A computational pipeline to discover highly phylogenetically informative genes in sequenced genomes: application to Saccharomyces cerevisiae natural strains.
A computational pipeline to discover highly phylogenetically informative genes in sequenced genomes: application to Saccharomyces cerevisiae natural strains.
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DOI：
10.1093/nar/gks005
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发表时间：
2012-05
影响因子：
14.9
通讯作者：
Cavalieri D
中科院分区：
文献类型：
--
作者：
Ramazzotti M;Berná L;Stefanini I;Cavalieri D
随着下一代测序（NGS）技术的发展，对代表种群内菌株或个体的遗传关系及其进化历史的基因的探索正在获得一个新的复杂维度。事实上，整个基因组测序揭示了编码和非编码区的遗传变异，并提供了在菌株水平上研究酿酒酵母种群的可能性。然而，不利的成本效益比（NGS披露的细节量对时间昂贵和专业知识要求的数据组装过程）仍然排除了这些技术的应用，酵母菌株的常规分配，使最可靠的分子标记的选择非常可取。在这项工作中，我们提出了一个原始的计算方法来发现基因，可以用作人口结构的描述符。我们发现了13个基因，其变异性可以用来概括从全基因组序列中获得的遗传学。我们证明在酵母中成功的相同方法可以推广到任何其他群体的个体，只要有高质量的基因组序列和明确的群体结构作为目标。
The quest for genes representing genetic relationships of strains or individuals within populations and their evolutionary history is acquiring a novel dimension of complexity with the advancement of next-generation sequencing (NGS) technologies. In fact, sequencing an entire genome uncovers genetic variation in coding and non-coding regions and offers the possibility of studying Saccharomyces cerevisiae populations at the strain level. Nevertheless, the disadvantageous cost-benefit ratio (the amount of details disclosed by NGS against the time-expensive and expertise-demanding data assembly process) still precludes the application of these techniques to the routinely assignment of yeast strains, making the selection of the most reliable molecular markers greatly desirable. In this work we propose an original computational approach to discover genes that can be used as a descriptor of the population structure. We found 13 genes whose variability can be used to recapitulate the phylogeny obtained from genome-wide sequences. The same approach that we prove to be successful in yeasts can be generalized to any other population of individuals given the availability of high-quality genomic sequences and of a clear population structure to be targeted.
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影响因子：
3.7
作者：
Diezmann S;Dietrich FS
通讯作者：
Dietrich FS
影响因子：
64.8
作者：
Kellis, M;Patterson, N;Lander, ES
通讯作者：
Lander, ES
DOI：
10.1073/pnas.1012544108
发表时间：
2011-02-01
影响因子：
11.1
作者：
Magwene, Paul M.;Kayikci, Oemuer;Murray, Debra
通讯作者：
Murray, Debra
影响因子：
2.6
作者：
SHARP, PM;COWE, E
通讯作者：
COWE, E
影响因子：
64.8
作者：
Schacherer J;Shapiro JA;Ruderfer DM;Kruglyak L
通讯作者：
Kruglyak L