https://github.com/karel-brinda/dymas
Dynamic Mapping Simulator.
https://github.com/karel-brinda/dymas
bioinformatics ngs read-mapping simulation
Last synced: 4 months ago
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Dynamic Mapping Simulator.
- Host: GitHub
- URL: https://github.com/karel-brinda/dymas
- Owner: karel-brinda
- Created: 2014-12-15T10:13:17.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2016-11-19T13:47:01.000Z (over 9 years ago)
- Last Synced: 2023-02-26T11:56:13.971Z (over 3 years ago)
- Topics: bioinformatics, ngs, read-mapping, simulation
- Language: HTML
- Homepage: http://arxiv.org/abs/1605.09070
- Size: 41.5 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 7
-
Metadata Files:
- Readme: readme.md
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README
# Dynamic Mapping Simulator
## Introduction
This is repository contains Dynamic Mapping Simulator and supporting information for the paper
K. Břinda, V. Boeva, G. Kucherov: *Dynamic read mapping and online consensus calling for better variant detection* ([arXiv:1605.09070](http://arxiv.org/abs/1605.09070)).
Dynamic mapping is mapping to a reference, which is continuously corrected according to alignments computed so far. *Dynamic Mapping Simulator* is a pipeline to simulate dynamic mapping using existing software to evaluate its benefits in comparison to standard *static mapping* and *iterative referencing*. For more details, see the paper.
## Simulation algorithm
Scheme of the simulation pipeline:
Reads are taken in the following way:
SM = static mapping, DM = dynamic mapping without remapping, DM-remap = dynamic mapping with remapping, IR = iterative referencing
## Structure of this repository
* [docs](docs) - supplementary materials (S1 and S2 files)
* [dymas](docs) - Dynamic Mapping Simulator (Python package)
* [experiments](docs) - all runs of all experiments
* [reports](reports) - generated reports
## Reports
## Replication of results
### Prerequisities
**Experiments**
* Python 3.3/3.4
* [OCOCO](http://github.com/karel-brinda/ococo)
* [RNFtools](http://github.com/karel-brinda/rnftools)
**Additional software for reports**
* GNU Parallel
* LaTeX
* Inkscape
* Gnuplot 5
#### Recommended way of installation using [Anaconda](https://www.continuum.io/downloads)
Environment installation:
```bash
conda create -y --name dymas \
-c bioconda \
python==3.4 \
snakemake samtools git cmake gnuplot ococo numpy biopython pysam==0.8.3
```
Environment activation:
```bash
source activate dymas
```
Installation of Python packages (in the activated environment)
```bash
pip install -r requirements.txt
```
### Replication steps
0. Install all required software and activate the corresponding Conda environment.
```bash
source activate dymas
```
1. Remove computed data
```bash
make clean
```
2. Download reference genomes
```bash
make -C experiments/exp0*
```
3. Run experiments (this step will take several hours)
```bash
make -C experiments -j 10
```
4. Generate reports
```bash
make -C reports -j 10
```