conLSH: Context based Locality Sensitive Hashing for mapping of noisy SMRT reads

Chakraborty, Angana; Bandyopadhyay, Sanghamitra

doi:10.1016/j.compbiolchem.2020.107206

Cited by 9 publications

(9 citation statements)

References 39 publications

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“…Six different real and simulated datasets of E.coli, A.thaliana, O.sativa, S.cerevisiae and H.sapiens have been used to benchmark the performance of S-conLSH in comparison to other state-of-the art aligners, viz., Minimap2 [7], lordFAST [8], Minimap [15], conLSH [16] and MUMmer4 [10]. All these methods are executed in a setting designed for PacBio read alignment (see Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…In biological applications, it is often useful to consider the local context of sequence positions and to consider matching subwords, as shown in conLSH [16]. It groups similar sequences in the localized slots of the hash tables considering the neighborhoods or contexts of the data points.…”

Section: Context Based Locality Sensitive Hashingmentioning

confidence: 99%

“…This makes it even more complicated to differentiate genomic variations from sequence errors. To resolve this issue, a concept of 'context-based' Locality Sensitive Hashing (conLSH) has been introduced by Chakraborty and Bandyopadhyay [16]. Locality Sensitive Hashing (LSH) [17,18] has been successfully applied in many real lifescience applications, ranging from genome comparison [19,20] to large scale genome assembly [21].…”

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confidence: 99%

“…However, in practice, the neighborhood or context of an object plays a key role in measuring its similarity to another object. Chakraborty and Bandyopadhyay [16] have shown that contexts of symbols (a base in reference to DNA) are important to decide the closeness of strings. They proposed conLSH to group sequences in localized slots of the hash table if they share a common context.…”

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confidence: 99%

“…Therefore, to address this problem, an idea of spaced-context is introduced in this article. Unlike conLSH [16] which produces base-level alignments of the sequences using Sparse Dynamic Programming (SDP) based algorithm, the proposed method (S-conLSH) is an alignment-free tool. It employs multiple spaced-seeds or patterns to find gapped mappings of noisy SMRT reads to reference genomes.…”

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confidence: 99%

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

Chakraborty¹,

Morgenstern

Bandyopadhyay

2021

BMC Bioinformatics

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Context Based Locality Sensitive Hashingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Chakraborty¹,

Morgenstern

Bandyopadhyay

2021

BMC Bioinformatics

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Sequence Alignment/Map format: a comprehensive review of approaches and applications

Liu,

Shen,

Gong

et al. 2023

Briefings in Bioinformatics

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The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

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BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis

Fırtına

Park

Alser

et al. 2023

NAR Genomics and Bioinformatics

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Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding exact-matching seeds as the conventional hashing methods assign distinct hash values for different seeds, including highly similar seeds. Finding only exact-matching seeds causes either (i) increasing the use of the costly sequence alignment or (ii) limited sensitivity. We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches. BLEND (i) utilizes a technique called SimHash, that can generate the same hash value for similar sets, and (ii) provides the proper mechanisms for using seeds as sets with the SimHash technique to find fuzzy seed matches efficiently. We show the benefits of BLEND when used in read overlapping and read mapping. For read overlapping, BLEND is faster by 2.4×–83.9× (on average 19.3×), has a lower memory footprint by 0.9×–14.1× (on average 3.8×), and finds higher quality overlaps leading to accurate de novo assemblies than the state-of-the-art tool, minimap2. For read mapping, BLEND is faster by 0.8×–4.1× (on average 1.7×) than minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND.

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conLSH: Context based Locality Sensitive Hashing for mapping of noisy SMRT reads

Cited by 9 publications

References 39 publications

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

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

Sequence Alignment/Map format: a comprehensive review of approaches and applications

BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis

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