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Modelling BioNano optical data and simulation study of genome map assembly
MOTIVATION: The launch of the BioNano next-generation mapping system has greatly enhanced the performance of physical map construction, thus rapidly expanding the application of optical mapping in genome research. Data biases have profound implications for downstream applications. However, very litt...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247929/ https://www.ncbi.nlm.nih.gov/pubmed/29893801 http://dx.doi.org/10.1093/bioinformatics/bty456 |
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author | Chen, Ping Jing, Xinyun Ren, Jian Cao, Han Hao, Pei Li, Xuan |
author_facet | Chen, Ping Jing, Xinyun Ren, Jian Cao, Han Hao, Pei Li, Xuan |
author_sort | Chen, Ping |
collection | PubMed |
description | MOTIVATION: The launch of the BioNano next-generation mapping system has greatly enhanced the performance of physical map construction, thus rapidly expanding the application of optical mapping in genome research. Data biases have profound implications for downstream applications. However, very little is known about the properties and biases of BioNano data, and the very factors that contribute to whole-genome optical map assembly. RESULTS: We generated BioNano molecule data from eight organisms with diverse base compositions. We first characterized the properties/biases of BioNano molecule data, i.e. molecule length distribution, false labelling signal, variation of optical resolution and coverage distribution bias, and their inducing factors such as chimeric molecules, fragile sites and DNA molecule stretching. Second, we developed the BioNano Molecule SIMulator (BMSIM), a novel computer simulation program for optical data. BMSIM, is of great use for future genome mapping projects. Third, we evaluated the experimental variables that impact whole-genome optical map assembly. Specifically, the effects of coverage depth, molecule length, false-positive and false-negative labelling signals, chimeric molecules and nicking enzyme and nick site density were investigated. Our simulation study provides the empirical findings on how to control experimental variables and gauge analytical parameters to maximize benefit and minimize cost on whole-genome optical map assembly. AVAILABILITY AND IMPLEMENTATION: BMSIM is freely available on: https://github.com/pingchen09990102/BMSIM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6247929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62479292018-11-28 Modelling BioNano optical data and simulation study of genome map assembly Chen, Ping Jing, Xinyun Ren, Jian Cao, Han Hao, Pei Li, Xuan Bioinformatics Original Papers MOTIVATION: The launch of the BioNano next-generation mapping system has greatly enhanced the performance of physical map construction, thus rapidly expanding the application of optical mapping in genome research. Data biases have profound implications for downstream applications. However, very little is known about the properties and biases of BioNano data, and the very factors that contribute to whole-genome optical map assembly. RESULTS: We generated BioNano molecule data from eight organisms with diverse base compositions. We first characterized the properties/biases of BioNano molecule data, i.e. molecule length distribution, false labelling signal, variation of optical resolution and coverage distribution bias, and their inducing factors such as chimeric molecules, fragile sites and DNA molecule stretching. Second, we developed the BioNano Molecule SIMulator (BMSIM), a novel computer simulation program for optical data. BMSIM, is of great use for future genome mapping projects. Third, we evaluated the experimental variables that impact whole-genome optical map assembly. Specifically, the effects of coverage depth, molecule length, false-positive and false-negative labelling signals, chimeric molecules and nicking enzyme and nick site density were investigated. Our simulation study provides the empirical findings on how to control experimental variables and gauge analytical parameters to maximize benefit and minimize cost on whole-genome optical map assembly. AVAILABILITY AND IMPLEMENTATION: BMSIM is freely available on: https://github.com/pingchen09990102/BMSIM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-12-01 2018-06-11 /pmc/articles/PMC6247929/ /pubmed/29893801 http://dx.doi.org/10.1093/bioinformatics/bty456 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Chen, Ping Jing, Xinyun Ren, Jian Cao, Han Hao, Pei Li, Xuan Modelling BioNano optical data and simulation study of genome map assembly |
title | Modelling BioNano optical data and simulation study of genome map assembly |
title_full | Modelling BioNano optical data and simulation study of genome map assembly |
title_fullStr | Modelling BioNano optical data and simulation study of genome map assembly |
title_full_unstemmed | Modelling BioNano optical data and simulation study of genome map assembly |
title_short | Modelling BioNano optical data and simulation study of genome map assembly |
title_sort | modelling bionano optical data and simulation study of genome map assembly |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247929/ https://www.ncbi.nlm.nih.gov/pubmed/29893801 http://dx.doi.org/10.1093/bioinformatics/bty456 |
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