An Open-Source Sandbox for Increasing the Accessibility of Functional Programming to the Bioinformatics and Scientific Communities

Fenwick, Matthew; Sesanker, Colbert; Schiller, Martin; Ellis, Heidi J. C.; Hinman, M. Lee; Vyas, Jay; Gryk, Michael R.

doi:10.1109/itng.2012.21

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…It has been shown that these features, together with other amenities like powerful debugging tools that Lisp programmers take for granted, offer a significant productivity boost to programmers [ 3 ]. Lisp also gives programmers the ability to implement complex data operations and mathematical constructs in an expressive and natural idiom [ 69 ].…”

Section: Other Unique Strengthsmentioning

confidence: 99%

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

et al. 2016

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We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the ‘programmable programming language’. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

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Section: Other Unique Strengthsmentioning

confidence: 99%

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

et al. 2016

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“…However, BioClojure does not have functions for SAM/BAM manipulation and is not fully implemented in Clojure. The CONNJUR-Sandbox source [ 11 ] contains examples of the visualization of protein structures using PDB data in Clojure and the prediction of neighboring amino acids with Support Vector Machine algorithms named Clojure Amino Acid Predictor.…”

Section: Methodsmentioning

confidence: 99%

cljam: a library for handling DNA sequence alignment/map (SAM) with parallel processing

Takeuchi¹,

Yamada²,

Aoki³

et al. 2016

Source Code Biol Med

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BackgroundNext-generation sequencing can determine DNA bases and the results of sequence alignments are generally stored in files in the Sequence Alignment/Map (SAM) format and the compressed binary version (BAM) of it. SAMtools is a typical tool for dealing with files in the SAM/BAM format. SAMtools has various functions, including detection of variants, visualization of alignments, indexing, extraction of parts of the data and loci, and conversion of file formats. It is written in C and can execute fast. However, SAMtools requires an additional implementation to be used in parallel with, for example, OpenMP (Open Multi-Processing) libraries. For the accumulation of next-generation sequencing data, a simple parallelization program, which can support cloud and PC cluster environments, is required.ResultsWe have developed cljam using the Clojure programming language, which simplifies parallel programming, to handle SAM/BAM data. Cljam can run in a Java runtime environment (e.g., Windows, Linux, Mac OS X) with Clojure.ConclusionsCljam can process and analyze SAM/BAM files in parallel and at high speed. The execution time with cljam is almost the same as with SAMtools. The cljam code is written in Clojure and has fewer lines than other similar tools.

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“…It has been shown that these features, together with other amenities like powerful debugging tools that Lisp programmers take for granted, offer a significant productivity boost to programmers [54,55]. Lisp also gives programmers the ability to implement complex data operations and mathematical constructs in an expressive and natural idiom [56].…”

Section: Other Unique Strengthsmentioning

confidence: 99%

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

Khomtchouk¹,

Weitz²,

Karp³

et al. 2017

Briefings in Bioinformatics

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We address the need for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the creation of powerful and flexible software models that are required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the "programmable programming language." We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology.

show abstract

An Open-Source Sandbox for Increasing the Accessibility of Functional Programming to the Bioinformatics and Scientific Communities

Cited by 6 publications

References 10 publications

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

cljam: a library for handling DNA sequence alignment/map (SAM) with parallel processing

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

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