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Methods Mol Biol. 2019;1910:747-766. doi: 10.1007/978-1-4939-9074-0_25.

Sharing Programming Resources Between Bio* Projects.

Author information

1
Istituto Nazionale Genetica Molecolare INGM Romeo ed Enrica Invernizzi, Milan, Italy.
2
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
3
Department of Genome Informatics, Genome Information Research Center, Research Institute for Microbial Diseases, Osaka University, Osaka, Japan.
4
DMAC, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark.
5
Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL, USA.
6
Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
7
Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Chiba, Japan.
8
Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. pjotr2018@thebird.nl.

Abstract

Open-source software encourages computer programmers to reuse software components written by others. In evolutionary bioinformatics, open-source software comes in a broad range of programming languages, including C/C++, Perl, Python, Ruby, Java, and R. To avoid writing the same functionality multiple times for different languages, it is possible to share components by bridging computer languages and Bio* projects, such as BioPerl, Biopython, BioRuby, BioJava, and R/Bioconductor.In this chapter, we compare the three principal approaches for sharing software between different programming languages: by remote procedure call (RPC), by sharing a local "call stack," and by calling program to programs. RPC provides a language-independent protocol over a network interface; examples are SOAP and Rserve. The local call stack provides a between-language mapping, not over the network interface but directly in computer memory; examples are R bindings, RPy, and languages sharing the Java virtual machine stack. This functionality provides strategies for sharing of software between Bio* projects, which can be exploited more often.Here, we present cross-language examples for sequence translation and measure throughput of the different options. We compare calling into R through native R, RSOAP, Rserve, and RPy interfaces, with the performance of native BioPerl, Biopython, BioJava, and BioRuby implementations and with call stack bindings to BioJava and the European Molecular Biology Open Software Suite (EMBOSS).In general, call stack approaches outperform native Bio* implementations, and these, in turn, outperform "RPC"-based approaches. To test and compare strategies, we provide a downloadable Docker container with all examples, tools, and libraries included.

KEYWORDS:

Bioinformatics; EMBOSS; Java; PAML; Perl; Python; R; RPC; Ruby; Web services

PMID:
31278684
DOI:
10.1007/978-1-4939-9074-0_25
[Indexed for MEDLINE]

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