MPRAflow | parallelized tool for complete processing | Genomics library

This pipeline uses python2.7 and python3.6 and is set up to run on a Linux system. Two .yml files are provided to create the appropriate environments. The general environment with nextflow located in the home directory called environment.yml and a specific python 2.7 environment in the conf folder: mpraflow_py27.yml. The different environments are handled internally by nextflow. Therefore your compute node, where you start MPRAflow, have to have access to the internet. Install the the conda environment. The general conda environment is called MPRAflow. If you do not have access to the internet, you have to run the previous command on a node with internet. Afterwards you need to start nextflow too (see Steps to run the pipeline). After creation of the second conda environment by nextflow you can cancel it and start it on your internal node. Be aware that folders must have access on all nodes.
Create an 'experiment' csv in the format below, including the header. DNA_R1 or RNA_R1 is name of the gzipped fastq of the forward read of the DNA or RNA from the defined condition and replicate. DNA_R2 or RNA_R2 is the corresponding index read with UMIs (excluding sample barcodes) and DNA_R3 or RNA_R3 of the reverse read. If you do not have UMIs remove the columns DNA_R2 and RNA_R2 or leave them empty.
Create an 'experiment' csv in the format below, including the header. DNA_R1 or RNA_R1 is name of the gzipped fastq of the forward read of the DNA or RNA from the defined condition and replicate. DNA_R2 or RNA_R2 is the corresponding index read with UMIs (excluding sample barcodes) and DNA_R3 or RNA_R3 of the reverse read. If you do not have UMIs remove the columns DNA_R2 and RNA_R2 or leave them empty. Condition,Replicate,DNA_BC_F,DNA_UMI,DNA_BC_R,RNA_BC_F,RNA_UMI,RNA_BC_R condition1,1,cond1_rep1_DNA_FWD_reads.fastq.gz,cond1_rep1_DNA_IDX_reads.fastq.gz,cond1_rep1_DNA_REV_reads.fastq.gz,cond1_rep1_RNA_FWD_reads.fastq.gz,cond1_rep1_RNA_IDX_reads.fastq.gz,cond1_rep1_RNA_REV_reads.fastq.gz condition1,2,cond1_rep2_DNA_FWD_reads.fastq.gz,cond1_rep2_DNA_IDX_reads.fastq.gz,cond1_rep2_DNA_REV_reads.fastq.gz,cond1_rep2_RNA_FWD_reads.fastq.gz,cond1_rep2_RNA_IDX_reads.fastq.gz,cond1_rep2_RNA_REV_reads.fastq.gz condition2,1,cond2_rep1_DNA_FWD_reads.fastq.gz,cond2_rep1_DNA_IDX_reads.fastq.gz,cond2_rep1_DNA_REV_reads.fastq.gz,cond2_rep1_RNA_FWD_reads.fastq.gz,cond2_rep1_RNA_IDX_reads.fastq.gz,cond2_rep1_RNA_REV_reads.fastq.gz condition2,2,cond2_rep2_DNA_FWD_reads.fastq.gz,cond2_rep2_DNA_IDX_reads.fastq.gz,cond2_rep2_DNA_REV_reads.fastq.gz,cond2_rep2_RNA_FWD_reads.fastq.gz,cond2_rep2_RNA_IDX_reads.fastq.gz,cond2_rep2_RNA_REV_reads.fastq.gz
If you would like each insert to be colored based on different user-specified categories, such as "positive control", "negative control", "shuffled control", and "putative enhancer", to assess the overall quality the user can create a 'label' tsv in the format below that maps the name to category: insert1_name insert1_label insert2_name insert2_label The insert names must exactly match the names in the design FASTA file.
Run Association if using a design with randomly paired candidate sequences and barcodes conda activate MPRAflow nextflow run association.nf --fastq-insert "${fastq_prefix}_R1_001.fastq.gz" --design "ordered_candidate_sequences.fa" --fastq-bc "${fastq_prefix}_R2_001.fastq.gz" NOTE: This will run in local mode, please submit this command to your cluster's queue if you would like to run a parallelized version.
Run Count conda activate MPRAflow nextflow run count.nf --dir "bulk_FASTQ_directory" --e "experiment.csv" --design "ordered_candidate_sequences.fa" --association "dictionary_of_candidate_sequences_to_barcodes.p" Be sure that the experiment.csv is correct. All fastq files must be in the same folder given by the --dir option. If you do not have UMIs please use the option --no-umi. Please specify your barcode length and umi-length with --bc-length and --umi-length.
Run association saturation mutagenesis conda activate MPRAflow nextflow run association_saturationMutagenesis.nf --fastq-insert SRR8646911_1.fastq.gz --fastq-insertPE SRR8646911_2.fastq.gz --fastq-bc SRR8646911_3.fastq.gz --design TERT.fa --name TERT --outdir out --bc-length 20
Run saturation mutagenesis conda activate MPRAflow nextflow run saturationMutagenesis.nf --dir "directory_of_DNA/RNA_counts" --e "satMutexperiment.csv" --assignment "yourSpecificAssignmentFile.variants.txt.gz" Note The experiment file is different from the count workflow. It should contain the condition, replicate and filename of the counts, like: Condition,Replicate,COUNTS condition1,1,cond1_1_counts.tsv.gz condition1,2,cond1_2_counts.tsv.gz condition1,3,cond1_3_counts.tsv.gz condition2,1,cond2_1_counts.tsv.gz condition2,2,cond2_2_counts.tsv.gz condition2,3,cond2_3_counts.tsv.gz The count files can be generated by the count workflow, are named: <condition>_<replicate>_counts.tsv.gz and can be found in the outs/<condition>/<replicate> folder. They have to be copied or linked into the --dir folder.