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 } }' | awk '{gsub(/[\]\[]/,"_")} $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:

HEPG2 association data

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:
  assignments:
    split_number: 30
assignments:
  assocBasic:
    bc_length: 15
    alignment_tool:
      tool: bbmap
      configs:
        sequence_length:
          min: 166
          max: 175
        alignment_start:
          min: 1
          max: 3
    FW:
      - data/SRR10800986_1.fastq.gz
    BC:
      - data/SRR10800986_2.fastq.gz
    REV:
      - data/SRR10800986_3.fastq.gz
    design_file: design.fa
    configs:
      default: {}

First we do a try run using snakemake -n option. The MPRAsnakeflow command is:

cd assoc_basic
conda activate mprasnakeflow
snakemake -c 1 --sdm conda --snakefile /home/user/MPRAsnakeflow/workflow/Snakefile --configfile /home/user/MPRAsnakeflow/resources/assoc_basic/config.yml -n -q --set-threads assignment_mapping_bwa=10

You should see a list of rules that will be executed. This is the summary:

Job stats:
job                                    count
-----------------------------------  -------
all                                        1
assignment_attach_idx                     60
assignment_bwa_ref                         1
assignment_check_design                    1
assignment_collect                         1
assignment_collectBCs                      1
assignment_fastq_split                     3
assignment_filter                          1
assignment_flagstat                        1
assignment_mapping_bwa_getBCs              30
assignment_idx_bam                         1
assignment_mapping_bwa                    30
assignment_merge                          30
assignment_statistic_assignedCounts        1
assignment_statistic_assignment            1
assignment_statistic_totalCounts           1
total                                    164

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 --sdm conda --snakefile /home/user/MPRAsnakeflow/workflow/Snakefile --configfile /home/user/MPRAsnakeflow/resources/assoc_basic/config.yml -n -q --set-threads assignment_mapping_bwa=10

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 15 rules showed above will run:

all

The overall all rule. Here is defined what final output files are expected.

assignment_attach_idx

Extract the index sequence and add it to the header.

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_bwa

Map the reads to the reference.

assignment_idx_bam

Index the BAM file

assignment_collect

Collect mapped reads into one BAM.

assignment_collectBCs

Get the barcodes.

assignment_flagstat

Run samtools flagstat. Results are in results/assignment/assocBasic/statistic/assignment/bam_stats.txt

assignment_statistic_totalCounts

Statistic of the total (unfiltered counts). Results are in results/assignment/assocBasic/statistic/total_counts.tsv

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.default.tsv.gz (default config).

assignment_statistic_assignedCounts

Statistic of filtered the assigned counts. Result is here results/assignment/assocBasic/statistic/assigned_counts.default.tsv (default)

assignment_statistic_assignment

Statistic of the filtered assignment. Result is here results/assignment/assocBasic/statistic/assignment.default.tsv.gz and a plot here results/assignment/assocBasic/statistic/assignment.default.png.

Results

All needed output files will be in the results/assignment/assocBasic folder. The final assignment is in results/assignment/assocBasic/assignment_barcodes.default.tsv.gz.

Note

Please note that for the experiment/count workflow you have to remove ambigous BCs. It is possible to retain ambigous BCs in the final file by configuring in the config file. But the default option will remove them from the final file.

Total file tree of the results folder:

results/
├── assignment
│   └── assocBasic
│       ├── aligned_merged_reads.bam
│       ├── aligned_merged_reads.bam.bai
│       ├── assignment_barcodes.default.tsv.gz
│       ├── barcodes_incl_other.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.default.tsv.gz
│           ├── assignment
│           │   └── bam_stats.txt
│           ├── assignment.default.png
│           ├── assignment.default.tsv.gz
│           └── total_counts.tsv.gz
└── logs