isONclust 0.0.4

De novo clustering of long-read transcriptome reads.

isONclust
========

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](https://www.biorxiv.org/content/early/2018/11/06/463463).


isONclust is distributed as a python package supported on Linux / OSX with python v>=3.4 as of version 0.0.2 and above (due to updates in python's multiprocessing library). [](https://travis-ci.org/ksahlin/isONclust).

Table of Contents
=================

* [INSTALLATION](#INSTALLATION)
* [Using conda](#Using-conda)
* [Using pip](#Using-pip)
* [Downloading source from GitHub](#Downloading-source-from-github)
* [Dependencies](#Dependencies)
* [Testing installation](#testing-installation)
* [USAGE](#USAGE)
* [Iso-Seq](#Iso-Seq)
* [Oxford Nanopore](#Oxford-Nanopore)
* [Output](#Output)
* [Parameters](#Parameters)
* [CREDITS](#CREDITS)
* [LICENCE](#LICENCE)



INSTALLATION
----------------

### Using conda
Conda is the preferred way to install isONclust.

1. Create and activate a new environment called isonclust

```
conda create -n isonclust python=3 pip
source activate isonclust
```

2. Install isONclust

```
pip install isONclust
```
3. You should now have 'isONclust' installed; try it:
```
isONclust --help
```

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

### Using pip

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](https://pip.pypa.io/en/stable/installing/) and upgraded with `pip install --upgrade pip`.


### Downloading source from GitHub

#### Dependencies

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.
* [parasail](https://github.com/jeffdaily/parasail-python)
* [pysam](http://pysam.readthedocs.io/en/latest/installation.html) (>= v0.11)


With these dependencies installed. Run

```sh
git clone https://github.com/ksahlin/isONclust.git
cd isONclust
./isONclust
```

### Testing installation

You can verify successul installation by running isONclust on this [small dataset](https://github.com/ksahlin/isONclust/tree/master/test/sample_alz_2k.fastq). Simply download the test dataset and run:

```
isONclust --fastq [test/sample_alz_2k.fastq] --outfolder [output path]
```


USAGE
-------

IsONclust can be used with either Iso-Seq or ONT reads. It takes either a fastq file or ccs.bam file.



### Iso-Seq

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:

1. Make sure quality values is output when running the circular consensus calling step (CCS), by running `ccs` with the parameter `--polish`.
2. Run PacBio's Iso-Seq pipeline step 2 and 3 (primer removal and extraction of flnc reads) [isoseq3](https://github.com/PacificBiosciences/IsoSeq3/blob/master/README_v3.1.md).

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.

<!--- If not, flnc reads can be generated as follows. Raw pacbio subreads needs to be proccesed with `ccs` with the command `--polish` (to get quality values), followed by `lima`, and `isoseq3 cluster` to get the flnc reads. The flnc file is generated at the very beginning of the `isoseq3 cluster` algorithm and it can be used once its created (no need to wait for isoseq3 to finish). See full documentation on generating flnc reads at [isoseq3](https://github.com/PacificBiosciences/IsoSeq3). After these three comands are run isONclust can be run as follows -->
```
isONclust --isoseq --ccs <ccs.bam> --flnc <flnc.bam> --outfolder </path/to/output>
```
Where `<ccs.bam>` is the file generated from `ccs` and `<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 `--t`.


### Oxford Nanopore
isONclust needs a fastq file generated by an Oxford Nanopore basecaller.

```
IsoCon pipeline --ont --fastq <reads.fastq> --outfolder </path/to/output>
```
The argument `--ont` simply means `--k 13 --w 20`. These arguments can be set manually without the `--ont` flag. Specify number of cores with `--t`.

## Output

### TSV

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).

### Fastq

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 `final_clusters.tsv` file.


### Parameters

```
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)
```

CREDITS
----------------

Please cite [1] when using IsoCon.

1. Kristoffer Sahlin, Paul Medvedev (2018) "De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm", bioRxiv [Link](https://www.biorxiv.org/content/early/2018/11/06/463463).

LICENCE
----------------

GPL v3.0, see [LICENSE.txt](https://github.com/ksahlin/IsoCon/blob/master/LICENCE.txt).