CamoTSS for alternative TSS analysis in single cells

Installation

You can install from this GitHub repository for latest (often development) version by following command line

pip install -U git+https://github.com/StatBiomed/CamoTSS

In either case, add --user if you don't have the write permission for your Python environment.

Quick start

Download test file

You can download test file from figshare.

Here, you can download some large file include genome.fa, possorted_genome_bam_filtered.bam.

Alternatively, you can also download the reference genome fasta file from Ensembl or Genecode or website of 10x Genomics.

Run CamoTSS

Here are three modes in CamoTSS : TC+CTSS , TC and CTSS.

When you run TC+CTSS mode, you will get TC result and then get the CTSS result based on the TC.

When you run TC mode, you will only get the TC result.

The TC+CTSS and TC mode have the same required files.

The --outdir is the only required parameter for CTSS mode. But the outdir should include output of TC.

If you want to run CTSS mode, you must based on the output of TC.

You can run CamoTSS TC+CTSS mode by using test file according to the following code.

For the remaining modes, you can check this document.

#!/bin/bash
gtfFile= $CamoTSS/test/Homo_sapiens.GRCh38.105.chr_test.gtf
fastaFile = $download/genome.fa
bamFile= $download/possorted_genome_bam_filtered.bam
cellbarcodeFile=$CamoTSS/test/cellbarcode_to_CamoTSS

CamoTSS --gtf gtfFile --refFasta fastaFile --bam bamFile -c cellbarcodeFile -o CamoTSS_out --mode TC+CTSS

Alternative TSS or CTSS detecting

To identify alternative TSS usage or alternative CTSS usage, Brie2 (Huang & Sanguinetti, 2021) is recommend to be used.

Here, we provide an example exploiting BRIE2 to detect alterntive TSS/CTSS usage.

You can check it in our manual.

Detailed Manual

The full manual is here, including:

Preprocess

Run CamoTSS

Detect alternative TSS/CTSS

Reference

Hou, R., Hon, C. C., & Huang, Y. (2023). CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5'scRNA-seq data. bioRxiv, 2023-04.