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kantorlab / hivmmer

Alignment and variant-calling pipeline for Illumina HIV sequences.
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hivmmer

An alignment and variant-calling pipeline for Illumina deep sequencing of HIV-1, based on the probabilistic aligner HMMER.

Pipeline steps

  1. Constructs an amino acid profile Hidden Markov Model (pHMM) from a multiple sequence alignment of all HIV-1 Group M amino acid sequences publicly available in the Los Alamos HIV Sequence Database for the a given gene or region of the HIV genome.
  2. Filters the NGS data, retaining reads with length >75 and mean PHRED quality score > 25, and consolidates duplicate sequences (with hivmmer-filter). The number of duplicates are tracked to enable correct inference of frequencies later in the pipeline.
  3. Translates each de-duplicated sequence into all six possible frames (forward and reverse), retaining only the translated sequences that contain no stop codons (with hivmmer-translate).
  4. Aligns the translated reads to the reference pHMM with hmmsearch, producing a multiple sequence alignment of translated reads.
  5. Constructs a sample-specific amino acid pHMM from the multiple sequence alignment of translated reads.
  6. Repeats the HMMER alignment against the sample-specific pHMM for increased sensitivity.
  7. Maps the translated amino acid coordinates in the alignment to the original frame and coordinates in the nucleotide reads to construct a codon frequency table (adjusting the counts for duplicate reads; with hivmmer-codons).

Usage

hivmmer --id ID --fq1 FASTQ1 --fq2 FASTQ2 --ref REFERENCE [--cpu N]
        [-h|--help] [-v|--version]

ID specifies a name for the analysis that will be used as the basename for all output.

FASTQ1 and FASTQ2 are the forward and reverse Illumina reads.

Optionally, you can use N threads to speed-up the HMMER stages of the pipeline.

Installation

hivmmer requires Python 3.7

Quick install with Anaconda Python

On 64-bit Linux, it is also possible to install hivmmer using prebuilt packages from the kantorlab Anaconda channel.

First, install the Anaconda or Miniconda distribution of Python 3.

Once the conda command is in your PATH, hivmmer and all its dependencies can be installed into its own isolated conda environment with the single command:

conda create -c kantorlab -n hivmmer hivmmer

Once installed, activate the hivmmer conda environment with:

source activate hivmmer

This will place hivmmer and all its dependencies in your PATH.

We have primarily tested hivmmer on CentOS 6.8, but in theory it should run on any 64-bit Linux system with glibc >= 2.12.

All relevant conda recipes are available from the Kantor Lab's conda-recipes repository.

Quick install with Docker

On systems other than 64-bit Linux, you can run hivmmer via a Docker container.

First, visit the Docker website to download and install Docker for your host operating system.

Second, pull the pre-compiled hivmmer Docker image, which includes all dependencies, from DockerHub:

docker pull kantorlab/hivmmer

Each time you want to use Agalma, run the docker image with:

docker run -it kantorlab/hivmmer

This will launch a new Docker container with hivmmer, and provide an interactive prompt to access to the container.

Manual installation

hivmmer can be installed with pip using the included setup.py, and has the following dependencies on external tools (which must be in your PATH):

Note: PEAR source code is available under an academic license from https://www.h-its.org/en/research/sco/software/#NextGenerationSequencingSequenceAnalysis.

pHMM references

hivmmer comes with prepackaged amino acid profile Hidden Markov Models for the entire HIV genome, based on curated multiple-sequence alignments downloaded from the Los Alamos HIV Sequence Database.

Funding

Development of hivmmer is made possible through funding from the National Institutes of Health under awards R01AI108441, R01AI120792, R01AI147333, and the Providence/Boston Center for AIDS Research (P30AI042853).

Authors

Mark Howison (mhowison@ripl.org)

For bug reports and questions, please create an issue on Github.

License

Copyright 2018, Brown University, Providence, RI. All Rights Reserved.

Copyright 2019-2020, Innovative Policy Lab (d/b/a Research Improving People's Lives), Providence, RI. All Rights Reserved.

See LICENSE for full terms of use.