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A comparison of common programming languages used in bioinformatics.

ABSTRACT: BACKGROUND: The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three standard bioinformatics methods, implemented in programs using one of six different programming languages. Programs for the Sellers algorithm, the Neighbor-Joining tree construction algorithm and an algorithm for parsing BLAST file outputs were implemented in C, C++, C#, Java, Perl and Python. RESULTS: Implementations in C and C++ were fastest and used the least memory. Programs in these languages generally contained more lines of code. Java and C# appeared to be a compromise between the flexibility of Perl and Python and the fast performance of C and C++. The relative performance of the tested languages did not change from Windows to Linux and no clear evidence of a faster operating system was found. Source code and additional information are available from http://www.bioinformatics.org/benchmark/. CONCLUSION: This benchmark provides a comparison of six commonly used programming languages under two different operating systems. The overall comparison shows that a developer should choose an appropriate language carefully, taking into account the performance expected and the library availability for each language.

SUBMITTER: Fourment M

PROVIDER: S-EPMC2267699 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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A comparison of common programming languages used in bioinformatics.

Fourment Mathieu M Gillings Michael R MR

BMC bioinformatics 20080205

<h4>Background</h4>The performance of different programming languages has previously been benchmarked using abstract mathematical algorithms, but not using standard bioinformatics algorithms. We compared the memory usage and speed of execution for three standard bioinformatics methods, implemented in programs using one of six different programming languages. Programs for the Sellers algorithm, the Neighbor-Joining tree construction algorithm and an algorithm for parsing BLAST file outputs were i ...[more]

PMID: 18251993

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Project description:Language WorkBenches (LWBs) are software engineering tools that help domain experts develop solutions to various classes of problems. Some of these tools focus on non-technical users and provide languages to help organize knowledge while other workbenches provide means to create new programming languages. A key advantage of language workbenches is that they support the seamless composition of independently developed languages. This capability is useful when developing programs that can benefit from different levels of abstraction. We reasoned that language workbenches could be useful to develop bioinformatics software solutions. In order to evaluate the potential of language workbenches in bioinformatics, we tested a prominent workbench by developing an alternative to shell scripting. To illustrate what LWBs and Language Composition can bring to bioinformatics, we report on our design and development of NYoSh (Not Your ordinary Shell). NYoSh was implemented as a collection of languages that can be composed to write programs as expressive and concise as shell scripts. This manuscript offers a concrete illustration of the advantages and current minor drawbacks of using the MPS LWB. For instance, we found that we could implement an environment-aware editor for NYoSh that can assist the programmers when developing scripts for specific execution environments. This editor further provides semantic error detection and can be compiled interactively with an automatic build and deployment system. In contrast to shell scripts, NYoSh scripts can be written in a modern development environment, supporting context dependent intentions and can be extended seamlessly by end-users with new abstractions and language constructs. We further illustrate language extension and composition with LWBs by presenting a tight integration of NYoSh scripts with the GobyWeb system. The NYoSh Workbench prototype, which implements a fully featured integrated development environment for NYoSh is distributed at http://nyosh.campagnelab.org.

Project description:BackgroundEvidence-based conservation is urgently needed to identify, apply and promote effective interventions for mitigation of threats and recovery of the natural environment. Estimation of intervention effectiveness is subject to robust study design and statistical analysis, and much progress is documented in these fields. In contrast, little is understood about the accuracy and biases (underestimation and overestimation) of different effectiveness metrics and how they are affected by sample size.MethodsIn this study, a dataset (n = 500 cases) consisting of random, positive, integer numbers was simulated to produce frequency input data for the 2 × 2 contingency table. For each case, three metrics of the relative risk, odds ratio and the magnitude of change were calculated, their disparity was estimated and the characteristics of treatment (with intervention) and control (without intervention) samples significantly affecting this disparity were studied by means of linear regression. Further, four case studies from different conservation interventions are provided to support the results.ResultsThe study has shown that the relative risk and the magnitude of change produce identical estimates of intervention effectiveness only when treatment and control samples are equal, or when the number of target outcomes (e.g., number of livestock killed by predators) in treatment sample reaches zero. In other situations, the magnitude of change gives overestimates or underestimates, depending on relationships between treatment and control sample sizes. The table summarizing the conditions of equalities and biases between these two metrics is provided. These conditions are valid for both reduction-aimed interventions reducing negative target outcomes (e.g., livestock protection to reduce livestock losses to predators) and for addition-aimed interventions increasing positive target outcomes (e.g., establishment of protected areas to increase species presence). No significant effects on the odds ratio were found.ConclusionResearchers should set equal treatment and control sample sizes so that to produce identical estimates of intervention effectiveness by the relative risk and the magnitude of change. Otherwise, these estimates are biased if produced by the magnitude of change and the relative risk should be used instead. As setting equal treatment and control samples can be impractical, I encourage researchers and practitioners to use the relative risk in estimation of intervention effectiveness. This will not take additional efforts as both metrics are calculated from the same contingency table. Treatment and control sample sizes, along with their sub-samples affected and not by an intervention, should be explicitly reported by researchers to allow independent evaluation of intervention effectiveness. This approach can help obtain more accurate information on intervention effectiveness for making better decisions in conservation actions.

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A comparison of common programming languages used in bioinformatics.

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A comparison of common programming languages used in bioinformatics.

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