- Introduction
- Dependencies
- Installation
- Running MMH
- About runtime
- Funding and acknowledgments
- References
Sequenced-based homology searches are usually performed with tools that search for similarity between proteins of interest and databases of sequences (e.g. BLAST) or with tools that search for known motifs (e.g. Pfam). However, if a biological sequence is highly divergent, sequenced-based tools may fail to find homology with known sequences or motifs. This lack of detectable homology renders functional assignment based on sequence similarity difficult, which can be problematic for groups of organisms with highly divergent sequences.
Homology searches against database motifs are inherently tied to the motifs themselves, and depending on how these motifs were generated, they may not accurately reflect the full spectrum of sequence diversity for the corresponding group of proteins. Therefore, by inverting the directionality of the search, i.e. by creating motifs from sets of divergent sequences and then searching these motifs against known proteins, we might be able to improve the sensitivity of these searches and/or detect new signals. This approach is similar to the reciprocity-scheme that is commonly used by the research community for BLAST searches, and should return similar hits when performed on proteins with known motifs.
We have implemented this approach in a simple to use pipeline. For this approach to work, at least a few datasets of proteins from (closely) related organisms should be available. The MMH pipeline leverages OrthoFinder, MAFTT and HMMER to identify single copy orthologs, align them, and generate hidden Markov models from the alignments. Those HMM models can then be searched against reference databases such as UniProt’s Swiss-Prot and trEMBL.
To download MMH from the command line (with git):
git clone https://github.com/PombertLab/MMH.git
cd MMH/
export PATH=$PATH:$(pwd)
export PATH=$PATH:$(pwd)/CoreHidden markov searches will be performed against a local copy of the UniProt Swiss-Prot and/or trEMBL databases. The UniProt databases can be downloaded with get_UniProt.pl as follows:
UNIPROT=~/UniProt ## Replace by desired download location
## Downloading the Swiss-Prot database
get_UniProt.pl \
-s \
-f $UNIPROT
## Downloading both the Swiss-Prot and trEMBL databases
get_UniProt.pl \
-s \
-t \
-f $UNIPROTNote that the trEMBL database is quite large (> 55 Gb as of June 2024). We recommend using the aria2 lightweight utility to download large files. If missing, get_UniProt.pl will use wget instead (or curl if the latter is missing as well).
Options for get_UniProt.pl are:
-s (--swiss) Download Swiss-Prot
-t (--trembl) Download trEMBL
-f (--folder) Download folder [Default: ./]
-l (--log) Print download information to log file
-dt (--dtool) Specify download tool: aria2c, wget or curl ## Tries to autodetect otherwise
-x (--connex) Number of aria connections [Default: 10]
-d (--decompress) Decompress downloaded files with gunzip ## trEMBL is huge, off by default
-v (--version) Show script version
The MMH pipeline consists of a few simple steps:
- It finds homologs (orthologs/paralogs) with OrthoFinder.
- It aligns homologs with MAFFT.
- It builds HMM models from these alignments with hmmbuild from HMMER.
- It searches these HMM models against a local copy of the UniProt databases.
- It reports matches as simple tab-delimited output files.
The MMH pipeline can be run via its run_mmh.pl master script. To run MMH, provide run_mmh.pl with the directory containing the FASTA files to query and the desired output directory as described below.
FASTA=~/FASTA ## Replace by FASTA file directory
OUTDIR=~/MMH ## Replace by desired output directory
DBLOC=~/UniProt ## Replace by UniProt database location
DB=swiss ## Desired database to query: swiss, trembl, both
run_mmh.pl \
-f $FASTA \
-o $OUTDIR \
-dbloc $DBLOC \
-db swissOptions for run_mmh.pl are:
-f (--fasta) Directory containing fasta files to create HMM models from
-o (--outdir) Output directory [Default: MMH]
-d (--db) Databases to query: swiss, trembl, or both [Default: swiss]
-l (--dbloc) Databases location
-t (--threads) Number of threads to use [Default: 16]
-e (--evalue) HMM search evalue cutoff [Default: 1e-10]
-v (--version) Show script version
The content of the results directory will look like this:
ls -lah MMH/
drwxr-xr-x 4 jpombert jpombert 4.0K Jun 3 14:19 Datasets
drwxr-xr-x 4 jpombert jpombert 4.0K Jun 3 14:19 HMM_motifs
drwxr-xr-x 4 jpombert jpombert 4.0K Jun 3 14:19 HMM_searches
drwxr-xr-x 3 jpombert jpombert 4.0K Jun 3 14:36 OrthoFinder
-rw-r--r-- 1 jpombert jpombert 11K Jun 3 14:36 hmmtable_sog.tsv
-rw-r--r-- 1 jpombert jpombert 1.9K Jun 3 14:36 mmh.log
The contents of the subdirectories are:
- DATASETS:
- Contains SOG/MOG datasets (in FASTA format)
- Contains tab-delimited SOG/MOG summaries (in TSV format)
- HMM_motifs:
- Contains HMM motifs (created with hmmbuild) for each SOG/MOG
- HMM_searches:
- Contains tables (.tbl) containing homology results for each SOG/MOG
- OrthoFinder:
- Contains the output from OrthoFinder searches
For ease of use, single copy orthogroups (sog) and multicopy orthogroups (mog) will be labelled distinctively in the files and subdirectories. Results will be contatenated in the corresponding tab-delimited hmmtable_{sog,mog}.tsv file(s). The TSV table(s) should look like this:
head -n 10 MMH/hmmtable_sog.tsv
Query Target E-value Product Genus Species OS descriptor
SOG00004 sp|P23968|VATO_YEAST 1.1e-18 V-type proton ATPase subunit c'' Saccharomyces Saccharomyces cerevisiae Saccharomyces cerevisiae (strain ATCC 204508 / S288c)
SOG00004 sp|Q9SLA2|VATO2_ARATH 1e-17 V-type proton ATPase subunit c''2 Arabidopsis Arabidopsis thaliana Arabidopsis thaliana
SOG00004 sp|Q9SZY7|VATO1_ARATH 1.1e-17 V-type proton ATPase subunit c''1 Arabidopsis Arabidopsis thaliana Arabidopsis thaliana
SOG00004 sp|O14046|VATO_SCHPO 1.1e-15 Probable V-type proton ATPase 20 kDa proteolipid subunit Schizosaccharomyces Schizosaccharomyces pombe Schizosaccharomyces pombe (strain 972 / ATCC 24843)
SOG00004 sp|G5EDB8|VATO_CAEEL 1.4e-15 V-type proton ATPase 21 kDa proteolipid subunit c'' Caenorhabditis Caenorhabditis elegans Caenorhabditis elegans
SOG00004 sp|Q99437|VATO_HUMAN 8.6e-15 V-type proton ATPase 21 kDa proteolipid subunit c'' Homo Homo sapiens Homo sapiens
SOG00004 sp|Q91V37|VATO_MOUSE 8.6e-15 V-type proton ATPase 21 kDa proteolipid subunit c'' Mus Mus musculus Mus musculus
SOG00004 sp|Q2TA24|VATO_BOVIN 1.8e-14 V-type proton ATPase 21 kDa proteolipid subunit c'' Bos Bos taurus Bos taurus
SOG00005 sp|P26659|RAD15_SCHPO 7.2e-233 General transcription and DNA repair factor IIH helicase subunit XPD Schizosaccharomyces Schizosaccharomyces pombe Schizosaccharomyces pombe (strain 972 / ATCC 24843) Note that due to its sheer size, running searches against the trEMBL databases will be slow. We recommend queries against the curated (but much smaller) Swiss-Prot database. For example, using the HMM models generated from the FASTA files in the Example/FASTA/ directory as queries, searches ran for 24 and 6573 seconds agaisnt the swiss and trembl databases, respectively (on an Intel i5-12500H mobile CPU).
This work was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number R15AI128627) to Jean-Francois Pombert. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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