Our tool is under active development and feedback is very much appreciated.

learnMSA: Learning and Aligning large Protein Families

Introduction

Multiple sequence alignment formulated as a statistical machine learning problem, where an optimal profile hidden Markov model for a potentially ultra-large family of protein sequences is learned from unaligned sequences and an alignment is decoded. We use a novel, automatically differentiable variant of the forward algorithm to train pHMMs via gradient descent.

Since version 2.0.0, learnMSA can utilize protein language models (--use_language_model) for significantly improved accuracy.

Features

• Aligns large numbers of protein sequences with state-of-the-art accuracy
• Can utilize protein language models (--use_language_model) for significantly improved accuracy
• Enables ultra-large alignment of millions of sequences
• GPU acceleration, multi-GPU support
• Scales linear in the number of sequences (does not require a guide tree)
• Memory efficient (depending on sequence length, aligning millions of sequences on a laptop is possible)
• Visualize a profile HMM or a sequence logo of the consensus motif

Current limitations

• Requires many sequences (in most cases starting at 1000, a few 100 might still be enough) to achieve state-of-the-art accuracy
• Only for protein sequences
• Increasingly slow for long proteins with a length > 1000 residues

Installation

Currently, learnMSA 2.0.0 is only supported for python <= 3.10, because of incompatibilities with TensorFlow. This is expected to change in the future.

Recommended way to install learnMSA:

conda create -n learnMSA_env python=3.10 pip
conda activate learnMSA_env
pip install learnMSA

Another quick option is to use mamba (if conda is too slow):

If you haven't done it yet, set up Bioconda channels first.

conda install mamba
mamba create -n learnMSA_env learnMSA
conda activate learnMSA_env

or simply with conda:

conda create -n learnMSA_env learnMSA
conda activate learnMSA_env

While in principle we attempt to support all tensorflow versions since 2.5.0, there are known incompatiblities with tf >= 2.12.0. We recommend tensorflow version 2.10.0 if there are no particular reasons to use something else.

Command line use after installing with Bioconda or pip

learnMSA -h

Align with language model support (recommended):

learnMSA -i INPUT_FILE -o OUTPUT_FILE --use_language_model

Without language model support:

learnMSA -i INPUT_FILE -o OUTPUT_FILE

We always recommend to run learnMSA with the --sequence_weights flag to improve accuracy. This requires mmseqs2 to be installed. You can use conda for this: conda install -c bioconda mmseqs2. Sequence weights (and thus the mmseqs2 requirement are turned off by default).

Since learnMSA version 1.2.0, insertions are aligned with famsa. This improves overall accuracy. The old behavior can be restored with the --unaligned_insertions flag.

To output a pdf with a sequence logo alongside the msa, use --logo. For a fun gif that visualizes the training process, you can use --logo_gif (attention, slows down training and should not be used for real alignments).

Interactive notebook with visualization:

Run the notebooks learnMSA_demo.ipynb or learnMSA_with_language_model_demo.ipynb with juypter.

Benchmark:

Publications

Becker F, Stanke M. learnMSA: learning and aligning large protein families. GigaScience. 2022