Academic Journal
ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data
| Τίτλος: | ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data |
|---|---|
| Συγγραφείς: | Yang, Zhiliang, Xu, Yuyang, Yao, Minhao, Wang, Gao, Liu, Zhonghua |
| Έτος έκδοσης: | 2023 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | Nucleotide sequence--Data processing, Genomes--Data processing, Python (Computer program language) |
| Περιγραφή: | Background Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software. Results We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory. Conclusion Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| DOI: | 10.7916/kx4x-e654 |
| Διαθεσιμότητα: | https://doi.org/10.7916/kx4x-e654 |
| Αριθμός Καταχώρησης: | edsbas.ECF4CBE0 |
| Βάση Δεδομένων: | BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Zhiliang%22">Yang, Zhiliang</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yuyang%22">Xu, Yuyang</searchLink><br /><searchLink fieldCode="AR" term="%22Yao%2C+Minhao%22">Yao, Minhao</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Gao%22">Wang, Gao</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhonghua%22">Liu, Zhonghua</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Columbia University: Academic Commons – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Nucleotide+sequence--Data+processing%22">Nucleotide sequence--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Genomes--Data+processing%22">Genomes--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Python+%28Computer+program+language%29%22">Python (Computer program language)</searchLink> – Name: Abstract Label: Description Group: Ab Data: Background Large-scale multi-ethnic DNA sequencing data is increasingly available owing to decreasing cost of modern sequencing technologies. Inference of the population structure with such sequencing data is fundamentally important. However, the ultra-dimensionality and complicated linkage disequilibrium patterns across the whole genome make it challenging to infer population structure using traditional principal component analysis based methods and software. Results We present the ERStruct Python Package, which enables the inference of population structure using whole-genome sequencing data. By leveraging parallel computing and GPU acceleration, our package achieves significant improvements in the speed of matrix operations for large-scale data. Additionally, our package features adaptive data splitting capabilities to facilitate computation on GPUs with limited memory. Conclusion Our Python package ERStruct is an efficient and user-friendly tool for estimating the number of top informative principal components that capture population structure from whole genome sequencing data. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: DOI Label: DOI Group: ID Data: 10.7916/kx4x-e654 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.7916/kx4x-e654 – Name: AN Label: Accession Number Group: ID Data: edsbas.ECF4CBE0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/kx4x-e654 Languages: – Text: English Subjects: – SubjectFull: Nucleotide sequence--Data processing Type: general – SubjectFull: Genomes--Data processing Type: general – SubjectFull: Python (Computer program language) Type: general Titles: – TitleFull: ERStruct: a fast Python package for inferring the number of top principal components from whole genome sequencing data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Zhiliang – PersonEntity: Name: NameFull: Xu, Yuyang – PersonEntity: Name: NameFull: Yao, Minhao – PersonEntity: Name: NameFull: Wang, Gao – PersonEntity: Name: NameFull: Liu, Zhonghua IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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