Biotechnological

Communication

Biosci. Biotech. Res. Comm. 10(2): 13-18 (2017)

Edi ce of vitiDB: A  rst ever structured portal for

vitiligo protein repository and its assessment

Anvita Gupta Malhotra, Sudha Singh, Mohit Jha and Khushhali M Pandey*

Department of Biological sciences and Engineering, MANIT, Bhopal (M.P) INDIA

ABSTRACT

vitiDB (http://vitidb.com/index.php) is the  rst ever structured repository exclusively for vitiligo disorder. This portal

brings together valuable but heterogeneous or disparate information available from a spectrum of public domain

databases, patents and published literature sources on vitiligo. The aim is to arm the research community with criti-

cal details extracted froma reviewed resource of vitiligo genes and proteins. In addition, the database throws light

on interectome of proteins and their targetability assessment. The current release of vitiDB contains 333 vitiligo

disease - associated proteins and nearly 5000 allied proteins. Each entry provides comprehensive data related to the

protein like its kinetic, pharmacological and ontological properties. Additionally, this portal provides browsing and

extracting information related to vitiligo protein interaction network and its topological properties. vitiDB catalogues

107416 unique interactions among 4845 proteins derived from 8 different databases. Detailed targetability analysis is

available at this portaland it includes druggability, assayability, essentiality, vulnerability and secretability analysis

for the disease proteins. This user-friendly web interface will help the user to have an informed opinion on disease

genes without having to plough through various databases. The additional information in the form of derived data

can potentially assist in the drug discovery process. There will be sustained effortsaimed at periodic updation of the

core data in the wake of their extension / modi cation as well as constructive feedback received from the users.

KEY WORDS: VITILIGO, INTERACTION NETWORK, TARGETABILITY, DRUGGABILITY, DATABASE

ARTICLE INFORMATION:

*Corresponding Author: kmpbiomanit@gmail.com,

khushhalimenariya@manit.ac.in

Received 27

Nov, 2016

Accepted after revision 26

Jan, 2017

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007 CODEN: USA BBRCBA

Thomson Reuters ISI ESC and Crossref Indexed Journal

NAAS Journal Score 2017: 4.31 Cosmos IF : 4.006

reserved.

Online Contents Available at: http//www.bbrc.in/

14 VITIDB: VITILIGO PROTEIN REPOSITORY AND ASSESSMENT PORTAL BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Anvita Gupta Malhotra et al.

Introduction

Vitiligo is a progressive depigmentation disorder affect-

ing nearly 1% of the world population (Guerra et al.,

2010). Vitiligo disorder is characterized by progressive

skin depigmentation phenomena, which is induced and

maintained by the loss of melanin - producing cells

at the cutaneous level. Vitiligo is known to be a skin

disorder with complex etiology. Its pathogenesis and

the inner causes are still unclear but, in recent years,

scienti c research identi ed various pathways and

processes for its onset and progression. There are sev-

eral proposed hypotheses for this depigmenting dis-

order which includes autoimmunity auto-cytotoxic/

metabolic mechanisms, (Reimann et al., 2012), and

impaired melanocyte migration and/or prolifera-

tion (Westerhof and d'Ischia, 2007, Speeckaert et al.,

2015).

Studies also show the important role played by genetic

susceptibility in the origin of vitiligo. These mechanisms

are neither mutually exclusive, noradequate enough to

explain the disease etiology on their own. An integrated

viewpoint can be formed by incorporating all the differ-

ent causal factors that could contribute to some extent to

melanocyte destruction into a ‘convergence theory’ (Le

Poole et al., 1993), (Schallreuter et al., 2008, Jin et al.,

2016, Spritz, 2011). Vitiligo involves a complex frame-

work of interactions between various proteins, pathways

and processes (Laddha et al., 2013). An attempt has been

made here to bring together these disease proteins on

a common platform to display their related properties.

Further protein-protein interaction (PPI) data for each

of these disease proteins were extracted to generate a

comprehensive vitiligo interectome map (Malhotra et al.,

2017).

Information regarding the interectome analysis is

also made readily available on this portal.A better

understanding of the disease could provide new targets

for prevention or treatment of vitiligo (Passeron and

Ortonne, 2012). An ideal drug target should compre-

hend following properties: favourable assayability for

high throughput screening, ability to modify a disease,

less effect on alteration of physiological conditions or

other diseases, differential expression across the body,

the availability of a biomarker, etc. Experimentally

evaluating all proteins for their targetability is an over-

whelming task (Gashaw et al., 2011, Dey-Rao and Sinha,

2017).

Hence, it’s not experimentally possible to analyse

and prioritize all the drug targets in a laboratory. This

necessitates employinga computational technique for

evaluation of proteins on the standards of potent drug

targets (Sakharkar and Sakharkar, 2007, Costa et al.,

2010, Kandoi et al., 2015). Thus targetability analysis of

these proteins will aid in shortlisting prospective thera-

peutic drug targetsfrom the entire proteome with high

speci city.

There is no exclusive database for vitiligo disease

proteins available till date. All related information is dis-

persed in various published literature and online data-

bases and is, therefore, extremely dif cult to access. For

assisting the vitiligo researchers, all scattered data has

been compiled together and adedicated and comprehen-

sive vitiligo proteins repository – vitiDB - is developed.

Methodology

Data collection and compilation

The organization of data was done systematically as pri-

mary and derived data [Figure-1]. The primary data con-

sists of the vitiligo disease proteins related information.

The secondary data includes disease proteins interaction

data and its analysis features. Alongside the targetability

related parameters were also computed and added to the

resource.

Disease Protein Information

In order to build a comprehensive resource for vitiligo

proteins an extensive search was carried out across vari-

ous online databases like NCBI (2017), Uniprot (2015),

AnyGene [AnyGenes® http://www.anygenes.com/index.

php], Harmonizome (Rouillard et al., 2016), vitivar [http://

vitivar.igib.res.in/genes], GeneCards (Rebhan et al.,

1998), DOlite (Du et al., 2009), DisGeNET (Pinero et al.,

2015), OMIM (Hamosh et al., 2005), Disease Ontology

(Schriml et al., 2012) and Human Phenotype Ontology

(Kohler et al., 2014). Along with these databases, infor-

mation was also manually curated from published lit-

erature, microarray experiments and SNP analysis. Gene

susceptibility studies (Passeron and Ortonne, 2005),

(Picardo et al., 2015) were also considered while collat-

ing the disease genes for vitiligo.

Vitiligo Interectome Data

PPI data for the disease proteins were extracted from

eight different databases. These includes BioGrid (Chatr-

Aryamontri et al., 2015), MINT (Ceol et al., 2010),

STRING (Szklarczyk et al., 2017), Reactome (Croft et al.,

2014), DIP (Xenarios et al., 2002), IntAct (Kerrien et al.,

2012), Spike (Paz et al., 2011) and GeneMania (Zuberi

et al., 2013). All this data was merged and the unique

interactions were employed for construction of unidirec-

tional vitiligo interaction map (Malhotra et al., 2017) in

Cytoscape (Kohl et al., 2011).

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS VITIDB: VITILIGO PROTEIN REPOSITORY AND ASSESSMENT PORTAL 15

Anvita Gupta Malhotra et al.

FIGURE 1. Architecture of vitiDB.

Cytoscape is a highly versatile program for the analy-

sis, operation and visualization of large networks. Later,

the topological properties (Degree, Betweeness centrality,

Topological coef cient, Clustering coef cient, Closeness

centrality, Radiality, Stress, Neighbourhood connectivity

and Average shortest path length) were calculated for

all the proteins in the map. These calculation were done

in Network Analyzer (Assenov et al., 2008), an inbuilt

plugin with cytoscape that is used for comprehensive

analysis of network topologies.

Targetability statistics for disease proteins

The key disease proteins were analysed for their ability

to be a good therapeutic target (Rask-Andersen et al.,

2011). This was done under  ve heads namely Drugga-

bility (Druggable Genome (Hopkins and Groom, 2002),

can SAR druggability (Bulusu et al., 2014), DrugBank

(Law et al., 2014), DGI druggability (Grif th et al., 2013),

PDTD (Gao et al., 2008) and Domain DrugEBIlity (EMBL)

[https://www.ebi.ac.uk/chembl/drugebility/]) Assay-

ability (Sigma Aldrich database, Brenda (Schomburg

et al., 2017), Ki database [https://kidbdev.med.unc.edu/

databases/kidb.php], PDB (Berman et al., 2000) or Bind-

ing DB (Gilson et al., 2016)), Essentiality (OGE (Chen

et al., 2012), MGD (Eppig et al., 2007), Human Pheno-

type project (Kohler et al., 2014), Part et al. (Park et al.,

2008) or Georgi et al. (Georgi et al., 2013)), Vulnerability

(Holme et al., 2002) and Secretability (Narayanan, 2015).

Database architecture and web interface development

All the collated data were entered in excel  les which

were converted into csv  les. These  les were later

imported into MySQL database. MySQL, an object-rela-

tional open source database management system, was

employed to manage the data at the back-end. There were

11 tables in the database. The database was launched

using Apache HTTP server online on Linux Platform and

on local machine via WAMP server 2.0. For the backend

support i.e., database interfacing scripts, we have used

PHP programming language. It queries backend data-

base to retrieve information. The front end was designed

using HTML 5.

Results and Discussion

Implementation

The database is sectioned into three categories: Vitiligo

Protein Repository, Vitiligo Interectome map and Targ-

etability Analysis. All the information can be browsed

under the sections mentioned below (Figure-2).

16 VITIDB: VITILIGO PROTEIN REPOSITORY AND ASSESSMENT PORTAL BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Anvita Gupta Malhotra et al.

FIGURE 2. Schematic representation of vitiDB web interface.

Vitiligo Protein Repository:

A total of 333 disease proteins were identi ed. Their

related information is made available from this section

under the following features:

Basic Information- This includes Gene name, Pro-

tein name, Uniprot id, status, Length of the protein,

gene name synonyms and also the source from which

this protein is obtained as vitiligo disease protein. This

source can be related to protein or pathway in which

the protein is involved like database, microarray experi-

mentation, autoimmunity, susceptibility studies, apop-

tosis, melanogenesis (Singh et al., 2013) or oxidative

stress.

Kinetic properties – Enzyme related information is

displayed here under the headings EC number, Catalytic

activity, KineticsPathway, Enzyme regulation, Active

site, Binding site, Site or Function.

Ontology Data – Gene Ontology details and identi-

 ers are made available.

PubmedDetails – Pubmed ids for the corresponding

protein is mentioned.

Cross-reference to other online databases - The vitil-

igo protein entries are linked to other biological data-

bases like PDB, SMR, Protein Model Portal, PDB-

sum, DisProt, STRING, MINT, IntAct, DIP, BioGrid, Guide

to PHARMACOLOGY, DrugBank, ChEMBL, BindingDB,

KEGG, GeneID, Ensembl, BRENDA, BioCyc, Reactome,

InterPro, PRINTS, PROSITE, Pfam, DisGeNET, Gene-

Cards, PharmGKB or GeneWiki.

Vitiligo Interaction Map

The disease proteins interaction network consists of

4845 nodes and 107416 unique interactions. The inter-

acting partners along with the topological properties are

available for each of 4845 proteins. Further, based on

the network topology analysis, the proteins were clas-

si ed as backbone and core proteins and are labeled

accordingly in the database under the section

Classi ca-

tion of proteins.

Targetability Analysis of Disease proteins

A comprehensive description of all the features pertain-

ing to the drug target identi cation was fetched from

various sources and displayed under this head. The pro-

teins that are in alignment with most of the parameters

are entitledto be the prospective target for the vitiligo

disorder. The later updates will aim at making the tar-

getability analysis available for all the human proteins.

Concluding Remarks

The increasing amount of data on protein interactions,

drug target features,and the knowledge of drug-like

properties can act as a very lucrative starting point for

vitiligo drug hunt. Vitiligo sufferers still have to face

ostracism and discrimination around the world. This is a

modest attempt to aid in its treatment. The enriched data

and search utilities available at vitiDB might assist other

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS VITIDB: VITILIGO PROTEIN REPOSITORY AND ASSESSMENT PORTAL 17

Anvita Gupta Malhotra et al.

online databases in providing all-inclusive information

about the drugs, targets and target identi cation proc-

ess aimed at further research and drug discovery efforts.

Availability

vitiDBis a free database that can be accessed at http://

vitidb.com

Acknowledgments

The authors thankfully acknowledge the help, support

and guidance provided by Dr. Ajay Pandey, Department

of Mechanical Engineering, MANIT, Bhopal.

References

Anonymous (2015) UniProt: a hub for protein information,

Nucleic Acids Res 43(Database issue), D204-212.

Anonymous (2017) Database Resources of the National Center

for Biotechnology Information, Nucleic Acids Res 45(D1), D12-

D17.

Assenov, Y., F. Ramirez, S. E. Schelhorn, T. Lengauer, M. Albre-

cht (2008), Computing topological parameters of biological

networks, Bioinformatics 24(2), 282-284.

Berman, H. M., J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat,

H. Weissig, I. N. Shindyalov, P. E. Bourne (2000) The Protein

Data Bank, Nucleic Acids Res 28(1), 235-242.

Bulusu, K. C., J. E. Tym, E. A. Coker, A. C. Schierz, B. Al-Lazi-

kani (2014) canSAR: updated cancer research and drug dis-

covery knowledgebase, Nucleic Acids Res 42(Database issue),

D1040- 1047.

Ceol, A., A. ChatrAryamontri, L. Licata, D. Peluso, L. Brig-

anti, L. Perfetto, L. Castagnoli, G. Cesareni (2010) MINT: the

molecular interaction database, 2009 update, Nucleic Acids

Res 38(Database issue), D532-539.

Chatr-Aryamontri, A., B. J. Breitkreutz, R. Oughtred, et al.,

(2015) The BioGRID interaction database: 2015 update, Nucleic

Acids Res 43(Database issue), D470-478.

Chen, W. H., P. Minguez, M. J. Lercher, P. Bork (2012) OGEE: an

online gene essentiality database, Nucleic Acids Res 40(Data-

base issue), D901-906.

Costa, P. R., M. L. Acencio, N. Lemke (2010) A machine learn-

ing approach for genome-wide prediction of morbid and drug-

gable human genes based on systems-level data, BMC Genom-

ics 11 Suppl 5, S9.

Croft, D., A. F. Mundo, R. Haw, M. Milacic, et al., (2014) The

Reactome pathway knowledgebase, Nucleic Acids Res 42(Data-

base issue), D472-477.

Dey-Rao, R., A. A. Sinha (2017) Vitiligo blood transcriptom-

ics provides new insights into disease mechanisms and identi-

 es potential novel therapeutic targets, BMC Genomics 18(1),

109.

Du, P., G. Feng, J. Flatow, J. Song, M. Holko, W. A. Kibbe, S.

M. Lin (2009) From disease ontology to disease-ontology lite,

statistical methods to adapt a general-purpose ontology for

the test of gene-ontology associations, Bioinformatics 25(12),

i63-68.

Eppig, J. T., J. A. Blake, C. J. Bult, J. A. Kadin, J. E. Richardson

(2007) The mouse genome database (MGD): new features facili-

tating a model system, Nucleic Acids Res 35(Database issue),

D630-637.

Gao, Z., H. Li, H. Zhang, X. Liu, L. Kang, X. Luo, W. Zhu, K.

Chen, X. Wang, H. Jiang (2008) PDTD: a web-accessible pro-

tein database for drug target identi cation, BMC Bioinformat-

ics 9, 104.

Gashaw, I., P. Ellinghaus, A. Sommer, K. Asadullah (2011)

What makes a good drug target?, Drug Discov Today 16(23-

24), 1037-1043.

Georgi, B., B. F. Voight, M. Bucan (2013) From mouse to

human, evolutionary genomics analysis of human orthologs

of essential genes, PLoS Genet 9(5), e1003484.

Gilson, M. K., T. Liu, M. Baitaluk, G. Nicola, L. Hwang, J.

Chong (2016) BindingDB in 2015: A public database for

medicinal chemistry, computational chemistry and systems

pharmacology, Nucleic Acids Res 44(Database issue), D1045-

1053.

Grif th, M., O. L. Grif th, A. C. Coffman, J. V. Weible, J. F.

McMichael, N. C. Spies, J. Koval, I. Das, M. B. Callaway, J. M.

Eldred, C. A. Miller, J. Subramanian, R. Govindan, R. D. Kumar,

R. Bose, L. Ding, J. R. Walker, D. E. Larson, D. J. Dooling, S.

M. Smith, T. J. Ley, E. R. Mardis, R. K. Wilson (2013) DGIdb

- Mining the druggable genome, Nat Methods 10(12), 1209-

1210.

Guerra, L., E. Dellambra, S. Brescia, D. Raskovic (2010) Vitiligo:

pathogenetic hypotheses and targets for current therapies, Curr

Drug Metab 11(5), 451-467.

Hamosh, A., A. F. Scott, J. S. Amberger, C. A. Bocchini, V.

A. McKusick (2005) Online Mendelian Inheritance in Man

(OMIM): a knowledgebase of human genes and genetic disor-

ders, Nucleic Acids Res 33(Database issue), D514-517.

Holme, P., B. J. Kim, C. N. Yoon, S. K. Han (2002) Attack vul-

nerability of complex networks, Phys Rev E Stat Nonlin Soft

Matter Phys 65(5 Pt 2), 056109.

Hopkins, A. L., C. R. Groom (2002) Thedruggable genome, Nat

Rev Drug Discov 1(9), 727-730.

Jin, Y., G. Andersen, D. Yorgov, T. M. Ferrara, S. Ben, K. M.

Brownson, P. J. Holland, S. A. Birlea, J. Siebert et al., (2016)

Genome-wide association studies of autoimmune vitiligo iden-

tify 23 new risk loci and highlight key pathways and regula-

tory variants, Nat Genet 48(11), 1418-1424.

Kandoi, G., M. L. Acencio, N. Lemke (2015) Prediction of Drug-

gable Proteins Using Machine Learning and Systems Biology,

A Mini-Review, Front Physiol 6.

Kerrien, S., B. Aranda, L. Breuza, A. Bridge, et al., (2012) The

IntAct molecular interaction database in 2012, Nucleic Acids

Res 40(Database issue), D841-846.

Anvita Gupta Malhotra et al.

18 VITIDB: VITILIGO PROTEIN REPOSITORY AND ASSESSMENT PORTAL BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Kohl, M., S. Wiese, B. Warscheid (2011) Cytoscape: software

for visualization and analysis of biological networks, Methods

MolBiol 696, 291-303.

Kohler, S., S. C. Doelken, C. J. Mungall, S. Baueret al., (2014)

The Human Phenotype Ontology project: linking molecular

biology and disease through phenotype data, Nucleic Acids Res

42(Database issue), D966-974.

Laddha, N. C., M. Dwivedi, M. S. Mansuri, A. R. Gani, M. et al

(2013) Vitiligo: interplay between oxidative stress and immune

system, ExpDermatol 22(4), 245-250.

Law, V., C. Knox, Y. Djoumbou, T. Jewison, et al., (2014) Drug-

Bank 4.0: shedding new light on drug metabolism, Nucleic

Acids Res 42(Database issue), D1091-1097.

Le Poole, I. C., P. K. Das, R. M. van den Wijngaard, J. D. Bos, W.

Westerhof (1993) Review of the etiopathomechanism of viti-

ligo: a convergence theory, ExpDermatol 2(4), 145-153.

Malhotra, A. G., M. Jha, S. Singh, K. M. Pandey (2017) Con-

struction of a Comprehensive Protein-Protein Interaction Map

for Vitiligo Disease to Identify Key Regulatory Elements: A

Systemic Approach, Interdiscip Sci.

Narayanan, R. (2015) Druggable Vitiligo Genome, A Fast Track

Approach to Take the Genome Wide Association to the Clinic,

MOJ Proteomics & Bioinformatics 2(3).

Park, D., J. Park, S. G. Park, T. Park, S. S. Choi (2008) Analysis

of human disease genes in the context of gene essentiality,

Genomics 92(6), 414-418.

Passeron, T., J. P. Ortonne (2005) Physiopathology and genetics

of vitiligo, J Autoimmun 25 Suppl, 63-68.

Passeron, T., J. P. Ortonne (2012) Activation of the unfolded

protein response in vitiligo: the missing link?" J Invest Derma-

tol 132(11), 2502-2504.

Paz, A., Z. Brownstein, Y. Ber, S. Bialik, E. et al., (2011) SPIKE: a

database of highly curated human signaling pathways, Nucleic

Acids Res 39(Database issue), D793-799.

Picardo, M., M. L. Dell'Anna, K. Ezzedine, I. Hamzavi, J. E.

Harris, D. Parsad, A. Taieb (2015) Vitiligo, Nat Rev Dis Primers

1, 15011.

Pinero, J., N. Queralt-Rosinach, A. Bravo, J. Deu-Pons, A. Bauer-

Mehren, M. Baron, F. Sanz, L. I. Furlong (2015) DisGeNET: a

discovery platform for the dynamical exploration of human dis-

eases and their genes, Database (Oxford) 2015, bav028.

Rask-Andersen, M., M. S. Almen, H. B. Schioth (2011) Trends

in the exploitation of novel drug targets, Nat Rev Drug Discov

10(8), 579-590.

Rebhan, M., V. Chalifa-Caspi, J. Prilusky, D. Lancet (1998) Gen-

eCards: a novel functional genomics compendium with auto-

mated data mining and query reformulation support, Bioinfor-

matics 14(8), 656-664.

Reimann, E., K. Kingo, M. Karelson, P. Reemann, U. Loite, M.

Keermann, K. Abram, E. Vasar, H. Silm, S. Koks (2012) Expres-

sion pro le of genes associated with the dopamine pathway

in vitiligo skin biopsies and blood sera, Dermatology 224(2),

168-176.

Rouillard, A. D., G. W. Gundersen, N. F. Fernandez, Z. Wang,

C. D. Monteiro, M. G. McDermott, A. Ma'ayan (2016) The

harmonizome: a collection of processed datasets gathered to

serve and mine knowledge about genes and proteins, Database

(Oxford) 2016.

Sakharkar, M. K., K. R. Sakharkar (2007) Targetability

of human disease genes, Curr Drug Discov Technol 4(1),

48-58.

Schallreuter, K. U., P. Bahadoran, M. Picardo, et al., (2008)

Vitiligo pathogenesis: autoimmune disease, genetic defect,

excessive reactive oxygen species, calcium imbalance, or what

else?" ExpDermatol 17(2), 139-140; discussion 141-160.

Schomburg, I., L. Jeske, M. Ulbrich, S. Placzek, A. Chang, D.

Schomburg (2017) The BRENDA enzyme information system-

From a database to an expert system, J Biotechnol.

Schriml, L. M., C. Arze, S. Nadendla, Y. W. Chang, M. Maza-

itis, V. Felix, G. Feng, W. A. Kibbe (2012) Disease Ontology: a

backbone for disease semantic integration, Nucleic Acids Res

40(Database issue), D940-946.

Singh, S., A. G. Malhotra, A. Pandey, K. M. Pandey (2013)

Computational model for pathway reconstruction to unravel

the evolutionary signi cance of melanin synthesis, Bioinfor-

mation 9(2), 94.

Speeckaert, R., M. M. Speeckaert, N. van Geel (2015) Why

treatments do(n't) work in vitiligo: An autoin ammatory per-

spective, Autoimmun Rev 14(4), 332-340.

Spritz, R. A. (2011) Recent progress in the genetics of general-

ized vitiligo, J Genet Genomics 38(7), 271-278.

Szklarczyk, D., J. H. Morris, H. Cook, M. Kuhn, S. Wyder, M.

Simonovic, A. Santos, N. T. Doncheva, A. Roth, P. Bork, L. J.

Jensen, C. von Mering (2017) The STRING database in 2017:

quality-controlled protein–protein association networks, made

broadly accessible, Nucleic Acids Res 45(Database issue),

D362-368.

Westerhof, W., M. d'Ischia (2007) Vitiligo puzzle, the pieces fall

in place, Pigment Cell Res 20(5), 345-359.

Xenarios, I., L. Salwinski, X. J. Duan, P. Higney, S. M. Kim, D.

Eisenberg (2002) DIP: the Database of Interacting Proteins, a

research tool for studying cellular networks of protein interac-

tions, Nucleic Acids Res 30(1), 303-305.

Zuberi, K., M. Franz, H. Rodriguez, J. Montojo, C. T. Lopes,

G. D. Bader, Q. Morris (2013) GeneMANIA prediction server

2013 update, Nucleic Acids Res 41(Web Server issue), W115-

122.