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SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand

The identification of conserved syntenic regions enables discovery of predicted locations for orthologous and homeologous genes, even when no such gene is present. This capability means that synteny-based methods are far more effective than sequence similarity-based methods in identifying true-negat...

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Autores principales: Tang, Haibao, Bomhoff, Matthew D., Briones, Evan, Zhang, Liangsheng, Schnable, James C., Lyons, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700967/
https://www.ncbi.nlm.nih.gov/pubmed/26560340
http://dx.doi.org/10.1093/gbe/evv219
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author Tang, Haibao
Bomhoff, Matthew D.
Briones, Evan
Zhang, Liangsheng
Schnable, James C.
Lyons, Eric
author_facet Tang, Haibao
Bomhoff, Matthew D.
Briones, Evan
Zhang, Liangsheng
Schnable, James C.
Lyons, Eric
author_sort Tang, Haibao
collection PubMed
description The identification of conserved syntenic regions enables discovery of predicted locations for orthologous and homeologous genes, even when no such gene is present. This capability means that synteny-based methods are far more effective than sequence similarity-based methods in identifying true-negatives, a necessity for studying gene loss and gene transposition. However, the identification of syntenic regions requires complex analyses which must be repeated for pairwise comparisons between any two species. Therefore, as the number of published genomes increases, there is a growing demand for scalable, simple-to-use applications to perform comparative genomic analyses that cater to both gene family studies and genome-scale studies. We implemented SynFind, a web-based tool that addresses this need. Given one query genome, SynFind is capable of identifying conserved syntenic regions in any set of target genomes. SynFind is capable of reporting per-gene information, useful for researchers studying specific gene families, as well as genome-wide data sets of syntenic gene and predicted gene locations, critical for researchers focused on large-scale genomic analyses. Inference of syntenic homologs provides the basis for correlation of functional changes around genes of interests between related organisms. Deployed on the CoGe online platform, SynFind is connected to the genomic data from over 15,000 organisms from all domains of life as well as supporting multiple releases of the same organism. SynFind makes use of a powerful job execution framework that promises scalability and reproducibility. SynFind can be accessed at http://genomevolution.org/CoGe/SynFind.pl. A video tutorial of SynFind using Phytophthrora as an example is available at http://www.youtube.com/watch?v=2Agczny9Nyc.
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spelling pubmed-47009672016-01-06 SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand Tang, Haibao Bomhoff, Matthew D. Briones, Evan Zhang, Liangsheng Schnable, James C. Lyons, Eric Genome Biol Evol Genome Resources The identification of conserved syntenic regions enables discovery of predicted locations for orthologous and homeologous genes, even when no such gene is present. This capability means that synteny-based methods are far more effective than sequence similarity-based methods in identifying true-negatives, a necessity for studying gene loss and gene transposition. However, the identification of syntenic regions requires complex analyses which must be repeated for pairwise comparisons between any two species. Therefore, as the number of published genomes increases, there is a growing demand for scalable, simple-to-use applications to perform comparative genomic analyses that cater to both gene family studies and genome-scale studies. We implemented SynFind, a web-based tool that addresses this need. Given one query genome, SynFind is capable of identifying conserved syntenic regions in any set of target genomes. SynFind is capable of reporting per-gene information, useful for researchers studying specific gene families, as well as genome-wide data sets of syntenic gene and predicted gene locations, critical for researchers focused on large-scale genomic analyses. Inference of syntenic homologs provides the basis for correlation of functional changes around genes of interests between related organisms. Deployed on the CoGe online platform, SynFind is connected to the genomic data from over 15,000 organisms from all domains of life as well as supporting multiple releases of the same organism. SynFind makes use of a powerful job execution framework that promises scalability and reproducibility. SynFind can be accessed at http://genomevolution.org/CoGe/SynFind.pl. A video tutorial of SynFind using Phytophthrora as an example is available at http://www.youtube.com/watch?v=2Agczny9Nyc. Oxford University Press 2015-11-11 /pmc/articles/PMC4700967/ /pubmed/26560340 http://dx.doi.org/10.1093/gbe/evv219 Text en © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Genome Resources
Tang, Haibao
Bomhoff, Matthew D.
Briones, Evan
Zhang, Liangsheng
Schnable, James C.
Lyons, Eric
SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand
title SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand
title_full SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand
title_fullStr SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand
title_full_unstemmed SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand
title_short SynFind: Compiling Syntenic Regions across Any Set of Genomes on Demand
title_sort synfind: compiling syntenic regions across any set of genomes on demand
topic Genome Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700967/
https://www.ncbi.nlm.nih.gov/pubmed/26560340
http://dx.doi.org/10.1093/gbe/evv219
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