Mining Next Generation Sequencing Data: How to Avoid “Treasure in, Error Out”

During the last ten years, next generation sequencing methods, technologies and platforms have revolutionized genomics and transcriptomics research fields and advanced their applications in agriculture and biomedicine [1–3]. To date, the Roche 454 GS FLX(+) system, Applied Biosystems SOLiD (supported oligonucleotide ligation and detection) and Ion Proton/PGM/Chef systems now owned by Life Technologies (Grand Island, NY); Solexa GA (Genome Analyzer)/HiSeq/MiSeq/NextSeq developed by Illumina (San Diego, CA); and PacBio RSII system made by Pacific Biosciences (Menlo Park, CA) present five major platforms in the market. They utilize different sequencing chemistries (e.g., sequencing by ligation vs. sequencing by synthesis); templates (e.g., single molecules vs. clusters amplified by emulsion or bridge PCR); product sizes (e.g., from 75 bp to 8,500 bp in length) and number of reads per run (e.g., from one million to 5,000 million) [2,3].