https://github.com/koesterlab/microphaser
https://github.com/koesterlab/microphaser
phasing software variant-calling
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/koesterlab/microphaser
- Owner: koesterlab
- License: mit
- Created: 2017-11-22T16:03:53.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-11-18T15:52:06.000Z (over 3 years ago)
- Last Synced: 2023-03-01T12:50:58.677Z (over 3 years ago)
- Topics: phasing, software, variant-calling
- Language: Rust
- Size: 1.91 MB
- Stars: 11
- Watchers: 3
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Microphaser
Microphaser is a tool for phasing small tumor DNA sequences - e.g. coding for small peptides - in linear time.
It can be used in tumor neoantigen prediction to generate the neo-peptidome.
## Installation
Microphaser is available for installation via conda.
Use `conda install -c biconda microphaser` to easily install the current version.
## Usage
### Input
To use microphaser, you need the following input files:
* a sorted and indexed bam file containing mapped tumor reads
* a reference genome in fasta format
* a matching gene and transcript annotation in gtf format
* a bcf/vcf file containing germline and somatic variants, where somatic variants should be flagged with a ```SOMATIC``` INFO tag
* optional: a bcf/vcf file containing only germline variants
### Output
Microphaser returns three important files:
* two filtered fasta files containing all neopeptides and their wildtype counterparts for further use with MHC-binding prediction tools
* an info file in tsv format containing meta-information about every neopeptide
The info table consist of the following fields:
* id: peptide identifier as found in the fasta files
* transcript: Ensembl transcript name
* gene_id: Ensembl gene name
* gene_name: Gene symbol
* chrom: Chromosome
* offset: Position of the neopeptide on the chromosome
* freq: Frequency of the neopeptide occurring in all reads overlapping the peptide position
* depth: Read depth at the peptide position
* nvar: number of variants in the neopeptide
* nsomatic: number of somatic variants in the neopeptide
* nvariant_sites: number of variant sites in the range of the neopeptide
* nsomvariant_sites: number of somatic variant sites in the range of the neopeptide
* strand: Strand orientation of the transcript (forward or reverse)
* somatic_positions: Positions of the somatic variants in the neopeptide
* somatic_aa_change: Somatic Amino Acid changes occuring in the neopeptide
* germline_positions: Positions of germline variants in the neopeptide
* germline_aa_change: Germline Amino Acid changes occuring in the neopeptide
* normal_sequence: Nucleotide sequence of the wildtype peptide
* mutant_sequence: Nucleotide sequence of the neopeptide
### Run
Currently, microphaser consists of four different submodules:
- somatic (returns neopeptides and their corresponding normal peptides)
- normal (returns all normal peptides of the patient)
- build_reference (returns a binary file representing the patients normal peptidome)
- filter (compares neopeptides against the normal peptidome and removes self-similar candidates)
You can run microphaser like this:
Phasing of the tumor reads and variants:
```
microphaser somatic tumor.bam -r reference.fasta -b all_variants.bcf -t neopeptides.info.tsv -n peptides.wt.fasta < annotation.gtf > peptides.mt.fasta
```
Generation of the patients healthy peptidome:
```
microphaser normal normal.bam -r reference.fasta -b germline_variants.bcf < annotation.gtf > healthy_peptides.fasta
```
Building the reference binary file of the healthy peptidome:
```
microphaser build_reference -r healthy_peptides.fasta -o peptides.bin > peptides.translated.fasta
```
Filtering the neopeptide candidates from subcommand ```microphaser somatic```:
```
microphaser filter -r peptides.bin -t neopeptides.info.tsv -o neopeptides.filtered.info.tsv -n normal_peptides.filtered.fasta > neopeptides.filtered.fasta
```
## Authors
* Jan Forster (https://github.com/jafors)
* Johannes Köster (https://koesterlab.github.io)