Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/zyxue/kleat

Cleavage site prediction via de novo assembly
https://github.com/zyxue/kleat

alternative-polyadenylation cleavage-sites de-novo-assembly rna-seq transcriptome-assembly

Last synced: about 1 month ago
JSON representation

Cleavage site prediction via de novo assembly

Awesome Lists containing this project

README 

        
======

KLEAT

======



.. image:: https://img.shields.io/pypi/v/kleat.svg

        :target: https://pypi.python.org/pypi/kleat



.. image:: https://img.shields.io/travis/zyxue/kleat.svg

        :target: https://travis-ci.org/zyxue/kleat



.. image:: https://coveralls.io/repos/github/zyxue/kleat/badge.svg

        :target: https://coveralls.io/github/zyxue/kleat



.. image:: https://readthedocs.org/projects/kleat/badge/?version=latest

        :target: https://kleat.readthedocs.io/en/latest/?badge=latest

        :alt: Documentation Status



Cleavage site prediction via de novo assembly.



**Note**: this is a reimplementation from scratch of the following paper (PMID: 25592595_),



.. _25592595: https://www.ncbi.nlm.nih.gov/pubmed/25592595



- Birol I, Raymond A, Chiu R, Nip KM, Jackman SD, Kreitzman M, et al. KLEAT:

  cleavage site analysis of transcriptomes. Pac Symp Biocomput. World

  Scientific. 2015;:347–58.



KLEAT works by



1. Collect polyA evidences (suffix, bridge, link, and blank) and their

   supporting clvs per contig

2. Aggregate polyA evidence per clv identified by (seqname, strand, clv)

3. Find the closest annotated clv per predicted clv and calculate the distance in between.



Suffix was named tail in the original paper, but tail collides with tail of a

bridge contig, which could be confused, so it is renamed to suffix in the code

base.



Blank indicates the contig without any indication of polyA, but could still

support predicting cleavage site since if a transcript is expressed, then a

cleavage site likely exists nearby.



..

   memo: adding hyperlink to a sentence is really awkward in rst!



..

   * Documentation: https://kleat.readthedocs.io.



Install

--------



KLEAT (>3.0.0) supports only Python3 (>=py34). A few key packages include

pysam_, pandas_, scikit-learn_.



.. _pysam: https://github.com/pysam-developers/pysam

.. _pandas: https://github.com/pandas-dev/pandas

.. _scikit-learn: https://github.com/scikit-learn/scikit-learn



First, it's recommended to create a virtual environment, using either

conda_



.. _conda: https://conda.io/miniconda.html



.. code-block:: bash



   conda create -p venv-kleat python=3

   source activate venv-kleat/



or pip_ + virtualenv_



.. _pip: https://github.com/pypa/pip

.. _virtualenv: https://github.com/pypa/virtualenv



.. code-block:: bash



   pip install virtualenv # skip this step if virtualenv is available already

   virtualenv venv-kleat

   . venv-kleat/bin/activate



Then install kleat with pip



.. code-block:: bash



   pip install git+https://github.com/zyxue/kleat.git#egg=kleat



Features

--------



* Suffix (previously known as tail), bridge, link, blank

* Search PAS hexamer on the contig

* Hardclip regions are considered, too, and well tested.

* Process all chromosomes in parallel, and parallized other steps as much as

  possible (e.g. aggregate polyA evidence per clv)



Usage

-----



* Inputs to `--contig-to-genome` and `--reads-to-contigs` should both be sorted

  and indexed with samtools_.



.. _samtools: http://samtools.sourceforge.net/



Notes on result interpretation

------------------------------



`ctg_hex_pos`, the distance between contig PAS hexamer and clv is not

interpretable in terms of reference genome because there might be insertion and

deletion, but `ctg_hex_dist`, the distance between contig PAS hexamer and clv is

interpretation.

  

We decided not to remove this column, and leave as a sanity check/indication of

indels. Indels can also be inferred from the difference between `ctg_hex_dist` and

`ref_hex_dist` if they exist.



`ctg_hex_pos` may become especially useful when the ref_hex is not found, as it

can be used as a rough estimate of the location of the hexamer, helpful for

locating the PAS hexamer on the contig in a genome browser (e.g. IGV), quickly.



Development

-----------



.. code-block:: bash



   virtualenv venv

   . venv/bin/activate

   pip install -r requirements_dev.txt

   python setup.py develop



.. code-block::



   kleat --help



   usage: kleat [-h] -c CONTIGS_TO_GENOME -r READS_TO_CONTIGS -f REFERENCE_GENOME

                -a KARBOR_CLV_ANNOTATION [-o OUTPUT] [-m OUTPUT_FORMAT]

                [-p NUM_CPUS] [--keep-pre-aggregation-tmp-file]

                [--bridge-skip-check-size BRIDGE_SKIP_CHECK_SIZE]

                [--cluster-first-then-aggregate]

                [--cluster-cutoff CLUSTER_CUTOFF]



   KLEAT: cleavage site detection via de novo assembly



   optional arguments:

     -h, --help            show this help message and exit

     -c CONTIGS_TO_GENOME, --contigs-to-genome CONTIGS_TO_GENOME

                           input contig-to-genome alignment BAM file

     -r READS_TO_CONTIGS, --reads-to-contigs READS_TO_CONTIGS

                           input read-to-contig alignment BAM file

     -f REFERENCE_GENOME, --reference-genome REFERENCE_GENOME

                           reference genome FASTA file, if provided, KLEAT will

                           search polyadenylation signal (PAS) hexamer in both

                           contig and reference genome, which is useful for

                           checking mutations that may affect PAS hexmaer. Note

                           this fasta file needs to be consistent with the one

                           used for generating the read-to-contig BAM alignments

     -a KARBOR_CLV_ANNOTATION, --karbor-clv-annotation KARBOR_CLV_ANNOTATION

                           the annotated clv pickle formatted for karbor with

                           (seqname, strand, clv, gene_ids, gene_names) columns

                           this file is processed from GTF annotation file

     -o OUTPUT, --output OUTPUT

                           output tsv file, if not specified, it will use prefix

                           output, and the extension depends on the value of

                           --output-format. e.g. output.csv, output.pickle, etc.

     -m OUTPUT_FORMAT, --output-format OUTPUT_FORMAT

                           also support tsv, pickle (python)

     -p NUM_CPUS, --num-cpus NUM_CPUS

                           parallize the step of aggregating polya evidence for

                           each (seqname, strand, clv)

     --keep-pre-aggregation-tmp-file

                           specify this if you would like to keep the tmp file

                           before aggregating polyA evidence per cleavage site,

                           mostly for debugging purpose

     --bridge-skip-check-size BRIDGE_SKIP_CHECK_SIZE

                           the size beyond which the clv is predicted be on the

                           next matched region. Otherwise, clv is predicted to be

                           at the edge of the prevision match. The argument is

                           added because inconsistent break points are observed

                           between read (softclip as in r2c alignment) and contig

                           (boundry between BAM_CMATCH and BAM_CREF_SKIP)

     --cluster-first-then-aggregate

                           the default approach is aggregate_polya_evidence ->

                           filter -> cluster, if this argument is specified, then

                           the order becomes cluster -> aggregate_polya_evidence

                           -> filter. The idea is that by clustering first,

                           neighbouring clvs would result in stronger polyA

                           evidence signal, but preliminary results show that it

                           does not make a big difference. Also, note that if the

                           data is noisy, cluster before filter would further

                           decrease the clv resolution since single-linkage

                           culstering would combine those noisy clvs in to the

                           clvs with real signal

     --cluster-cutoff CLUSTER_CUTOFF

                           the cutoff for single-linkage clustering



To uninstall



.. code-block:: bash



   python setup.py develop --uninstall



Debug instruction

-----------------



For a particular contig, you could insert pdb such as below



.. code-block::



    @@ -32,6 +32,11 @@ def collect_polya_evidence(c2g_bam, r2c_bam, ref_fa, csvwriter, bridge_skip_chec

             if contig.is_unmapped:

                 continue



    +        if contig.query_name == "" and contig.reference_name == "chrX":

    +            pass

    +        else:

    +            continue

    +

             ascs = []           # already supported clvs

             rec = process_suffix(

                 contig, r2c_bam, ref_fa, csvwriter)



Zero-based index

----------------



Every index is 0-based, including ascii visualization such as



.. code-block::



   Symbols:

   --: ref_skip

   //: hardclip at right

   \\: hardclip at left

   __: deletion

   ┬ : insertion

    └: softclip at left

    ┘: softclip at right



   Abbreviation:

    cc: ctg_clv, clv in contig coordinate

    rc: ref_clv, clv in reference coordinate



   icb: init_clv_beg, initialized beginning index in contig coordinate (for - strand clv)

   irb: init_ref_beg, initialized beginning index in reference coordinate (for - strand clv)



   ice: init_clv_end, initialized end index in contig coordinate (for + strand clv)

   ire: init_ref_end, initialized end index in reference coordinate (for + strand clv)



    TTT

      └AT

    89012 <- one coord (0-based)

      1   <- ten coord



which is different from the display on IGV that is 1-based (although its

underlying system is still 0-based_).



.. _0-based: https://software.broadinstitute.org/software/igv/IGV.



Some key concepts in the code:



- ctg_clv: clv in contig coordinate including clipped regions and indels



- gnm_clv: or ref_clv. clv in genome coordinate



- gnm_offset: ctg_clv converted genome coordinate with proper handling of skips,

clips, indels, so that gnm_offset is addable to the genome coordinate directly.



Credits

-------



This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.



.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage