isONclust is a tool for clustering either PacBio Iso-Seq reads, or Oxford Nanopore reads into clusters, where each cluster represents all reads that came from a gene. Output is a tsv file with each read assigned to a cluster-ID. Detailed information is available in preprint.
Table of Contents
Conda is the preferred way to install isONclust.
- Create and activate a new environment called isonclust
conda create -n isonclust python=3 pip source activate isonclust
- Install isONclust
pip install isONclust
- You should now have 'isONclust' installed; try it:
Upon start/login to your server/computer you need to activate the conda environment "isonclust" to run isONclust as:
source activate isonclust
To install isONclust, run:
pip install isONclust
pip will install the dependencies automatically for you.
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.
Downloading source from GitHub
Make sure the below listed dependencies are installed (installation links below). Versions in parenthesis are suggested as IsoCon has not been tested with earlier versions of these libraries. However, IsoCon may also work with earliear versions of these libaries.
With these dependencies installed. Run
git clone https://github.com/ksahlin/isONclust.git cd isONclust ./isONclust
You can verify successul installation by running isONclust on this small dataset. Simply download the test dataset and run:
isONclust --fastq [test/sample_alz_2k.fastq] --outfolder [output path]
IsONclust can be used with either Iso-Seq or ONT reads. It takes either a fastq file or ccs.bam file.
IsONclust works with full-lengh non-chimeric (flnc) reads that has quality values assigned to bases. The flnc reads with quality values can be generated as follows:
- Make sure quality values is output when running the circular consensus calling step (CCS), by running
ccswith the parameter
- Run PacBio's Iso-Seq pipeline step 2 and 3 (primer removal and extraction of flnc reads) isoseq3.
Flnc reads can be submitted as either a fastq file or bam file. A fastq file is created from a BAM by running e.g
bamtools convert -format fastq -in flnc.bam -out flnc.fastq. isONclust is called as follows
isONclust pipeline --isoseq --fastq <reads.fastq> --outfolder </path/to/output>
isONclust also supports older versions of the isoseq3 pipeline by taking the
ccs.bam file together with the
flnc.bam. In this case, isONclust can be run as follows.
isONclust --isoseq --ccs <ccs.bam> --flnc <flnc.bam> --outfolder </path/to/output>
<ccs.bam> is the file generated from
<flnc.bam> is the file generated from
isoseq3 cluster. The argument
--isoseq simply means
--k 15 --w 50. These arguments can be set manually without the
--isoseq flag. Specify number of cores with
isONclust needs a fastq file generated by an Oxford Nanopore basecaller.
IsoCon pipeline --ont --fastq <reads.fastq> --outfolder </path/to/output>
--ont simply means
--k 13 --w 20. These arguments can be set manually without the
--ont flag. Specify number of cores with
The output consists of a tsv file
final_clusters.tsv present in the specified output folder. In this file, the first column is the cluster ID and the second column is the read accession. For example:
0 read_X_acc 0 read_Y_acc ... n read_Z_acc
if there are n reads there will be n rows. Some reads might be singletons. The rows are ordered with respect to the size of the cluster (largest first).
isONclust can also print separate fastq files for each cluster with more than N reads (N is a parameter to the program). After clustering, simply run
isONclust write_fastq --N [int] --fastq <reads.fastq> --clusters <path/to/final_clusters.tsv> --outfolder </path/to/output>
This will print out separate fastq files in
</path/to/output> for all clusters with more than
[int] reads. The names of the files are the cluster IDs assigned by isONclust, and matches the ID's found in the
optional arguments: -h, --help show this help message and exit --version show program's version number and exit --fastq FASTQ Path to consensus fastq file(s) (default: False) --flnc FLNC The flnc reads generated by the isoseq3 algorithm (BAM file) (default: False) --ccs CCS Path to consensus BAM file(s) (default: False) --t NR_CORES Number of cores allocated for clustering (default: 8) --ont Clustering of ONT transcript reads. (default: False) --isoseq Clustering of PacBio Iso-Seq reads. (default: False) --k K Kmer size (default: 15) --w W Window size (default: 50) --min_shared MIN_SHARED Minmum number of minimizers shared between read and cluster (default: 5) --mapped_threshold MAPPED_THRESHOLD Minmum mapped fraction of read to be included in cluster. The density of minimizers to classify a region as mapped depends on quality of the read. (default: 0.7) --aligned_threshold ALIGNED_THRESHOLD Minmum aligned fraction of read to be included in cluster. Aligned identity depends on the quality of the read. (default: 0.4) --min_fraction MIN_FRACTION Minmum fraction of minimizers shared compared to best hit, in order to continue mapping. (default: 0.8) --min_prob_no_hits MIN_PROB_NO_HITS Minimum probability for i consecutive minimizers to be different between read and representative and still considered as mapped region, under assumption that they come from the same transcript (depends on read quality). (default: 0.1) --outfolder OUTFOLDER A fasta file with transcripts that are shared between samples and have perfect illumina support. (default: None)
Please cite  when using IsoCon.
- Kristoffer Sahlin, Paul Medvedev (2018) "De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm", bioRxiv Link.
GPL v3.0, see LICENSE.txt.