.0%
GAG000ZA6	1.2	82.9%	1.07	6.5M	2.9M	1.9M	0.7M	0.5M	0.59%	1.8M	6.5M	100.0%	0.0%	1.9%	6.5M	0.0bp	8.3M	8.3M	100.0%
GAG000ZA7	1.1	81.6%	1.53	5.5M	2.4M	1.6M	0.6M	0.4M	0.59%	2.4M	5.5M	100.0%	0.0%	3.4%	5.5M	0.0bp	7.9M	7.9M	100.0%
GAG000ZA8	1.3	85.1%	1.20	5.5M	2.4M	1.5M	0.8M	0.4M	0.68%	1.8M	5.5M	100.0%	0.0%	2.3%	5.5M	0.0bp	7.3M	7.3M	100.0%
GAG000ZB1	1.6	87.6%	1.51	5.5M	2.4M	1.6M	0.6M	0.4M	0.61%	1.6M	5.5M	100.0%	0.0%	2.1%	5.5M	0.0bp	7.1M	7.1M	100.0%
GAG000ZB2	1.4	85.0%	1.67	4.9M	2.2M	1.5M	0.5M	0.4M	0.58%	1.3M	4.9M	100.0%	0.0%	1.9%	4.9M	0.0bp	6.2M	6.2M	100.0%
GAG000ZB3	1.1	81.4%	1.17	4.6M	2.0M	1.4M	0.4M	0.3M	0.58%	1.5M	4.6M	100.0%	0.0%	2.3%	4.6M	0.0bp	6.1M	6.1M	100.0%
GAG000ZB4	1.5	86.1%	1.07	7.5M	3.2M	2.4M	0.7M	0.5M	0.55%	2.4M	7.5M	100.0%	0.0%	2.1%	7.5M	0.0bp	9.9M	9.9M	100.0%
GAG000ZB5	0.6	76.2%	0.68	10.6M	4.7M	3.3M	0.9M	0.8M	0.52%	3.1M	10.6M	100.0%	0.0%	2.0%	10.6M	0.0bp	13.7M	13.7M	100.0%
GAG000ZB6	1.5	86.8%	1.50	6.4M	2.8M	1.9M	0.6M	0.5M	0.61%	2.1M	6.4M	100.0%	0.0%	2.1%	6.4M	0.0bp	8.4M	8.4M	100.0%
GAG000ZB7	1.5	87.5%	1.36	7.0M	3.1M	2.1M	0.7M	0.5M	0.57%	2.2M	7.0M	100.0%	0.0%	2.2%	7.0M	0.0bp	9.2M	9.2M	100.0%
GAG000ZB8	1.4	85.8%	0.80	7.7M	3.2M	2.4M	0.8M	0.5M	0.56%	2.4M	7.7M	100.0%	0.0%	2.2%	7.7M	0.0bp	10.1M	10.1M	100.0%
GAG007LA1	1.3	85.5%	2.56	5.1M	2.2M	1.4M	0.7M	0.4M	0.69%	1.6M	5.1M	100.0%	0.0%	2.1%	5.1M	0.0bp	6.7M	6.7M	100.0%
GAG007LA2	1.3	84.5%	1.37	6.2M	2.7M	1.8M	0.7M	0.4M	0.63%	1.9M	6.2M	100.0%	0.0%	2.2%	6.2M	0.0bp	8.1M	8.1M	100.0%
GAG007LA3	1.7	85.1%	84.14	1.9M	0.8M	0.5M	0.2M	0.1M	0.56%	1.5M	1.9M	100.0%	0.0%	6.4%	1.9M	0.0bp	3.5M	3.5M	100.0%
GAG007LA4	1.6	87.7%	1.37	6.1M	2.6M	2.1M	0.6M	0.4M	0.55%	1.8M	6.1M	100.0%	0.0%	2.2%	6.1M	0.0bp	7.9M	7.9M	100.0%
GAG007LB1	1.3	84.8%	1.10	6.2M	2.8M	1.9M	0.6M	0.4M	0.57%	1.8M	6.2M	100.0%	0.0%	2.0%	6.2M	0.0bp	8.0M	8.0M	100.0%
GAG007LB2	1.6	86.1%	2.90	4.0M	1.6M	1.2M	0.4M	0.3M	0.61%	1.6M	4.0M	100.0%	0.0%	3.2%	4.0M	0.0bp	5.6M	5.6M	100.0%
GAG007LB3	1.4	86.0%	1.12	6.8M	3.1M	2.0M	0.7M	0.5M	0.56%	1.9M	6.8M	100.0%	0.0%	2.0%	6.8M	0.0bp	8.7M	8.7M	100.0%
GAG007LB4	1.9	89.7%	2.30	7.1M	3.1M	2.2M	0.7M	0.5M	0.56%	2.2M	7.1M	100.0%	0.0%	2.0%	7.1M	0.0bp	9.3M	9.3M	100.0%
GAH000ZA1	0.9	77.4%	1.65	3.0M	1.3M	0.9M	0.4M	0.2M	0.66%	1.0M	3.0M	100.0%	0.0%	2.3%	3.0M	0.0bp	4.1M	4.1M	100.0%
GAH000ZA2	0.8	77.2%	1.02	6.4M	2.8M	1.9M	0.7M	0.5M	0.56%	2.1M	6.4M	100.0%	0.0%	2.2%	6.4M	0.0bp	8.5M	8.5M	100.0%
GAH000ZA3	0.7	74.6%	1.29	5.3M	2.3M	1.6M	0.5M	0.4M	0.57%	1.8M	5.3M	100.0%	0.0%	2.3%	5.3M	0.0bp	7.0M	7.0M	100.0%
GAH000ZA4	0.7	73.8%	1.48	3.8M	1.6M	1.1M	0.4M	0.3M	0.65%	1.4M	3.8M	100.0%	0.0%	2.3%	3.8M	0.0bp	5.2M	5.2M	100.0%
GAH000ZA5	0.4	66.0%	0.96	3.9M	1.7M	1.2M	0.4M	0.3M	0.57%	1.2M	3.9M	100.0%	0.0%	2.2%	3.9M	0.0bp	5.1M	5.1M	100.0%
GAH000ZA6	0.8	77.4%	0.93	6.4M	2.8M	1.9M	0.7M	0.5M	0.58%	1.9M	6.4M	100.0%	0.0%	2.0%	6.4M	0.0bp	8.2M	8.2M	100.0%
GAH000ZA7	0.8	73.9%	1.51	3.3M	1.3M	1.0M	0.3M	0.2M	0.62%	1.5M	3.3M	100.0%	0.0%	3.3%	3.3M	0.0bp	4.8M	4.8M	100.0%
GAH000ZA8	0.6	74.2%	0.99	5.8M	2.5M	1.8M	0.6M	0.4M	0.58%	2.1M	5.8M	100.0%	0.0%	2.5%	5.8M	0.0bp	7.9M	7.9M	100.0%
GAH000ZB1	0.6	71.3%	1.21	3.5M	1.6M	1.0M	0.4M	0.3M	0.59%	1.1M	3.5M	100.0%	0.0%	2.3%	3.5M	0.0bp	4.6M	4.6M	100.0%
GAH000ZB2	0.8	76.4%	1.41	4.8M	2.2M	1.4M	0.5M	0.3M	0.57%	1.4M	4.8M	100.0%	0.0%	2.0%	4.8M	0.0bp	6.2M	6.2M	100.0%
GAH000ZB3	0.4	66.7%	1.38	3.7M	1.5M	1.1M	0.4M	0.2M	0.58%	1.7M	3.7M	100.0%	0.0%	3.1%	3.7M	0.0bp	5.4M	5.4M	100.0%
GAH000ZB4	0.8	75.8%	1.02	4.7M	2.0M	1.5M	0.4M	0.3M	0.54%	1.5M	4.7M	100.0%	0.0%	2.1%	4.7M	0.0bp	6.2M	6.2M	100.0%
GAH000ZB5	0.6	71.1%	0.80	6.0M	2.5M	1.9M	0.5M	0.4M	0.52%	2.0M	6.0M	100.0%	0.0%	2.4%	6.0M	0.0bp	8.0M	8.0M	100.0%
GAH000ZB6	0.5	70.1%	1.04	4.7M	2.0M	1.5M	0.4M	0.3M	0.56%	1.6M	4.7M	100.0%	0.0%	2.3%	4.7M	0.0bp	6.3M	6.3M	100.0%
GAH000ZB7	1.1	79.7%	1.60	3.7M	1.6M	1.1M	0.4M	0.2M	0.56%	1.4M	3.7M	100.0%	0.0%	2.6%	3.7M	0.0bp	5.1M	5.1M	100.0%
GAH000ZB8	0.7	73.7%	0.81	4.6M	2.1M	1.4M	0.4M	0.3M	0.54%	1.3M	4.6M	100.0%	0.0%	2.0%	4.6M	0.0bp	6.0M	6.0M	100.0%
GAH078LA1	0.6	73.0%	0.93	5.0M	2.0M	1.6M	0.5M	0.3M	0.56%	1.7M	5.0M	100.0%	0.0%	2.2%	5.0M	0.0bp	6.8M	6.8M	100.0%
GAH078LA2	0.7	72.6%	1.16	3.0M	1.3M	0.9M	0.3M	0.2M	0.61%	1.0M	3.0M	100.0%	0.0%	2.3%	3.0M	0.0bp	3.9M	3.9M	100.0%
GAH078LA3	0.4	66.1%	0.99	3.1M	1.3M	0.9M	0.3M	0.2M	0.60%	1.4M	3.1M	100.0%	0.0%	3.1%	3.1M	0.0bp	4.4M	4.4M	100.0%
GAH078LA4	0.5	69.5%	1.14	3.5M	1.4M	1.1M	0.4M	0.2M	0.60%	1.2M	3.5M	100.0%	0.0%	2.3%	3.5M	0.0bp	4.7M	4.7M	100.0%
GAH078LB1	0.6	70.6%	1.66	4.0M	1.7M	1.3M	0.4M	0.3M	0.60%	1.4M	4.0M	100.0%	0.0%	2.4%	4.0M	0.0bp	5.4M	5.4M	100.0%
GAH078LB2	0.7	73.9%	0.92	4.0M	1.6M	1.3M	0.4M	0.3M	0.60%	1.5M	4.0M	100.0%	0.0%	2.6%	4.0M	0.0bp	5.5M	5.5M	100.0%
GAH078LB3	0.6	73.7%	1.03	4.1M	1.8M	1.2M	0.4M	0.3M	0.60%	1.3M	4.1M	100.0%	0.0%	2.1%	4.1M	0.0bp	5.4M	5.4M	100.0%
GAH078LB4	0.6	72.5%	1.46	4.8M	1.9M	1.5M	0.5M	0.3M	0.57%	1.9M	4.8M	100.0%	0.0%	2.7%	4.8M	0.0bp	6.7M	6.7M	100.0%

Expand table

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

Sort	Group	Column	Description	ID	Scale
\|\|	Custom content: DupRadar	dupInt	Intercept value from DupRadar	`custom_content_dupradar-dupRadar_intercept`
\|\|	Picard: Mark Duplicates	Duplication	Mark Duplicates - Percent Duplication	`picard_mark_duplicates-PERCENT_DUPLICATION`
\|\|	QualiMap: RNASeq	5'-3' bias	5'-3' bias	`qualimap_rnaseq-5_3_bias`
\|\|	QualiMap: RNASeq	M Aligned	Reads Aligned (millions)	`qualimap_rnaseq-reads_aligned`	read_count
\|\|	QualiMap: RNASeq	Exonic	Reads aligned to exonic regions	`qualimap_rnaseq-reads_aligned_exonic`	read_count
\|\|	QualiMap: RNASeq	Intronic	Reads aligned to intronic regions	`qualimap_rnaseq-reads_aligned_intronic`	read_count
\|\|	QualiMap: RNASeq	Intergenic	Reads aligned to intergenic regions	`qualimap_rnaseq-reads_aligned_intergenic`	read_count
\|\|	QualiMap: RNASeq	Overlapping Exon	Reads aligned to overlapping exon regions	`qualimap_rnaseq-reads_aligned_overlapping_exon`	read_count
\|\|	Samtools: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_stats-error_rate`
\|\|	Samtools: stats	Non-primary	Non-primary alignments (millions)	`samtools_stats-non_primary_alignments`	read_count
\|\|	Samtools: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_stats-reads_mapped`	read_count
\|\|	Samtools: stats	% Mapped	% Mapped reads	`samtools_stats-reads_mapped_percent`
\|\|	Samtools: stats	% Proper pairs	% Properly paired reads	`samtools_stats-reads_properly_paired_percent`
\|\|	Samtools: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_stats-reads_MQ0_percent`
\|\|	Samtools: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_stats-raw_total_sequences`	read_count
\|\|	Samtools: stats	Mean insert	Average insert size	`samtools_stats-insert_size_average`
\|\|	Samtools: flagstat	Reads	Total reads in the bam file (millions)	`samtools_flagstat-flagstat_total`	read_count
\|\|	Samtools: flagstat	Reads mapped	Reads mapped in the bam file (millions)	`samtools_flagstat-mapped_passed`	read_count
\|\|	Samtools: flagstat	% Reads mapped	% Reads mapped in the bam file	`samtools_flagstat-mapped_passed_pct`

Sample status checks

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

Strandedness Checks

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

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

Showing ⁴⁸/₄₈ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
GAG000ZA1	fail	RSeQC	auto	forward	89.6	2.3	8.1
GAG000ZA2	fail	RSeQC	auto	forward	89.9	2.3	7.8
GAG000ZA3	fail	RSeQC	auto	forward	89.1	2.7	8.2
GAG000ZA4	fail	RSeQC	auto	forward	89.5	2.7	7.8
GAG000ZA5	fail	RSeQC	auto	forward	89.5	2.4	8.1
GAG000ZA6	fail	RSeQC	auto	forward	89.5	2.4	8.1
GAG000ZA7	fail	RSeQC	auto	forward	88.3	3.2	8.5
GAG000ZA8	fail	RSeQC	auto	forward	89.4	2.6	8.0
GAG000ZB1	fail	RSeQC	auto	forward	89.3	2.8	7.9
GAG000ZB2	fail	RSeQC	auto	forward	89.6	2.6	7.8
GAG000ZB3	fail	RSeQC	auto	forward	89.3	2.8	7.9
GAG000ZB4	fail	RSeQC	auto	forward	89.2	2.7	8.1
GAG000ZB5	fail	RSeQC	auto	forward	91.9	2.5	5.6
GAG000ZB6	fail	RSeQC	auto	forward	89.3	2.7	7.9
GAG000ZB7	fail	RSeQC	auto	forward	89.0	2.9	8.1
GAG000ZB8	fail	RSeQC	auto	forward	88.9	2.9	8.3
GAG007LA1	fail	RSeQC	auto	forward	89.8	2.4	7.8
GAG007LA2	fail	RSeQC	auto	forward	89.5	2.5	8.0
GAG007LA3	fail	RSeQC	auto	forward	82.7	9.7	7.5
GAG007LA4	fail	RSeQC	auto	forward	88.8	3.0	8.1
GAG007LB1	fail	RSeQC	auto	forward	89.2	2.6	8.1
GAG007LB2	fail	RSeQC	auto	forward	88.3	3.9	7.8
GAG007LB3	fail	RSeQC	auto	forward	88.9	2.5	8.5
GAG007LB4	fail	RSeQC	auto	forward	89.4	2.7	7.9
GAH000ZA1	fail	RSeQC	auto	forward	89.8	2.3	7.9
GAH000ZA2	fail	RSeQC	auto	forward	90.4	2.5	7.1
GAH000ZA3	fail	RSeQC	auto	forward	89.9	2.6	7.5
GAH000ZA4	fail	RSeQC	auto	forward	89.5	2.4	8.1
GAH000ZA5	fail	RSeQC	auto	forward	90.2	2.4	7.4
GAH000ZA6	fail	RSeQC	auto	forward	90.6	2.5	6.9
GAH000ZA7	fail	RSeQC	auto	forward	89.1	3.0	7.9
GAH000ZA8	fail	RSeQC	auto	forward	90.4	2.8	6.8
GAH000ZB1	fail	RSeQC	auto	forward	89.3	2.5	8.2
GAH000ZB2	fail	RSeQC	auto	forward	89.4	2.3	8.3
GAH000ZB3	fail	RSeQC	auto	forward	89.4	2.9	7.6
GAH000ZB4	fail	RSeQC	auto	forward	88.8	2.8	8.4
GAH000ZB5	fail	RSeQC	auto	forward	90.6	2.6	6.8
GAH000ZB6	fail	RSeQC	auto	forward	89.7	2.8	7.5
GAH000ZB7	fail	RSeQC	auto	forward	89.3	2.7	8.0
GAH000ZB8	fail	RSeQC	auto	forward	89.3	2.6	8.1
GAH078LA1	fail	RSeQC	auto	forward	90.2	2.8	7.0
GAH078LA2	fail	RSeQC	auto	forward	89.7	2.3	8.0
GAH078LA3	fail	RSeQC	auto	forward	89.4	2.6	8.0
GAH078LA4	fail	RSeQC	auto	forward	89.9	2.4	7.7
GAH078LB1	fail	RSeQC	auto	forward	90.1	2.5	7.4
GAH078LB2	fail	RSeQC	auto	forward	89.4	2.7	7.9
GAH078LB3	fail	RSeQC	auto	forward	89.6	2.3	8.1
GAH078LB4	fail	RSeQC	auto	forward	89.9	2.7	7.4

Expand table

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

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

DupRadar

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

Created with MultiQC

Picard

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

Mark Duplicates

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

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

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

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

Created with MultiQC

Preseq

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

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

Complexity curve (molecule count)

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

Created with MultiQC

QualiMap

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

Genomic origin of reads

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

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

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

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

Created with MultiQC

Gene Coverage Profile

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

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

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

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

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

Created with MultiQC

RSeQC

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

Read Distribution

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

Created with MultiQC

Read Duplication

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

Created with MultiQC

Junction Annotation

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

Created with MultiQC

Junction Saturation

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

Created with MultiQC

Infer experiment

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

Created with MultiQC

Bam Stat

All numbers reported in millions.

Created with MultiQC

Showing ⁴⁸/₄₈ rows and ¹⁰/₁₀ columns.

Sample Name	Total records	QC failed	Duplicates	Non primary hit	Unmapped	Unique	+ve strand	-ve strand	Non-splice reads	Splice reads
GAG000ZA1	5.4M	0.0M	3.6M	1.2M	0.0M	0.5M	0.2M	0.2M	0.4M	0.0M
GAG000ZA2	9.1M	0.0M	5.9M	2.1M	0.0M	1.0M	0.5M	0.5M	0.9M	0.1M
GAG000ZA3	8.9M	0.0M	5.7M	2.2M	0.0M	0.8M	0.4M	0.4M	0.7M	0.1M
GAG000ZA4	6.4M	0.0M	3.9M	1.8M	0.0M	0.6M	0.3M	0.3M	0.5M	0.0M
GAG000ZA5	7.0M	0.0M	4.3M	1.7M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
GAG000ZA6	8.3M	0.0M	5.4M	1.8M	0.0M	1.0M	0.5M	0.5M	0.9M	0.1M
GAG000ZA7	7.9M	0.0M	4.5M	2.4M	0.0M	0.8M	0.4M	0.4M	0.8M	0.1M
GAG000ZA8	7.3M	0.0M	4.7M	1.8M	0.0M	0.7M	0.4M	0.3M	0.6M	0.1M
GAG000ZB1	7.1M	0.0M	4.8M	1.6M	0.0M	0.6M	0.3M	0.3M	0.5M	0.0M
GAG000ZB2	6.2M	0.0M	4.1M	1.3M	0.0M	0.6M	0.3M	0.3M	0.6M	0.1M
GAG000ZB3	6.1M	0.0M	3.7M	1.5M	0.0M	0.7M	0.4M	0.4M	0.7M	0.1M
GAG000ZB4	9.9M	0.0M	6.5M	2.4M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
GAG000ZB5	13.7M	0.0M	8.0M	3.1M	0.0M	2.2M	1.1M	1.1M	2.0M	0.2M
GAG000ZB6	8.4M	0.0M	5.5M	2.1M	0.0M	0.7M	0.4M	0.3M	0.6M	0.1M
GAG000ZB7	9.2M	0.0M	6.2M	2.2M	0.0M	0.7M	0.4M	0.4M	0.7M	0.1M
GAG000ZB8	10.1M	0.0M	6.6M	2.4M	0.0M	0.9M	0.5M	0.5M	0.8M	0.1M
GAG007LA1	6.7M	0.0M	4.4M	1.6M	0.0M	0.6M	0.3M	0.3M	0.6M	0.1M
GAG007LA2	8.1M	0.0M	5.2M	1.9M	0.0M	0.8M	0.4M	0.4M	0.7M	0.1M
GAG007LA3	3.5M	0.0M	1.7M	1.5M	0.0M	0.2M	0.1M	0.1M	0.2M	0.0M
GAG007LA4	7.9M	0.0M	5.4M	1.8M	0.0M	0.6M	0.3M	0.3M	0.6M	0.0M
GAG007LB1	8.0M	0.0M	5.3M	1.8M	0.0M	0.8M	0.4M	0.4M	0.7M	0.1M
GAG007LB2	5.6M	0.0M	3.4M	1.6M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
GAG007LB3	8.7M	0.0M	5.8M	1.9M	0.0M	0.8M	0.4M	0.4M	0.7M	0.1M
GAG007LB4	9.3M	0.0M	6.4M	2.2M	0.0M	0.6M	0.3M	0.3M	0.6M	0.1M
GAH000ZA1	4.1M	0.0M	2.4M	1.0M	0.0M	0.6M	0.3M	0.3M	0.5M	0.0M
GAH000ZA2	8.5M	0.0M	5.0M	2.1M	0.0M	1.3M	0.7M	0.6M	1.2M	0.1M
GAH000ZA3	7.0M	0.0M	3.9M	1.8M	0.0M	1.2M	0.6M	0.6M	1.1M	0.1M
GAH000ZA4	5.2M	0.0M	2.8M	1.4M	0.0M	0.9M	0.4M	0.4M	0.8M	0.1M
GAH000ZA5	5.1M	0.0M	2.6M	1.2M	0.0M	1.1M	0.6M	0.6M	1.1M	0.1M
GAH000ZA6	8.2M	0.0M	4.9M	1.9M	0.0M	1.3M	0.6M	0.6M	1.1M	0.1M
GAH000ZA7	4.8M	0.0M	2.4M	1.5M	0.0M	0.7M	0.4M	0.3M	0.7M	0.1M
GAH000ZA8	7.9M	0.0M	4.3M	2.1M	0.0M	1.3M	0.7M	0.6M	1.2M	0.1M
GAH000ZB1	4.6M	0.0M	2.5M	1.1M	0.0M	0.9M	0.4M	0.4M	0.8M	0.1M
GAH000ZB2	6.2M	0.0M	3.7M	1.4M	0.0M	1.0M	0.5M	0.5M	0.9M	0.1M
GAH000ZB3	5.4M	0.0M	2.5M	1.7M	0.0M	1.0M	0.5M	0.5M	1.0M	0.1M
GAH000ZB4	6.2M	0.0M	3.6M	1.5M	0.0M	1.0M	0.5M	0.5M	0.9M	0.1M
GAH000ZB5	8.0M	0.0M	4.3M	2.0M	0.0M	1.5M	0.8M	0.7M	1.4M	0.1M
GAH000ZB6	6.3M	0.0M	3.3M	1.6M	0.0M	1.2M	0.6M	0.6M	1.1M	0.1M
GAH000ZB7	5.1M	0.0M	3.0M	1.4M	0.0M	0.6M	0.3M	0.3M	0.6M	0.1M
GAH000ZB8	6.0M	0.0M	3.4M	1.3M	0.0M	1.1M	0.5M	0.5M	1.0M	0.1M
GAH078LA1	6.8M	0.0M	3.7M	1.7M	0.0M	1.2M	0.6M	0.6M	1.1M	0.1M
GAH078LA2	3.9M	0.0M	2.2M	1.0M	0.0M	0.7M	0.4M	0.3M	0.7M	0.1M
GAH078LA3	4.4M	0.0M	2.0M	1.4M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
GAH078LA4	4.7M	0.0M	2.4M	1.2M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
GAH078LB1	5.4M	0.0M	2.9M	1.4M	0.0M	1.0M	0.5M	0.5M	0.9M	0.1M
GAH078LB2	5.5M	0.0M	3.0M	1.5M	0.0M	0.9M	0.5M	0.4M	0.8M	0.1M
GAH078LB3	5.4M	0.0M	3.0M	1.3M	0.0M	0.9M	0.5M	0.5M	0.9M	0.1M
GAH078LB4	6.7M	0.0M	3.5M	1.9M	0.0M	1.1M	0.6M	0.6M	1.1M	0.1M

Expand table

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

Sort	Column	Description	ID	Scale
\|\|	Total records	Total records	`total_records`	read_count
\|\|	QC failed	QC failed	`qc_failed`	read_count
\|\|	Duplicates	Optical/PCR duplicate	`optical_pcr_duplicate`	read_count
\|\|	Non primary hit	Non primary hit	`non_primary_hits`	read_count
\|\|	Unmapped	Unmapped reads	`unmapped_reads`	read_count
\|\|	Unique	mapq >= mapq_cut (unique)	`mapq_gte_mapq_cut_unique`	read_count
\|\|	+ve strand	Reads map to '+'	`reads_map_to_sense`	read_count
\|\|	-ve strand	Reads map to '-'	`reads_map_to_antisense`	read_count
\|\|	Non-splice reads	Non-splice reads	`non_splice_reads`	read_count
\|\|	Splice reads	Splice reads	`splice_reads`	read_count

Samtools

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

Percent mapped

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

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

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

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

Created with MultiQC

Alignment stats

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

Created with MultiQC

Showing ⁴⁸/₄₈ rows and ¹³/₁₃ columns.

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
GAG000ZA1	4.2M	0.0M	0.0M	3.6M	0.0M	0.1M	436.4Mb	0.0Mb	418.2Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA2	7.0M	0.0M	0.0M	5.9M	0.0M	0.1M	723.2Mb	0.0Mb	676.3Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA3	6.7M	0.0M	0.0M	5.7M	0.0M	0.2M	657.5Mb	0.0Mb	636.9Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA4	4.6M	0.0M	0.0M	3.9M	0.0M	0.1M	448.2Mb	0.0Mb	429.3Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA5	5.3M	0.0M	0.0M	4.3M	0.0M	0.1M	544.0Mb	0.0Mb	486.0Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA6	6.5M	0.0M	0.0M	5.4M	0.0M	0.1M	687.9Mb	0.0Mb	630.8Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA7	5.5M	0.0M	0.0M	4.5M	0.0M	0.2M	505.7Mb	0.0Mb	468.7Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZA8	5.5M	0.0M	0.0M	4.7M	0.0M	0.1M	554.0Mb	0.0Mb	529.5Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB1	5.5M	0.0M	0.0M	4.8M	0.0M	0.1M	559.5Mb	0.0Mb	547.4Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB2	4.9M	0.0M	0.0M	4.1M	0.0M	0.1M	508.6Mb	0.0Mb	481.4Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB3	4.6M	0.0M	0.0M	3.7M	0.0M	0.1M	457.2Mb	0.0Mb	417.5Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB4	7.5M	0.0M	0.0M	6.5M	0.0M	0.2M	754.3Mb	0.0Mb	729.1Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB5	10.6M	0.0M	0.0M	8.0M	0.0M	0.2M	1046.7Mb	0.0Mb	894.8Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB6	6.4M	0.0M	0.0M	5.5M	0.0M	0.1M	649.6Mb	0.0Mb	632.8Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB7	7.0M	0.0M	0.0M	6.2M	0.0M	0.2M	708.1Mb	0.0Mb	698.9Mb	0.0M	0.0M	0.0M	0.0M
GAG000ZB8	7.7M	0.0M	0.0M	6.6M	0.0M	0.2M	773.4Mb	0.0Mb	741.5Mb	0.0M	0.0M	0.0M	0.0M
GAG007LA1	5.1M	0.0M	0.0M	4.4M	0.0M	0.1M	528.1Mb	0.0Mb	501.8Mb	0.0M	0.0M	0.0M	0.0M
GAG007LA2	6.2M	0.0M	0.0M	5.2M	0.0M	0.1M	627.7Mb	0.0Mb	594.2Mb	0.0M	0.0M	0.0M	0.0M
GAG007LA3	1.9M	0.0M	0.0M	1.7M	0.0M	0.1M	134.7Mb	0.0Mb	141.3Mb	0.0M	0.0M	0.0M	0.0M
GAG007LA4	6.1M	0.0M	0.0M	5.4M	0.0M	0.1M	626.3Mb	0.0Mb	615.8Mb	0.0M	0.0M	0.0M	0.0M
GAG007LB1	6.2M	0.0M	0.0M	5.3M	0.0M	0.1M	631.0Mb	0.0Mb	600.0Mb	0.0M	0.0M	0.0M	0.0M
GAG007LB2	4.0M	0.0M	0.0M	3.4M	0.0M	0.1M	379.1Mb	0.0Mb	372.6Mb	0.0M	0.0M	0.0M	0.0M
GAG007LB3	6.8M	0.0M	0.0M	5.8M	0.0M	0.1M	710.8Mb	0.0Mb	676.1Mb	0.0M	0.0M	0.0M	0.0M
GAG007LB4	7.1M	0.0M	0.0M	6.4M	0.0M	0.1M	737.7Mb	0.0Mb	738.8Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA1	3.0M	0.0M	0.0M	2.4M	0.0M	0.1M	294.8Mb	0.0Mb	254.0Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA2	6.4M	0.0M	0.0M	5.0M	0.0M	0.1M	629.4Mb	0.0Mb	538.1Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA3	5.3M	0.0M	0.0M	3.9M	0.0M	0.1M	503.2Mb	0.0Mb	420.4Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA4	3.8M	0.0M	0.0M	2.8M	0.0M	0.1M	373.9Mb	0.0Mb	306.6Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA5	3.9M	0.0M	0.0M	2.6M	0.0M	0.1M	373.8Mb	0.0Mb	274.0Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA6	6.4M	0.0M	0.0M	4.9M	0.0M	0.1M	629.5Mb	0.0Mb	538.9Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA7	3.3M	0.0M	0.0M	2.4M	0.0M	0.1M	285.3Mb	0.0Mb	239.2Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZA8	5.8M	0.0M	0.0M	4.3M	0.0M	0.1M	543.9Mb	0.0Mb	454.1Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB1	3.5M	0.0M	0.0M	2.5M	0.0M	0.1M	337.3Mb	0.0Mb	268.3Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB2	4.8M	0.0M	0.0M	3.7M	0.0M	0.1M	479.9Mb	0.0Mb	404.0Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB3	3.7M	0.0M	0.0M	2.5M	0.0M	0.1M	325.0Mb	0.0Mb	243.2Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB4	4.7M	0.0M	0.0M	3.6M	0.0M	0.1M	464.3Mb	0.0Mb	390.3Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB5	6.0M	0.0M	0.0M	4.3M	0.0M	0.1M	554.0Mb	0.0Mb	437.7Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB6	4.7M	0.0M	0.0M	3.3M	0.0M	0.1M	446.8Mb	0.0Mb	347.5Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB7	3.7M	0.0M	0.0M	3.0M	0.0M	0.1M	348.1Mb	0.0Mb	310.7Mb	0.0M	0.0M	0.0M	0.0M
GAH000ZB8	4.6M	0.0M	0.0M	3.4M	0.0M	0.1M	465.4Mb	0.0Mb	379.2Mb	0.0M	0.0M	0.0M	0.0M
GAH078LA1	5.0M	0.0M	0.0M	3.7M	0.0M	0.1M	490.7Mb	0.0Mb	400.2Mb	0.0M	0.0M	0.0M	0.0M
GAH078LA2	3.0M	0.0M	0.0M	2.2M	0.0M	0.1M	292.2Mb	0.0Mb	235.0Mb	0.0M	0.0M	0.0M	0.0M
GAH078LA3	3.1M	0.0M	0.0M	2.0M	0.0M	0.1M	272.6Mb	0.0Mb	202.2Mb	0.0M	0.0M	0.0M	0.0M
GAH078LA4	3.5M	0.0M	0.0M	2.4M	0.0M	0.1M	335.2Mb	0.0Mb	258.2Mb	0.0M	0.0M	0.0M	0.0M
GAH078LB1	4.0M	0.0M	0.0M	2.9M	0.0M	0.1M	384.1Mb	0.0Mb	304.2Mb	0.0M	0.0M	0.0M	0.0M
GAH078LB2	4.0M	0.0M	0.0M	3.0M	0.0M	0.1M	379.1Mb	0.0Mb	313.8Mb	0.0M	0.0M	0.0M	0.0M
GAH078LB3	4.1M	0.0M	0.0M	3.0M	0.0M	0.1M	403.5Mb	0.0Mb	331.2Mb	0.0M	0.0M	0.0M	0.0M
GAH078LB4	4.8M	0.0M	0.0M	3.5M	0.0M	0.1M	439.6Mb	0.0Mb	357.3Mb	0.0M	0.0M	0.0M	0.0M

Expand table

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

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

Flagstat

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁴⁸/₄₈ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
GAG000ZA1	5.4M	5.4M	5.4M	1.2M	3.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA2	9.1M	9.1M	9.1M	2.1M	5.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA3	8.9M	8.9M	8.9M	2.2M	5.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA4	6.4M	6.4M	6.4M	1.8M	3.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA5	7.0M	7.0M	7.0M	1.7M	4.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA6	8.3M	8.3M	8.3M	1.8M	5.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA7	7.9M	7.9M	7.9M	2.4M	4.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZA8	7.3M	7.3M	7.3M	1.8M	4.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB1	7.1M	7.1M	7.1M	1.6M	4.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB2	6.2M	6.2M	6.2M	1.3M	4.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB3	6.1M	6.1M	6.1M	1.5M	3.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB4	9.9M	9.9M	9.9M	2.4M	6.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB5	13.7M	13.7M	13.7M	3.1M	8.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB6	8.4M	8.4M	8.4M	2.1M	5.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB7	9.2M	9.2M	9.2M	2.2M	6.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG000ZB8	10.1M	10.1M	10.1M	2.4M	6.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LA1	6.7M	6.7M	6.7M	1.6M	4.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LA2	8.1M	8.1M	8.1M	1.9M	5.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LA3	3.5M	3.5M	3.5M	1.5M	1.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LA4	7.9M	7.9M	7.9M	1.8M	5.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LB1	8.0M	8.0M	8.0M	1.8M	5.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LB2	5.6M	5.6M	5.6M	1.6M	3.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LB3	8.7M	8.7M	8.7M	1.9M	5.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAG007LB4	9.3M	9.3M	9.3M	2.2M	6.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA1	4.1M	4.1M	4.1M	1.0M	2.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA2	8.5M	8.5M	8.5M	2.1M	5.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA3	7.0M	7.0M	7.0M	1.8M	3.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA4	5.2M	5.2M	5.2M	1.4M	2.8M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA5	5.1M	5.1M	5.1M	1.2M	2.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA6	8.2M	8.2M	8.2M	1.9M	4.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA7	4.8M	4.8M	4.8M	1.5M	2.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZA8	7.9M	7.9M	7.9M	2.1M	4.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB1	4.6M	4.6M	4.6M	1.1M	2.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB2	6.2M	6.2M	6.2M	1.4M	3.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB3	5.4M	5.4M	5.4M	1.7M	2.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB4	6.2M	6.2M	6.2M	1.5M	3.6M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB5	8.0M	8.0M	8.0M	2.0M	4.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB6	6.3M	6.3M	6.3M	1.6M	3.3M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB7	5.1M	5.1M	5.1M	1.4M	3.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH000ZB8	6.0M	6.0M	6.0M	1.3M	3.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LA1	6.8M	6.8M	6.8M	1.7M	3.7M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LA2	3.9M	3.9M	3.9M	1.0M	2.2M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LA3	4.4M	4.4M	4.4M	1.4M	2.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LA4	4.7M	4.7M	4.7M	1.2M	2.4M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LB1	5.4M	5.4M	5.4M	1.4M	2.9M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LB2	5.5M	5.5M	5.5M	1.5M	3.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LB3	5.4M	5.4M	5.4M	1.3M	3.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
GAH078LB4	6.7M	6.7M	6.7M	1.9M	3.5M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M

Expand table

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

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

Flagstat: Percentage of total

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁴⁸/₄₈ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
GAG000ZA1	100.0%	100.0%	100.0%	22.6%	66.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA2	100.0%	100.0%	100.0%	23.2%	64.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA3	100.0%	100.0%	100.0%	24.9%	64.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA4	100.0%	100.0%	100.0%	27.8%	61.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA5	100.0%	100.0%	100.0%	24.2%	60.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA6	100.0%	100.0%	100.0%	21.6%	65.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA7	100.0%	100.0%	100.0%	30.6%	56.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZA8	100.0%	100.0%	100.0%	24.1%	64.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB1	100.0%	100.0%	100.0%	23.0%	67.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB2	100.0%	100.0%	100.0%	21.4%	66.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB3	100.0%	100.0%	100.0%	25.3%	60.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB4	100.0%	100.0%	100.0%	24.3%	65.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB5	100.0%	100.0%	100.0%	22.7%	58.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB6	100.0%	100.0%	100.0%	24.3%	65.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB7	100.0%	100.0%	100.0%	23.8%	66.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG000ZB8	100.0%	100.0%	100.0%	23.9%	65.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LA1	100.0%	100.0%	100.0%	23.7%	65.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LA2	100.0%	100.0%	100.0%	23.4%	64.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LA3	100.0%	100.0%	100.0%	44.0%	47.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LA4	100.0%	100.0%	100.0%	22.8%	67.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LB1	100.0%	100.0%	100.0%	22.5%	65.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LB2	100.0%	100.0%	100.0%	29.4%	60.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LB3	100.0%	100.0%	100.0%	22.1%	67.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAG007LB4	100.0%	100.0%	100.0%	23.4%	68.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA1	100.0%	100.0%	100.0%	25.0%	58.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA2	100.0%	100.0%	100.0%	24.5%	58.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA3	100.0%	100.0%	100.0%	25.2%	55.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA4	100.0%	100.0%	100.0%	26.4%	54.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA5	100.0%	100.0%	100.0%	23.9%	50.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA6	100.0%	100.0%	100.0%	22.7%	59.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA7	100.0%	100.0%	100.0%	31.8%	50.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZA8	100.0%	100.0%	100.0%	26.5%	54.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB1	100.0%	100.0%	100.0%	24.3%	54.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB2	100.0%	100.0%	100.0%	23.1%	58.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB3	100.0%	100.0%	100.0%	30.8%	46.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB4	100.0%	100.0%	100.0%	23.8%	57.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB5	100.0%	100.0%	100.0%	25.3%	53.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB6	100.0%	100.0%	100.0%	25.3%	52.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB7	100.0%	100.0%	100.0%	27.2%	58.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH000ZB8	100.0%	100.0%	100.0%	22.4%	57.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LA1	100.0%	100.0%	100.0%	25.3%	54.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LA2	100.0%	100.0%	100.0%	24.5%	54.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LA3	100.0%	100.0%	100.0%	30.8%	45.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LA4	100.0%	100.0%	100.0%	25.5%	51.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LB1	100.0%	100.0%	100.0%	25.2%	52.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LB2	100.0%	100.0%	100.0%	26.9%	54.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LB3	100.0%	100.0%	100.0%	24.3%	55.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
GAH078LB4	100.0%	100.0%	100.0%	28.0%	52.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%

Expand table

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

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

Mapped reads per contig

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

Created with MultiQC

Sample relationships

Plots interrogating sample relationships, based on final count matrices.

STAR_SALMON DESeq2 sample similarity

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

Created with MultiQC

STAR_SALMON DESeq2 PCA plot

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

Created with MultiQC

Software Versions

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

Group	Software	Version
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DESEQ2_QC_STAR_SALMON	bioconductor-deseq2	`1.46.0`
	r-base	`4.4.2`
DupRadar	bioconductor-dupradar	`1.32.0`
GTF2BED	perl	`5.26.2`
GTF_FILTER	python	`3.9.5`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
PICARD_MARKDUPLICATES	picard	`3.1.1`
PRESEQ_LCEXTRAP	preseq	`3.2.0`
QUALIMAP_RNASEQ	qualimap	`2.3`
RSEQC_BAMSTAT	rseqc	`5.0.2`
RSEQC_INFEREXPERIMENT	rseqc	`5.0.2`
RSEQC_INNERDISTANCE	rseqc	`5.0.2`
RSEQC_JUNCTIONANNOTATION	rseqc	`5.0.2`
RSEQC_JUNCTIONSATURATION	rseqc	`5.0.2`
RSEQC_READDISTRIBUTION	rseqc	`5.0.2`
RSEQC_READDUPLICATION	rseqc	`5.0.2`
SALMON_QUANT	salmon	`1.10.3`
SAMTOOLS_FLAGSTAT	samtools	`1.21`
SAMTOOLS_IDXSTATS	samtools	`1.21`
SAMTOOLS_INDEX	samtools	`1.21`
SAMTOOLS_SORT_QUALIMAP	samtools	`1.21`
SAMTOOLS_STATS	samtools	`1.21`
SE_GENE_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
SE_TRANSCRIPT_UNIFIED	bioconductor-summarizedexperiment	`1.32.0`
SORTMERNA_INDEX	sortmerna	`4.3.7`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
Workflow	Nextflow	`25.04.7`
	nf-core/rnaseq	`v3.22.2-g3816d48`

nf-core/rnaseq Methods Description

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

Methods

Data was processed using nf-core/rnaseq v3.22.2 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v25.04.7 (Di Tommaso et al., 2017) with the following command:

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

References

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

Notes:

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

nf-core/rnaseq Workflow Summary

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

Input/output options

input: ../samplesheet_HepG2.csv
multiqc_title: PTox-Production-phase2-HepG2-include-dups
outdir: ./rnaseq_output

Reference genome options

fasta: ../../refs/HepG2/genome.fa
gtf: ../../refs/HepG2/genome_annotation.gtf
gtf_extra_attributes: gene
star_index: ../../rnaseq_run-HepG2/rnaseq_output/genome/index/star

Read trimming options

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

Read filtering options

remove_ribo_rna: true

UMI options

umi_discard_read: 0

Alignment options

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

Process skipping options

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

Generic options

monochrome_logs: true
trace_report_suffix: 2026-04-01_10-23-06

Core Nextflow options

configFiles: /users/rg/vntasis/.nextflow/assets/nf-core/rnaseq/nextflow.config, /users/project/precisiontox_008279_no_backup/production_phase2_data_dec2025/analyse_duplicated_data/rnaseq_run-HepG2/../rnaseq.config
containerEngine: apptainer
launchDir: /users/project/precisiontox_008279_no_backup/production_phase2_data_dec2025/analyse_duplicated_data/rnaseq_run-HepG2
profile: crg_slurm
projectDir: /users/rg/vntasis/.nextflow/assets/nf-core/rnaseq
revision: 3.22.2
runName: disturbed_kimura
userName: vntasis
workDir: /users/project/precisiontox_008279_no_backup/production_phase2_data_dec2025/analyse_duplicated_data/rnaseq_run-HepG2/work

PTox-Production-phase2-HepG2-include-dups

MultiQC Toolbox

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About MultiQC

PTox-Production-phase2-HepG2-include-dups

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General Statistics

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General Statistics: Columns

Sample status checks

Strandedness Checks

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Samples strandedness check: Columns

DupRadar

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Picard

Mark Duplicates Help

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Preseq

Complexity curve (molecule count)

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QualiMap

Genomic origin of reads Help

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Gene Coverage Profile Help

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RSeQC

Read Distribution

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Read Duplication

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Junction Annotation

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Junction Saturation

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Infer experiment

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Bam Stat

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RSeQC: Bam Stat: Columns

Samtools

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Alignment stats

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Samtools: stats: Alignment Stats: Columns

Flagstat

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Samtools: flagstat: read count: Columns

Flagstat: Percentage of total

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Samtools: flagstat: percentage of total: Columns

Mapped reads per contig

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Sample relationships

STAR_SALMON DESeq2 sample similarity

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STAR_SALMON DESeq2 PCA plot

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Software Versions

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nf-core/rnaseq Methods Description

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Methods

References

Notes:

nf-core/rnaseq Workflow Summary

AI Summary

Highlight Samples

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Mark Duplicates

Genomic origin of reads

Gene Coverage Profile

Percent mapped