With the rapid decrease in the experimental cost of NGS, human whole-genome sequencing (WGS) at 30× depth can be performed at the cost of $700, and an mRNA sequencing (RNA-seq) costs only $80. Next-generation sequencing (NGS) is a key cornerstone in precision medicine. The FANSe3 algorithm can be downloaded from the website. The novel algorithm, designed for high-performance cloud-computing after infrastructures, will break the bottleneck of speed and accuracy in NGS data analysis and promote NGS applications in various fields.
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Error allowance hardly affected the identification of a driver somatic mutation in clinically relevant WGS data and provided robust gene expression profiles regardless of the parameter settings and sequencer used. Like its predecessor FANSe2, the error rate of FANSe3 can be kept as low as 10 –9 in most cases, this is more robust than the Burrows–Wheeler transform-based algorithms. Therefore, we developed the FANSe3 mapping algorithm, which can map a 30 × human whole-genome sequencing (WGS) dataset within 30 min, a 50 × human whole exome sequencing (WES) dataset within 30 s, and a typical mRNA-seq dataset within seconds in a single-server node without the need for any hardware acceleration feature.
A Fast, accurate and robust mapping algorithm is highly needed. Aligning billions of reads generated by the next-generation sequencing (NGS) to reference sequences, termed “mapping”, is the time-consuming and computationally-intensive process in most NGS applications.
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