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ACTG: novel peptide mapping onto gene models
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Choi, Seunghyuk | - |
| dc.contributor.author | Kim, Hyunwoo | - |
| dc.contributor.author | Paek, Eunok | - |
| dc.date.accessioned | 2022-07-14T10:41:06Z | - |
| dc.date.available | 2022-07-14T10:41:06Z | - |
| dc.date.issued | 2017-04 | - |
| dc.identifier.issn | 1367-4803 | - |
| dc.identifier.issn | 1367-4811 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/152630 | - |
| dc.description.abstract | In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input. | - |
| dc.format.extent | 3 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Oxford University Press | - |
| dc.title | ACTG: novel peptide mapping onto gene models | - |
| dc.type | Article | - |
| dc.publisher.location | 영국 | - |
| dc.identifier.doi | 10.1093/bioinformatics/btw787 | - |
| dc.identifier.scopusid | 2-s2.0-85019160786 | - |
| dc.identifier.wosid | 000400985900016 | - |
| dc.identifier.bibliographicCitation | Bioinformatics, v.33, no.8, pp 1218 - 1220 | - |
| dc.citation.title | Bioinformatics | - |
| dc.citation.volume | 33 | - |
| dc.citation.number | 8 | - |
| dc.citation.startPage | 1218 | - |
| dc.citation.endPage | 1220 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | sci | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
| dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
| dc.relation.journalResearchArea | Computer Science | - |
| dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
| dc.relation.journalResearchArea | Mathematics | - |
| dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
| dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
| dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
| dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
| dc.subject.keywordPlus | RNA-SEQ | - |
| dc.subject.keywordPlus | DATABASE | - |
| dc.identifier.url | https://academic.oup.com/bioinformatics/article/33/8/1218/2748210 | - |
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