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Splice junction scoring tool

Project description

Italian Trulli

License: MIT version Downloads GitHub Downloads os

Splam is a splice junction recognition model based on a deep residual convolutional neural network that offers fast and precise assessment of splice junctions. It was trained on combined donor-acceptor pairs and focuses on a narrow window of 400 base pairs surrounding each splice site, inspired by the understanding that the splicing process primarily depends on signals within this region.

Why Splam❓#

  1. We need a tool to evaluate splice junctions & spliced alignments. Thousands of RNA-Seq datasets are generated every day, but there are no tools available for cleaning up spurious spliced alignments in these data. Splam addresses this problem!
  2. Splam-cleaned alignments lead to improved transcript assembly, which, in turn, may enhance all downstream RNA-Seq analyses, including transcript quantification, differential gene expression analysis, and more.

Who is it for❓#

If you are (1) doing RNA-Seq data analysis or (2) seeking a trustworthy way to evaluate splice junctions (introns), then Splam is the tool that you are looking for!


What does Splam do❓#

There are two main use case scenarios:

  1. Improving your alignment file. Splam evaluates the quality of spliced alignments and removes those containing spurious splice junctions. This significantly enhances the quality of downstream transcriptome assemblies [Link].

  2. Evaluating the quality of introns in your annotation file or assembled transcripts [Link].


Documentation#

📒 The full user manual is available here

Table of contents#


Installation#

Splam is on PyPi. This is the easiest installation approach. Check out all the releases here.

$ pip install splam

You can also install Splam from source

$ git clone https://github.com/Kuanhao-Chao/splam --recursive

$ cd splam/src/

$ python setup.py install

Quick Start#

Running Splam is simple. It only requires three lines of code!

See these examples on Google Colab:

Example 1: clean up alignment files (BAM)

$ cd test

# Step 1: extract splice junctions in the alignment file
$ splam extract -P SRR1352129_chr9_sub.bam -o tmp_out_alignment

# Step 2: score all the extracted splice junctions
$ splam score -G chr9_subset.fa -m ../model/splam_script.pt -o tmp_out_alignment tmp_out_alignment/junction.bed

#Step 3: output a cleaned and sorted alignment file
$ splam clean -o tmp_out_alignment

Example 2: evaluate annotation files / assembled transcripts (GFF)

$ cd test

# Step 1: extract introns in the annotation
$ splam extract refseq_40_GRCh38.p14_chr_fixed.gff -o tmp_out_annotation

# Step 2: score introns in the annotation
$ splam score -G chr9_subset.fa -m ../model/splam_script.pt -o tmp_out_annotation tmp_out_annotation/junction.bed

#Step 3: output statistics of each transcript
$ splam clean -o tmp_out_annotation

Example 3: evaluate mouse annotation files (GFF)

$ cd test

# Step 1: extract introns in the annotation
$ splam extract mouse_chr19.gff -o tmp_out_generalization

# Step 2: score introns in the annotation
$ splam score -A GRCm39_assembly_report.txt -G mouse_chr19.fa -m ../model/splam_script.pt -o tmp_out_generalization tmp_out_generalization/junction.bed

# Step 3: output statistics of each transcript
$ splam clean -o tmp_out_generalization

Scripts for Splam model training & analysis#

All the scripts for Splam training and data analysis are in this GitHub repository.


Citation#

Kuan-Hao Chao*, Alan Mao, Steven L Salzberg, Mihaela Pertea*, "Splam: a deep-learning-based splice site predictor that improves spliced alignments ", bioRxiv 2023.07.27.550754, doi: https://doi.org/10.1101/2023.07.27.550754, 2023

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