Fast and accurate large multiple sequence alignments using root-to-leave regressive computation

This repository contains data, documentation, analysis and Nextflow workflow for the manuscript "Fast and accurate large multiple sequence alignments using root-to-leave regressive computation".

For details on how to use the Regressive Multiple Sequence Alignment method, see the T-Coffee documentation.

Credits

This workflow was written by Evan Floden (evanfloden) and Edgar(edgano) at the Center for Genomic Regulation (CRG).

The authors who contributed to the analysis and manuscript are:

• Edgar Garriga Nogales
• Paolo Di Tommaso
• Cedrik Magis
• Ionas Erb
• Hafid Laayouni
• Fyodor Kondrashov
• Evan Floden
• Cedric Notredame

Notebooks

This repository contains a series of Jupyter Notebooks that contain the steps for replicating the analysis, tables and figures in the manuscript.

The index jupyter notebook can be found here.

The notebook executes the pipeline, some steps of which require a lot of resources.

Pipeline

The pipeline for generating trees, alignments and performing the evaluations is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with a docker container making installation trivial and results highly reproducible.

Pipeline Quick Start

Make sure you have either docker/singularity installed or the required dependencies listed in the last section.

Install the Nextflow runtime by running the following command:

$ curl -fsSL get.nextflow.io | bash

When done, you can launch the pipeline execution by entering the command shown below:

$ nextflow run evanfloden/dpa-analysis

By default the pipeline is executed against the provided example dataset. Check the Pipeline parameters section below to see how enter your data on the program command line.

Containers

All the methods above are available in a Docker image on DockerHub here and the image is tested to be compatible with the Singularity.

The container also contains test data consisting of protein sequences, reference alignments and trees in the directory /test_data.

To launch the container interactively with Docker run:

docker run cbcrg/regressive-msa

To launch the container interactivly with Singularity run:

singularity shell docker://cbcrg/regressive-msa

Pipeline parameters

--seqs

• Specifies the location of the input fasta file(s).
• Multiple files can be specified using the usual wildcards (*, ?), in this case make sure to surround the parameter string value by single quote characters (see the example below)

Example:

$ nextflow run evanfloden/dpa-analysis --seqs '/home/seqs/*.fasta'

This will handle each fasta file as a seperate sample.

--refs

• Specifies the location of the reference aligned fasta file(s).

--trees

• Specifies the location of input tree file(s).

--align_method

• Specifies which alignment methods should be used.
• Options include: "CLUSTALO,MAFFT-FFTNS1,MAFFT-SPARSECORE,MAFFT-GINSI,PROBCONS,UPP"

--tree_method

• Specifies which guide-tree / clustering methods should be used.
• Options include: "CLUSTALO,MAFFT_PARTTREE"

--regressive_align

• Flag to generate regressive MSAs.
• See templates/dpa_align for the specific commands executed.

--stardard_align

• Flag to perform standard MSAs.
• Standard MSA is alignment where the guide-tree is provided as input.
• See templates/std_align for the specific commands executed.

--default_align

• Flag to perform default MSAs.
• Default MSA is alignment where the alignment software uses an internally generated guide-tree.
• See templates/default_align for the specific commands executed.

--evaluate

• Flag to perform evaluation of the alignments.
• Requires reference sequences to be provided with the --refs parameter.

--buckets

• List of bucket sizes or maximum size of the subMSAs in the regressive proceedure.
• Default value is "1000" sequences.

--output

• Location of the results.
• Default locations is results directory.