The Ultrafast and Accurate Mapping Algorithm FANSe3: Mapping a Human Whole-Genome Sequencing Dataset Within 30 Minutes

Abstract

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. A Fast, accurate and robust mapping algorithm is highly needed. 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. 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. 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. 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. The FANSe3 algorithm can be downloaded from the website: http://www.chi-biotech.com/fanse3/.

Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data

“…Adapters were trimmed from the reads by Cutadapt, and the reads shorter than 17 nt, and low-quality sequence were discarded. The quantity of gene expression was calculated by the RPKM method (reads per kilobase per million reads), The mRNA reads were mapped to the RefSeq mRNA reference using FANSe3 algorithm FANSe3 algorithm [ 35 ] (-E5% --indel -S14). The gene expression level was quantified using RPKM method (reads per kilobase per million reads) [ 36 ].…”

Efficient Detection of the Alternative Spliced Human Proteome Using Translatome Sequencing

“…We extract the ribosomal RNA sequences in the NCBI RefSeq-RNA database (downloaded on 6 December 2019) according to refFlat annotation (downloaded on 17 January 2020) as a human rRNA reference dataset. For full-length translating (RNC) mRNA-seq datasets, reads were mapped to rRNA reference sequences using FANSe3 ( Zhang et al, 2021 ) (Release version 3.13) with the parameters–S12 –E4 in HeLa (50bp read lengths), and–S14 -E4 in MHCC97H (100bp read lengths). For Ribo-seq datasets, reads were mapped to rRNA reference sequences using FANSe3 with the parameters–S10 -E2 -U1.…”

Functional evolution of SARS-COV-2 Spike protein: adaptation on translation and infection via surface charge of spike protein

“…The original RNA-seq and Ribo-seq data (Calu3 cells infected with SARS-COV-2 for 7h) were downloaded form Gene Expression Omnibus database (GEO) under accession number GSE149973 [9], In brief, all raw sequencing data low quality reads and linker were removed using fastp [10]. For SARS-COV-2 gene expression quantification, cleaning reads were aligned to human refmRNA (UCSC) in order to remove human reads using Fanse3 program [11,12], after human reads removal, the remaining reads mapped to the Wuhan Hu-1 (NCBI Accession NC_045512.2. Mapping parameter, Ribo-seq: -S10 -E1 -U1, RNA-seq: -S12 -E2), reads per kilobase per Million mapped reads (RPKM) of each genes were calculated in golang program.…”