Assignment of Orthologous Genes by Utilization of Multiple Databases

The Orthology Package in R

Steffen Priebe and Uwe Menzel

Systems Biology and Bioinformatics Group, Leibniz Institute for Natural Product Research and Infection Biology

Hans-Kn

oll-Institute, Jena D-07745, Germany

Keywords:

Orthologs, Databases, Multi-species, R Programming Language.

Abstract:

The assignment of orthologous genes between species is a key issue when multiple-species approaches are

conducted. This has become even more relevant over the past years, triggered by the development of high-

throughput genome sequencing technologies, which enable access to complete genomes in a rapid and cost-

effective way. In this paper, we present a new software that allows the user to access orthology relationships

across multiple species in an easy, fast, and ﬂexible manner. The tool collects data from three prominent freely

available databases, and presents it to the user in a convenient, easily accessible way. Once the package is

installed, the software works on the local computer, therewith circumventing runtime delay caused by network

trafﬁc often being a critical performance bottleneck when large datasets are studied or many organisms are

investigated simultaneously. By the consequent internal usage of unique identiﬁers, the software disburdens

the user from problems connected with the existence of synonyms or ambiguous gene denotations, a problem

that often hampers a clear-cut assignment of orthologs. The software is able to display frequently occurring,

complicated many-to-many orthology relationships in a visual manner. It is written in the R programming

language and freely available.

1 INTRODUCTION

Multiple-species approaches become more and more

important when universal, cross-species biological

principles are to be investigated, for example in sys-

tems biology, cancer- or age research. Furthermore,

ethical or practical reasons - e.g. a limited population

frequency - make the usage of model organisms in bi-

ological experiments often unavoidable, and conclu-

sions of general relevance have to be derived starting

from the results obtained for the model organisms. It

appears self-evident that the accurate determination of

orthologs - genes with a shared ancestor separated by

a speciation event - adopts a key role when using such

approaches. However, a researcher aiming at carrying

out an orthology search is frequently confronted with

a number of problems.

First of all, by committing oneself to a sin-

gle database only, important orthology relationships

might be missed because of the incompleteness of

present-day databases. Secondly, the search for or-

thologous genes is often hampered - if not made im-

possible - by the existence of a multitude of synonyms

or other ambiguous denotations that exist for the va-

st majority of annotated genes. Thirdly, if web-based

databases have to be used, access can be slow if large

datasets are investigated or many organisms are stud-

ied at the same time. Last but not least, orthol-

ogy relationships are often not in a 1-to-1 manner,

so that graphical presentations of many-to-many re-

lationships are desirable, while simple gene lists are

inconvenient and hard to perceive.

Up to now, various methods for the identiﬁcation

of orthologous genes between species have been de-

veloped, starting from simple blast searches aiming

at the identiﬁcation of bi-directional hits between the

genes of two species, up to advanced clustering or

tree-based methods (Kuzniar et al., 2008; Kristensen

et al., 2011). The results of this work have been stored

in a number of publicly available databases, providing

orthology information covering a growing number of

species to the research community. In this paper, we

utilize three prominent and comprehensive databases

that have been established in order to identify ortholo-

gous genes: i) the HomoloGene database maintained

at the National Center for Biotechnology Information

- NCBI (Geer et al., 2010), ii) the Ensembl Com-

para database driven by the Ensembl project (Vilella

105

Priebe S. and Menzel U..

Assignment of Orthologous Genes by Utilization of Multiple Databases - The Orthology Package in R.

DOI: 10.5220/0004193201050110

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2013), pages 105-110

ISBN: 978-989-8565-35-8

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: The main commands of the R orthology package and their function.

Subroutine Task

get.gene.info access different identiﬁers and descriptive information for genes

get.orthologe get the orthologs in other species for a given gene and a given species

is.orthologe test if two genes from different species are orthologous

get.ortholog.table visualize orthology relationships between two or more species

and return table (see ﬁgure 1)

get.orthologe.set return a set of all orthologous genes between the input species

et al., 2009), and iii) the Inparanoid database held at

the University of Stockholm, Sweden (Ostlund et al.,

2010).

The HomoloGene database at NCBI is a system

for the automated detection of homologs among the

annotated genes of several completely sequenced eu-

karyotic genomes. The approach used to establish

this database relies on clustering the input sequences

from the different species based on sequence similar-

ity on the protein level, using the blastp protein se-

quence alignment tool (Altschul et al., 1990). As a

key feature, database creation considers taxonomy by

matching sequences from closely related species ﬁrst,

subsequently adding sequences originating from or-

ganisms with bigger evolutionary distances to a tree

constructed during this procedure (Wheeler et al.,

2006). The HomoloGene database currently includes

21 species (Release 66).

The Ensembl Compara database is driven by the

Ensembl project. In order to establish this database,

orthology was predicted by gene tree generation us-

ing the TreeFam methodology (Ruan et al., 2008).

This involves building a graph of protein similarity

utilizing blastp, extracting clusters from this graph,

and generating multiple alignments within each clus-

ter. Based on these alignments, a gene tree is built

and reconciled with the taxonomy tree (Vilella et al.,

2009). An important feature of this approach is

that sequence similarity is connected with informa-

tion originating from the phylogenetic tree of the

species. The respective data can be accessed using

a Perl Application Programme Interface (Perl API) or

the BioMart portal (Haider et al., 2009).

As a third source of orthology information, we uti-

lize the Inparanoid database. Here, pairwise similar-

ity scores between complete proteomes were calcu-

lated in order to construct orthology groups, seeded

by the reciprocally best-matching pair. So far, this

database contains 100 eukaryotic species. While In-

Paranoid essentially relies on pairwise ortholog rela-

tionships, both HomoloGene and Ensembl Compara

use heuristic as well as phylogenetic methods to in-

fer orthologs, even though with considerably different

methods. The use of multiple databases - with varying

approaches to infer orthology - increases the number

of identiﬁable orthology relationships between genes

of different species. Furthermore, by using multiple

databases, it becomes possible to conﬁne oneself to

robust predictions of orthology by merely consider-

ing data that are conﬁrmed by more than one database

because biases in one database can be expected to be

overcome by another.

> get.gene.info("glr-2","external_gene_id","","ce")[1:5]

ensembl_gene_id ensembl_peptide_id external_gene_id entrezgene

13751 B0280.12 B0280.12b glr-2 175999

13752 B0280.12 B0280.12a glr-2 175999

wikigene_description

13751 GLutamate Receptor family (AMPA)

13752 GLutamate Receptor family (AMPA)

> get.orthologe("B0280.12","ce","mm")

$ensembl

ENSMUSG00000001986 ENSMUSG00000025892 ENSMUSG00000033981

"ENSMUSG00000001986" "ENSMUSG00000025892" "ENSMUSG00000033981"

ENSMUSG00000020524

"ENSMUSG00000020524"

$homologene

ENSMUSG00000001986

"ENSMUSG00000001986"

> get.orthologe.table(gene=get.gene.info("mod-5","external_gene_id",

"ensembl_gene_id","ce"),species="ce",plot=TRUE,self=FALSE)

$danrer

[1] "ENSDARG00000061165" "ENSDARG00000057017"

$mm

[1] "ENSMUSG00000020838"

$hs

[1] "ENSG00000108576"

$ce

[1] "Y54E10BR.7"

$ortho_table

ENSDARG00000061165 ENSDARG00000057017 ENSMUSG00000020838

ENSDARG00000061165 0 0 3

ENSDARG00000057017 0 0 0

ENSMUSG00000020838 3 0 0

ENSG00000108576 3 0 7

Y54E10BR.7 1 3 1

ENSG00000108576 Y54E10BR.7

ENSDARG00000061165 3 1

ENSDARG00000057017 0 3

ENSMUSG00000020838 7 1

ENSG00000108576 0 1

Y54E10BR.7 1 0

Figure 1: Usage of the main commands of the package

and example output. A: Accessing different gene IDs for

gene symbol glr-2 (C. elegans). B: Retrieval of orthologs in

mouse for the selected Ensembl gene ID. In this case only

Ensembl and HomoloGene provide orthologs. C: Listing

of all orthologs for the mod-5 gene (C. elegans) in human,

mouse and zebraﬁsh. The number in the orthology table de-

ﬁnes the source of information. If the parameter plot is set

to TRUE, a network plot will be drawn (see Figure 2).

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Orthologous gene relationship

for mod−5 in ce

Y54E10BR.7

mod−5

ENSDARG00000061165

slc6a4a

ENSDARG00000057017

slc6a4b

ENSG00000108576

SLC6A4

ENSMUSG00000020838

Slc6a4

Node color: Red = C.elegans, Green = D.rerio, Blue = H.sapiens, Orange = M.musculus

Edge color: Red = Ensembl, Blue = Homologene, Yellow = Inparanoid

Violet = Ensembl+Homologene, Orange = Ensembl+Inparanoid, Green = Homologene+Inparanoid

Black = Ensembl+Homologene+Inparanoid

Figure 2: Graphical representation of the orthology table

produced using the command stated in ﬁgure 1 C. Here the

orthology relationship is less complex, with only four or-

thologous genes in three other species. The orthologous

connection of SLC6A5 (human) and Slc6a4 (mouse) is cov-

ered by all three databases (thick black edge), which is rep-

resented by a binary 111 (decimal 7) in ﬁgure 1 C). Self-

edges are not included.

2 RESULTS

The software described in this paper allows the user

to access information stored in several orthology

databases with one interface only, with the advantage

that the user does not have to know how the individual

databases are to be operated. The software provides

a collection of features which, amongst other things,

allow the translation of gene names to gene identi-

ﬁers, the accession of functional gene descriptions,

and the retrieval of orthology relationships between

two or more organisms. A few of the subroutines of

the software package and their respective functions

are listed in table 1.

2.1 Example Commands

The command get.orthologe takes a gene identiﬁer

or gene symbol as input, and outputs the orthologs in

the organisms speciﬁed by the user. Upon request, all

three databases listed in the introduction can be taken

into account, or a subset of them.

Orthology relationships often are complicated

when more than two species are involved. In this case,

a graphical representation can help to understand this

complexity. The latter can be accomplished using the

function get.ortholog.table. This function dis-

plays orthology relationships in tabular form (ﬁgure

1) and visualizes these relationships in form of a net-

work graph.

In the table, gene names of two or more organ-

isms serve as column and row headers. Numbers in

the cells of the table encode the databases that re-

port orthology between the corresponding genes. We

use binary numbers for encoding, where 1 (2

) repre-

sents the Ensembl database, 2 (2

) represents the Ho-

moloGene database, and 4 (2

) stands for an orthol-

ogy relationship reported by the Inparanoid database.

The actual number shown in the table is then the sum

of the numbers representing the individual databases.

For example, 3 in a cell of the table means that or-

thology of the corresponding genes is reported by

the Ensembl database as well as by the HomoloGene

database. Certainly, every gene is orthologous to it-

self. Therefore, by default, self hits are labeled 0 in

the table and are suppressed in the graphical represen-

tation. However, if the parameters plot and self are

set TRUE, the self-hits appear as 10 in the table, and

self-edges are added to the plot.

Figure 2 shows a relatively simple example for

a graphical representation of orthology relationships

between genes, while ﬁgure 3 displays a more com-

plicated graph. It displays the glr-2 gene in C. elegans

and its orthologs in three other vertebrate species. In

this plots, nodes deﬁne genes and edges connect or-

thologous genes across species. While the node color

indicates species, the color of the edges stands for

the database the orthology is derived from. In this

case, like in many others, there is no simple 1-to-1 or-

thology relationship between the genes but there ex-

ists a multitude of (putative) orthologs in the organ-

isms considered, covered by different databases (1-

to-many and many-to-many).

An issue in orthology identiﬁcation is if and how

the order of input affects the results. For example, if

the orthology request shown in ﬁgure 2 started from

the human SLC6A4 gene (marked blue in the ﬁgure),

the slc6a4b D. rerio gene (marked green, to the right)

would not appear in the resulting plot, since this gene

is solely listed as ortholog of the mod-5 C. elegans

gene. In order to overcome this discrepancy, a special

parameter loop - with default 0 - can be set to a posi-

tive integer value n. This causes the tool to carry out n

iterative loops of orthology searches, in which every

loop uses the results of the previous one as input.

2.2 Applications

The orthology package described here has been suc-

cessfully applied in the framework of the JenAge

project, a collaboration between several institutions

AssignmentofOrthologousGenesbyUtilizationofMultipleDatabases-TheOrthologyPackageinR

107

Orthologous gene relationship

for glr−2 in ce

B0280.12

glr−2

ENSDARG00000037496

gria4a

ENSDARG00000059368

gria4b

ENSDARG00000037498

gria3b

ENSDARG00000032737

gria3a

ENSDARG00000052765

gria2b

ENSDARG00000070173

gria2a

ENSDARG00000021352

gria1a

ENSDARG00000032714

gria1b

ENSG00000125675

GRIA3

ENSG00000152578

GRIA4

ENSG00000120251

GRIA2

ENSG00000155511

GRIA1

ENSMUSG00000001986

Gria3

ENSMUSG00000025892

Gria4

ENSMUSG00000033981

Gria2

ENSMUSG00000020524

Gria1

Node color: Red = C.elegans, Green = D.rerio, Blue = H.sapiens, Orange = M.musculus

Edge color: Red = Ensembl, Blue = Homologene, Yellow = Inparanoid

Violet = Ensembl+Homologene, Orange = Ensembl+Inparanoid, Green = Homologene+Inparanoid

Black = Ensembl+Homologene+Inparanoid

Figure 3: Example R plot for the orthologous relationship of the glr-2 gene in C.elegans

using the command: get.orthologe.table("B0280.12","ce",plot=TRUE,self=FALSE) or

get.orthologe.table(get.gene.info("glr-2","external gene id","ensembl gene id","ce"), +

"ce",plot=TRUE,self=FALSE).

in Jena, Germany

. In this project, normal and

perturbed ageing is investigated by measuring the

complete transcriptomes of ﬁve organisms (round-

worm, zebraﬁsh, killiﬁsh, mouse and human) at dif-

ferent age levels using next-generation-sequencing

technologies. The goal of this project is to study

the complex interplay of maintenance and repair net-

works in the process of ageing in these organisms.

The software introduced in this paper has essentially

contributed to the evaluation of the transcriptome

data, for instance by clustering temporal expression

patterns across species, by enabling a gene set enrich-

ment analysis (GSEA) with four organisms included,

and by facilitating the construction of decision trees

for gene expression levels by inclusion of multiple

species.

In addition, the methods presented here were used

in a cross-species comparison (between M. musculus

and C. elegans) of genes that were differentially ex-

pressed as a consequence of artiﬁcially induced im-

paired insulin/IGF1 signalling, which extends lifes-

pan in both species. Thereby, six commonly downreg-

ulated and thirty commonly upregulated genes could

be identiﬁed (Zarse et al., 2012).

Another successful application of the orthology

package was presented at the RoSyBa

in 2011. Here,

genes of three species including different tissues,

http://www.jenage.de

http://goethe.informatik.uni-

rostock.de/ibima/rosyba2011/

which were differentially expressed during the pro-

cess of aging were compared using the methods de-

scribed in this paper, yielding 49 orthologous upregu-

lated (amongst others mod-5, shown in ﬁgure 2) and

66 downregulated genes (Fuellen et al., 2012).

3 DISCUSSION

The package presented here is capable of support-

ing genome-based multi-species approaches where

the knowledge of orthology relationships between the

genes of two or more organisms is required. Ex-

amples for areas of application are systems biol-

ogy, cancer- or age research. The package sub-

sumes data from three prominent open-access orthol-

ogy databases, and allows the user to retrieve the in-

formation contained in these databases in a consis-

tent manner. By collecting information from several

databases, the package gives a more complete picture

of orthology compared to each individual database. It

allows for selection of robust predictions of orthology

if the user conﬁnes the search to orthologies predicted

by more than one database. The package is free of

idle time caused by network trafﬁc because the data

related to the investigated organisms is downloaded

and processed locally before the package is used for

the ﬁrst time, and is therewith faster than web-based

tools. Problems arising from the existence of syn-

onyms for genes or from the occurrence of duplicate

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gene names and non-unique gene identiﬁers are over-

come by using unique (Ensembl-) identiﬁers inter-

nally. The latter liberates the user from such issues,

and in many cases is a prerequisite for a successful or-

thology search. Orthology relationships between two

or more species can be drawn schematically as shown

in ﬁgure 2, which allows the user to gain insight even

if complicated many-to-many orthology relationships

between genes prevail.

By default, the current release of the orthol-

ogy package comprises data for four organisms:

C. elegans, D. rerio, M. musculus and H. sapiens. The

extension towards a larger set of supported species

is simply feasible as long as the referring data is

available in the HomoloGene database, the Ensembl

Compara database, and the Inparanoid database. Fu-

ture work could be the inclusion of more orthology

databases or species into the package. The software

is written using the R programming language and is

freely available at the Comprehensive R Archive Net-

work - CRAN

4 METHODS

In order to build the package described here, data

from different sources, with different structures, had

to be combined. The orthology information stored

in the databases involved was downloaded, and

the datasets were synchronized by cross-mapping

the corresponding identiﬁers used in the individual

databases. A diagram showing the databases used and

the mappings carried out is depicted in ﬁgure 4.

As internal identiﬁers for the orthology package,

Ensembl gene IDs were chosen since two of the in-

cluded databases work with Ensembl IDs. Informa-

tion from Ensembl Compara was accessed via the

biomaRt R package (Durinck et al., 2005) which facil-

itates connection to the Biomart portal. The Homolo-

Gene database was downloaded in ﬂat-ﬁle format

and accessed using the R package annotationTools

(Kuhn et al., 2008). HomoloGene uses Entrez gene

IDs as internal identiﬁers which can be mapped

to Ensembl gene IDs using the biomaRt package.

Data from the Inparanoid database was retrieved with

the corresponding homology information R packages

from Bioconductor (e.g. hom.Hs.inp.db for human

or hom.Mm.inp.db for mouse) (Carlson and Pages,

2012). Inparanoid is based on Ensembl protein IDs

and, once again, these IDs were mapped using the

www.cran.r-project.org

ftp://ftp.ncbi.nih.gov/pub/HomoloGene/

current/homologene.data

biomaRt package. The complete datasets were pre-

processed and assembled into a compact R package

that is run on the local computer independently from

online resources. Because of the compact structure,

the commands shown in table 1 can be run in par-

allel on multiple CPUs, although this is not manda-

tory. The example commands used in this paper only

needed a few seconds on a standard desktop com-

puter.

BioMart via biomaRt R package

FTP data download

annotationTools R package

Entrez gene IDs

Ensembl gene IDs

biomaRt R package

homology information R packages:

hom.Hs.inp.db, hom.Mm.inp.db,

hom.Dr.inp.db, hom.Ce.inp.db

Ensembl protein IDs

local R orthology

package for fast access

Figure 4: Scheme of the accessed databases and of the gen-

eral data ﬂow.

5 CONCLUSIONS

We present a software package that is able to access

and display orthology relationships between multiple

species. The software circumvents a number of is-

sues usually connected with orthology searches and

provides a convenient and consistent interface to the

user. The software has been proven to be useful in

several research projects, and is freely available at the

Comprehensive R Archive Network (CRAN).

ACKNOWLEDGEMENTS

This work is part of the research program of

the Jena Centre for Systems Biology of Ageing

(JenAge) funded by the German Ministry for Ed-

ucation and Research (Bundesministerium fur Bil-

dung und Forschung - BMBF; support code BMBF

0315581).

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