evanfloden/regressive-msa repository overview

⁠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.