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Characterisation data of simple sequence repeats of phages closely related to T7M.

ABSTRACT: Coliphages T7M and T3, Yersinia phage ?YeO3-12, and Salmonella phage ?SG-JL2 share high homology in genomic sequences. Simple sequence repeats (SSRs) are found in their genomes and variations of SSRs among these phages are observed. Analyses on regions of sequences in T7M and T3 genomes that are likely derived from phage recombination, as well as the counterparts in ?YeO3-12 and ?SG-JL2, have been discussed by Lin in "Simple sequence repeat variations expedite phage divergence: mechanisms of indels and gene mutations" [1]. These regions are referred to as recombinant regions. The focus here is on SSRs in the whole genome and regions of sequences outside the recombinant regions, referred to as non-recombinant regions. This article provides SSR counts, relative abundance, relative density, and GC contents in the complete genome and non-recombinant regions of these phages. SSR period sizes and motifs in the non-recombinant regions of phage genomes are plotted. Genomic sequence changes between T7M and T3 due to insertions, deletions, and substitutions are also illustrated. SSRs and nearby sequences of T7M in the non-recombinant regions are compared to the sequences of ?YeO3-12 and ?SG-JL2 in the corresponding positions. The sequence variations of SSRs due to vertical evolution are classified into four categories and tabulated: (1) insertion/deletion of SSR units, (2) expansion/contraction of SSRs without alteration of genome length, (3) changes of repeat motifs, and (4) generation/loss of repeats.

SUBMITTER: Lin TY

PROVIDER: S-EPMC4956903 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Characterisation data of simple sequence repeats of phages closely related to T7M.

Lin Tiao-Yin TY

Data in brief 20160629

Coliphages T7M and T3, Yersinia phage ϕYeO3-12, and Salmonella phage ϕSG-JL2 share high homology in genomic sequences. Simple sequence repeats (SSRs) are found in their genomes and variations of SSRs among these phages are observed. Analyses on regions of sequences in T7M and T3 genomes that are likely derived from phage recombination, as well as the counterparts in ϕYeO3-12 and ϕSG-JL2, have been discussed by Lin in "Simple sequence repeat variations expedite phage divergence: mechanisms of ind ...[more]

PMID: 27500195

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Project description:BackgroundSimple Sequence Repeats (SSRs) are short tandem repeats of nucleotide sequences. It has been shown that SSRs are associated with human diseases and are of medical relevance. Accordingly, a variety of computational methods have been proposed to mine SSRs from genomes. Conventional methods rely on a high-quality complete genome to identify SSRs. However, the sequenced genome often misses several highly repetitive regions. Moreover, many non-model species have no entire genomes. With the recent advances of next-generation sequencing (NGS) techniques, large-scale sequence reads for any species can be rapidly generated using NGS. In this context, a number of methods have been proposed to identify thousands of SSR loci within large amounts of reads for non-model species. While the most commonly used NGS platforms (e.g., Illumina platform) on the market generally provide short paired-end reads, merging overlapping paired-end reads has become a common way prior to the identification of SSR loci. This has posed a big data analysis challenge for traditional stand-alone tools to merge short read pairs and identify SSRs from large-scale data.ResultsIn this study, we present a new Hadoop-based software program, termed BigFiRSt, to address this problem using cutting-edge big data technology. BigFiRSt consists of two major modules, BigFLASH and BigPERF, implemented based on two state-of-the-art stand-alone tools, FLASH and PERF, respectively. BigFLASH and BigPERF address the problem of merging short read pairs and mining SSRs in the big data manner, respectively. Comprehensive benchmarking experiments show that BigFiRSt can dramatically reduce the execution times of fast read pairs merging and SSRs mining from very large-scale DNA sequence data.ConclusionsThe excellent performance of BigFiRSt mainly resorts to the Big Data Hadoop technology to merge read pairs and mine SSRs in parallel and distributed computing on clusters. We anticipate BigFiRSt will be a valuable tool in the coming biological Big Data era.

Dataset Information

Characterisation data of simple sequence repeats of phages closely related to T7M.

Publications

Characterisation data of simple sequence repeats of phages closely related to T7M.

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