Basic assignment workflow
This example runs the assignment workflow on 5’/5’ WT MRPA data in the HEPG2 cell line from Klein J., Agarwal, V., Keith, A., et al. 2019.
Prerequirements
This example depends on the following data and software:
Installation of MPRAsnakeflow
Please install conda, the MPRAsnakeflow environment and clone the actual MPRAsnakeflow master branch. You will find more help under Installation.
Meta Data
It is necessary to get the ordered oligo array so that each enhancer sequence can be labeled in the analysis and to trim any adaptors still in the sequence, in this case we trim off 15bp from the end of each sequence
mkdir -p assoc_basic/data
cd assoc_basic/data
wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM4237nnn/GSM4237954/suppl/GSM4237954_9MPRA_elements.fa.gz
zcat GSM4237954_9MPRA_elements.fa.gz |awk '{ count+=1; if (count == 1) { print } else { print substr($1,1,171)}; if (count == 2) { count=0 } }' > design.fa
Reads
There is one set of association sequencing for this data, which contains a forward (CRS-forward), reverse (CRS-reverse), and index (barcode) read for DNA and RNA. These data must be downloaded. All data is publically available on the short read archive (SRA). We will use SRA-toolkit to obtain the data.
Note
You need 10 GB disk space to download the data!
conda install sra-tools
cd assoc_basic/data
fastq-dump --gzip --split-files SRR10800986
cd ..
For large files and unstable internet connection we reccommend the comand prefetch from SRA tools before running fastq-dump. This command is much smarter in warnings when something went wrong.
conda install sra-tools
cd assoc_basic/data
prefetch SRR10800986
fastq-dump --gzip --split-files SRR10800986
cd ..
Note
Please be sure that all files are downloaded completely without errors! Depending on your internet connection this can take a while. If you just want some data to run MPRsnakeAflow you can just limit yourself to one condition and/or just one replicate.
With
tree data
the folder should look like this:
data
├── design.fa
├── SRR10800986_1.fastq.gz
├── SRR10800986_2.fastq.gz
└── SRR10800986_3.fastq.gz
Here is an overview of the files:
Condition |
GEO Accession |
SRA Accession |
SRA Runs |
|---|---|---|---|
HEPG2-association: HEPG2 library association |
GSM4237954 |
SRX7474872 |
SRR10800986 |
MPRAsnakeflow
Now we are ready to run MPRAsnakeflow and create CRS-barcode mappings.
Run snakemake
Now we have everything at hand to run the count MPRAsnakeflow pipeline. We will run the pipeline directly in the assoc_basic folder. The MPRAsnakeflow workflow can be in a different directory. Let’s assume /home/user/MPRAsnakeflow.
First we have to configure the config file and save it to the assoc_basic folder. The config file is a simple text file with the following content:
---
global:
threads: 10
assignments:
split_number: 30
assignments:
assocBasic:
bc_length: 15
sequence_length:
min: 166
max: 175
alignment_start:
min: 1
max: 3
R1:
- data/SRR10800986_1.fastq.gz
R2:
- data/SRR10800986_2.fastq.gz
R3:
- data/SRR10800986_3.fastq.gz
reference: data/design.fa
configs:
exampleConfig:
min_support: 3
fraction: 0.7
unknown_other: true
ambiguous: true
exampleConfigTrueMatches:
min_support: 3
fraction: 0.7
unknown_other: false
ambiguous: false
First we do a try run using snakemake -n option. The MPRAsnakeflow command is:
cd assoc_basic
conda activate mprasnakeflow
snakemake -c 1 --use-conda --snakefile /home/user/MPRAsnakeflow/workflow/Snakefile --configfile config.yml -n
You should see a list of rules that will be executed. This is the summary:
Job stats:
job count min threads max threads
----------------------------------- ------- ------------- -------------
all 1 1 1
assignment_bwa_ref 1 1 1
assignment_fastq_split 3 1 1
assignment_filter 2 1 1
assignment_flagstat 1 1 1
assignment_getBCs 1 1 1
assignment_idx_bam 1 1 1
assignment_mapping 1 1 1
assignment_merge 30 10 10
assignment_statistic_assignedCounts 2 1 1
assignment_statistic_assignment 2 1 1
assignment_statistic_totalCounts 1 1 1
total 46 1 1
When dry-drun does not give any errors we will run the workflow. We use a machine with 30 threads/cores to run the workflow. Therefore split_number is set to 30 to parallize the workflow. Also we are using 10 threads for mapping (bwa mem). But snakemake takes care that no more than 30 threads are used.
snakemake -c 30 --use-conda --snakefile /home/user/MPRAsnakeflow/workflow/Snakefile --configfile /home/user/MPRAsnakeflow/resources/assoc_basic/config.yml
Note
Please modify your code when running in a cluster environment. We have an example SLURM config file here config/sbatch.yml.
If everything works fine the 12 rules showed above will run:
- all
The overall all rule. Here is defined what final output files are expected.
- assignment_bwa_ref
Create mapping reference for BWA from design file.
- assignment_fastq_split
Split the fastq files into n files for parallelisation. N is given by split_read in the configuration file.
- assignment_merge
Merge the FW,REV and BC fastq files into one. Extract the index sequence from the middle and end of an Illumina run. Separates reads for Paired End runs. Merge/Adapter trim reads stored in BAM.
- assignment_mapping
Map the reads to the reference.
- assignment_idx_bam
Index the BAM file
- assignment_flagstat
Run samtools flagstat. Results are in
results/assignment/assocBasic/statistic/assignment/bam_stats.txt- assignment_getBCs
Get the barcodes (not filtered). Results are in
results/assignment/assocBasic/barcodes_incl_other.sorted.tsv.gz- assignment_statistic_totalCounts
Statistic of the total (unfiltered counts). Results are in
results/assignment/assocBasic/statistic/total_counts.tsv.gz- assignment_filter
Filter the barcodes file based on the config given in the config-file. Results for this run are here
results/assignment/assocBasic/assignment_barcodes.exampleConfigTrueMatches.sorted.tsv.gz(exampleConfigTrueMatches) and hereresults/assignment/assocBasic/assignment_barcodes.exampleConfig.sorted.tsv.gz(exampleConfig)- assignment_statistic_assignedCounts
Statistic of filtered the assigned counts. Result is here
results/assignment/assocBasic/statistic/assigned_counts.exampleConfigTrueMatches.tsv.gz(exampleConfigTrueMatches) orresults/assignment/assocBasic/statistic/assigned_counts.exampleConfig.tsv.gz(exampleConfig)- assignment_statistic_assignment
Statistic of the filtered assignment. Result is here
results/assignment/assocBasic/statistic/assignment.exampleConfigTrueMatches.tsv.gzand a plot hereresults/assignment/assocBasic/statistic/assignment.exampleConfigTrueMatches.png. (also files are available for the configexampleConfig).
Results
All needed output files will be in the results/assignment/assocBasic folder. The final assignment is in results/assignment/assocBasic/assignment_barcodes.exampleConfigTrueMatches.sorted.tsv.gz or results/assignment/assocBasic/assignment_barcodes.exampleConfig.sorted.tsv.gz depeding on the filtering in the config file.
Note
Please note that for the experiment/count workflow you have to remove ambigous BCs. Therefore the file results/assignment/assocBasic/assignment_barcodes.exampleConfigTrueMatches.sorted.tsv.gz is the correct wone
Total file tree of the results folder:
results
├── assignment
│ └── assocBasic
│ ├── aligned_merged_reads.bam
│ ├── aligned_merged_reads.bam.bai
│ ├── assignment_barcodes.exampleConfig.sorted.tsv.gz
│ ├── assignment_barcodes.exampleConfigTrueMatches.sorted.tsv.gz
│ ├── barcodes_incl_other.sorted.tsv.gz
│ ├── reference
│ │ ├── reference.fa
│ │ ├── reference.fa.amb
│ │ ├── reference.fa.ann
│ │ ├── reference.fa.bwt
│ │ ├── reference.fa.dict
│ │ ├── reference.fa.fai
│ │ ├── reference.fa.pac
│ │ └── reference.fa.sa
│ └── statistic
│ ├── assigned_counts.exampleConfigTrueMatches.tsv.gz
│ ├── assigned_counts.exampleConfig.tsv.gz
│ ├── assignment
│ │ └── bam_stats.txt
│ ├── assignment.exampleConfig.png
│ ├── assignment.exampleConfigTrueMatches.png
│ ├── assignment.exampleConfigTrueMatches.tsv.gz
│ ├── assignment.exampleConfig.tsv.gz
│ └── total_counts.tsv.gz