S-conLSH: alignment-free gapped mapping of noisy long reads

Abstract

Background The advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate. Results We present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the recently developed method lordFAST. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing. Conclusions S-conLSH is one of the first alignment-free reference genome mapping tools achieving a high level of sensitivity. The spaced-context is especially suitable for extracting distant similarities. The variable-length spaced-seeds or patterns add flexibility to the proposed algorithm by introducing gapped mapping of the noisy long reads. Therefore, S-conLSH may be considered as a prominent direction towards alignment-free sequence analysis.

Benchmarking long-read genome sequence alignment tools for human genomics applications

“…Sixteen tools were excluded because they were superseded by other tools, produced an alignment file in a non-SAM format, were designed for only one type of input data, or did not work on our cluster ( Kiełbasa et al, 2011 ; Li, 2013 ; Chaisson & Tesler, 2012 ; Faust & Hall, 2012 ; Liu et al, 2016 ; Sović et al, 2016 ; Liu, Gao & Wang, 2017 ; Jain et al, 2018a ; Jain et al, 2018b ; Nashta-Ali et al, 2017 ; Xiao et al, 2017 ; Haghshenas, Sahinalp & Hach, 2019 ; Li, 2018 ; Sedlazeck et al, 2018 ; Marić et al, 2019 ; Kumar, Agarwal & Ranvijay, 2019 ; Wei, Zhang & Liu, 2020 ; Jain et al, 2020 ; Jain et al, 2022 ; Ren & Chaisson, 2021 ; Chakraborty, Morgenstern & Bandyopadhyay, 2021 ).…”

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.…”

“…conLSH ( Chakraborty and Bandyopadhyay, 2020 ) computes context-based locality-sensitive hashing values of genomes to facilitate seed search and then generates a series of sites for candidate alignment after extension; finally, it produces the best possible alignment results by applying the sparse dynamic programming-based approach. Later, S-conLSH ( Chakraborty et al, 2021 ), an improved version of conLSH, was developed by introducing the spaced context-based locality-sensitive hashing for mapping long noisy SMS reads.…”

A survey of mapping algorithms in the long-reads era

“…While this survey covers the genomic mapping aspects, other important contributions have dealt with adapted procedures in the case of long-read RNA mapping [67,72,86,99], and structural variant identification [33,65,89,98], or alignment through large repeats [12,74]. Other related research focused on read-to-read overlap detection [26,100] 1 , or extension-free (pseudo-alignment) approaches [16,25,46]. Finally, here we describe algorithmic solutions working on the nucleotide sequence, but raw signal mappers for Nanopore long-reads is also an active area of research [39,54,101].…”