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A hybrid computational method for the discovery of novel reproduction-related genes.

ABSTRACT: Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

SUBMITTER: Chen L

PROVIDER: S-EPMC4358884 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A hybrid computational method for the discovery of novel reproduction-related genes.

Chen Lei L Chu Chen C Kong Xiangyin X Huang Guohua G Huang Tao T Cai Yu-Dong YD

PloS one 20150313 3

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecti ...[more]

PMID: 25768094

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Project description:BackgroundThe reproductive mechanisms of mollusk species have been interesting targets in biological research because of the diverse reproductive strategies observed in this phylum. These species have also been studied for the development of fishery technologies in molluscan aquaculture. Although the molecular mechanisms underlying the reproductive process have been well studied in animal models, the relevant information from mollusks remains limited, particularly in species of great commercial interest. Crassostrea hongkongensis is the dominant oyster species that is distributed along the coast of the South China Sea and little genomic information on this species is available. Currently, high-throughput sequencing techniques have been widely used for investigating the basis of physiological processes and facilitating the establishment of adequate genetic selection programs.ResultsThe C.hongkongensis transcriptome included a total of 1,595,855 reads, which were generated by 454 sequencing and were assembled into 41,472 contigs using de novo methods. Contigs were clustered into 33,920 isotigs and further grouped into 22,829 isogroups. Approximately 77.6% of the isogroups were successfully annotated by the Nr database. More than 1,910 genes were identified as being related to reproduction. Some key genes involved in germline development, sex determination and differentiation were identified for the first time in C.hongkongensis (nanos, piwi, ATRX, FoxL2, β-catenin, etc.). Gene expression analysis indicated that vasa, nanos, piwi, ATRX, FoxL2, β-catenin and SRD5A1 were highly or specifically expressed in C.hongkongensis gonads. Additionally, 94,056 single nucleotide polymorphisms (SNPs) and 1,699 simple sequence repeats (SSRs) were compiled.ConclusionsOur study significantly increased C.hongkongensis genomic information based on transcriptomics analysis. The group of reproduction-related genes identified in the present study constitutes a new tool for research on bivalve reproduction processes. The large group of molecular markers discovered in this study will be useful for population screening and marker assisted selection programs in C.hongkongensis aquaculture.

Dataset Information

A hybrid computational method for the discovery of novel reproduction-related genes.

Publications

A hybrid computational method for the discovery of novel reproduction-related genes.

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