stRainy

stRainy is a graph-based phasing algorithm, that takes a de novo assembly graph (in gfa format) and simplifies it by combining phasing information and graph structure.

Conda Installation

The recommended way of installing is though conda:

git clone https://github.com/katerinakazantseva/stRainy
cd stRainy
git submodule update --init
make -C submodules/Flye
conda env create -f environment.yml -n strainy

Note that if you use an M1 conda installation, you should run conda config --add subdirs osx-64 before installation. Find details here

Once installed, you will need to activate the conda environemnt prior running:

conda activate strainy
./strainy.py

Quick usage example

After successful installation, you should be able to run:

conda activate strainy
./strainy.py phase  -o out_strainy -b test_set/toy.bam -g test_set/toy.gfa -t 4 -m hifi 
./strainy.py transform  -o out_strainy -b test_set/toy.bam -g test_set/toy.gfa -t 4 -m hifi

Limitations

stRainy is under active development! The current version is optimized for a relatively simple bacterial communities (one or a few bacterial species, 2-5 strains each). Extending stRainy to larger metagenomes is a work in progress.

Input requirements

stRainy supports PacBio HiFi and Nanopore (Guppy5+) sequencing.

The inputs to metaPhase are:

GFA file (can be produced with metaFlye or minigraph) and
BAM file (reads aligned to the fasta reference generated from the GFA file).

How to get fasta from gfa and perform alignment (assuming ONT reads):

awk '/^S/{print ">"$2"\n"$3}’ assembly_graph.gfa > assembly_graph.fasta
minimap2 -ax map-ont assembly_graph.gfa reads.fastq | samtools sort -@4 -t 8 > assembly_graph.bam
samtools index assembly_graph.bam

Usage and outputs

strainy.py phase - performs reads clustering according to SNP positions using community detection approach

strainy.py transfom - transforms assembly graph

./strainy.py phase -o output_dir -b bam_file -g gfa_graph -m mode -t threads

Phasing stage clusters reads and produce csv files with read names and corresponding cluster names and BAM file wich visualise reads clustering

./strainy.py transform -o output_dir -b bam_file -g gfa_graph -m mode -t threads

Transform stage transform and simplify initial assembly graph, produce the final gfa file: transformed_after_simplification_merged.gfa

Parameters desciption

strainy.py [-h] [-s SNP] [-t THREADS] [-f FASTA] -o OUTPUT -b BAM -g GFA -m {hifi,nano} stage

positional arguments:
  stage                 stage to run: either phase or transform

optional arguments:
  -h, --help            show this help message and exit
  -s SNP, --snp SNP     vcf file
  -t THREADS, --threads THREADS
                        number of threads
  -f FASTA, --fasta FASTA
                        fasta file

required named arguments:
  -o OUTPUT, --output OUTPUT
                        output dir
  -b BAM, --bam BAM     bam file
  -g GFA, --gfa GFA     gfa file
  -m {hifi,nano}, --mode {hifi,nano}

Acknowledgements

Consensus function of stRainy is Flye

Community detection algorithm is Karate club

Credits

stRainy was originally developed at at Kolmogorov lab at NCI

Code contributors:

Ekaterina Kazantseva
Ataberk Donmez
Mikhail Kolmogorov

Citation

Ekaterina Kazantseva, Ataberk Donmez, Mihai Pop, Mikhail Kolmogorov. "stRainy: assembly-based metagenomic strain phasing using long reads" bioRxiv 2023, https://doi.org/10.1101/2023.01.31.526521

License

Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

jelber2 / stRainy