ArticleBMC bioinformatics2018
HapCHAT: adaptive haplotype assembly for efficiently leveraging high coverage in long reads.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Graph-based deep reinforcement learning for haplotype assembly with Ralphi.Genome research · 2025Article
- Pairwise comparative analysis of six haplotype assembly methods based on users' experience.BMC genomic data · 2023Article
- Benchmarking machine learning robustness in Covid-19 genome sequence classification.Scientific reports · 2023Article
- GAMIBHEAR: whole-genome haplotype reconstruction from Genome Architecture Mapping data.Bioinformatics (Oxford, England) · 2021Article
- scHaplotyper: haplotype construction and visualization for genetic diagnosis using single cell DNA sequencing data.BMC bioinformatics · 2020Article
- GenHap: a novel computational method based on genetic algorithms for haplotype assembly.BMC bioinformatics · 2019Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHaplotype assembly is the process of assigning the different alleles of the variants covered by mapped sequencing reads to the two haplotypes of the genome of a human individual. Long reads, which are nowadays cheaper to produce and more widely available than ever before, have been used to reduce the fragmentation of the assembled haplotypes since their ability to span several variants along the genome. These long reads are also characterized by a high error rate, an issue which may be mitigated, however, with larger sets of reads, when this error rate is uniform across genome positions. Unfortunately, current state-of-the-art dynamic programming approaches designed for long reads deal only with limited coverages.
resultsHere, we propose a new method for assembling haplotypes which combines and extends the features of previous approaches to deal with long reads and higher coverages. In particular, our algorithm is able to dynamically adapt the estimated number of errors at each variant site, while minimizing the total number of error corrections necessary for finding a feasible solution. This allows our method to significantly reduce the required computational resources, allowing to consider datasets composed of higher coverages. The algorithm has been implemented in a freely available tool, HapCHAT: Haplotype Assembly Coverage Handling by Adapting Thresholds. An experimental analysis on sequencing reads with up to 60 × coverage reveals improvements in accuracy and recall achieved by considering a higher coverage with lower runtimes.
conclusionsOur method leverages the long-range information of sequencing reads that allows to obtain assembled haplotypes fragmented in a lower number of unphased haplotype blocks. At the same time, our method is also able to deal with higher coverages to better correct the errors in the original reads and to obtain more accurate haplotypes as a result. AVAILABILITY: HapCHAT is available at http://hapchat.algolab.eu under the GNU Public License (GPL).
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