The emergence of high-throughput data in biology has increased the need for functional in silico analysis and prompted the development of integrative bioinformatics tools to facilitate the obtainment of biologically meaningful data. In this paper, we present DoriTool, a comprehensive, easy, and friendly pipeline integrating biological data from different functional tools. The tool was designed with the aim to maximize reproducibility and reduce the working time of the researchers, especially of those with limited bioinformatics skills, and to help them with the interpretation of the results. DoriTool is based upon an integrative strategy implemented following a modular design pattern. Using scripts written in Bash, Perl and R, it performs a functional in silico analysis annotation at mutation/variant level, gene level, pathway level and network level by combining up-to-date functional and genomic data and integrating also third-party bioinformatics tools in a pipeline. DoriTool uses GRCh37 human assembly and online mode. DoriTool provides nice visual reports including variant annotation, linkage disequilibrium proxies, gene annotation, gene ontology analysis, expression quantitative trait loci results from Genotype-Tissue Expression (GTEx) and coloured pathways. Here, we also show DoriTool functionalities by applying it to a dataset of 13 variants associated with prostate cancer. Project development, released code libraries, GitHub repository (https://github.com/doritool) and documentation are hosted at https://doritool.github.io/. DoriTool is, to our knowledge, the most complete bioinformatics tool offering functional in silico annotation of variants previously associated with a trait of interest, shedding light on the underlying biology and helping the researchers in the interpretation and discussion of the results.

Keywords:
Genome-wide association study, Association, Cancer, Functional annotation, Genomics, Next-generation sequencing
1.
McVean GA, et al: An integrated map of genetic variation from 1,092 human genomes. Nature 2012;491:56-65.
2.
UK10K Consortium: The UK10K project identifies rare variants in health and disease. Nature 2015;526:82-90.
3.
NHLBI GO Exome Sequencing Project (ESP): Exome Variant Server. 2014. http://evs.gs.washington.edu/EVS/.
4.
Genomics England: The 100,000 Genomes Project. 2014. http://www.genomicsengland.co.uk/the-100000-genomes-project/.
5.
Harrow J, et al: GENCODE: producing a reference annotation for ENCODE. Genome Biol 2006;7(suppl 1):S4.
6.
Huang Q: Genetic study of complex diseases in the post-GWAS era. J Genet Genomics 2015;42:87-98.
7.
Freedman ML, et al: Principles for the post-GWAS functional characterization of cancer risk loci. Nat Genet 2011;43:513-518.
8.
Reimand J, et al: g:Profiler - a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 2016;44:W83-W89.
9.
Falcon S, Gentleman R: Using GOstats to test gene lists for GO term association. Bioinformatics 2007;23:257-258.
10.
Bauer S, Grossmann S, Vingron M, Robinson PN: Ontologizer 2.0 - a multifunctional tool for GO term enrichment analysis and data exploration. Bioinformatics 2008;24:1650-1651.
11.
González-Pérez A, López-Bigas N: Improving the assessment of the outcome of nonsynonymous SNVs with a consensus deleteriousness score, Condel. Am J Hum Genet 2011;88:440-449.
12.
Kumar P, Henikoff S, Ng PC: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009;4:1073-1081.
13.
Adzhubei IA, et al: A method and server for predicting damaging missense mutations. Nat Methods 2010;7:248-249.
14.
Flicek P, et al: Ensembl 2012. Nucleic Acids Res 2012;40:D1.
15.
McLaren W, et al: The Ensembl variant effect predictor. Genome Biol 2016;17:122.
16.
Yates A, et al: Ensembl 2016. Nucleic Acids Res 2016;44:D710-D716.
17.
McLaren W, et al: The Ensembl variant effect predictor. bioRxiv 2016;42374.
18.
GTEx Consortium: The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013;45:580-585.
19.
Yates A, et al: The Ensembl REST API: Ensembl data for any language. Bioinformatics 2015;31:143-145.
20.
Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009;37:1-13.
21.
Carlson M: org.Hs.eg.db: Genome Wide Annotation for Human. R Package 2015, version 3.1.2.
22.
Alexa A, Rahnenfuhrer J: topGO: Enrichment Analysis for Gene Ontology. October, 2010.
23.
Zhao S, Guo Y, Shyr Y: KEGGprofile: An Annotation and Visualization Package for Multi-Types and Multi-Groups Expression Data in KEGG Pathway. R Package, 2015, version 1.18.0.
24.
Aibar S, Fontanillo C, Droste C, De Las Rivas J: Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering. Bioinformatics 2015;31:1686-1688.
25.
Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57.
26.
Fresno C, Fernández EA: RDAVIDWebService: a versatile R interface to DAVID. Bioinformatics 2013;29:2810-2811.
© 2017 S. Karger AG, Basel
2017
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