Proteinortho is a tool to detect orthologous genes within different species.
Input: Multiple fasta files (orange boxes) with many proteins/genes (circles). Output: Groups (*.proteinortho) and pairs (*.proteinortho-graph) of orthologs proteins/genes.
For doing so, it compares similarities of given gene sequences and clusters them to find significant groups. The algorithm was designed to handle large-scale data and can be applied to hundreds of species at one. Details can be found in (doi:10.1186/1471-2105-12-124). To enhance the prediction accuracy, the relative order of genes (synteny) can be used as additional feature for the discrimination of orthologs. The corresponding extension, namely PoFF (doi:10.1371/journal.pone.0105015), is already build in Proteinortho. The general workflow of proteinortho:
First an initial all vs. all comparison between all proteins of all species is performed to determine protein similarities (upper right image).
The second stage is the clustering of similar genes to meaningful co-orthologous groups (lower right image).
Connected components within this graph can be considered as putative co-orthologous groups in theory and are returned in the output (lower left image).
A more detailed list of all changes: CHANGELOG
Bug reports: Please have a look at chapter 2. first or send a mail to incoming+paulklemm-phd-proteinortho-7278443-issue-@incoming.gitlab.com. (please include the ‘parameter-vector’ that is printed for all errors) You can also send mails to lechner@staff.uni-marburg.de. Any suggestions, feedback and comments are welcome!
Proteinortho comes with precompiled binaries of all executables (Linux/x86) so you should be able to run perl proteinortho6.pl in the downloaded directory for Linux/x86. You could also move all executables to a local bin directory (e.g. with make install PREFIX=~/bin). If you cannot execute the src/BUILD/Linux_x86_64/proteinortho_clustering, then you have to recompile with make, see the section 2. Building and installing proteinortho from source.
conda install -c bioconda proteinorthoIf you need conda (see here) and the bioconda channel: conda config --add channels bioconda.
brew install proteinorthoIf you need brew (see here)
docker pull quay.io/biocontainers/proteinortho:TAGyou can find the TAG here (e.g. 6.0.23–hfd40d39_0).
how to docker (Click to expand)
First define a TAG with:
export TAG='put the version tag here'
To start a simple bash shell with the proteinortho container use:
docker run --rm -it quay.io/biocontainers/proteinortho:$TAG bash
Here you can start/use proteinortho. You can change “6.0.22–hfd40d39_0” with any tag/version that is available here. Sadly there is no “:latest” tag available …
This is neccessary if you want to access your local files:
docker run --rm --mount "type=bind,src=/home/$(id -un),dst=/home/$(id -un)" -u $(id -u):$(id -g) -it quay.io/biocontainers/proteinortho:$TAG bash
now you have your home directory mounted to /home/YOURNAME. (load your bashrc within the container : source /home/YOURNAME/.bashrc)
Simply go to the european galaxy server and search for proteinortho:
https://usegalaxy.euOr you can integrate proteinortho into your own galaxy instance using: proteinortho (iuc repository)
Disclamer: Be aware that this method usually lacks 6-12 months behind the latest version
The deb package can be downloaded here: unstable or stable. Afterwards the deb package can be installed with sudo dpkg -i proteinortho*deb.
Disclamer: Be aware that this method usually lacks 6-12 months behind the latest version (current version)
proteinortho is released to stable Debian 11 (2021), so you can install it with (sudo) apt install proteinortho
Proteinortho uses standard software which is often installed already or is part of then package repositories and can thus easily be installed. The sources come with a precompiled version of Proteinortho for 64bit Linux x86.
To run Proteinortho, you need: (Click to expand)
At least one of the following the following programs (default is diamond):
cpan install ... )To compile Proteinortho (linux/osx), you need: (Click to expand)
You need to have a working lapack library, check this e.g. with ‘dpkg –get-selections | grep lapack’. Install lapack e.g. with ‘apt-get install libatlas3-base’ or liblapack3.
If you dont have Lapack, then ‘make’ will automatically compiles an old lapack (v3.8.0) for you automatically as fallback !
Fetch the latest source code archive downloaded from hereor from here (Click to expand)
git clone https://gitlab.com/paulklemm_PHD/proteinortho
wget https://gitlab.com/paulklemm_PHD/proteinortho/-/archive/master/proteinortho-master.zip
tar -xzvf proteinortho*.tar.gz or unzip proteinortho*.zip : Extract the filescd proteinortho* : Change directory into the extracted foldermake clean && make : If you want to recompile Proteinortho. (For osx you need a newer g++ compiler to support multithreading, see below)make test : To make sure Proteinortho works as expected. The output should look like below (‘Make test output’).make install or make install PREFIX=~/bin for local installation.Install a newer g++ compiler for -fopenmp support (multithreading) with brew (get brew here https://brew.sh/index_de) ``` brew install gcc --without-multilib ``` Then you should have a g++-7 or whatever newer version that there is (g++-8,9,...). Next you have to tell make to use this new compiler with one of the following: ``` ln -s /usr/local/bin/gcc-7 /usr/local/bin/gcc ln -s /usr/local/bin/g++-7 /usr/local/bin/g++ ``` OR(!) specify the new g++ in 'make CXX=/usr/local/bin/g++-7 all'
[ 0%] Prepare proteinortho_clustering ... [ 20%] Building **proteinortho_clustering** with LAPACK (static/dynamic linking) [ 25%] Building **graphMinusRemovegraph** [ 50%] Building **cleanupblastgraph** [ 75%] Building **po_tree** [100%] Everything is compiled with no errors.
The compilation of proteinortho_clustering has multiple fall-back routines. If everything fails please look here Troubleshooting (proteinortho wiki).
Everything is compiled with no errors. [TEST] 1. basic proteinortho6.pl -step=2 tests [1/11] -p=blastp+ test: passed [2/11] -p=blastp+ synteny (PoFF) test: passed [3/11] -p=diamond test: passed [4/11] -p=diamond (--moresensitive) test (subparaBlast): passed [5/11] -p=lastp (lastal) test: passed [6/11] -p=topaz test: passed [7/11] -p=usearch test: passed [8/11] -p=ublast test: passed [9/11] -p=rapsearch test: passed [10/11] -p=blatp (blat) test: passed [11/11] -p=mmseqsp (mmseqs) test: passed [TEST] 2. -step=3 tests (proteinortho_clustering) [1/2] various test functions of proteinortho_clustering (-test): passed [2/2] Compare results of 'with lapack' and 'without lapack': passed [TEST] Clean up all test files... [TEST] All tests passed
If you have problems compiling/running the program go to Troubleshooting (proteinortho wiki).
proteinortho options <fasta file(s)>
one fasta file for each input species; at least two species are required
proteinortho is a tool to detect orthologous genes within different species.
Proteinortho assumes, that you have all your gene sequences in FASTA format either represented as amino acids or as nucleotides. The source code archive contains some examples, namely C.faa, E.faa, L.faa, M.faa located in the test/ directory. By default Proteinortho assumes amino acids sequences and thus uses diamond (-p=diamond) to compare sequences. If you have nucleotide sequences, you need to change this by adding the parameter -p=blastn+ (or some other algorithm). (In case you have only have NCBI BLAST legacy installed, you need to tell this too - either by adding -p=blastp or -p=blastn respectively.) The full command for the example files would thus be > proteinortho6.pl -project=test test/C.faa test/E.faa
test/L.faa test/M.faa. Instead of naming the FASTA files one by one, you could also use test/*.faa. Please note that the parameter -project=test is optional, for naming the output. With this, you can set the prefix of the output files generated by Proteinortho. If you skip the project parameter, the default project name will be myproject.
Open proteinorthoHelper.html in your favorite browser or visit lechnerlab.de/proteinortho online for an interactiv exploration of the different options of proteinortho.
Main parameters (can be used with – or -)
–project=name (default: myproject) prefix for all resulting file names
–inproject=name (default: same as –project) load data from this namespace instead (works with intermediate files for step=2 and blast-graph for step=3)
–cpus=number (default: all available) the number of processors to use (multicore/processor support)
–verbose={0,1,2} (default: 1) verbose level. 1:keeps you informed about the progress
–silent sets verbose level to 0.
–temp=directory(.) path to the temporary files
–force forces the recalculation of the blast results in any case in step=2. Also forces the recreation of the database generation in step=1
–clean removes all database-index-files generated by the -p algorithm afterwards
–step={0,1,2,3} (default: 0) 0 -> all. 1 -> prepare blast (build db). 2 -> run all-versus-all blast. 3 -> run the clustering.
(Show more information)
proteinortho test/*faa
# the following 3 commands are producing the same results as the command above proteinortho -step=1 test/faa proteinortho -step=2 test/faa proteinortho -step=3
–keep stores temporary blast results for reuse (proteinortho_cache_project directory). In a second run the intermediate blast results are loaded instead of calculated. You can adjust the parameters e.g. a more strict -e evalue cut off and write the output to a different namespace using –inproject.
(Show more information)
# 1. generate db files
proteinortho -step=1 -project=test -keep infile/*fasta
# 2. run the all-versus-all blast of some input files (infile/)
proteinortho -step=2 -project=test -keep infile/*fasta
# now you can insert more fasta files to infile/ and reuse everything computed
proteinortho -step=2 -project=test -keep infile/*fasta
# finally run clustering
proteinortho -step=3 -project=test -keep
–isoform={ncbi,uniprot,trinity} more information about –isoform
Merge isoforms to a single entity.
ncbi
isoforms are specified in ncbi style
---
>**ENSMUSP00000021091.8** pep chromosome:GRCm38:11:74673949:74724670:-1 **gene:ENSMUSG00000020745.15** transcript:ENSMUST00000021091.14 gene_biotype:protein_coding transcript_biotype:protein_coding gene_symbol:Pafah1b1 description:platelet-activating factor acetylhydrolase, **isoform** 1b, subunit 1 [Source:MGI Symbol;Acc:MGI:109520]
>**ENSMUSP00000099578.2** pep chromosome:GRCm38:11:74673950:74723858:-1 **gene:ENSMUSG00000020745.15** transcript:ENSMUST00000102520.8 gene_biotype:protein_coding transcript_biotype:protein_coding gene_symbol:Pafah1b1 description:platelet-activating factor acetylhydrolase, **isoform** 1b, subunit 1 [Source:MGI Symbol;Acc:MGI:109520]
---
Different protein identifier (ENSMUSP00000021091.8, ENSMUSP00000099578.2) but the same gene id (ENSMUSG00000020745.15). The word 'isoform' is also mandatory!uniprot
isoforms are specified in uniprot style using the '*_additional.fa' files
E.g. C.fa:
---
>tr|ADHA2|R4GDP1_DANRE Gamma-aminobutyric
(...)
---
C_additional.fa:
---
>tr|QDHQ4|R4GDP1_DANRE isoform of ADHA2
(...)
---
QDHQ4 is the isoform of ADHA2. Please simply add the *_additional.fa files to the proteinortho call!trinity
isoforms are specified in trinity style:
---
>TRINITY_DN1000_c115_g5_i1 len=247 path=[31015:0-148 23018:149-246]
(...)
---
The protein id is TRINITY_DN1000_c115_g5a and the isoform id is specified with i1Search options (step 1-2) (output:
–p=algorithm (default: diamond)
show all options (Click to expand)
autoblast : automatically detects the blast+ program (blastp,blastn,tblastn,blastx) depending on the input (can also be mixed together!)
blastn_legacy,blastp_legacy,tblastx_legacy : legacy blast family (shell commands: blastall -) family. The suffix ‘n’ or ‘p’ indicates nucleotide or protein input files.
blastn+,blastp+,tblastx+ : standard blast family (shell commands: blastn,blastp,tblastx) family. The suffix ‘n’ or ‘p’ indicates nucleotide or protein input files.
diamond : Only for protein files! standard diamond procedure with –sensitive flag Warning: Please use version 0.9.29 or later to avoid this known bug: #24
lastn,lastp : lastal. -n : dna files, -p protein files (BLOSUM62 scoring matrix)!
rapsearch : Only for protein files!
mmseqsp,mmseqsn : mmseqs2. -n : dna files, -p protein files
topaz : Only for protein files!
usearch : usearch_local procedure with -id 0 (minimum identity percentage).
ublast : usearch_ublast procedure.
–sim=float (default: 0.95) min. reciprocal similarity for additional hits. 1 : only the best reciprocal hits are reported, 0 : all possible reciprocal blast matches (within the -e) are reported.
More (Click to expand)
–e=evalue (default: 1e-05) E-value for blast (column 11 of blast outfmt 6 output)
–selfblast apply selfblast, detects paralogs without orthologs
–identity=number (default: 25) min. percent identity of best blast hits (column 3 (pident) of blast outfmt 6 output)
–cov=number (default: 50) min. coverage of best blast alignments in % coverage between protein A and B = min ( alignment_length_A_B/length_A, alignment_length_A_B/length_B ) (alignment_length_A_B = column 4 of blast outfmt 6 output)
–subparaBlast=‘options’ additional parameters for the search tool (-p=blastp+,diamond,…) example -subpara=‘-seg no’ or -subpara=‘–more-sensitive’ for diamond
–identical only return entries that are 100% identical
–range=number (default:disabled) maximal length difference for any blast hit. e.g. 0 = filter for hits between proteins of same length
Synteny options (optional, step 2) (output:
More (Click to expand)
–synteny activate PoFF extension to separate similar by contextual adjacencies (requires .gff for each .fasta)
–dups=number (default: 0) PoFF: number of reiterations for adjacencies heuristic, to determine duplicated regions
–cs=number (default: 3) PoFF: Size of a maximum common substring (MCS) for adjacency matches
Clustering options (step 3) (output:
More (Click to expand)
–singles report singleton genes without any hit
–purity=float (default: 1e-7) avoid spurious graph assignments
–minspecies=float (default: 1, must be >=0) min. number of genes per species. If a group is found with up to (minspecies) genes/species, it wont be split again (regardless of the connectivity).
–nograph do not generate *-graph file (pairwise orthology relations)
–subparaCluster=‘options’ additional parameters for the clustering algorithm (proteinortho_clustering) example -subparaCluster=‘-maxnodes 10000’. Note: -rmgraph cannot be set. All other parameters of subparaCluster are replacing the default values (like -cpus or -minSpecies)
–xml do generate an orthologyXML file (see http://www.orthoxml.org for more information). You can also use proteinortho2xml.pl <myproject.proteinortho>.
–core stop clustering if a split would result in groups that do not span across all species of the inital connected component. Overrules the -conn threshold.
–coreMinSpecies sets the minimal number of species for the -core option (default:0)
–coreMaxProts sets the maximal number of proteins per species for the -core option (default:100)
Misc options
(Click to expand)
–cleanblast cleans blast-graph with proteinortho_cleanupblastgraph
–desc write description files (for NCBI FASTA input only)
–binpath=directory (default: $PATH) path to your local executables (blast, diamond, mcl, …)
–debug gives detailed information for bug tracking
Large compute jobs - –jobs=M/N If you want to involve multiple machines or separate a Proteinortho run into smaller chunks, use the -jobs=M/N option. First, run ‘proteinortho6.pl -steps=1 …’ to generate the indices. Then you can run ‘proteinortho6.pl -steps=2 -jobs=M/N …’ to run small chunks separately. Instead of M and N numbers must be set representing the number of jobs you want to divide the run into (M) and the job division to be performed by the process. E.g. to divide a Proteinortho run into 4 jobs to run on several machines, use ‘proteinortho6.pl -steps=2 -jobs=1/4’, ‘proteinortho6.pl -steps=2 -jobs=1/4’, ‘proteinortho6.pl -steps=2 -jobs=2/4’, ‘proteinortho6.pl -steps=2 -jobs=3/4’, ‘proteinortho6.pl -steps=2 -jobs=4/4’.
See [Large compute jobs, the --jobs option (proteinortho wiki)](https://gitlab.com/paulklemm_PHD/proteinortho/wikis/Large-compute-jobs-(the--jobs-option)) for more details.The PoFF extension allows you to use the relative order of genes (synteny) as an additional criterion to disentangle complex co-orthology relations. To do so, add the parameter -synteny.
You can use it to either come closer to one-to-one orthology relations by preferring synthetically conserved copies in the presence of two very similar paralogs (default), or just to reduce noise in the predictions by detecting multiple copies of genomic areas (add the parameter -dups=3).
Please note that you need additional data to include synteny, namely the gene positions in GFF3 format. As Proteinortho is primarily made for proteins, it will only accept GFF entries of type CDS (column #3 in the GFF-file). The attributes column (#9) must contain Name=GENE IDENTIFIER where GENE IDENTIFIER corresponds to the respective identifier in the FASTA format. It may not contain a semicolon (;)! Alternatively, you can also set ID=GENE IDENTIFIER. Example files are provided in the source code archive. Hence, we can run proteinortho6.pl -project=test -synteny test/A1.faa test/B1.faa test/E1.faa test/F1.faa to add synteny information to the calculations. Of course, this only makes sense if species are sufficiently similar. You won’t gain much when comparing e.g. bacteria with fungi.
When the analysis is done you will find an additional file in your current working directory, namely myproject.poff.tsv (tab separated file). This file is equivalent to the test.proteinortho.tsv file (above) but can be considered more accurate as synteny was involved for its construction. More information about the output files are described below and a full example is described here: Synteny example (proteinortho wiki).
BLAST Search Output (step 1-2)
filtered blast data based on adaptive reciprocal best blast matches, the reciprocal best match matches within a range of 95% by default (-sim).
A line starting with ‘#’ indicates the two species that are analysed below. E.g. ‘# M.faa L.faa’ tells that the next lines are for species M.faa versus species L.faa.
All matches are reciprocal matches. If e.g. a match for M_15 L_15 is shown, L_15 M_15 exists implicitly.
E-Values and bit scores for both directions A->B and B->A are printed behind each match. (e.g. 5.9e-51 190.7 5.6e-50 187.61)
The 4 numbers below the species (e.g. ‘# 3.8e-124 434.9…’) are representing the median evalue/bitscore values for this comparison.
myproject.blast-graph (Click to expand)
# file_a file_b
# a b evalue_ab bitscore_ab evalue_ba bitscore_ba
# E.faa C.faa
# 3.8e-124 434.9 2.8e-126 442.2
E_11 C_11 5.9e-51 190.7 5.6e-50 187.61
E_10 C_10 3.8e-124 434.9 2.8e-126 442.2
...Clustering Output (step 3)
clustered version of the myproject.blast-graph is printed in myproject.proteinortho-graph.
Its connected components are represented in myproject.proteinortho.tsv / myproject.proteinortho.html.
The format of myproject.blast-graph is the equivalent to the myproject.blast-graph (see above).
myproject.proteinortho-graph (Click to expand)
# file_a file_b
# a b evalue_ab bitscore_ab evalue_ba bitscore_ba
# E.faa C.faa
E_10 C_10 3.8e-124 434.9 2.8e-126 442.2
E_11 C_11 5.9e-51 190.7 5.6e-50 187.6
...The connected components of myproject.proteinortho-graph.
The very first column indicates the number of species covered by this group. The second column indicates the number of genes included in this group.
If the number of genes is bigger than the number of species, there are co-orthologs present.
The third column gives the algebraic connectivity of the respective group. This indicates how densely the genes are connected in the orthology graph that was used for clustering. A connectivity of 1 indicates a perfect dense cluster with each gene beeing connected/orthologous to each other gene.
By default, Proteinortho splits each group into two more dense subgroups when the connectivity is below 0.1 (default -conn).
Hint: you can open this file in Excel / Numbers / Open Office as a tab separated file.
myproject.proteinortho.tsv (Click to expand)
# Species Genes Alg.-Conn. C.faa C2.faa E.faa L.faa M.faa
2 5 0.16 * * * L_643,L_641 M_649,M_640,M_642
3 6 0.138 C_164,C_166,C_167,C_2 * * L_2 M_2
2 4 0.489 * * * L_645,L_647 M_644,M_646
...myproject.proteinortho-graph.summary
POFF Output (-synteny)
The synteny based graph files (before clustering = myproject.ffadj-graph and after clustering = myproject.poff-graph) have two additional columns: same_strand and simscore, e.g.
# M.faa C_clean.faa # Scores: 7 15 5.500909 13.039009 M_10 C_10;test 1.71e-150 445 3.49e-150 444 1 1.0 M_11 C_11 2.34e-68 208 5.41e-67 204 1 0.997001020511701 M_14 C_14 1.68e-145 440 6.72e-150 451 1 1.0 M_15 C_15 8.33e-96 292 1.64e-99 301 1 1.0 M_16 C_16 5.15e-42 138 4.18e-42 138 1 0.620002623668878 …
The first one indicates if two genes from a match are located at the same strands (1) or not (-1). The second one is an internal score which can be interpreted as a normalized weight ranging from 0 to 1 based on the respective e-values.
myproject.ffadj-graph (Click to expand)
filtered blast data based on adaptive reciprocal best blast matches and synteny parameters
output columns are: geneA, geneB, evalue_A_vs_B, bitscore_A_vs_B, evalue_B_vs_A, bitscore_B_vs_A, same_strand, simscore
evalue* and bitscore* are computed by the specified -p blast algorithm (default: diamond) same_strand and simscore are described above
# file_a file_b # a b evalue_ab bitscore_ab evalue_ba bitscore_ba same_strand simscore # C2.faa L.faa # Scores: 0 3 2.000000 3.000000 C_10 L_10 4.30e-151 446 6.13e-153 447 1 1.0 # C2.faa M.faa # Scores: 0 3 2.000000 3.000000 C_10 M_10 3.49e-150 444 2.43e-152 445 1 1.0 # C2.faa E.faa # Scores: 0 3 2.000000 3.000000 …
myproject.poff-graph (Click to expand)
clustered ffadj graph. Its connected components are represented in myproject.poff.tsv (tab separated file) (only if -synteny is set)
output columns are same as for ffadj-graph
myproject.poff.tsv (Click to expand)
The connected components of myproject.poff-graph (clustering output), same format as the myproject.proteinortho.tsv.
output columns are: number_species, number_genes, algebraic_connectivity, speciesA_proteins, …
algebraic_connectivity defines how well this group is connected (if all proteins have a connection to all other proteins, this score is high)
if multiple proteins are present in one group they are separated by comma (if they have a comma in their name it is replaced by semicolon)
# Species Genes Alg.-Conn. C2.faa C_clean.faa E.faa L.faa M.faa 5 5 0.169 C_10 C_10;test E_10 L_10 M_10 4 4 0.449 * C_11 E_11 L_11 M_11 4 4 0.441 * C_63 E_19 L_19 M_19 4 4 0.44 * C_15 E_15 L_15 M_15 4 4 0.439 * C_64 E_18 L_18 M_18 4 4 0.435 * C_17 E_17 L_17 M_17 4 4 0.425 * C_14 E_14 L_14 M_14 4 4 0.39 * C_12 E_313 L_313 M_313 4 4 0.387 * C_16 E_16 L_16 M_16
Calling proteinortho Sequences are typically given in plain fasta format like the files in test/
test/C.faa:
C_10 VVLCRYEIGGLAQVLDTQFDMYTNCHKMCSADSQVTYKEAANLTARVTTDRQKEPLTGGY HGAKLGFLGCSLLRSRDYGYPEQNFHAKTDLFALPMGDHYCGDEGSGNAYLCDFDNQYGR …
test/E.faa:
E_10 CVLDNYQIALLRNVLPKLFMTKNFIEGMCGGGGEENYKAMTRATAKSTTDNQNAPLSGGF NDGKMGTGCLPSAAKNYKYPENAVSGASNLYALIVGESYCGDENDDKAYLCDVNQYAPNV …
To run proteinortho for these sequences, simply call
perl proteinortho6.pl test/C.faa test/E.faa test/L.faa test/M.faa
To give the outputs the name ‘test’, call
perl proteinortho6.pl -project=test test/*faa
To use blast instead of the default diamond, call
perl proteinortho6.pl -project=test -p=blastp+ test/*faa
If installed with make install, you can also call
proteinortho -project=test -p=blastp+ test/*faa
Using .faa to indicate that your file contains amino acids and .fna to show it contains nucleotides makes life much easier but is not required.
Sequence IDs must be unique within a single FASTA file. Consider renaming otherwise. Note: Till version 5.15 sequences IDs had to be unique among the whole dataset. Proteinortho now keeps track of name and species to avoid the necessissity of renaming.
You need write permissions in the directory of your FASTA files as Proteinortho will create blast databases. If this is not the case, consider using symbolic links to the FASTA files.
The directory src/ contains useful tools, e.g. proteinortho_grab_proteins.pl which fetches protein sequences of orthologous groups from Proteinortho output table. (These files are installed during ‘make install’)
For download and online information, see https://www.bioinf.uni-leipzig.de/Software/proteinortho/ or https://gitlab.com/paulklemm_PHD/proteinortho
Lechner, M., Findeisz, S., Steiner, L., Marz, M., Stadler, P. F., & Prohaska, S. J. (2011). Proteinortho: detection of (co-) orthologs in large-scale analysis. BMC bioinformatics, 12(1), 124.