| Literature DB >> 31278684 |
Raoul J P Bonnal1, Andrew Yates2, Naohisa Goto3, Laurent Gautier4, Scooter Willis5, Christopher Fields6, Toshiaki Katayama7, Pjotr Prins8.
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.Entities:
Keywords: Bioinformatics; EMBOSS; Java; PAML; Perl; Python; R; RPC; Ruby; Web services
Mesh:
Year: 2019 PMID: 31278684 PMCID: PMC7212028 DOI: 10.1007/978-1-4939-9074-0_25
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745