MultiQC Report

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MultiQC: Summarize analysis results for multiple tools and samples in a single report
Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
Bioinformatics (2016)
doi: 10.1093/bioinformatics/btw354
PMID: 27312411

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About MultiQC

This report was generated using MultiQC, version 1.35

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MultiQC is published in Bioinformatics:

MultiQC: Summarize analysis results for multiple tools and samples in a single report
Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
Bioinformatics (2016)
doi: 10.1093/bioinformatics/btw354
PMID: 27312411

MultiQC is developed by Seqera.

A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

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Report generated on 2026-05-28, 10:49 CST based on data in: /home/kyhan/test/rna-seq/yeast-standard-out/03_results/alignment_qc

General Statistics

Showing ²⁴/₂₄ rows and ³/₉ columns.

Sample Name	5'-3' bias	M Aligned	Exonic	Intronic	Intergenic	Overlapping Exon	Reads	Reads mapped	% Reads mapped
SNF2KO_01	1.25	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	97.5%
SNF2KO_02	1.82	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.2%
SNF2KO_03	2.54	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	97.1%
SNF2KO_04	1.07	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	98.6%
SNF2KO_05	1.17	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	97.0%
SNF2KO_06	1.70	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	96.9%
SNF2KO_07	1.64	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	96.8%
SNF2KO_08	1.70	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	96.8%
SNF2KO_09	2.11	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	97.1%
SNF2KO_10	3.11	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	97.1%
SNF2KO_11	1.12	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	96.5%
SNF2KO_12	1.00	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.5M	97.3%
WT_01	3.23	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	96.9%
WT_02	1.51	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.4%
WT_03	3.66	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	96.5%
WT_04	1.17	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.6M	97.5%
WT_05	1.93	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.6M	97.3%
WT_06	1.57	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.6M	97.8%
WT_07	1.82	0.5M	0.4M	0.0M	0.0M	0.0M	0.6M	0.6M	97.4%
WT_08	2.32	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.4%
WT_09	10.15	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.6%
WT_10	1.60	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.2%
WT_11	6.38	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.5%
WT_12	1.84	0.5M	0.3M	0.0M	0.0M	0.0M	0.6M	0.6M	97.0%

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
\|\|	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: 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`

QualiMap

RNASeq:

2.3

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.

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

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 ²⁴/₂₄ 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
SNF2KO_01	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_02	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_03	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_04	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_05	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_06	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_07	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_08	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_09	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_10	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_11	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
SNF2KO_12	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_01	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_02	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_03	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_04	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_05	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_06	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_07	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_08	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_09	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_10	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_11	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M
WT_12	0.6M	0.0M	0.0M	0.1M	0.0M	0.4M	0.2M	0.2M	0.4M	0.0M

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

Flagstat

This module parses the output from samtools flagstat

Created with MultiQC

Showing ²⁴/₂₄ 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)
SNF2KO_01	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_02	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_03	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_04	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_05	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_06	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_07	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_08	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_09	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_10	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_11	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
SNF2KO_12	0.6M	0.6M	0.5M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_01	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_02	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_03	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_04	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_05	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_06	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_07	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_08	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_09	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_10	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_11	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M
WT_12	0.6M	0.6M	0.6M	0.1M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M	0.0M

Expand table

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

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

Flagstat: Percentage of total

This module parses the output from samtools flagstat

Created with MultiQC

Showing ²⁴/₂₄ 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)
SNF2KO_01	100.0%	100.0%	97.5%	10.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_02	100.0%	100.0%	97.2%	15.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_03	100.0%	100.0%	97.1%	10.1%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_04	100.0%	100.0%	98.6%	20.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_05	100.0%	100.0%	97.0%	11.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_06	100.0%	100.0%	96.9%	10.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_07	100.0%	100.0%	96.8%	10.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_08	100.0%	100.0%	96.8%	13.7%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_09	100.0%	100.0%	97.1%	10.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_10	100.0%	100.0%	97.1%	10.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_11	100.0%	100.0%	96.5%	10.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
SNF2KO_12	100.0%	100.0%	97.3%	9.9%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_01	100.0%	100.0%	96.9%	13.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_02	100.0%	100.0%	97.4%	14.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_03	100.0%	100.0%	96.5%	13.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_04	100.0%	100.0%	97.5%	13.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_05	100.0%	100.0%	97.3%	14.2%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_06	100.0%	100.0%	97.8%	12.6%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_07	100.0%	100.0%	97.4%	13.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_08	100.0%	100.0%	97.4%	14.4%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_09	100.0%	100.0%	97.6%	13.8%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_10	100.0%	100.0%	97.2%	18.3%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_11	100.0%	100.0%	97.5%	16.5%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%
WT_12	100.0%	100.0%	97.0%	14.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%	0.0%

Expand table

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

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

Mapped reads per contig

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

Created with MultiQC

Software Versions

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

Group	Software	Version
QualiMap	RNASeq	`2.3`

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