Abstract
BackgroundIn [Prezza et al., AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory consuming due to the use of additional heavy data structures (namely, the Suffix and LCP arrays), besides the BWT.ResultsIn this paper, we introduce a new algorithm and the corresponding tool ebwt2InDel that (i) extend the framework of [Prezza et al., AMB 2019] to detect also INDELs, and (ii) implements recent algorithmic findings that allow to perform the whole analysis using just the BWT, thus reducing the working space by one order of magnitude and allowing the analysis of full genomes. Finally, we describe a simple strategy for effectively parallelizing our tool for SNP detection only. On a 24-cores machine, the parallel version of our tool is one order of magnitude faster than the sequential one. The tool ebwt2InDel is available at github.com/nicolaprezza/ebwt2InDel.ConclusionsResults on a synthetic dataset covered at 30x (Human chromosome 1) show that our tool is indeed able to find up to 83% of the SNPs and 72% of the existing INDELs. These percentages considerably improve the 71% of SNPs and 51% of INDELs found by the state-of-the art tool based on de Bruijn graphs. We furthermore report results on larger (real) Human whole-genome sequencing experiments. Also in these cases, our tool exhibits a much higher sensitivity than the state-of-the art tool.
Highlights
In [Prezza et al, AMB 2019], a new reference-free and alignment-free framework for the detection of Single nucleotide polymorphism (SNP) was suggested and tested
We compared our EBWT2INDEL with DISCOSNP++ [8], that is an improvement of the DISCOSNP [1, 5] algorithm: while DISCOSNP only detects isolated SNPs from any number of read datasets without a reference genome, DISCOSNP++ detects and ranks all kinds of SNPs as well as small INsertions and/or DELetions (INDEL)
DISCOSNP++ builds the de Bruijn graph (dBG) of the input datasets taking into account both the size of the k-mers and the minimum coverage c (DBGH5 module), and presumed erroneous k-mers are removed based on their frequency
Summary
In [Prezza et al, AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The most typical workflow for variant calling downstream of a genome(s) or exome(s) sequencing, is to map the obtained reads onto a reference genome by means of some alignment tool, and highlight loci where the reads differ from the reference Such mapping, is time consuming, error prone, and it can even be unfeasible when a reference genome is not available (in this case the analysis should start with an assembly process that reconstructs the genomes before comparing/analysing them, but this is often out of reach in practice for several computational and experimental reasons). In the literature one can find reference-free methods and tools for detecting SNPs [1, 4, 7, 8], small INDELs [4, 8, 9], sequencing errors [10,11,12], rearrangement breakpoints [13] in genomic data, haplotype assembly [14,15,16], as well as alternative splicing events [2] on RNA-Seq data
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