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https://github.com/NCBI-Hackathons/rnaseqview
RNA-seq Viewer Team at the NCBI-assisted Boston Genomics Hackathon
https://github.com/NCBI-Hackathons/rnaseqview
Last synced: about 2 months ago
JSON representation
RNA-seq Viewer Team at the NCBI-assisted Boston Genomics Hackathon
- Host: GitHub
- URL: https://github.com/NCBI-Hackathons/rnaseqview
- Owner: NCBI-Hackathons
- License: cc0-1.0
- Created: 2016-04-14T17:08:56.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-07-24T15:36:19.000Z (almost 7 years ago)
- Last Synced: 2024-04-14T09:31:37.156Z (3 months ago)
- Language: JavaScript
- Size: 6.26 MB
- Stars: 36
- Watchers: 13
- Forks: 17
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-expression-browser - RNA-Seq Viewer
README
# rnaseqview
*Visualize genome-wide RNA-Seq data*
[](https://zenodo.org/badge/latestdoi/56255701)
The Genome-Wide RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI's [Sequence Read Archive](https://www.ncbi.nlm.nih.gov/sra) (SRA) and [Gene Expression Omnibus](https://www.ncbi.nlm.nih.gov/geo) (GEO) databases.
This repository contains a data pipeline written in Python. It extracts aligned RNA-Seq data from SRA or GEO and transforms it into a format used by [Ideogram.js](https://github.com/eweitz/ideogram), a JavaScript library for chromosome visualization. The minimal front-end allows users to see the distribution of genes across the entire human genome, and filter them by expression levels in the SRA/GEO sample or gene type.
# How to
Broadly, the pipeline does the following:
1. Get data for an [SRR accession](https://www.ncbi.nlm.nih.gov/books/NBK49167/#SRA_Analysis_BK.sec2) from NCBI SRA
2. Count reads for each gene and normalize expression values to [TPM units](http://www.ncbi.nlm.nih.gov/pubmed/22872506)
3. Get coordinates and type for each gene from [a GFF file](http://ftp.ncbi.nih.gov/genomes/Homo_sapiens/ARCHIVE/ANNOTATION_RELEASE.105/GFF/ref_GRCh37.p13_top_level.gff3.gz) in the [NCBI Homo sapiens Annotation Release](https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/105/)
4. Format coordinates and TPM values for each gene into JSON used by Ideogram.js
## Counter
### Counter dependencies
Read `counter/deps.txt` to know the tools needed to run. You can install all of them from the `bioconda` channel if you have an enviroment running.
An easy way to install conda:
```
wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
bash Miniconda-latest-Linux-x86_64.sh -b -p ~/install
```
### Counter how to
First, `cd counter`.
`counter.py` script gets gene expression stored in NCBI's SRA database.
Run `python counter.py` to show information on how to use the script. It accepts SAM/BAM files or SRA accession numbers like `SRR562646`.
`python $PATH/counter.py --inp SRR562645 --out SRR562645_counts`
This will connect to NCBI and gets the genome reference used for the alignment. In case there is no alignment information,
it will stop. It will download the gene annotation from NCBI; only GRCh37 and GRCh38 are supported right now.
You can use it like so:
`python $PATH/counter.py --inp SRR562645.bam --out SRR562645_counts --gtf GTF_file`
and it will use the given GTF to create the count data. GTF needs to have `ID` and `gene` in the attributes field.
### Counter outputs
`counter.py` creates 2 outputs:
* `*.tsv`: with absolute read counts per gene
* `*_norm.tsv`: with counts/kb per gene (TPM)
## Formatter
Run the formatter.py script which converts the output from the Counter to JSON format. Example
`formatter.py --type srr --lookup gene_lookup_GRCh37.tsv --inp SRR562645_counts_norm.tsv --out SRR562645.json`
# Visualization
After running the steps above, you can [plug the JSON data into Ideogram.js](https://github.com/NCBI-Hackathons/rnaseqview/blob/master/SRR562646.html#L159) to view and filter RNA-Seq data on the entire human genome.
