Proteinortho

Proteinortho is a tool to detect orthologous genes within different species.

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:

proteinortho.workflow.png

Input: Multiple fasta files (orange boxes) with many proteins/genes (circles).

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).

Output: Groups (*.proteinortho) and pairs (*.proteinortho-graph) of orthologs proteins/genes.

New Features of Proteinortho Version 6

6.0.14 : public release to https://usegalaxy.eu/

A more detailed list of all changes: CHANGELOG

Table of Contents

  1. Installation
  2. Synopsis and Description
  3. Options/Parameters
  4. PoFF synteny extension
  5. Output description
  6. Examples

Proteinortho-Wiki Table of Contents

  1. Tools and additional programs
  2. Error Codes and Troubleshooting <- look here if you cannot compile/run proteinortho
  3. Large compute jobs example)
  4. FAQ
    (...)

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!

Installation

Proteinortho comes with precompiled binaries of all executables (Linux/x86) so you should be able to run perl proteinortho6.pl in the downloaded directory. You could also move all executables to your favorite directory (e.g. with make install PREFIX=/home/paul/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.


Easy installation with (bio)conda (for Linux + OSX) install with bioconda alt

conda install proteinortho

If you need conda (see here) and the bioconda channel: conda config --add channels defaults && conda config --add channels bioconda && conda config --add channels conda-forge.


Easy installation with brew (for OSX) install with brew dl

brew install proteinortho

If you need brew (see here)


Easy installation with docker install with docker

docker pull quay.io/biocontainers/proteinortho:TAG

with TAG specified here (e.g. 6.0.23--hfd40d39_0).

how to docker (Click to expand)
To start a bash shell docker run --rm -it quay.io/biocontainers/proteinortho:6.0.22--hfd40d39_0 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 ... ### Now lets try to mount your home in docker 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:6.0.22--hfd40d39_0 bash now you have your home directory mounted to /home/YOURNAME. (load your bashrc within docker : source /home/YOURNAME/.bashrc)


Available at Galaxy Europe

Simply go to the european galaxy server and search for proteinortho:

https://usegalaxy.eu

Or you can integrate proteinortho into your own galaxy instance using: proteinortho (iuc repository)


Easy installation with dpkg (root privileges are required)

The deb package can be downloaded here: https://packages.debian.org/unstable/proteinortho. Afterwards the deb package can be installed with sudo dpkg -i proteinortho*deb.


(Easy installation with apt-get)

! Disclamer: Work in progress ! proteinortho will be released to stable with Debian 11 (~2021), then proteinortho can be installed with apt-get install proteinortho (currently this installes the outdated version v5.16b)


Prerequisites for compiling proteinortho from source

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): - NCBI BLAST+ or NCBI BLAST legacy (to test this, type tblastn. apt-get install ncbi-blast+) - Diamond (apt-get install diamond, brew install diamond, conda install diamond, https://github.com/bbuchfink/diamond) - Last (http://last.cbrc.jp/) - Rapsearch (https://github.com/zhaoyanswill/RAPSearch2) - Topaz (https://github.com/ajm/topaz) - usearch (https://www.drive5.com/usearch/download.html) - ublast (is part of usearch) - blat (http://hgdownload.soe.ucsc.edu/admin/) - mmseqs2 (conda install mmseqs2, https://github.com/soedinglab/MMseqs2) - Perl v5.08 or higher (to test this, type perl -v in the command line) - (optional) Python v3.0 or higher to include synteny analysis (to test this, type 'python -V' in the command line) - Perl standard modules (these should come with Perl): Thread::Queue, File::Basename, Pod::Usage, threads (if you miss one just install with cpan install ... )


To compile Proteinortho (linux/osx), you need: (Click to expand) - GNU make (to test this, type 'make' in the command line) - GNU g++ v4.1 or higher (to test this, type 'g++ --version' in the command line) - openmp (to test this, type 'g++ -fopenmp' in the command line) - (optional) gfortran for compiling LAPACK (to test this, type 'whereis gfortran' in the command line) - (optional) CMake for compiling LAPACK (to test this, type 'cmake' in the command line), OR you can use your own compiled version of lapack (you can get this with 'apt-get install liblapack3') and run 'make USEPRECOMPILEDLAPACK=TRUE'


Building and installing proteinortho from source (linux and osx)

Here you can use a working lapack library, check this 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 Lapack v3.8.0 for you !

Fetch the latest source code archive downloaded from here

or 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 files - cd proteinortho* : Change directory into the extracted folder - You can now run proteinortho6.pl directly (linux only). - make clean && make : If you want to recompile Proteinortho. (For osx you need a newer g++ compiler to support multithreading, see below) - make install or make install PREFIX=~/bin if you dont have root privileges. - make test : To make sure Proteinortho works as expected. The output should look like below (3. Make test output).
OSX additional informations (the -fopenmp error) 
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'
'make' successful output (Click to expand) 
[  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).

3. Make test output

'make test' successful output (Click to expand) 
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).


SYNOPSIS

proteinortho [options] \

one fasta for each species; at least 2

DESCRIPTION

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.

OPTIONS graphical user interface

Open proteinorthoHelper.html in your favorite browser or visit lechnerlab.de/proteinortho online for an interactiv exploration of the different options of proteinortho.

OPTIONS

Main parameters (can be used with -- or -)

More (Click to expand) - --e=evalue (default: 1e-05) E-value for blast - --selfblast apply selfblast, detects paralogs without orthologs - --identity=number (default: 25) min. percent identity of best blast hits - --cov=number (default: 50) min. coverage of best blast alignments in % - --subparaBlast='options' additional parameters for the search tool (-p=blastp+,diamond,...) example -subpara='-seg no' or -subpara='--more-sensitive' for diamond

Synteny options (optional, step 2) (output: .ffadj-graph, .poff.tsv (tab separated file)-graph)
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 - --alpha=number (default: .5) PoFF: weight of adjacencies vs. sequence similarity

Clustering options (step 3) (output: .proteinortho.tsv, .proteinortho.html, .proteinortho-graph) - --conn=float (default: 0.1) min. algebraic connectivity. This is the main parameter for the clustering step. Choose larger values then more splits are done, resulting in more and smaller clusters. (There are still cluster with an alg. conn. below this given threshold allowed if the protein to species ratio is good enough, see -minspecies option below)
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 . - --exactstep3 perform step 3 without the k-mere heuristic (much slower for huge datasets but more precise) - --mcl perform the clustering without the k-mere heuristic. The k-mere heuristic is only applied for very large connected components (>1e+6 nodes) and if the algorithm would start to iteratate very slowly

Misc options - --checkfasta checks input fasta files if the given algorithm can process the given fasta file.
(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)) for more details.
# PoFF 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. AsProteinortho 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 test.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. # Output BLAST Search (step 1-2)
myproject.blast-graph (Click to expand) filtered raw blast data based on adaptive reciprocal best blast matches (= reciprocal best match matches within a range of 95% by default) 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 versus species L. 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 are given behind each match. The 4 numbers below the species (e.g. '# 3.8e-124 434.9...') are representing the median values for this comparison. # 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 (step 3)
myproject.proteinortho-graph (Click to expand) clustered version of the myproject.blast-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). # 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 ...

myproject.proteinortho.tsv (Click to expand) 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 (can be user defined). Hint: you can open this file in Excel / Numbers / Open Office. # 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
myproject.proteinortho.html (Click to expand) The html version of the myproject.proteinortho.tsv file

POFF (-synteny) The synteny based graph files (myproject.ffadj-graph and myproject.poff.tsv (tab separated file)-graph) have two additional columns: same_strand and simscore. 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. Moreover, a second comment line is followed after the species lines, e.g. # M.faa L.faa # Scores: 4 39 34.000000 39.000000
myproject.ffadj-graph (Click to expand) filtered blast data based on adaptive reciprocal best blast matches and synteny (only if -synteny is set)

myproject.poff.tsv (tab separated file)-graph (Click to expand) clustered ffadj graph. Its connected components are represented in myproject.poff.tsv (tab separated file) (only if -synteny is set)

# EXAMPLES 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 # Hints Using .faa to indicate that your file contains amino acids and .fna to show it contains nucleotides makes life much easier. 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') # Kmere Heuristic ## Example 1 In the following example a huge blast graph is used for step 3 (clustering). The first connected component contains 7410694 nodes, hence the kmere heuristic is activated. Since the fiedler vector would result in a good split, the kmere heuristic is then deactivated immediatly.
as fallback (Click to expand) ... [CRITICAL WARNING] Failed to partition subgraph with 6929 nodes into (6929,0,0) sized groups, now using kmere heuristic as fall-back. ...
working example for large graphs (Click to expand) ... 17:32:15 [DEBUG] (kmere-heuristic) The current connected component is so large that the k-mere heuristic can be used. First: Testing if a normal split would result in a good partition (|.|>20%) of the CC. [WARNING] (kmere-heuristic) A normal split would NOT result in a good partition (|.|>20%) of the CC, therefore the k-mere heuristic is now used. The current connected component will be split in 3.85373 (= number of proteins <6929> / ( n odes per species <1> * number of species <1798>)) groups greedily accordingly to the fiedler vector. ...
example for large graphs, where kmere is tested but not needed (Click to expand) ... 20:27:07 [DEBUG] (kmere-heuristic) The current connected component is so large that the k-mere heuristic can be used. First: Testing if a normal split would result in a good partition (|.|>20%) of the CC. 20:27:09 [DEBUG] (kmere-heuristic) A normal split would result in a good partition (|.|>20%) of the CC, therefore returning now to the normal algorithm (no k-mere heuristic). ...

Credit where credit is due

ONLINE INFORMATION

For download and online information, see https://www.bioinf.uni-leipzig.de/Software/proteinortho/ or https://gitlab.com/paulklemm_PHD/proteinortho

REFERENCES

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.