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HaploConduct

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Haplotype-aware genome assembly toolkit

// repository documentation

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# HaploConduct

[![license](https://img.shields.io/badge/license-GPL%20v3.0-blue.svg)](http://www.gnu.org/licenses/)

## Synopsis

HaploConduct is a package designed for reconstruction of individual haplotypes
from next generation sequencing data, in particular Illumina. Currently,
HaploConduct consists of two methods: [SAVAGE](https://github.com/HaploConduct/HaploConduct/tree/master/savage) and
[POLYTE](https://github.com/HaploConduct/HaploConduct/tree/master/polyte).

### SAVAGE: Strain Aware VirAl GEnome assembly

SAVAGE is a computational tool for reconstructing individual
haplotypes of intra-host virus strains (a viral quasispecies) without
the need for a high quality reference genome. SAVAGE makes use of
either FM-index based data structures or ad-hoc consensus reference
sequence for constructing overlap graphs from patient sample data.
In this overlap graph, nodes represent reads and/or contigs, while
edges reflect that two reads/contigs, based on sound statistical
considerations, represent identical haplotypic sequence.
Following an iterative scheme, a new overlap assembly algorithm that
is based on the enumeration of statistically well-calibrated groups
of reads/contigs then efficiently reconstructs the individual
haplotypes from this overlap graph.

*If you are using SAVAGE, please cite our paper:*
De novo assembly of viral quasispecies using overlap graphs,
**J. Baaijens, A. Zine El Aabidine, E. Rivals, and A. Schoenhuth**,  
Genome Res. 2017; 27: 835-848,
[doi:10.1101/gr.215038.116](https://doi.org/10.1101/gr.215038.116).

### POLYTE: POLYploid genome fitTEr

POLYTE is a method for reconstructing haplotigs for diploid and polyploid
genome, specifically designed for a low-coverage setting where the ploidy of
the organism is known. POLYTE follows an iterative scheme where in each
iteration reads or contigs are joined, based on their interplay in terms of
an underlying haplotype-aware overlap graph. Along the iterations,
contigs grow while preserving their haplotype identity. POLYTE has been shown
to produce very accurate and complete assemblies, reaching high target genome
reconstruction values at extremely low error rates.

*If you are using POLYTE, please cite our paper:*
Overlap graph-based generation of haplotigs for diploids and polyploids,  
**J.A. Baaijens and A. Schoenhuth**,  
Bioinformatics 2019; 35(21): 4281--4289,
[doi:10.1101/378356](https://doi.org/10.1101/378356).

## Installation and dependencies

*Please note that HaploConduct is built for linux-based systems only.*

**The easiest and recommended way to install HaploConduct is through Conda, 
using the [Bioconda](https://bioconda.github.io/) channel:**
- First, install [miniconda](https://conda.io/miniconda.html)
- Setup your channels as described [here](https://bioconda.github.io/user/install.html#install-conda)
- Now install HaploConduct with one simple command: `conda install haploconduct`

**Installation through Conda (see above) takes care of all dependencies and allows you to
skip the steps below.**

SAVAGE and POLYTE make use of the same C++ codebase but have completely
different workflows (implemented in Python2).
The C++ part requires several boost libraries (boost::timer,
boost::system, and boost::program_options) and it needs a compiler
that supports [OpenMP](http://openmp.org/wp/) (such as `g++`).

For maximal clique enumeration, HaploConduct depends on the [quick-cliques package](https://github.com/darrenstrash/quick-cliques) which is already included.

For suffix-prefix overlap computations, HaploConduct uses the [rust-overlaps package](https://github.com/jbaaijens/rust-overlaps) for
computing suffix-prefix overlaps in de novo mode.

For reference-guided assembly, HaploConduct depends on [samtools](http://samtools.sourceforge.net) and the [bwa mem](http://bio-bwa.sourceforge.net/) aligner.

To summarize, please download and install the following dependencies:

* g++ with boost libraries
* [rust-overlaps](https://github.com/jbaaijens/rust-overlaps)
* [bwa mem](http://bio-bwa.sourceforge.net/)
* [samtools](http://samtools.sourceforge.net)
* python2.7 + scipy

*Each of these tools can also be installed using [Bioconda](https://bioconda.github.io/),
a distribution of bioinformatics software realized as a channel for the
versatile Conda package manager. This comes down to one simple command, creating a conda environment that has all required dependencies:
`conda create --name haploconduct-deps python=2.7 scipy bwa samtools rust-overlaps`
Then activate the environment with `source activate haploconduct-deps` and you're ready to go!*

Once all dependencies are installed, download the [latest release](https://github.com/HaploConduct/HaploConduct/releases) of the HaploConduct package, enter the repository and type `make`.

## Input

Both methods are designed for Illumina paired-end sequencing reads. SAVAGE
assumes typical viral sequencing data consisting of at least 10.000x total coverage;
POLYTE requires only 10x coverage per haplotype and achieves optimal performance
at 15-20x coverage per haplotype.

## Usage

*Note: the HaploConduct workflows are currently implemented in Python2, we plan
to move to Python3 in the future.*

For SAVAGE, run `haploconduct savage` and for POLYTE run `haploconduct polyte`.
Detailed user instructions can be found in the respective [SAVAGE](https://github.com/HaploConduct/HaploConduct/tree/master/savage) and
[POLYTE](https://github.com/HaploConduct/HaploConduct/tree/master/polyte) manuals.

## Contact

Please report any questions or issues [here](https://github.com/HaploConduct/HaploConduct/issues).

HaploConduct

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