Using MultiQC to produce consolidated QC Reports
Byte Club, October 18 2017
Anna Battenhouse, CSSB & CCBB.
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.
For data, we will use some ATAC-seq datasets produced in Igor Ponomarev's lab in WCAAR. As a proof-of-concept for future work, they performed the ATAC-seq protocol on 5k and 50k cell nuclei from mouse brain, producing 2 paired-end datasets.
Login to ls5 at TACC. Execute these commands to set up access to the multiqc binary:
module load python export PATH="/work/projects/BioITeam/ls5/bin/multiqc-1.0:$PATH" export PYTHONPATH="/work/projects/BioITeam/ls5/lib/python2.7/annab-packages:$PYTHONPATH" # make sure it is working... multiqc --help |
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:
mkdir -p $SCRATCH/byteclub/multiqc/01_fastq cd $SCRATCH/byteclub/multiqc/01_fastq ln -s -f /work/01063/abattenh/projects/byteclub/multiqc/fastqc |
Now this is all it takes to produce a basic MultiQC report:
cd $SCRATCH/byteclub/multiqc/01_fastq multiqc . |
When this completes you'll see a new file and directory:
Here's what this basic FastQC report looks like: http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/01_basic.multiqc_report.html
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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MultiQC reports can be customized by creating a file called multiqc_config.yaml in the directory where you call multiqc.
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.
# 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 |
To catch up, just stage Anna's pre-made files:
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After saving this file, remove the previous MultiQC outputs and re-run the program:
cd $SCRATCH/byteclub/multiqc/01_fastq rm -rf multiqc_data multiqc_report.html multiqc . |
If all went well, you should now see a mqc_report.html file and a mqc_report_data directory. Your newly-generated mqc_report.html report file in should look like this (note the new title and header): http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/02_custom.mqc_report.html.
Here are a few tips for working with the MultiQC configuration file.
First stage some mm10 bowtie2 alignment data:
mkdir -p $SCRATCH/byteclub/multiqc/02_bowtie cd $SCRATCH/byteclub/multiqc/02_bowtie ln -s -f /work/01063/abattenh/projects/byteclub/multiqc/fastqc rsync -avrP /work/01063/abattenh/projects/byteclub/multiqc/bowtie2/ bowtie2/ |
Take a look at the contents of the bowtie2 directory. It contains typical output files from running Anna's align_bowtie2_illumina.sh alignment script.
MultiQC will look at all files in this directory looking for report formats it understands. Here, reports that MultiQC will recognize as-is include:
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!
Get ready to re-run MultiQC using the configuration created above.
mkdir -p $SCRATCH/byteclub/multiqc/02_bowtie cd $SCRATCH/byteclub/multiqc/02_bowtie cp ../01_fastq/multiqc_config.yaml . |
To catch up, just use Anna's pre-made files:
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Now run multiqc again:
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.
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.
Take a look at one of the <prefix>.dupinfo.txt files to see what might be going on. Below I've added line breaks to the command line info for clarity.
## htsjdk.samtools.metrics.StringHeader # picard.sam.markduplicates.MarkDuplicates INPUT=[brain_5k_nuclei.sort.bam] OUTPUT=brain_5k_nuclei.sort.dup.bam METRICS_FILE=brain_5k_nuclei.dupinfo.txt ASSUME_SORTED=true VALIDATION_STRINGENCY=LENIENT MAX_SEQUENCES_FOR_DISK_READ_ENDS_MAP=50000 MAX_FILE_HANDLES_FOR_READ_ENDS_MAP=8000 SORTING_COLLECTION_SIZE_RATIO=0.25 TAG_DUPLICATE_SET_MEMBERS=false REMOVE_SEQUENCING_DUPLICATES=false TAGGING_POLICY=DontTag REMOVE_DUPLICATES=false DUPLICATE_SCORING_STRATEGY=SUM_OF_BASE_QUALITIES PROGRAM_RECORD_ID=MarkDuplicates PROGRAM_GROUP_NAME=MarkDuplicates READ_NAME_REGEX=<optimized capture of last three ':' separated fields as numeric values> OPTICAL_DUPLICATE_PIXEL_DISTANCE=100 VERBOSITY=INFO QUIET=false COMPRESSION_LEVEL=5 MAX_RECORDS_IN_RAM=500000 CREATE_INDEX=false CREATE_MD5_FILE=false GA4GH_CLIENT_SECRETS=client_secrets.json ## htsjdk.samtools.metrics.StringHeader # Started on: Wed Jul 05 23:20:57 CDT 2017 ## METRICS CLASS picard.sam.DuplicationMetrics LIBRARY UNPAIRED_READS_EXAMINED READ_PAIRS_EXAMINED SECONDARY_OR_SUPPLEMENTARY_RDS UNMAPPED_READS UNPAIRED_READ_DUPLICATES READ_PAIR_DUPLICATES READ_PAIR_OPTICAL_DUPLICATES PERCENT_DUPLICATION ESTIMATED_LIBRARY_SIZE brain_5k_nuclei 0 28666117 0 16562322 0 12504025 902024 0.436 195 23118972 ## HISTOGRAM java.lang.Double BIN VALUE 1.0 1.016471 ... |
This is a standard metrics file produced by running Picard MarkDuplicates with a METRICS_FILE option specified. Note the INPUT bam filename: brain_5k_nuclei.sort.bam. MultiQC parses this report looking for the INPUT file, and uses it (minus the .bam suffix) as the sample name. So brain_5k_nuclei.sort.bam becomes sample name brain_5k_nuclei.sort.
The solution is to modify the metrics so that it has brain_5k_nuclei.bam as its INPUT option. This sounds straightforward but there are a couple of gocha's.
For the first part of the solution, we'll create a modified version of the metrics files, but not in the alignment directory, but in a new for_multiqc directory.
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:
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.
# 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' |
To catch up, just use Anna's pre-made files:
|
After making this config file modification, you can now run multiqc again:
cd $SCRATCH/byteclub/multiqc/02_bowtie; rm -rf mqc_report*; multiqc . |
The resulting report should look like this: , with a cleaned up General Statistics table.
You may have noticed that MultiQC lists its report sections in fairly random order. If you're Type-A like me, you'll want sections ordered to reflect the sequence of analyses performed. This can be controlled by adding a list of top_module entries.
# 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' |
To catch up, just use Anna's pre-made files:
|
After making this config file modification, you can now run multiqc again:
cd $SCRATCH/byteclub/multiqc/02_bowtie; rm -rf mqc_report*; multiqc . |
Producing a report like this: http://web.corral.tacc.utexas.edu/iyer/byteclub/multiqc/05_section_order.mqc_report.html, with a section order that more closely follows workflow processing steps.
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.)
To add a section for custom data:
See http://multiqc.info/docs/#configuration for more details about the structure of custom data sections in multiqc_config.yaml.
MultiQC supports these custom plot types:
Plot-type-specific plotting options can be specified in each custom data report's pconfig section. See http://multiqc.info/docs/#plotting-functions for more information. While this section is written for Python programming, the options listed in each plot type's "config" block can be used in the plot's plot's pconfig section (modulo the Python versus YAML formatting differences).
Here we'll create a linegraph report of insert sizes for the bowtie2 alignments.
We start with the <prefix>.insertsz.txt files produced by Anna's align_bowtie2_illumina.sh script. That file has both positive and "negative" insert sizes with read counts for all mates, proper pairs, and R1s and R2s individually. For a data file compatible with MultiQC's linegraph, we want only two columns: the positive insert size and count of proper pairs with that size (negative sizes are redundant since each proper pair will have one positive and one negative size entry of the same magnitude).
So a bit of command line reformatting is needed to produce files for MultiQC, which we will save in our for_multiqc directory.
cd $SCRATCH/byteclub/multiqc/02_bowtie/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 |
Next we edit the multiqc_config.yaml configuration file to add appropriate custom data sections:
# 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' |
x
To catch up, just use Anna's pre-made files:
|
Then the usual...
cd $SCRATCH/byteclub/multiqc/02_bowtie; 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!
Below are descriptions of two projects I've assisted with lately using MultiQC to help pull together visualizations assessing experiment quality.
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. |
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.
Igor Ponomarev's lab (in WCAAR) performed the ATAC-seq protocol on 5k and 50k cell nuclei from mouse brain, producing 2 paired-end datasets.
The Marcotte lab is working on a deep mutational screening project of a human gene transformed into yeast as an amplicon on a plasmid. Here, the gene is MVK, a gene in the yeast cholesterol biosynthesis pathway. The hsMVK gene is amplified with an error-prone polymerase to produce point mutations. Both the native yeast gene and the human ortholog (with which it shares no sequence similarity) are under on/off promoter control. The idea is to compare the mutations that accumulate in the active hsMVK gene, after many growth cycles, with a background in which the hsMVK gene is present but not active (the yeast MVKis doing the work) to see which mutations are favored or disfavored. As part of this project, Riddhiman Garge produced 19 datasets.