BFG-Y2H 0.1.2

Analysis scripts for BFG-Y2H data

BFG Y2H Analysis Pipeline

Requirements

• Python 3.7
• Bowtie 2 and Bowtie2 build

Files required

The pipeline requires reference files before running. They can be found on GALEN:

all reference files contain all the barcodes in fasta format
path: /home/rothlab/rli/02_dev/08_bfg_y2h/bfg_data/reference/

Before running the pipeline, you need to copy everything in these two folders to your designated directory.

Build new reference

If you need to build a new reference for your analysis, please follow:

1. You can refer to the create_fasta.py script to build the new fasta file
2. Make sure the name for the sequences follows the format: >*;ORF-BC-ID;*;up/dn. In other words, the ORF-ID should always be the second item, and the up/dn identifier should always be the last item. (see examples below)
3. Example sequences in output fasta file:

>G1;YDL169C_BC-1;7;up
CCCTTAGAACCGAGAGTGTGGGTTAAATGGGTGAATTCAGGGATTCACTCCGTTCGTCACTCAATAA

>G1;YMR206W_BC-1;1.0;DB;up
CCATACGAGCACATTACGGGGCTTGAGTTATATAGTCGATCCGGGCTAACTCGCATACCTCTGATAAC

>G09;56346_BC-1;24126.0;DB;dn
TCGATAGGTGCGTGTGAAGGATGTTCCCCCGGTCACCGGGCCAGTCCTCAGTCGCTCAGTCAAG

4. After making the fasta file, build index with bowtie2-build bowtie2-build filename.fasta filename
5. Update main.py to use the summary files you generated
   • Edit parse_input_files() to add a case

Running the pipeline

• Install from pypi (recommend): python -m pip install BFG-Y2H

• Install and build from github, the update.sh might need to be modified before you install

1. download the package from github
2. inside the root folder, run ./update.sh

1. Input arguments:

usage: bfg [-h] [--fastq FASTQ] [--output OUTPUT] --mode MODE [--alignment]
           [--ref REF] [--cutOff CUTOFF]

BFG-Y2H

optional arguments:
  -h, --help       show this help message and exit
  --fastq FASTQ    Path to all fastq files you want to analyze
  --output OUTPUT  Output path for sam files
  --mode MODE      pick yeast or human or virus or hedgy or LAgag
  --alignment      turn on alignment
  --ref REF        path to all reference files
  --cutOff CUTOFF  assign cut off

2. All the input fastq files should have names following the format: y|hADDBGFP(pre|med|high) (for human and yeast)

3. Run the pipeline on GALEN

# this will run the pipeline using slurm         
# all the fastq files in the given folder will be processed
# run with alignment 
bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --alignment --ref path/to/reference

# if alignment was finished, you want to only do read counts
bfg --fastq /path/to/fastq_files/ --output /path/to/output_dir/ --mode yeast/human/virus/hedgy --ref path/to/reference

Output files

• After running the pipeline, one folder will be generated for each group pair (yADDB)

• The folder called GALEN_jobs saves all the bash scripts submited to GALEN

• In the output folder for each group pair, we aligned R1 and R2 separately to the reference sequences for GFP_pre, GFP_med and GFP_high.

• *_sorted.sam: Raw sam files generated from bowtie2

• *_noh.csv: shrinked sam files, used for scoring

• *_counts.csv: barcode counts for uptags, dntags, and combined (up+dn)