orthologs are defined as genes in different species that diverged at their speciation event and , therefore , directly derive from a single gene in their last common ancestor ( 1 ) .
paralogs , on the other hand , are genes separated by duplication and may be found either in the same or in different species .
it is common that orthologs undergo duplications after the speciation event , generating multiple co - orthologs or inparalogs ( 2 ) .
in contrast , outparalogs are genes , either in the same or different species , that derive from a duplication event prior to a given speciation event .
orthology detection is a challenging task , partly because of the mentioned complications with gene duplications , but also because of gene loss , lateral gene transfer or other mechanisms that produce incongruent evolutionary patterns ( 3 ) .
inparanoid is an algorithm designed with the aim to generate ortholog groups that include all inparalogs but no outparalogs .
it is a graph - based method that starts with an exhaustive all - vs - all basic local alignment search tool ( blast ) comparison of all protein sequences in two species and then applies a number of clustering rules to build ortholog groups ( 4 ) .
it operates on two species at a time , which may be considered a limitation , but also has advantages .
the most typical orthology query is indeed pairwise , either one - to - one species queries , e.g.  find the fly ortholog to human gene x , or one - to - many , e.g.  find all orthologs in any other species to human gene x. both queries are supported on the inparanoid website .
an advantage of the pairwise nature of inparanoid is that the orthology assignments are not affected by other species .
when building multi - species ortholog groups of more than two species , a compromise has to be made when conflicting signals are merged , and inevitably more errors are made , such as including outparalogs in ortholog groups .
this typically happens when a gene in a distant species is orthologous to multiple inparalogs in two or more closely related species . from the point of view of the gene in the distant species ,
all genes are orthologous to it as they are related via a speciation event . however , the genes in the closely related species may not all be orthologous to each other , but instead make up separate ortholog groups , generated by duplications after the divergence from the distant species but before the closely related species split .
aside from the website , which mainly contains eukaryotes , the software is available for stand - alone usage .
it runs reasonably fast for a handful of species and has a good balance between false positives and false negatives , as testified in several benchmarks ( 57 ) . on the online orthology benchmarks at http://orthology.benchmarkservice.org ,
inparanoid is generally ranked among the best methods , especially when looking at both coverage and accuracy .
a drawback of inparanoid 's pairwise approach , which it shares with most other ortholog inference methods , is however the quadratic runtime scaling with the number of species . with 273 species ,
we here present release 8 of inparanoid , for which the gathering of proteomes was radically different from previous releases .
thanks to the  quest for orthologs community efforts , we are now able to use pre - defined reference proteomes from uniprot , which allowed us to skip the time - consuming and error - prone step of curating proteomes from multiple sources .
we analyze how this has affected the content of inparanoid , and also provide a number of use cases .
a slightly modified version of the inparanoid 4.1 software ( 4,8 ) was used for computing inparanoid 8 .
the difference is in how the two blast ( 9 ) passes are run . in the distributed version 4.1 ,
the two blast passes are run after each other  the first run to find all homologs between two species , and then a second run is launched per query sequence to make accurate alignments with only the homologs found in pass 1 .
we have not yet found a blast setting that simultaneously makes accurate alignments and efficiently avoids false low - complexity matches .
the default composition - based score adjustment ( 10 ) in blast does the latter , and is used in pass 1 , but it often truncates the alignments which may cause inparanoid to miss true orthologs ( 8) , hence it is not used in pass 2 . in order to improve computational throughput , we ran both blast passes in parallel and after both were done , extracted matches from pass 2 for homologs found in pass 1 ; see figure 1
this can also save total real runtime as only two blast runs are launched instead of thousands of tiny runs per species comparison , which causes a lot of input / output ( i / o ) overhead .
there are some drawbacks however : the pass 2 computation and results become much larger , and the infrastructure and work required to synchronize , supervise and load balance the increased number of computational jobs is considerable .
blast runs are launched for all pairs of proteomes , running both passes in parallel .
when both passes are finished , their outputs are validated by checking for truncation or failure to complete .
intra - proteome matches are checked against the proteome sequences to ensure inclusion of all genes .
pass 1 pairs are combined with pass 2 results such that only pairs accepted in pass 1 are kept , but with alignments from pass 2 .
a failed validation will either lead to a whole proteome rerun for failed / truncated results or individual serial pass2 reruns for pass1 pairs lacking pass2 results . to speed up the parallel pass 2 , the blast parameter z ( effective database size )
this can reduce blast 's ability to find matches , and it therefore happens at low frequency that homologs found in pass 1 are not reported in the parallel pass 2 . to handle this ,
a quality control and repair step was added where missing pairs were rerun with a normal pass 2 .
it also catches other problems , such as failed or truncated runs , and repairs them too .
hence the quality control is currently more rigorous for the parallel method than for the serial .
the computations were run on a linux cluster with around 300 8-core nodes , and took in total about 113 core years of which nearly all was spent on running blast ( blastall 2.2.18 ) . only about 0.5% of the computation time
was spent on running the inparanoid orthology detection algorithm , implemented in perl . even though the procedure was modified , the parameters were effectively the same as by default . compared to the previous release , the number of pairwise species comparisons increased by 650% , from 4950 to 37 128 .
inparanoid takes as input complete proteomes with one representative protein sequence per gene , normally the splice form that gives the longest protein or the canonical form . in the past
, a large effort was spent on collecting proteomes from various sources and parsing annotation to trace proteins to genes .
this often introduced errors which could lead to incorrect orthology assignments . as this is a general problem in the orthology field , the  quest for orthologs ( qfo ; http://questfororthologs.org/ ) community has agreed on establishing and using standardized reference proteomes that are curated at the ebi ( http://www.ebi.ac.uk/reference_proteomes ) .
the qfo reference proteomes are a subset of the uniprot reference proteomes ( 11 ) and currently comprise 66 species .
the uniprot reference proteomes are in turn a subset of the uniprot complete proteomes . for inparanoid 8
we used the qfo reference proteomes and added ( i ) all proteomes that were included in inparanoid 7 and are found in the uniprot complete proteomes , and ( ii ) additional eukaryotic proteomes from the uniprot complete proteomes , limited to one species for each genus not already covered .
this resulted in 273 species , which constitutes a representative set of all completely sequenced ( eukaryotic ) species .
it is a superset of the qfo reference proteomes and a subset of the uniprot complete proteomes . as in previous inparanoid
releases it is strongly biased toward eukaryotes , with 246 eukaryotes , 20 bacteria and seven archaea . in total , 3 718 323 sequences were used as input .
since the previous release , the number of species has increased by 173% from 100 to 273 .
many of the new proteomes are smaller than the old ones , hence the increase in protein sequences is only 120% , from 1.7 to 3.7 million . the number of ortholog groups has increased by 423% , from 1.5 to 8.0 million , and the unique orthologous proteins by 141% , from 1.2 to 3.0 million . the discrepancy between these is due to the fact that the total number of ortholog groups grows quadratically with the number of species , while the unique orthologous proteins are limited to the number of total proteins . still , the addition of new species in release 8 increased the average fraction of proteins that have an ortholog in another species from 0.74 to 0.81 .
the average number of inparalogs per species across all ortholog groups is 1.41 , slightly lower than in release 7 ( 1.46 ) .
very closely related species still have an average of 1.00 inparalogs , but the highest average number for two species has increased to 10.94 , about twice as high as in release 7 , for glycine max ( soybean ) when compared to auricularia delicata ( jelly fungus ) .
we show in figure 2 an example of an inparalog - rich ortholog group with soybean .
a likely reason for the high number of inparalogs in soybean is increased ploidy from genome - wide duplications .
example of online output when browsing inparanoid 8 , showing the neighbor - joining tree and pfam ( 20 ) domain architectures of the proteins in ortholog group 99 between human and soybean ( glycine max ) .
all proteins have the same pfam domain architecture  a tubulin ( green ) and a tubulin_c ( red ) domain .
the tree indicates that these tubulin - alpha proteins have been duplicated many times independently in the two lineages since they diverged , giving rise to seven human and 10 soybean inparalogs .
all human inparalogs are orthologous to all soybean inparalogs as they are all related via the inferred speciation event at the root of the tree .
overall , most of the proteomes have not changed drastically in the number of sequences or inparalogs , as shown in figures 3 and 4 .
if the number of inparalogs changed , then this is normally directly proportional to changes in the number of sequences , and a consequence of a major update in the genome project .
we searched for species pairs in which a species had changed more than 1.5-fold in the number of inparalogs and found 28 cases of increase and 170 of decrease .
however , all species except three only occurred once ; these highly changed species are brugia malayi , branchiostoma floridae and trypanosoma cruzi .
their relative change in the number of sequences is 48% increase , 44% decrease and 45% decrease .
this correlates well with the overall change in the number of inparalogs : 45% increase , 45% decrease and 55% decrease .
they show just how different a proteome can be defined in different resources ; in inparanoid 7 they were taken from ncbi , jgi and genedb , respectively . for some species
the number of sequences has changed drastically without affecting the overall number of inparalogs much .
for example , the aphid acyrthosiphon pisum has 227% more sequences but overall only 7% more inparalogs , indicating that the added sequences are highly species - specific or that the genome annotation now includes lower quality gene predictions .
scatterplot of the number of inparalogs between species pairs in inparanoid 8 and inparanoid 7 , for the species common to both releases .
the number of inparalogs has generally not changed much , with some exceptions that are highlighted in color ( orange for b. malayi , green for t. cruzi and blue for b. floridae ) .
distributions of the relative number of inparalogs ( yellow ) , inparalogs per sequence ( black ) and sequences ( blue ) , comparing inparanoid8 to inparanoid 7 . as before
, we generated an orthology - based phylogenetic tree by upgma clustering of pairwise species distances derived from shared ortholog content .
the distances were calculated as 1 minus the fraction of orthologous proteins , averaged over both directions ( 12 ) . this 
orthophylogram is too large to be shown as a figure but can be accessed on the inparanoid website in the download directory in newick or pdf format .
for instance , the northern greater galago ( otolemur garnettii ) , which is taxonomically classified as a primate , does not cluster with other primates but instead with various domesticated and wild non - primate mammals .
this could reflect a problem with its genome sequence , but we note that it shares physical characteristics with e.g. cats and rodents . the gray short - tailed opossum ( malus domestica ) , a marsupial which we previously felt was misplaced inside the placental mammalian lineage , has now been joined by another marsupial , the tasmanian devil ( sarcophilus harrisii ) .
macaque monkey ( macaca mulatta ) is still oddly placed , clearly outside the other primates , indicating that the annotation of this genome still needs to be improved ( 8) .
we recommend using the orthoxml ( 13 ) format as this is structured , flexible and resistant to errors .
the sql format is probably easiest to parse for simple purposes , but lacks additional information available in other formats such as annotations , alternative seed orthologs , statistics , etc .
inparanoid is used widely , testified by a total of 1977 citations to all inparanoid papers ( google scholar , september 18 , 2014 ) .
the inparanoid website http://inparanoid.sbc.su.se receives on average about 1500 sessions per month ( google analytics ) .
the inparanoid software is one of the few downloadable fully automatic ortholog - inference programs and has become popular in many applications that require orthologs .
it is made available through a webpage ( linked from the inparanoid homepage ) that sends a copy by email ; we have received 1609 download requests from august 2011 to august 2014 .
some examples of inparanoid uses , either using the database directly or running the software , are listed below .
( 14 ) compared phenotype - based gene groups between species , looking for groups with significant enrichment of orthologs identified by inparanoid .
they thus connected disease phenotypes in human with other phenotypes in chicken , zebrafish , escherichia coli and caenorhabditis elegans , and called such pairs  orthologous phenotypes.
they report a number of compelling examples and used the method to predict novel candidate disease genes .
( 15 ) built a database called fsrd of 1985 fungal stress response proteins , and used inparanoid to identify their orthologs in 28 species including human and plant pathogens and other fungi .
( 16 ) used inparanoid orthologs to predict whether human genes are generated by retroposition .
they found 20 new human candidate retrogenes that lack introns , yet their c. elegans orthologs have introns .
hoeppner and poole ( 17 ) used inparanoid in their pipeline to identify orthologous snorna - bearing host genes across 44 eukaryote genomes . using dollo parsimony
, they reconstructed the pattern of snorna conservation across the eukaryote tree and showed that dozens of snornas are traceable to the last eukaryotic common ancestor ( leca ) . finally , we note that the orthophylogram generated from inparanoid is somewhat unique as it provides quantitative distances between species based on their entire proteomes .
these distances have been used to calculate phylogenetic profile scores in the funcoup database ( 18 ) .
if we could find a way to retain high accuracy with just one blast pass , this would save about 50% of the computation and greatly simplify the procedure .
another possibility is to use pre - calculated homology data , but that would require developing a whole new pipeline to ensure high quality , which may not be achievable . a third solution would be to develop an incremental updating scheme that only reruns blast on new and modified sequences . a fourth scenario is to move to linearly scaling hieranoid ( 19 ) .
it is however still unclear whether hieranoid 's multi - species approach , which has to compromise when merging conflicting orthology evidences , will be able to match inparanoid 's quality .
 the inparanoid database ( http://inparanoid.sbc.su.se ) provides a user interface to orthologs inferred by the inparanoid algorithm . 
 as there are now international efforts to curate and standardize complete proteomes , we have switched to using these resources rather than gathering and curating the proteomes ourselves . 
 inparanoid release 8 is based on the 66 reference proteomes that the  quest for orthologs community has agreed on using , plus 207 additional proteomes from the uniprot complete proteomes  in total 273 species . 
 these represent 246 eukaryotes , 20 bacteria and seven archaea . compared to the previous release , this increases the number of species by 173% and the number of pairwise species comparisons by 650% . in turn , the number of ortholog groups has increased by 423% . 
 we present the contents and usages of inparanoid 8 , and a detailed analysis of how the proteome content has changed since the previous release . 
INTRODUCTION
MATERIALS AND METHODS
DATA
INPARANOID CONTENT
INPARANOID USAGE
FUTURE PERSPECTIVES
FUNDING