smsMap: mapping single molecule sequencing reads by locating the alignment starting positions

Abstract

Background Single Molecule Sequencing (SMS) technology can produce longer reads with higher sequencing error rate. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. Most existing mapping tools generally adopt the traditional seed-and-extend strategy, and the candidate aligned regions for each query read are selected either by counting the number of matched seeds or chaining a group of seeds. However, for all the existing mapping tools, the coverage ratio of the alignment region to the query read is lower, and the read alignment quality and efficiency need to be improved. Here, we introduce smsMap, a novel mapping tool that is specifically designed to map the long reads of SMS to a reference genome. Results smsMap was evaluated with other existing seven SMS mapping tools (e.g., BLASR, minimap2, and BWA-MEM) on both simulated and real-life SMS datasets. The experimental results show that smsMap can efficiently achieve higher aligned read coverage ratio and has higher sensitivity that can align more sequences and bases to the reference genome. Additionally, smsMap is more robust to sequencing errors. Conclusions smsMap is computationally efficient to align SMS reads, especially for the larger size of the reference genome (e.g., H. sapiens genome with over 3 billion base pairs). The source code of smsMap can be freely downloaded from https://github.com/NWPU-903PR/smsMap .

Nanopore sequencing technology, bioinformatics and applications

“…288 ), mashmap2 (ref. 289 ), NanoBLASTer 290 , mapAlign 291 , GraphAligner 292 , smsmap 293 , lordFAST 294 , S-conLSH 295 , QAlign 296 Splice-aware minimap2, Graphmap2 (ref. 102 ), GmAP 100 , STAR 101 , deSALT 103 , magic-BLAST 297 , Deep-Long 298 , uLTRA 299 Genome assembly Canu, miniasm 107 , Flye 110 , Redbean/wtdbg2 (ref.…”

kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the K-Mer Neighborhood Graph

“…The performance of kngMap was evaluated against 10 state-of-the-art long read aligners: BLASR (v) ( Chaisson and Tesler, 2012 ), minimap2 (v2.12-r829) ( Li, 2018 ), BWA-MEM (v0.7.17-r1194) ( Li, 2013 ), GraphMap (v0.5.2) ( Ivan et al, 2016 ), rHAT (v0.1.2) ( Liu et al, 2015 ), NGMLR (v0.2.7) ( Sedlazeck et al, 2018 ), lordFAST (v0.0.9) ( Haghshenas et al, 2018 ), smsMap ( Wei et al, 2020 ), conLSH (v0.0.1) ( Chakraborty and Bandyopadhyay, 2020 ), and S-conLSH (v0.0.1) ( Chakraborty et al, 2021 ). All methods were benchmarked on simulated and real-life SMS sequencing datasets.…”

“…BWT-FM index-based methods find the matched seeds by constructing the BWT-FM index of the reference genome; such methods include BLASR ( Chaisson and Tesler, 2012 ), BWA-MEM ( Li, 2013 ), lordFAST ( Haghshenas et al, 2018 ), and smsMap ( Wei et al, 2020 ). BLASR ( Chaisson and Tesler, 2012 ) initially builds the BWT-FM index of the genome to find short exact matches; then, the candidate aligned region is generated by using sparse dynamic programming (SDP), and the final detailed alignment within the area defined by SDP is performed by dynamic programming.…”

“…lordFAST ( Haghshenas et al, 2018 ) selects the candidate alignment regions in the genome using the longest matches identified by the BWT-FM index; then, the base-to-base alignment between consecutive seeds is obtained by performing dynamic programming. Recently, the smsMap ( Wei et al, 2020 ) mapper proposed by us also utilizes the BWT-FM index to quickly find the matches in the reference genome and then locates the best starting positions in genome and query read by defining a credibility function. Finally, the detailed alignment result is obtained with the column reduction banded alignment method.…”

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

“…Numerous OTU picking methods have been developed, which can be categorized as closed-reference clustering, de novo clustering (also called taxonomy independent), and open-reference clustering ( Lawley and Tannock, 2017 ; Whelan and Surette, 2017 ; De Filippis et al, 2018 ). The closed-reference clustering involves comparing each query sequence to an annotated reference taxonomy database by utilizing the sequence classification or searching methods ( Liu et al, 2017 , 2018 ; Matias Rodrigues et al, 2017 ; Wei et al, 2020 ), then sequences matched to the same reference sequence are grouped into the same OTU. However, if a large portion of microbes in a sample has not yet been well defined, that is, not recorded in databases (i.e., unknown taxa), then they cannot be assigned to an OTU.…”