Cargando…
Reconstructing cancer karyotypes from short read data: the half empty and half full glass
BACKGROUND: During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions.The result is a highly complex, patient-specific cancer karyotype. Using high-throughput techno...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688766/ https://www.ncbi.nlm.nih.gov/pubmed/29141589 http://dx.doi.org/10.1186/s12859-017-1929-9 |
_version_ | 1783279235548315648 |
---|---|
author | Eitan, Rami Shamir, Ron |
author_facet | Eitan, Rami Shamir, Ron |
author_sort | Eitan, Rami |
collection | PubMed |
description | BACKGROUND: During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions.The result is a highly complex, patient-specific cancer karyotype. Using high-throughput technologies of deep sequencing and microarrays it is possible to interrogate a cancer genome and produce chromosomal copy number profiles and a list of breakpoints (“jumps”) relative to the normal genome. This information is very detailed but local, and does not give the overall picture of the cancer genome. One of the basic challenges in cancer genome research is to use such information to infer the cancer karyotype. We present here an algorithmic approach, based on graph theory and integer linear programming, that receives segmental copy number and breakpoint data as input and produces a cancer karyotype that is most concordant with them. We used simulations to evaluate the utility of our approach, and applied it to real data. RESULTS: By using a simulation model, we were able to estimate the correctness and robustness of the algorithm in a spectrum of scenarios. Under our base scenario, designed according to observations in real data, the algorithm correctly inferred 69% of the karyotypes. However, when using less stringent correctness metrics that account for incomplete and noisy data, 87% of the reconstructed karyotypes were correct. Furthermore, in scenarios where the data were very clean and complete, accuracy rose to 90%–100%. Some examples of analysis of real data, and the reconstructed karyotypes suggested by our algorithm, are also presented. CONCLUSION: While reconstruction of complete, perfect karyotype based on short read data is very hard, a large fraction of the reconstruction will still be correct and can provide useful information. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1929-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5688766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56887662017-11-24 Reconstructing cancer karyotypes from short read data: the half empty and half full glass Eitan, Rami Shamir, Ron BMC Bioinformatics Methodology Article BACKGROUND: During cancer progression genomes undergo point mutations as well as larger segmental changes. The latter include, among others, segmental deletions duplications, translocations and inversions.The result is a highly complex, patient-specific cancer karyotype. Using high-throughput technologies of deep sequencing and microarrays it is possible to interrogate a cancer genome and produce chromosomal copy number profiles and a list of breakpoints (“jumps”) relative to the normal genome. This information is very detailed but local, and does not give the overall picture of the cancer genome. One of the basic challenges in cancer genome research is to use such information to infer the cancer karyotype. We present here an algorithmic approach, based on graph theory and integer linear programming, that receives segmental copy number and breakpoint data as input and produces a cancer karyotype that is most concordant with them. We used simulations to evaluate the utility of our approach, and applied it to real data. RESULTS: By using a simulation model, we were able to estimate the correctness and robustness of the algorithm in a spectrum of scenarios. Under our base scenario, designed according to observations in real data, the algorithm correctly inferred 69% of the karyotypes. However, when using less stringent correctness metrics that account for incomplete and noisy data, 87% of the reconstructed karyotypes were correct. Furthermore, in scenarios where the data were very clean and complete, accuracy rose to 90%–100%. Some examples of analysis of real data, and the reconstructed karyotypes suggested by our algorithm, are also presented. CONCLUSION: While reconstruction of complete, perfect karyotype based on short read data is very hard, a large fraction of the reconstruction will still be correct and can provide useful information. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-017-1929-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-15 /pmc/articles/PMC5688766/ /pubmed/29141589 http://dx.doi.org/10.1186/s12859-017-1929-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Eitan, Rami Shamir, Ron Reconstructing cancer karyotypes from short read data: the half empty and half full glass |
title | Reconstructing cancer karyotypes from short read data: the half empty and half full glass |
title_full | Reconstructing cancer karyotypes from short read data: the half empty and half full glass |
title_fullStr | Reconstructing cancer karyotypes from short read data: the half empty and half full glass |
title_full_unstemmed | Reconstructing cancer karyotypes from short read data: the half empty and half full glass |
title_short | Reconstructing cancer karyotypes from short read data: the half empty and half full glass |
title_sort | reconstructing cancer karyotypes from short read data: the half empty and half full glass |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688766/ https://www.ncbi.nlm.nih.gov/pubmed/29141589 http://dx.doi.org/10.1186/s12859-017-1929-9 |
work_keys_str_mv | AT eitanrami reconstructingcancerkaryotypesfromshortreaddatathehalfemptyandhalffullglass AT shamirron reconstructingcancerkaryotypesfromshortreaddatathehalfemptyandhalffullglass |