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Error correction of ONT transcript reads
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README.md

isONcorrect

isONcorrect is a tool for error-correcting Oxford Nanopore cDNA reads. It is designed to handle highly variable coverage and exon variation within reads and achieves about a 0.5-1% median error rate after correction (see preprint for details). It leverages regions shared between reads from different isoforms achieve low error rates even for low abundant transcripts. See preprint for details.

Processing and error correction of full-length ONT cDNA reads is acheved by the pipeline of running pychopper --> isONclust --> isONcorrect

isONcorrect is distributed as a python package supported on Linux / OSX with python v>=3.4. Build Status.

Table of Contents

INSTALLATION

Typical install time on a desktop computer is about 10 minutes with conda for this software.

Using conda

Conda is the preferred way to install isONcorrect.

  1. Create and activate a new environment called isoncorrect
conda create -n isoncorrect python=3 pip 
source activate isoncorrect
  1. Install isONcorrect and its dependency spoa.
pip install isONcorrect
conda install -c bioconda spoa
  1. You should now have 'isONcorrect' installed; try it:
isONcorrect --help

Upon start/login to your server/computer you need to activate the conda environment "isonclust" to run isONcorrect as:

source activate isoncorrect

Using pip

pip is pythons official package installer and is included in most python versions. If you do not have pip, it can be easily installed from here and upgraded with pip install --upgrade pip.

To install isONcorrect, run:

pip install isONcorrect

Then install spoa and include it in your path.

Downloading source from GitHub

Dependencies

Make sure the below listed dependencies are installed (installation links below). Versions in parenthesis are suggested as isONcorrect has not been tested with earlier versions of these libraries. However, isONcorrect may also work with earliear versions of these libaries.

In addition, please make sure you use python version >=3.

With these dependencies installed. Run

git clone https://github.com/ksahlin/isONcorrect.git
cd isONcorrect
./isONcorrect

Testing installation

You can verify successul installation by running isONcorrect on this small dataset of 100 reads. Assuming you have cloned this repository and the repository is found in /my/path/isONcorrect, simply run:

isONcorrect --fastq /my/path/isONcorrect/test_data/isoncorrect/0.fastq \
            --outfolder [output path]

Expected runtime for this test data is about 15 seconds. The output will be found in [output path]/corrected_reads.fastq where the 100 reads have the same headers as in the original file, but with corrected sequence. Testing the paralleized version (by separate clusters) of isONcorrect can be done by running

./run_isoncorrect --t 3 --fastq_folder /my/path/isONcorrect/test_data/isoncorrect/ \
                  --outfolder [output path]

This will perform correction on 0.fastq and 1.fastq in parallel. Expected runtime for this test data is about 15 seconds. The output will be found in [output path]/0/corrected_reads.fastq and [output path]/1/corrected_reads.fastq where the 100 reads in each separate cluster have the same headers as in the respective original files, but with corrected sequence.

USAGE

Running

For a single file with raw ONT cDNA reads, the following pipeline is recommended (bash script to run this provided below)

  1. Get full-length ONT cDNA sequences (pychopper (a.k.a. cdna_classifier))
  2. Cluster the full length reads where a cluster corresponds to a gene/gene-family (isONclust)
  3. Make fastq files of each cluster
  4. Correct individual clusters (isONcorrect)
  5. Optional (join reads from separate clusters back to a single file)

Below shows specific pipeline script to go from raw reads raw_reads.fq to corrected full-length reads all_corrected_reads.fq (please modify/remove arguments as needed).

#!/bin/bash

# isonano pipeline to get high quality full length reads from transcriots

cdna_classifier.py  raw_reads.fq outfolder/reads_full_length.fq \
                      [-t cores]  [-w outfolder/rescued.fq  \
                      -u outfolder/unclassified.fq  -S outfolder/stats.txt] 

isONclust  [--t cores]  --ont --fastq outfolder/reads_full_length.fq \
             --outfolder outfolder/clustering

isONclust write_fastq --N 1 --clusters outfolder/clustering/final_clusters.csv \
                      --fastq reads_full_length.fq --outfolder  outfolder/clustering/fastq_files 

run_isoncorrect [--t cores]  --fastq_folder outfolder/clustering/fastq_files  --outfolder /outfolder/correction/ 

# OPTIONAL BELOW TO MERGE ALL CORRECTED READS INTO ONE FILE
touch all_corrected_reads.fq
for f in in outfolder/clustering/fastq_files/*/corrected_reads.fastq; 
do 
  cat {f} >> all_corrected_reads.fq
done

isONcorrect does not need ONT reads to be full-length (i.e., produced by pychopper), but unless you have specific other goals, it is advised to run pychopper for any kind of downstream analysis to guarantee full-length reads.

Output

The output of run_isoncorrect is one file per cluster with identical headers to the original reads.

Parallelization

isONcorrect currently supports parallelization across cores on a node, but not across several nodes. There is a way to overcome this limitation if you have access to multiple nodes as follows. The run_isoncorrect step can be parallilized across n nodes by (in bash or other environment, e.g., snakemake) parallelizing the following commands

run_isoncorrect --fastq_folder outfolder/clustering/fastq_files  --outfolder /outfolder/correction/ --split_mod n --residual 0
run_isoncorrect --fastq_folder outfolder/clustering/fastq_files  --outfolder /outfolder/correction/ --split_mod n --residual 1
run_isoncorrect --fastq_folder outfolder/clustering/fastq_files  --outfolder /outfolder/correction/ --split_mod n --residual 2
...
run_isoncorrect --fastq_folder outfolder/clustering/fastq_files  --outfolder /outfolder/correction/ --split_mod n --residual n-1

Which tells isONcorrect to only work with distinct cluster IDs.

CREDITS

Please cite [1] when using isONcorrect.

  1. Kristoffer Sahlin, Botond Sipos, Phillip L James, Daniel Turner, Paul Medvedev, Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis (bioRxiv, 2020) Link.

Bib record:

LICENCE

GPL v3.0, see LICENSE.txt.

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