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SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning
The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using...
| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208262/ https://www.ncbi.nlm.nih.gov/pubmed/35725628 http://dx.doi.org/10.1186/s13059-022-02695-x |
| _version_ | 1784729705340993536 |
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| author | Balaji, Advait Kille, Bryce Kappell, Anthony D. Godbold, Gene D. Diep, Madeline Elworth, R. A. Leo Qian, Zhiqin Albin, Dreycey Nasko, Daniel J. Shah, Nidhi Pop, Mihai Segarra, Santiago Ternus, Krista L. Treangen, Todd J. |
| author_facet | Balaji, Advait Kille, Bryce Kappell, Anthony D. Godbold, Gene D. Diep, Madeline Elworth, R. A. Leo Qian, Zhiqin Albin, Dreycey Nasko, Daniel J. Shah, Nidhi Pop, Mihai Segarra, Santiago Ternus, Krista L. Treangen, Todd J. |
| author_sort | Balaji, Advait |
| collection | PubMed |
| description | The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02695-x. |
| format | Online Article Text |
| id | pubmed-9208262 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2022 |
| publisher | BioMed Central |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-92082622022-06-21 SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning Balaji, Advait Kille, Bryce Kappell, Anthony D. Godbold, Gene D. Diep, Madeline Elworth, R. A. Leo Qian, Zhiqin Albin, Dreycey Nasko, Daniel J. Shah, Nidhi Pop, Mihai Segarra, Santiago Ternus, Krista L. Treangen, Todd J. Genome Biol Software The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02695-x. BioMed Central 2022-06-20 /pmc/articles/PMC9208262/ /pubmed/35725628 http://dx.doi.org/10.1186/s13059-022-02695-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| spellingShingle | Software Balaji, Advait Kille, Bryce Kappell, Anthony D. Godbold, Gene D. Diep, Madeline Elworth, R. A. Leo Qian, Zhiqin Albin, Dreycey Nasko, Daniel J. Shah, Nidhi Pop, Mihai Segarra, Santiago Ternus, Krista L. Treangen, Todd J. SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| title | SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| title_full | SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| title_fullStr | SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| title_full_unstemmed | SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| title_short | SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| title_sort | seqscreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning |
| topic | Software |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208262/ https://www.ncbi.nlm.nih.gov/pubmed/35725628 http://dx.doi.org/10.1186/s13059-022-02695-x |
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