Skip to main content

CamoTSS: Detection alternative TSS in single cells

Project description

pypi

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= $download/Homo_sapiens.GRCh38.105.chr_test.gtf
fastaFile = $download/genome.fa
bamFile= $download/possorted_genome_bam_filtered.bam
cellbarcodeFile=$download/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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

CamoTSS-0.1.7.tar.gz (20.8 kB view details)

Uploaded Source

File details

Details for the file CamoTSS-0.1.7.tar.gz.

File metadata

  • Download URL: CamoTSS-0.1.7.tar.gz
  • Upload date:
  • Size: 20.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for CamoTSS-0.1.7.tar.gz
Algorithm Hash digest
SHA256 c0996506d6ddf7b1418bdc3842d70662a7ca4e36d65b5e06baae89374ce86e77
MD5 428bf46decefcf479e61a10f522631be
BLAKE2b-256 f36383929393bc24e6c9365d2ad05fda819fdf0c14bf413784a2bcb285a0af0f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page