homologene

An r package that works as a wrapper to homologene

Available species are

homologene::taxData

##    tax_id                      name_txt
## 1   10090                  Mus musculus
## 2   10116             Rattus norvegicus
## 3   28985          Kluyveromyces lactis
## 4  318829            Magnaporthe oryzae
## 5   33169         Eremothecium gossypii
## 6    3702          Arabidopsis thaliana
## 7    4530                  Oryza sativa
## 8    4896     Schizosaccharomyces pombe
## 9    4932      Saccharomyces cerevisiae
## 10   5141             Neurospora crassa
## 11   6239        Caenorhabditis elegans
## 12   7165             Anopheles gambiae
## 13   7227       Drosophila melanogaster
## 14   7955                   Danio rerio
## 15   8364 Xenopus (Silurana) tropicalis
## 16   9031                 Gallus gallus
## 17   9544                Macaca mulatta
## 18   9598               Pan troglodytes
## 19   9606                  Homo sapiens
## 20   9615        Canis lupus familiaris
## 21   9913                    Bos taurus

Installation

install.packages('homologene')

or

devtools::install_github('oganm/homologene')

Usage

Basic homologene function requires a list of gene symbols or NCBI ids, and an inTax and an outTax. In this example, inTax is the taxon id of mus musculus while outTax is for humans.

homologene(c('Eno2','Mog'), inTax = 10090, outTax = 9606)

##   10090 9606 10090_ID 9606_ID
## 1  Eno2 ENO2    13807    2026
## 2   Mog  MOG    17441    4340

homologene(c('Eno2','17441'), inTax = 10090, outTax = 9606)

##   10090 9606 10090_ID 9606_ID
## 1  Eno2 ENO2    13807    2026
## 2   Mog  MOG    17441    4340

For mouse and humans two convenience functions exist that removes the need to provide taxonomic identifiers. Note that the column names are not the same as the homologene output.

mouse2human(c('Eno2','Mog'))

##   mouseGene humanGene mouseID humanID
## 1      Eno2      ENO2   13807    2026
## 2       Mog       MOG   17441    4340

human2mouse(c('ENO2','MOG','GZMH'))

##   humanGene mouseGene humanID mouseID
## 1      ENO2      Eno2    2026   13807
## 2       MOG       Mog    4340   17441
## 3      GZMH      Gzmd    2999   14941
## 4      GZMH      Gzme    2999   14942
## 5      GZMH      Gzmg    2999   14944
## 6      GZMH      Gzmf    2999   14943

homologeneData2

Original homologene database has not been updated since 2014. This package also includes an updated version of the homologene database that replaces gene symbols and identifiers with the their latest version. For the procedure followed for updating, see this blog post and/or see the processing code.

Using the updated version can help you match genes that cannot matched due to out of date annotations.

mouse2human(c('Mesd',
              'Trp53rka',
              'Cstdc4',
              'Ifit3b'))

## [1] mouseGene humanGene mouseID   humanID  
## <0 rows> (or 0-length row.names)

mouse2human(c('Mesd',
              'Trp53rka',
              'Cstdc4',
              'Ifit3b'),
            db = homologeneData2)

##   mouseGene humanGene mouseID humanID
## 1      Mesd      MESD   67943   23184
## 2  Trp53rka    TP53RK  381406  112858
## 3    Cstdc4      CSTA  433016    1475
## 4    Ifit3b     IFIT3  667370    3437

The homologeneData2 object that comes with the GitHub version of this package is updated weekly but if you are using the CRAN version and want the latest annotations, or if you want to keep a frozen version homologene, you can use the updateHomologene function.

homologeneDataVeryNew = updateHomologene() # update the homologene database with the latest identifiers

mouse2human(c('Mesd',
              'Trp53rka',
              'Cstdc4',
              'Ifit3b'),
            db = homologeneDataVeryNew)

Gene ID syncronization

The package also includes functions that were used to create the homologeneData2, for updating outdated gene symbols and identifiers.

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:testthat':
## 
##     matches

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

gene_history = getGeneHistory()
oldIds = c(4340964, 4349034, 4332470, 4334151, 4323831)
newIds = updateIDs(oldIds,gene_history)
print(newIds)

## [1] "9267698" "4349033" "4332468" "4334150" "4324017"

# get the latest gene symbols for the ids

gene_info = getGeneInfo()

gene_info %>%
    dplyr::filter(GeneID %in% as.integer(newIds)) # faster to match integers

## # A tibble: 5 x 3
##   tax_id  GeneID Symbol    
##    <int>   <int> <chr>     
## 1  39947 4324017 LOC4324017
## 2  39947 4332468 LOC4332468
## 3  39947 4334150 LOC4334150
## 4  39947 4349033 LOC4349033
## 5  39947 9267698 LOC9267698

Querying DIOPT

Instead of using just homologene, one can also make queries into the DIOPT database. Diopt uses multiple databases to find gene homolog/orthologues. Note that this function has a delay parameter that is set to 10 seconds by default. This was done to obey the robots.txt of their website.

diopt(c('GZMH'),inTax = 9606, outTax = 10090) %>% 
    knitr::kable()

## Submitting with 'submit'

Input Order	Search Term	Human GeneID	HGNCID	Human Symbol	Species 2	Mouse GeneID	Mouse Species Gene ID	Mouse Symbol	DIOPT Score	Weighted Score	Rank	Best Score	Best Score Reverse	Prediction Derived From
1	GZMH	2999	4710	GZMH	Mouse	14944	109253	Gzmg	8	8.40	high	Yes	Yes	Compara, HGNC, Homologene, Isobase, OrthoDB, OrthoFinder, Panther, Phylome
1	GZMH	2999	4710	GZMH	Mouse	14941	109255	Gzmd	7	7.45	moderate	No	Yes	Compara, HGNC, Homologene, OrthoDB, OrthoFinder, Panther, Phylome
1	GZMH	2999	4710	GZMH	Mouse	14943	109254	Gzmf	7	7.45	moderate	No	Yes	Compara, HGNC, Homologene, OrthoDB, OrthoFinder, Panther, Phylome
1	GZMH	2999	4710	GZMH	Mouse	14942	109265	Gzme	7	7.45	moderate	No	Yes	Compara, HGNC, Homologene, OrthoDB, OrthoFinder, Panther, Phylome
1	GZMH	2999	4710	GZMH	Mouse	245839	2675494	Gzmn	6	5.96	moderate	No	Yes	Compara, OMA, OrthoDB, OrthoFinder, Panther, Phylome
1	GZMH	2999	4710	GZMH	Mouse	14940	109256	Gzmc	5	5.03	moderate	No	No	OMA, OrthoDB, OrthoFinder, Panther, Phylome
1	GZMH	2999	4710	GZMH	Mouse	14939	109267	Gzmb	5	4.94	moderate	No	No	OrthoFinder, orthoMCL, Panther, Phylome, RoundUp
1	GZMH	2999	4710	GZMH	Mouse	17231	1261780	Mcpt8	4	4.07	moderate	No	No	OrthoDB, OrthoFinder, Panther, TreeFam
1	GZMH	2999	4710	GZMH	Mouse	13035	88563	Ctsg	2	2.01	low	No	No	OrthoDB, OrthoFinder
1	GZMH	2999	4710	GZMH	Mouse	14938	109266	Gzma	1	1.03	low	No	No	RoundUp
1	GZMH	2999	4710	GZMH	Mouse	19144	1343166	Klk6	1	1.01	low	No	No	OrthoDB
1	GZMH	2999	4710	GZMH	Mouse	17227	96940	Mcpt4	1	1.01	low	No	No	OrthoDB
1	GZMH	2999	4710	GZMH	Mouse	545055	88426	Cma2	1	1.01	low	No	No	OrthoDB
1	GZMH	2999	4710	GZMH	Mouse	17224	96937	Mcpt1	1	1.01	low	No	No	OrthoDB
1	GZMH	2999	4710	GZMH	Mouse	17232	1194491	Mcpt9	1	1.01	low	No	No	OrthoDB
1	GZMH	2999	4710	GZMH	Mouse	17228	96941	Cma1	1	1.01	low	No	No	OrthoDB

diopt(c('Eno2','Mog'),inTax = 10090, outTax =9606) %>%
    knitr::kable()

## Submitting with 'submit'

Input Order	Search Term	Mouse GeneID	MGIID	Mouse Symbol	Species 2	Human GeneID	Human Species Gene ID	Human Symbol	DIOPT Score	Weighted Score	Rank	Best Score	Best Score Reverse	Prediction Derived From
1	Eno2	13807	95394	Eno2	Human	2026	3353	ENO2	14	14.29	high	Yes	Yes	Compara, eggNOG, HGNC, Hieranoid, Homologene, Inparanoid, OMA, OrthoFinder, OrthoInspector, orthoMCL, Panther, Phylome, RoundUp, TreeFam
1	Eno2	13807	95394	Eno2	Human	2023	3350	ENO1	4	3.83	moderate	No	No	eggNOG, OrthoFinder, orthoMCL, RoundUp
1	Eno2	13807	95394	Eno2	Human	2027	3354	ENO3	4	3.83	moderate	No	No	eggNOG, OrthoFinder, orthoMCL, RoundUp
1	Eno2	13807	95394	Eno2	Human	387712	31670	ENO4	1	0.90	low	No	No	eggNOG
2	Mog	17441	97435	Mog	Human	4340	7197	MOG	13	13.28	high	Yes	Yes	Compara, eggNOG, HGNC, Hieranoid, Homologene, Inparanoid, OrthoFinder, OrthoInspector, orthoMCL, Panther, Phylome, RoundUp, TreeFam

Mishaps

As of version version 1.1.68, the output now includes NCBI ids. Since it doesn’t change any of the existing column names or their order, this shouldn’t cause problems in most use cases.

If a you can’t find a gene you are looking for it may have synonyms. See geneSynonym package to find them. If you have other problems open an issue or send a mail.