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SWORD - a highly efficient protein database search

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# SWORD

[![Latest GitHub release](https://img.shields.io/github/release/rvaser/sword.svg)](https://github.com/rvaser/sword/releases/latest)
[![Published in Oxford Bioinformatics](https://img.shields.io/badge/published%20in-Oxford%20Bioinformatics-blue.svg)](https://doi.org/10.1093/bioinformatics/btw445)

SWORD (Smith Waterman On Reduced Database) is a fast and sensitive software for protein sequence alignment. SWORD consists of two steps, first being a heuristic and the second being optimal alignment phase. In the first step, for each query, it reduces the database to A sequences that score best with the query, which are in the second step aligned to the query using [OPAL](https://github.com/Martinsos/opal) library. SWORD utilizes multithreading.

## DEPENDENCIES

### LINUX and MAC OS

Application uses following software:

1. SSE4.1 or higher
2. gcc 4.8+
3. cmake 3.2+

## INSTALLATION

### LINUX

To build SWORD run the following commands from your terminal:
```bash
git clone --recursive https://github.com/rvaser/sword.git sword
cd sword/
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make
```

After running make, an executable named `sword` will appear in the `build` directory.

#### Troubleshooting

If you have cloned the repository without `--recursive`, run the following commands:
```bash
git submodule init
git submodule update
```

The software should also run on MAC OS, but it was not tested in that environment.

## EXAMPLES

All examples assume that make has been run and that SWORD was successfully compiled.
Simplest protein FASTA database search can be executed using the following command:

```bash
./sword -i <query> -j <database>
```
This will run a search using the default, sensitive mode.

For the complete list of parameters and their descriptions run the following command:

```bash
./sword -h (or ./sword --help)
```

## Contact information

For additional information, help and bug reports please send an email to: robert.vaser@fer.hr.

## Acknowledgement

This work has been supported in part by Croatian Science Foundation under the project UIP-11-2013-7353 and in part
by the Foundation of the Croatian Academy of Sciences and Arts.

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