A Novel Gap-Filling Method Based on Hybrid Read Information Analysis

De novo assembly, which discovers the entire nucleotide sequence by reconstructing the reads resulting from next-generation sequencing, is a subject that must be studied for genetic information analysis. The recombination of reads is performed in several steps, but gaps that cannot be resolved occur...

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Bibliographic Details
Published in2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 3827 - 3829
Main Authors Kan, Yejin, Kim, Dongyeon, Yi, Gangman
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.12.2022
Subjects
Data mining
De Bruijn graph
de novo assembly
Estimation
Filling
Filtering
gap-filling
Genetics
hybrid reads
next-generation sequencing
Noise measurement
Sequential analysis
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Summary:De novo assembly, which discovers the entire nucleotide sequence by reconstructing the reads resulting from next-generation sequencing, is a subject that must be studied for genetic information analysis. The recombination of reads is performed in several steps, but gaps that cannot be resolved occur even after scaffolding. Gap-filling is performed as the last assembly stage to fill the unidentified regions called gaps, significantly improving overall assembly performance. We propose a gap-filling method using hybrid reads to resolve gaps based on sequence similarity estimation and graph searches. The proposed method consists of three key steps: extracting the candidate sequence, estimating similarity, and filling the gaps based on the graph. Hybrid reads extract sequences with more accurate information, and candidate sequences corresponding to noise are effectively removed based on the similarity estimation. In conclusion, a graph search using statistical information derives a final sequence that guarantees high coverage, resolves gaps, reduces misassemblies, and improves accuracy.
DOI:10.1109/BIBM55620.2022.9994889