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- MultiQC produces neat, interactive plots in an HTML file.
- So it can be used as a basic plotting tool for many kinds of reports and data, not just those produced by NGS tools!
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I recommend using Chrome to view MultiQC reports. The HTML reports generated by MultQC rely heavily on JavaScript and other dynamic web content scripting tools, and not all browsers support them equally well. |
Code Workshop
ATAC-seq is a transposon-insertion sequencing method where an engineered, activate transposon inserts in accessible ("open") chromatin. It is considered to be a much simpler protocol to standard DNase-seq, and requires less starting material as well.
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Setup to follow along
Login to ls5 or stampede at TACCTACCTACC. Execute these commands to set up access to the the multiqc binary:
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module load python export PATH="/work/projects/BioITeam/ls5/binopt/multiqc-1.0:$PATH" export PYTHONPATH="/work/projects/BioITeam/ls5/lib/python2.7/annab-packages:$PYTHONPATH" # make sure it is working... multiqc --help |
Produce a consolidated FastQC report
The FastQC took is great for producing detailed reports for every individual fastq file. For example, for Igor's 2 PE datasets, 4 reports are produced from running fastqc (http://web.corral.tacc.utexas.edu/iyer/igor/fastqc/).
The shortcoming is that you have to browse through all the individual reports one at a time, which can be tedious for large experiments.
This is where MultiQC's power comes in. You can point MultiQC to a directory where FastQC has been run and it will magically produce a consolidated report.
For example, logged in to ls5 at TACC, first stage a directory where FastQC has been run:
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module load python
export PATH="/work/projects/BioITeam/stampede/opt/multiqc-1.0:$PATH"
export PYTHONPATH="/work/projects/BioITeam/stampede/lib/python2.7/annab-packages:$PYTHONPATH"
# make sure it is working...
multiqc --help |
Produce a consolidated FastQC report
The FastQC took is great for producing detailed reports for every individual fastq file. For example, for Igor's 2 PE datasets, 4 reports are produced from running fastqc (http://web.corral.tacc.utexas.edu/iyer/igor/fastqc/).
The shortcoming is that you have to browse through all the individual reports one at a time, which can be tedious for large experiments.
This is where MultiQC's power comes in. You can point MultiQC to a directory where FastQC has been run and it will magically produce a consolidated report.
For example, logged in to ls5 at TACC, first stage a directory where FastQC has been run:
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mkdir -p $SCRATCH/byteclub/multiqc/01_fastq cd $SCRATCH/byteclub/multiqc/01_fastq ln -s -f /work/01063projects/abattenhBioITeam/projects/byteclub/multiqc/fastqc |
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cd $SCRATCH/byteclub/multiqc/01_fastq
multiqc . |
When this completes you'll see a new file and directory:
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To view the file you created in a web browser, it must be copied somwhere where a browser can open it. An easy way to do this is to copy it to your laptop like this, for example, changing the user name from abattenh and scratch path as appropriate.
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Add a few customizations
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Use your favorite text editor to create a a file called multiqc_config.yaml in your $SCRATCH/byteclub/multiqc/01_fastq directory as shown below. This will add report title lines and change the names of the MultiQC output files.
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To catch up, just use stage Anna's pre-made files:
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After saving this file, remove the previous MultiQC outputs and re-run the program:
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cd $SCRATCH/byteclub/multiqc/01_fastq
rm -rf multiqc_data multiqc_report.html
multiqc . |
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- Always use spaces (not tabs!) in the multiqc_config.yaml file.
- Make sure the file is saved with Unix line endings (not Windows or Mac).
- Pay attention to the output when running multiqc. It will tell you if there are issues parsing the config file.
- Always delete any previous MultiQC output files before running multiqc
- While their documentation says existing files will just be updated, I have seen MultiQC get confused when previous reports exist.
- It is a good idea to change the name of the MultiQC output files
- If output files with those names are not created, something went wrong!
- Consult example config files
- An example multiqc_config.yaml file: https://github.com/ewels/MultiQC/blob/master/multiqc_config_example.yaml
- All multiqc_config.yaml defaults: https://github.com/ewels/MultiQC/blob/master/multiqc/utils/config_defaults.yaml
Add reports from a bowtie2 alignment
First stage some mm10 bowtie2 alignment data:
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language | bash |
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- Avoid running multiqc on large complex directory trees.
- Instead, create a separate directory (or directory tree) only for MultiQC
- Copy or link the files you want MultiQC to look for there, and use it as MultiQC's target directory.
- MultiQC will run much faster and have fewer confusions.
- Instead, create a separate directory (or directory tree) only for MultiQC
Add reports from a bowtie2 alignment
First stage some mm10 bowtie2 alignment data:
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cd $SCRATCH/byteclub/multiqc rsync -avrP /work/01063projects/abattenhBioITeam/projects/byteclub/multiqc/bowtie2/ bowtie2/ |
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- <prefix>.flagstat.txt - output from running samtools flagstat
- <prefix>.idxstats.txt - output from running samtools idxstats
- <prefix>.dupinfo.txt - output from running Picard MarkDuplicates
Note that output from samtools flagstat and samtools idxstats will only be recognized by MultiQC if the files names include the words flagstat and idxstats. Fortunately, Anna's script created files with those names!
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To catch up, just use Anna's pre-made files:
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To catch up, just use Anna's pre-made files
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Now run multiqc again:
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mkdir -p $SCRATCH/byteclub/multiqc/02_bowtie
cd $SCRATCH/byteclub/multiqc /02_bowtie
lnrm -s -f /work/01063/abattenh/projects/byteclub/multiqc/fastqc rsync -avrP /work/01063/abattenh/projects/byteclub/multiqc/bowtie2/ bowtie2/ cp /work/01063/abattenh/projects/byteclub/multiqc/conf/02_simple_custom.yaml multiqc_config.yaml . |
Now run multiqc again:
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cd $SCRATCH/byteclub/multiqc/02_bowtie
rm -rf mqc_report*
multiqc . |
If all went well, you should now see a mqc_report.html file that looks like this: http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/03_bowtie.mqc_report.html, with new sections for Picard and Samtools reports.
Fix the Picard MarkDuplicates sample name
Notice there is something odd going on in the new General Statistics section. We see M Reads Mapped entries for samples called brain_50k_nuclei and brain_5k_nuclei, but % Dups entries for samples named brain_50k_nuclei.sort and brain_5k_nuclei.sort.
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rf mqc_report*
multiqc . |
If all went well, you should now see a mqc_report.html file that looks like this: http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/03_bowtie.mqc_report.html, with new sections for Picard and Samtools reports.
Fix the Picard MarkDuplicates sample name
Notice there is something odd going on in the new General Statistics section. We see M Reads Mapped entries for samples called brain_50k_nuclei and brain_5k_nuclei, but % Dups entries for samples named brain_50k_nuclei.sort and brain_5k_nuclei.sort.
To see where a General Statistics column comes from, hover over the column header. Doing this tells us that the the M Reads Mapped figures came from the samtools flagstat report, while the % Dups comes from Picard MarkDuplicates.
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mkdir -p $SCRATCH/byteclub/multiqc/02_bowtie/for_multiqc cd $SCRATCH/byteclub/multiqc/02_bowtie/for_multiqc for f in ../bowtie2/*.dupinfo.txt; do bn=`basename $f` pfx=${bn%%.dupinfo.txt} echo "$f - $pfx" cat $f | sed 's/[.]sort//g' > ${pfx}.dupmetrics.txt done |
Your $SCRATCH/byteclub/multiqc/02_bowtie/for_multiqc directory should have 2 files:
- brain_50k_nuclei.fixed.dupmetrics.txt
- brain_50k_nuclei.fixed.dupmetrics.txt
The final piece of the puzzle is to tell MultiQC to ignore the original <prefix>.dupinfo.txt files by modifying the multiqc_config.yaml file, adding a fn_ignore_files list entry.
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# Titles to use for the report. title: "ATAC-Seq QC Reports" subtitle: null intro_text: "MultiQC reports for Igor's ATAC-Seq proof-of-concept project." report_header_info: - Sequenced by: 'GSAF' - Job: 'JA17277' - Run: 'SA17121' - Setup: '2x150' # Change the output filenames output_fn_name: mqc_report.html data_dir_name: mqc_report_data # Ignore these files / directories / paths when searching for reports fn_ignore_files: - '*.dupinfo.txt' |
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To catch up, just use Anna's pre-made files:
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After making this config file modification, you can now run multiqc again:
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cd $SCRATCH/byteclub/multiqc/bowtie2/ bowtie2/ cp /work/01063/abattenh/projects/byteclub/multiqc/conf/04_fix_dupinfo.yaml multiqc_config.yaml mkdir for_multiqc cp /work/01063/abattenh/projects/byteclub/multiqc/for_multiqc/brain*.dupmetrics.txt for_multiqc/ |
After making this config file modification, you can now run multiqc again:
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cd $SCRATCH/byteclub/multiqc/02_bowtie; rm -rf mqc_report*; multiqc . |
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; rm -rf mqc_report*; multiqc . |
The resulting report should look like this: , with a cleaned up General Statistics table.
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# Titles to use for the report. title: "ATAC-Seq QC Reports" subtitle: null intro_text: "MultiQC reports for Igor's ATAC-Seq proof-of-concept project." report_header_info: - Sequenced by: 'GSAF' - Job: 'JA17277' - Run: 'SA17121' - Setup: '2x150' # Change the output filenames output_fn_name: mqc_report.html data_dir_name: mqc_report_data # Ignore these files / directories / paths when searching for reports fn_ignore_files: - '*.dupinfo.txt' # Modules that should come at the top of the report top_modules: - 'generalstats' - 'fastqc' - 'samtools' - 'picard' |
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To catch up, just use Anna's pre-made files:
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After making this config file modification, you can now run multiqc again:
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cd $SCRATCH/byteclub/multiqc/02_bowtie; rm -rf mqc_report*; multiqc . |
Producing a report like this: . ftphttp://gapdhweb.corral.icmbtacc.utexas.edu/misciyer/byteclub/multiqc/05_section_order.mqc_report.changed_section_order.htmlhtml, with a section order that more closely follows workflow processing steps.
About MultiQC custom data
When MultiQC does not know about data produced by a program it doesn't know about, it has a mechanisms for adding custom report sections. The simple way to do this is declaratively, (i.e., via configuation parameters) as described below. (You can also write a Python module for very fine-grained control, but that is a lot more work.)
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To add a section for custom data:
- Format the data appropriately
- MultiQC supports a number of data file formats (yaml, comma-separated values, etc.)
- I recommend using simple tab-delimited text files, with MultiQC's preferred .tsv extension.
- data can be provided as one file per sample (where the sample name is part of the file name)
- or as a single table-like file containing data for all samples
- MultiQC supports a number of data file formats (yaml, comma-separated values, etc.)
- Add two required custom data section entries in the multiqc_config.yaml configuration file
- a sp (search path) section for finding report data
- specifying a wildcard pattern, if data is supplied as one file per sample
- or a single file name for a consolidated data file
- each report has a user-named section under a single custom_data section.
- the required id attribute must be unique, and ties the custom_data, sp and custom_content sections
- other important attributes include description, file_format, and plot_type.
- a pconfig sub-section contains plot configuration options
- a sp (search path) section for finding report data
- Specify the ordering of the custom report section (optional)
- add a custom_content section order list entry
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So a bit of command line reformatting is needed to produce files for MultiQC, which we will save in our for_multiqc directory.
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mkdir -p ~/playtime/multiqc/atacseq/for_multiqc cd ~$SCRATCH/playtimebyteclub/multiqc/atacseq/for_multiqc for f in ../bowtie2/*.insertsz.txt; do bn=`basename $f` pfx=${bn%%.insertsz.txt} echo "$f - $pfx" tail -n +2 $f | grep -v -P '^-' | cut -f 1,3 > ${pfx}.bowtie2_isizes.tsv done |
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# Titles to use for the report. title: "ATAC-Seq QC Reports" subtitle: null intro_text: "MultiQC reports for Igor's ATAC-Seq proof-of-concept project." report_header_info: - Sequenced by: 'GSAF' - Job: 'JA17277' - Run: 'SA17121' - Setup: '2x150' # Change the output filenames output_fn_name: mqc_report.html data_dir_name: mqc_report_data # Ignore these files / directories / paths when searching for reports fn_ignore_files: - '*.dupinfo.txt' # Modules that should come at the top of the report top_modules: - 'generalstats' - 'fastqc' - 'samtools' - 'picard' # -------------------------------- # Custom data # -------------------------------- custom_content: order: - bowtie2_isize_section custom_data: bowtie2_isize: id: 'bowtie2_isize_section' section_name: 'Bowtie2 insert size' description: 'distribution for alignments (bowtie2 --local -X2000 --no-mixed --no-discordant)' file_format: 'tsv' plot_type: 'linegraph' pconfig: id: 'bowtie2_isize_plot' title: 'Insert sizes for proper pairs' xlab: 'Insert size' ylab: 'Count' sp: bowtie2_isize_section: fn: '*.bowtie2_isizes.tsv' |
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: 'Insert sizes for proper pairs'
xlab: 'Insert size'
ylab: 'Count'
sp:
bowtie2_isize_section:
fn: '*.bowtie2_isizes.tsv'
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To catch up, just use Anna's pre-made files:
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Then the usual...
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cd $SCRATCH/byteclub/multiqc; rm -rf mqc_report*; multiqc . |
Resulting in a report that includes our inset size distribution data the custom data section we configured: http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/06_custom_linegraph.mqc_report.html, with a new section called Bowtie2 insert size.
What's cool is that this "sawtooth" insert size distribution occurs because of the way transposons insert into the major groove of DNA at regular intervals. So this graph shows Igor that his ATAC-seq proof-of-concept experiment worked!
Adding custom bargraphs
Here we'll create two custom bargraph reports, one for bowtie2 mapping qualities and a second showing genome coverage of the alignments.
The data files for both reports are pretty simple, but it took a bit of scripting to create them. So let's just use pre-made copies:
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cd $SCRATCH/byteclub/multiqc
cp /work/projects/BioITeam/projects/byteclub/multiqc/07_custom_bargraph/for_multiqc/*mapq* for_multiqc/
cp /work/projects/BioITeam/projects/byteclub/multiqc/07_custom_bargraph/for_multiqc/*genomecov* for_multiqc/ |
There is one mapping quality histogram for each dataset, with category names in the 1st column and counts in the 2nd. The 50k dataset file looks like this:
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q0 137354
1-9 671546
10-19 1081868
20-29 1945926
30-39 1508496
40+ 12930272 |
There is just one data file for genome coverage. Unlike the per-sample files, it has a header, with dataset names in the 1st column, followed by category names and their counts in subsequent columns. (I've re-formatted the data below for readability, but remember that all .tsv file data must be tab-separated.)
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sample none 1-2 3-10 11-50 51+
5k_nuclei 2140984435 237947623 308665107 38729079 4545530
50k_nuclei 2175228345 351105871 186361275 17356704 819579 |
Here we edit the multiqc_config.yaml configuration file to add appropriate custom data sections:
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# Titles to use for the report.
title: "ATAC-Seq QC Reports"
subtitle: null
intro_text: "MultiQC reports for Igor's ATAC-Seq proof-of-concept project."
report_header_info:
- Sequenced by: 'GSAF'
- Job: 'JA17277'
- Run: 'SA17121'
- Setup: '2x150'
# Change the output filenames
output_fn_name: mqc_report.html
data_dir_name: mqc_report_data
# Ignore these files / directories / paths when searching for reports
fn_ignore_files:
- '*.dupinfo.txt'
# Modules that should come at the top of the report
top_modules:
- 'generalstats'
- 'fastqc'
- 'samtools'
- 'picard'
# --------------------------------
# Custom data
# --------------------------------
custom_content:
order:
- bowtie2_isize_section
- bowtie2_mapq_section
- genome_coverage_section
custom_data:
bowtie2_isize:
id: 'bowtie2_isize_section'
section_name: 'Bowtie2 insert size'
description: 'distribution for alignments (bowtie2 --local -X2000 --no-mixed --no-discordant)'
file_format: 'tsv'
plot_type: 'linegraph'
pconfig:
id: 'bowtie2_isize_plot'
title: 'Insert sizes for proper pairs'
xlab: 'Insert size'
ylab: 'Count'
bowtie2_mapq:
id: 'bowtie2_mapq_section'
section_name: 'Mapping quality'
description: 'distribution for aligned reads before filtering'
file_format: 'tsv'
plot_type: 'bargraph'
pconfig:
id: 'bowtie2_mapq_plot'
title: 'Mapping quality scores'
ymax: 60000000
genome_coverage:
id: 'genome_coverage_section'
section_name: 'Genome coverage'
description: 'of mapped inserts (bedtools genomecov -fs), grouped into coverage count catgories'
file_format: 'tsv'
plot_type: 'bargraph'
pconfig:
id: 'genome_coverage_plot'
title: 'Position coverage by coverage count category'
logswitch: True
stacking: null
sp:
bowtie2_isize_section:
fn: '*.bowtie2_isizes.tsv'
bowtie2_mapq_section:
fn: '*.mapq_histogram.tsv'
genome_coverage_section:
fn: 'combined_genomecov.tsv'
# file suffixes to remove when generating sample names...
extra_fn_clean_exts:
- type: 'replace'
pattern: '.mapq_histogram.tsv'
- type: 'replace'
pattern: '.genomecov.tsv' |
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To catch up, just use Anna's pre-made files:
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Then the usual...
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cd $SCRATCH/byteclub/multiqc; rm -rf mqc_report*; multiqc . |
Resulting in a report that includes our new Mapping quality and Genome coverage sections, that should look like this: http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/07_custom_bargraph.mqc_report.html.
Making MultiQC run faster and be less confused
By default, MultiQC scans all files in the analysis directory you specify. This can take quite a while for complex directory hierarchies with many files that will not be used by MultiQC.
Additionally, MultiQC can get confused when the same (or similar) data is found in different files, or in different directories.
To address these issues, it is a good practice to copy everything you want MultiQC to process into a single directory, then either specify just that directory on the multiqc command line (e.g. multiqc for_multiqc), or exclude other directories in the multiqc_config.yaml file.
For example, here we can stage all the reports we want MultiQC to process in our for_multiqc directory:
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cd $SCRATCH/byteclub/multiqc/for_fastqc
ln -s -f ../fastqc
cp -p ../bowtie2/*.flagstat.txt .
cp -p ../bowtie2/*.idxstats.txt . |
Your for_multiqc directory should now everything we want MultiQC to use:
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brain_50k_nuclei.bowtie2_isizes.tsv
brain_50k_nuclei.dupmetrics.txt
brain_50k_nuclei.flagstat.txt
brain_50k_nuclei.idxstats.txt
brain_50k_nuclei.mapq_histogram.tsv
brain_5k_nuclei.bowtie2_isizes.tsv
brain_5k_nuclei.dupmetrics.txt
brain_5k_nuclei.flagstat.txt
brain_5k_nuclei.idxstats.txt
brain_5k_nuclei.mapq_histogram.tsv
combined_genomecov.tsv
fastqc |
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To catch up, just use Anna's pre-made files:
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Run MultiQC again, but this time just point it
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cd ~$SCRATCH/playtimebyteclub/multiqc/atacseq; rm -rf mqc_report*; multiqc . |
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rf mqc_report*
multiqc for_multiqc |
Alternatively, you could exclude the bowtie2 directory entirely via a fn_ignore_dirs section list item in multiqc_config.yaml, like this:
References
Main MultiQC links
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Below are descriptions of two projects I've assisted with lately using MultiQC to help pull together visualizations assessing experiment quality.
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I recommend using Chrome to view MultiQC reports.
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- These example MultiQC reports below were generated by running the multiqc binary on a command line.
- After inspecting them locally (by just opening them as files in a web browser), they were copied to a web-accessible location to share with others. Here, that location is Iyer Lab's web-accessible directory on corral.
Igor Ponomarev ATAC-seq data
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