SeqDev: An Algorithm for Constructing Genetic Elements Using Comparative Assembly

With the availability of recent next generation sequencing technologies and their low cost, genomes of different organisms are being sequenced frequently. Therefore, quick assembly of genome, transcriptome, and target contigs from the raw data generated through the sequencing technologies has become necessary for better understanding of different biological systems. This article proposes an algorithm, namely SeqDev (Sequence Developer) for constructing contigs from raw reads using reference sequences. For this, we considered a weighted frequency?based consensus mechanism named BlastAssemb for primary construction of a sequence with gaps. Then, we adopted suffix array and proposed a gap filling search (GFS) algorithm for searching the missing sequences in the primary construct. For evaluating our algorithm, we have chosen Pokkali (rice) raw genome and Japonica (rice) as our reference data. Experimental results demonstrated that our proposed algorithm accurately constructs promoter sequences of Pokkali from its raw genome data. These constructed promoter sequences were 93 ? 100% identical with the reference and also aligned with 96 ? 100% of corresponding reference sequences with eValue ranging from 0.0 ? 2e-14. All these results indicated that our proposed method could be a potential algorithm to construct target contigs from raw sequences with the help of reference sequences. Further wet lab validation with specific Pokkali promoter sequence will boost this method as a robust algorithm for target contig assembly.Plant Tissue Cult. & Biotech. 26(1): 105-121, 2016 (June)

Utilization of high throughput genome sequencing technology for large scale single nucleotide polymorphism discovery in red deer and Canadian elk

Deer farming is a significant international industry. For genetic improvement, using genomic tools, an ordered array of DNA variants and associated flanking sequence across the genome is required. This work reports a comparative assembly of the deer genome and subsequent DNA variant identification. Next generation sequencing combined with an existing bovine reference genome enabled the deer genome to be assembled sufficiently for large-scale SNP discovery. In total, 28 Gbp of sequence data were generated from seven Cervus elaphus (European red deer and Canadian elk) individuals. After aligning sequence to the bovine reference genome build UMD 3.0 and binning reads into one Mbp groups; reads were assembled and analyzed for SNPs. Greater than 99% of the non-repetitive fraction of the bovine genome was covered by deer chromosomal scaffolds. We identified 1.8 million SNPs meeting Illumina InfiniumII SNP chip technical threshold. Markers on the published Red x Pere David deer linkage map were aligned to both UMD3.0 and the new deer chromosomal scaffolds. This enabled deer linkage groups to be assigned to deer chromosomal scaffolds, although the mapping locations remain based on bovine order. Genotyping of 270 SNPs on a Sequenom MS system showed that 88% of SNPs identified could be amplified. Also, inheritance patterns showed no evidence of departure from Hardy-Weinberg equilibrium. A comparative assembly of the deer genome, alignment with existing deer genetic linkage groups and SNP discovery has been successfully completed and validated facilitating application of genomic technologies for subsequent deer genetic improvement.