Dataset Information

ProGeRF: proteome and genome repeat finder utilizing a fast parallel hash function.

ABSTRACT: Repetitive element sequences are adjacent, repeating patterns, also called motifs, and can be of different lengths; repetitions can involve their exact or approximate copies. They have been widely used as molecular markers in population biology. Given the sizes of sequenced genomes, various bioinformatics tools have been developed for the extraction of repetitive elements from DNA sequences. However, currently available tools do not provide options for identifying repetitive elements in the genome or proteome, displaying a user-friendly web interface, and performing-exhaustive searches. ProGeRF is a web site for extracting repetitive regions from genome and proteome sequences. It was designed to be efficient, fast, and accurate and primarily user-friendly web tool allowing many ways to view and analyse the results. ProGeRF (Proteome and Genome Repeat Finder) is freely available as a stand-alone program, from which the users can download the source code, and as a web tool. It was developed using the hash table approach to extract perfect and imperfect repetitive regions in a (multi)FASTA file, while allowing a linear time complexity.

SUBMITTER: Lopes Rda S

PROVIDER: S-EPMC4355816 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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ProGeRF: proteome and genome repeat finder utilizing a fast parallel hash function.

Lopes Robson da Silva Rda S Moraes Walas Jhony Lopes WJ Rodrigues Thiago de Souza Tde S Bartholomeu Daniella Castanheira DC

BioMed research international 20150225

Repetitive element sequences are adjacent, repeating patterns, also called motifs, and can be of different lengths; repetitions can involve their exact or approximate copies. They have been widely used as molecular markers in population biology. Given the sizes of sequenced genomes, various bioinformatics tools have been developed for the extraction of repetitive elements from DNA sequences. However, currently available tools do not provide options for identifying repetitive elements in the geno ...[more]

PMID: 25811026

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Project description:BACKGROUND: The high throughput of modern NGS sequencers coupled with the huge sizes of genomes currently analysed, poses always higher algorithmic challenges to align short reads quickly and accurately against a reference sequence. A crucial, additional, requirement is that the data structures used should be light. The available modern solutions usually are a compromise between the mentioned constraints: in particular, indexes based on the Burrows-Wheeler transform offer reduced memory requirements at the price of lower sensitivity, while hash-based text indexes guarantee high sensitivity at the price of significant memory consumption. METHODS: In this work we describe a technique that permits to attain the advantages granted by both classes of indexes. This is achieved using Hamming-aware hash functions--hash functions designed to search the entire Hamming sphere in reduced time--which are also homomorphisms on de Bruijn graphs. We show that, using this particular class of hash functions, the corresponding hash index can be represented in linear space introducing only a logarithmic slowdown (in the query length) for the lookup operation. We point out that our data structure reaches its goals without compressing its input: another positive feature, as in biological applications data is often very close to be un-compressible. RESULTS: The new data structure introduced in this work is called dB-hash and we show how its implementation--BW-ERNE--maintains the high sensitivity and speed of its (hash-based) predecessor ERNE, while drastically reducing space consumption. Extensive comparison experiments conducted with several popular alignment tools on both simulated and real NGS data, show, finally, that BW-ERNE is able to attain both the positive features of succinct data structures (that is, small space) and hash indexes (that is, sensitivity). CONCLUSIONS: In applications where space and speed are both a concern, standard methods often sacrifice accuracy to obtain competitive throughputs and memory footprints. In this work we show that, combining hashing and succinct indexing techniques, we can attain good performances and accuracy with a memory footprint comparable to that of the most popular compressed indexes.

Project description:BackgroundWhole-genome sequencing is performed routinely as a means to identify polymorphic genetic loci such as short tandem repeat loci. We have developed a simple tool, called pSTR Finder, which is freely available as a means of identifying putative polymorphic short tandem repeat (STR) loci from data generated from genome-wide sequences. The program performs cross comparisons on the STR sequences generated using the Tandem Repeats Finder based on multiple-genome samples in a FASTA format. These comparisons generate reports listing identical, polymorphic, and different STR loci when comparing two samples.MethodsThe web site http://forensic.mc.ntu.edu.tw:9000/PSTRWeb/Default has been developed as a means to identify polymorphic STR loci within complex mass genome sequences. The program was developed to generate a series of user-friendly reports.ResultsAs proof of concept for the program, four FASTA genome sequence samples of human chromosome X (AC_000155.1, CM000685.1, NC_018934.2, and CM000274.1) were obtained from GenBank and were analyzed for the presence of putative STR regions. The sequences within AC-000155.1 were used as an initial reference sequence from which there were 5443 identical and 4305 polymorphic STR loci identified using a repeat unit of 1-6 and 10 bp as the flanking sequence either side of the putative STR loci. A reliability test was used to compare five FASTA samples, which had sections of DNA sequence removed to mimic partial or fragmented DNA sequences, to determine whether pSTR Finder can efficiently and consistently find identical, polymorphic, and different STR loci.ConclusionsFrom the mass of DNA sequence data, the project was found to reproducibly identify polymorphic STR loci and generate user-friendly reports detailing the number and location of these potential polymorphic loci. This freely available program was found to be a useful tool to find polymorphic STR within whole-genome sequence data in forensic genetic studies.

Dataset Information

ProGeRF: proteome and genome repeat finder utilizing a fast parallel hash function.

Publications

ProGeRF: proteome and genome repeat finder utilizing a fast parallel hash function.

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