A rapid and cost-effective approach for the development of polymorphic microsatellites in non-model species using paired-end RAD sequencing

BigFiRSt: A Software Program Using Big Data Technique for Mining Simple Sequence Repeats From Large-Scale Sequencing Data

Background

Simple 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.

Results

In 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.

Conclusions

The 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.

Mining the red deer genome (CerEla1.0) to develop X-and Y-chromosome-linked STR markers

Microsatellites are widely applied in population and forensic genetics, wildlife studies and parentage testing in animal breeding, among others, and recently, high-throughput sequencing technologies have greatly facilitated the identification of microsatellite markers. In this study the genomic data of Cervus elaphus (CerEla1.0) was exploited, in order to identify microsatellite loci along the red deer genome and for designing the cognate primers. The bioinformatics pipeline identified 982,433 microsatellite motifs genome-wide, assorted along the chromosomes, from which 45,711 loci mapped to the X- and 1096 to the Y-chromosome. Primers were successfully designed for 170,873 loci, and validated with an independently developed autosomal tetranucleotide STR set. Ten X- and five Y-chromosome-linked microsatellites were selected and tested by two multiplex PCR setups on genomic DNA samples of 123 red deer stags. The average number of alleles per locus was 3.3, and the average gene diversity value of the markers was 0.270. The overall observed and expected heterozygosities were 0.755 and 0.832, respectively. Polymorphic Information Content (PIC) ranged between 0.469 and 0.909 per locus with a mean value of 0.813. Using the X- and Y-chromosome linked markers 19 different Y-chromosome and 72 X-chromosome lines were identified. Both the X- and the Y-haplotypes split to two distinct clades each. The Y-chromosome clades correlated strongly with the geographic origin of the haplotypes of the samples. Segregation and admixture of subpopulations were demonstrated by the use of the combination of nine autosomal and 16 sex chromosomal STRs concerning southwestern and northeastern Hungary. In conclusion, the approach demonstrated here is a very efficient method for developing microsatellite markers for species with available genomic sequence data, as well as for their use in individual identifications and in population genetics studies.