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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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Tool Citations

Please remember to cite the tools that you use in your analysis.

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About MultiQC

This report was generated using MultiQC, version 1.13

You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

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

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

This report has been generated by the Arcadia-Science/metagenomics analysis pipeline using the Nanopore workflow.

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Report generated on 2023-05-19, 13:05 UTC based on data in: /tmp/nxf.tq8He2WwdV

General Statistics

Showing ¹⁸/₁₈ rows and ⁷/₉ columns.

Sample Name	N50 (Kbp)	Assembly Length (Mbp)	Error rate	M Non-Primary	M Reads Mapped	% Mapped	% Proper Pairs	% MapQ 0 Reads	M Total seqs
EL12weeks			2.81%	0.2	1.0	97.5%	0.0%	4.3%	1.0
EL12weeks_polished	220.2Kbp	67.3Mbp
EL2weeks			4.04%	0.4	1.4	99.3%	0.0%	1.8%	1.4
EL2weeks_polished	1026.8Kbp	73.3Mbp
EL4weeks			2.46%	0.4	1.2	99.4%	0.0%	3.0%	1.2
EL4weeks_polished	832.1Kbp	62.5Mbp
OM2weeks			2.69%	0.3	1.8	99.5%	0.0%	2.6%	1.8
OM2weeks_polished	266.9Kbp	76.7Mbp
OM4weeks			2.74%	0.3	1.2	98.4%	0.0%	2.7%	1.2
OM4weeks_polished	210.7Kbp	34.0Mbp
OM8weeks			2.61%	0.2	1.2	98.5%	0.0%	1.7%	1.2
OM8weeks_polished	272.7Kbp	81.5Mbp
WH1month			4.51%	0.4	1.6	98.5%	0.0%	1.7%	1.6
WH1month_polished	91.6Kbp	99.3Mbp
WH2months			3.73%	0.5	1.7	98.4%	0.0%	2.0%	1.7
WH2months_polished	73.2Kbp	88.9Mbp
WH4months			3.11%	0.2	1.0	98.6%	0.0%	3.8%	1.0
WH4months_polished	195.8Kbp	84.6Mbp

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

Sort	Group	Column	Description	ID	Scale
\|\|	QUAST	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly (kilo base pairs)	`N50`	None
\|\|	QUAST	Assembly Length (Mbp)	The total number of bases in the assembly (Mbp).	`Total length`	None
\|\|	Samtools	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`error_rate`	None
\|\|	Samtools	M Non-Primary	Non-primary alignments (millions)	`non-primary_alignments`	read_count
\|\|	Samtools	M Reads Mapped	Reads Mapped in the bam file (millions)	`reads_mapped`	read_count
\|\|	Samtools	% Mapped	% Mapped Reads	`reads_mapped_percent`	None
\|\|	Samtools	% Proper Pairs	% Properly Paired Reads	`reads_properly_paired_percent`	None
\|\|	Samtools	% MapQ 0 Reads	% of Reads that are Ambiguously Placed (MapQ=0)	`reads_MQ0_percent`	None
\|\|	Samtools	M Total seqs	Total sequences in the bam file (millions)	`raw_total_sequences`	read_count

NanoStat

NanoStat various statistics from a long read sequencing dataset in fastq, bam or sequencing summary format.DOI: 10.1093/bioinformatics/bty149.

Fastq stats

NanoStat statistics from FastQ files.

Showing ⁹/₉ rows and ⁵/₇ columns.

Sample Name	Median length	Mean length	Read N50	Median Qual	Mean Qual	# Reads (K)	Total Bases (Mb)
EL12weeks_nanoplot_stats	2028 bp	3902 bp	6990 bp	20.4	20.2	1010.1	3941.9
EL2weeks_nanoplot_stats	2030 bp	2966 bp	3705 bp	15.7	15.6	1365.8	4051.3
EL4weeks_nanoplot_stats	2020 bp	3920 bp	6758 bp	19.9	19.7	1183.7	4640.5
OM2weeks_nanoplot_stats	2957 bp	5067 bp	8371 bp	18.4	18.0	1812.1	9181.5
OM4weeks_nanoplot_stats	2002 bp	3340 bp	4967 bp	19.0	18.8	1203.6	4020.6
OM8weeks_nanoplot_stats	2594 bp	4776 bp	8563 bp	20.0	19.7	1247.8	5959.5
WH1month_nanoplot_stats	3107 bp	4495 bp	6150 bp	17.1	16.8	1605.9	7218.9
WH2months_nanoplot_stats	1958 bp	2872 bp	3623 bp	17.7	17.5	1741.3	5000.4
WH4months_nanoplot_stats	1996 bp	3514 bp	5587 bp	20.1	19.7	995.7	3499.4

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Sort	Group	Column	Description	ID	Scale
\|\|	NanoStat	Median length	Median read length (bp)	`Median read length_fastq`	nucleotides
\|\|	NanoStat	Mean length	Mean read length (bp)	`Mean read length_fastq`	nucleotides
\|\|	NanoStat	Read N50	Read length N50	`Read length N50_fastq`	None
\|\|	NanoStat	Median Qual	Median read quality (Phred scale)	`Median read quality_fastq`	phred_score
\|\|	NanoStat	Mean Qual	Mean read quality (Phred scale)	`Mean read quality_fastq`	phred_score
\|\|	NanoStat	# Reads (K)	Number of reads (thousands)	`Number of reads_fastq`	long_read_count
\|\|	NanoStat	Total Bases (Mb)	Total bases (millions)	`Total bases_fastq`	base_count

Reads by quality

Read counts categorised by read quality (phred score).

Sequencing machines assign each generated read a quality score using the Phred scale. The phred score represents the liklelyhood that a given read contains errors. So, high quality reads have a high score.

Data may come from NanoPlot reports generated with sequencing summary files or alignment stats. If a sample has data from both, the sequencing summary is preferred.

QUAST

QUAST is a quality assessment tool for genome assemblies, written by the Center for Algorithmic Biotechnology.DOI: 10.1093/bioinformatics/btt086.

Assembly Statistics

Showing ⁹/₉ rows and ⁴/₄ columns.

Sample Name	N50 (Kbp)	L50 (K)	Largest contig (Kbp)	Length (Mbp)
EL12weeks_polished	220.2Kbp	0.0K	4096.3Kbp	67.3Mbp
EL2weeks_polished	1026.8Kbp	0.0K	3926.7Kbp	73.3Mbp
EL4weeks_polished	832.1Kbp	0.0K	3928.8Kbp	62.5Mbp
OM2weeks_polished	266.9Kbp	0.1K	3928.1Kbp	76.7Mbp
OM4weeks_polished	210.7Kbp	0.0K	3931.7Kbp	34.0Mbp
OM8weeks_polished	272.7Kbp	0.0K	4456.2Kbp	81.5Mbp
WH1month_polished	91.6Kbp	0.1K	3823.9Kbp	99.3Mbp
WH2months_polished	73.2Kbp	0.2K	1956.0Kbp	88.9Mbp
WH4months_polished	195.8Kbp	0.1K	3932.6Kbp	84.6Mbp

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

Sort	Group	Column	Description	ID	Scale
\|\|	QUAST	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly.	`N50`	None
\|\|	QUAST	L50 (K)	L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length.	`L50`	None
\|\|	QUAST	Largest contig (Kbp)	The size of the largest contig of the assembly	`Largest contig`	None
\|\|	QUAST	Length (Mbp)	The total number of bases in the assembly.	`Total length`	None

Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

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).

Alignment metrics

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

Arcadia-Science/metagenomics Software Versions

are collected at run time from the software output.

Process Name	Software	Version
`CHECK_SAMPLESHEET`	`python`	`3.9.5`
`CUSTOM_DUMPSOFTWAREVERSIONS`	`python`	`3.10.6`
	`yaml`	`6.0`
`FLYE`	`flye`	`2.9-b1768`
`MEDAKA`	`medaka`	`1.4.4`
`METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS`	`metabat2`	`2.15`
`MINIMAP2_ALIGN`	`minimap2`	`2.24-r1122`
`MINIMAP2_INDEX`	`minimap2`	`2.24-r1122`
`NANOPLOT`	`nanoplot`	`1.41.0`
`PORECHOP_ABI`	`porechop_abi`	`0.5.0`
`PRODIGAL`	`pigz`	`2.6`
	`prodigal`	`2.6.3`
`SAMTOOLS_STATS`	`samtools`	`1.16.1`
`SOURMASH_COMPARE`	`sourmash`	`4.6.1`
`SOURMASH_GATHER`	`sourmash`	`4.6.1`
`SOURMASH_SKETCH`	`sourmash`	`4.6.1`
`SOURMASH_TAXANNOTATE`	`sourmash`	`4.6.1`
`Workflow`	`Arcadia-Science/metagenomics`	`1.0dev`
	`Nextflow`	`23.04.1`

Arcadia-Science/metagenomics Workflow Summary

- this information is collected when the pipeline is started.

Core Nextflow options

runName: TMI_Nanopore_final_2
containerEngine: docker
launchDir: /
workDir: /nf-metagenomics/scratch/3rKCsU9t1AF4t3
projectDir: /.nextflow/assets/Arcadia-Science/metagenomics
userName: root
profile: docker
configFiles: /.nextflow/assets/Arcadia-Science/metagenomics/nextflow.config, /nextflow.config

Input/output options

input: s3://nf-metagenomics/metagenomics_samplesheets/TMI_nanopore_samplesheet_s3_uri.csv
outdir: s3://nf-metagenomics/TMI_nanopore_processing
platform: nanopore
email: [email protected]
sourmash_dbs: s3://nf-metagenomics/databases/sourmash-cover-dbs.csv
diamond_db: s3://software-databases/uniprot/2023-04-26-uniref90.dmnd
diamond_columns: qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qlen slen staxids sscinames stitle
multiqc_title: TMI Nanopore

Max job request options

max_memory: 400.GB

Other parameters

config_profile_description: N/A
config_profile_contact: N/A
config_profile_url: N/A
config_profile_name: N/A

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General Statistics

General Statistics: Columns

NanoStat

Fastq stats

NanoStat fastq: Columns

Reads by quality Help

QUAST

Assembly Statistics

Quast Table: Columns

Number of Contigs

Samtools

Percent Mapped Help

Alignment metrics

Arcadia-Science/metagenomics Software Versions

Arcadia-Science/metagenomics Workflow Summary

v1.13

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