Evidence map›Paper›PMID 39815230›Full record

ArticleBMC bioinformatics2025

TopoQual polishes circular consensus sequencing data and accurately predicts quality scores.

Minindu Weerakoon, Sangjin Lee, Emily Mitchell, Haynes Heaton

Abstract read
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Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Evaluation of sequencing reads at scale using rdeval.Bioinformatics (Oxford, England) · 2025
    Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Minindu WeerakoonAuburn University, Auburn, AL, 36849, USA. wmw0016@auburn.edu.
Sangjin LeeWellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.
Emily MitchellWellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.
Haynes HeatonAuburn University, Auburn, AL, 36849, USA. haynesheaton@auburn.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPacific Biosciences (PacBio) circular consensus sequencing (CCS), also known as high fidelity (HiFi) technology, has revolutionized modern genomics by producing long (10 + kb) and highly accurate reads. This is achieved by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently, the accuracy and quality value estimation provided by HiFi technology are more than sufficient for applications such as genome assembly and germline variant calling. However, there are limitations in the accuracy of the estimated quality scores when it comes to somatic variant calling on single reads.

resultsTo address the challenge of inaccurate quality scores for somatic variant calling, we introduce TopoQual, a novel tool designed to enhance the accuracy of base quality predictions. TopoQual leverages techniques including partial order alignments (POA), topologically parallel bases, and deep learning algorithms to polish consensus sequences. Our results demonstrate that TopoQual corrects approximately 31.9% of errors in PacBio consensus sequences. Additionally, it validates base qualities up to q59, which corresponds to one error in 0.9 million bases. These improvements will significantly enhance the reliability of somatic variant calling using HiFi data.

conclusionTopoQual represents a significant advancement in genomics by improving the accuracy of base quality predictions for PacBio HiFi sequencing data. By correcting a substantial proportion of errors and achieving high base quality validation, TopoQual enables confident and accurate somatic variant calling. This tool not only addresses a critical limitation of current HiFi technology but also opens new possibilities for precise genomic analysis in various research and clinical applications.

Indexed as

Consensus SequenceHigh-Throughput Nucleotide SequencingSequence Analysis, DNASoftwareAlgorithmsDeep LearningGenomicsHumansCircular consensus sequencingDeep consensusError correctingHigh fidelityPacbioQuality scoresSomatic mutationsTopoqual

Identifiers

PMID39815230
PMCPMC11737182

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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.