![]() ![]() ![]() We also wanted to examine the memory requirements of each program/language combination, since although memory capacity increases constantly and hardware gets cheaper, the large datasets in bioinformatics analyses can be a problem for desktop computers. We specifically wanted to determine if C would be faster than Java for performing recombination detection, which is an inherently difficult computational exercise. There were several reasons for this benchmarking exercise. This benchmark was conducted on both Linux and Windows, since the computer used had a dual boot. In each case we tested the programs using different languages. In this paper we examined three commonly used tasks in biology, the Sellers algorithm the Neighbor-Joining NJ algorithm and a program parsing the output of BLAST. Consequently, any bioinformatics procedure has a number of areas where programming might be improved, these being: the space required to temporarily store data, the speed of computation, linkage between programs, and presentation of analyses. These quick scripts are usually implemented in Perl or Python. Scripts are also used to extract information from large data files, thus enhancing the presentation of results. Because file formats can be different, linking programs in a pipeline is difficult, hence scripts are written to act as interfaces between programs performing the sequential parts of an analysis. Large amounts of data can be generated in different formats. Another common task in bioinformatics is text mining or text parsing. Ī typical bioinformatics program reads FASTA files, holds the DNA sequences in memory, performs different computing tasks on the sequences, and finally writes the results to a file. While languages themselves have been benchmarked, such comparisons have not been done using algorithms that are relevant to bioinformatics. There is, at present, little direct data on the underlying speed and efficiency of equivalent algorithms written in different languages. However, it is possible that the same program, written in different languages, or running under different operating systems, may exhibit significant differences in speed and efficiency. Comparisons of the algorithm accuracy of different programs that undertake similar tasks have been published allowing assessment of the best algorithms to use for specific tasks. Because of the size of bioinformatics datasets, computation time is not trivial, and efficiencies in computational speed are desirable. Diverse programs have been written for various bioinformatics applications using every available language. ![]() Bioinformatic analyses involve a range of tasks and processes. ![]()
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