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Article: Assembly-free discovery of human novel sequences using long reads.

TitleAssembly-free discovery of human novel sequences using long reads.
Authors
Keywordsassembly-free approach
human references
long reads
novel sequences
Issue Date1-Dec-2022
PublisherOxford University Press
Citation
DNA Research, 2022, v. 29, n. 6 How to Cite?
Abstract

DNA sequences that are absent in the human reference genome are classified as novel sequences. The discovery of these missed sequences is crucial for exploring the genomic diversity of populations and understanding the genetic basis of human diseases. However, various DNA lengths of reads generated from different sequencing technologies can significantly affect the results of novel sequences. In this work, we designed an assembly-free novel sequence (AF-NS) approach to identify novel sequences from Oxford Nanopore Technology long reads. Among the newly detected sequences using AF-NS, more than 95% were omitted from those using long-read assemblers and 85% were not present in short reads of Illumina. We identified the common novel sequences among all the samples and revealed their association with the binding motifs of transcription factors. Regarding the placements of the novel sequences, we found about 70% enriched in repeat regions and generated 430 for one specific subpopulation that might be related to their evolution. Our study demonstrates the advance of the assembly-free approach to capture more novel sequences over other assembler based methods. Combining the long-read data with powerful analytical methods can be a robust way to improve the completeness of novel sequences.


Persistent Identifierhttp://hdl.handle.net/10722/340462
ISSN
2023 Impact Factor: 3.9
2023 SCImago Journal Rankings: 1.131
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Q-
dc.contributor.authorYan, B-
dc.contributor.authorLam, TW-
dc.contributor.authorLuo, R-
dc.date.accessioned2024-03-11T10:44:49Z-
dc.date.available2024-03-11T10:44:49Z-
dc.date.issued2022-12-01-
dc.identifier.citationDNA Research, 2022, v. 29, n. 6-
dc.identifier.issn1340-2838-
dc.identifier.urihttp://hdl.handle.net/10722/340462-
dc.description.abstract<p>DNA sequences that are absent in the human reference genome are classified as novel sequences. The discovery of these missed sequences is crucial for exploring the genomic diversity of populations and understanding the genetic basis of human diseases. However, various DNA lengths of reads generated from different sequencing technologies can significantly affect the results of novel sequences. In this work, we designed an assembly-free novel sequence (AF-NS) approach to identify novel sequences from Oxford Nanopore Technology long reads. Among the newly detected sequences using AF-NS, more than 95% were omitted from those using long-read assemblers and 85% were not present in short reads of Illumina. We identified the common novel sequences among all the samples and revealed their association with the binding motifs of transcription factors. Regarding the placements of the novel sequences, we found about 70% enriched in repeat regions and generated 430 for one specific subpopulation that might be related to their evolution. Our study demonstrates the advance of the assembly-free approach to capture more novel sequences over other assembler based methods. Combining the long-read data with powerful analytical methods can be a robust way to improve the completeness of novel sequences.</p>-
dc.languageeng-
dc.publisherOxford University Press-
dc.relation.ispartofDNA Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectassembly-free approach-
dc.subjecthuman references-
dc.subjectlong reads-
dc.subjectnovel sequences-
dc.titleAssembly-free discovery of human novel sequences using long reads.-
dc.typeArticle-
dc.identifier.doi10.1093/dnares/dsac039-
dc.identifier.pmid36308393-
dc.identifier.scopuseid_2-s2.0-85142940438-
dc.identifier.volume29-
dc.identifier.issue6-
dc.identifier.eissn1756-1663-
dc.identifier.isiWOS:000892327200001-
dc.identifier.issnl1340-2838-

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