Evidence map›Paper›PMID 33573603›Full record

ArticleBMC bioinformatics2021

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

Angana Chakraborty, Burkhard Morgenstern, Sanghamitra Bandyopadhyay

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 18 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Angana ChakrabortyDepartment of Computer Science, West Bengal Education Service, Kolkata, India.
Burkhard MorgensternDepartment of Bioinformatics (IMG), University of Göttingen, 37077, Göttingen, Germany. bmorgen@gwdg.de.
Sanghamitra BandyopadhyayMachine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in.ORCID http://orcid.org/0000-0001-6370-2083
Indian Statistical Institute · INUniversity of Göttingen · DE

Funding

Govt. of India J. C. Bose Fellowship (SB/S1/JCB-033/2016)
6 · The paper itself

Abstract

backgroundThe 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.

resultsWe 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.

conclusionsS-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.

Indexed as

AlgorithmsHigh-Throughput Nucleotide SequencingHumansSequence AlignmentSequence Analysis, DNASoftwareAlignment-free sequence comparisonLocality sensitive hashingNoisy long SMRT readsSequence analysis

Identifiers

PMID33573603
PMCPMC7879691
OpenAlexW3126397633

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.