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DOI:	10.1371/journal.pone.0098011
论文题名:	Back to BaySICS: A User-Friendly Program for Bayesian Statistical Inference from Coalescent Simulations
作者:	Edson Sandoval-Castellanos; Eleftheria Palkopoulou; Love Dalén
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2014
发表日期:	2014-5-27
卷:	9, 期:5
语种:	英语
英文关键词:	Computer software ; Biochemical simulations ; Simulation and modeling ; Ancient DNA ; DNA sequence analysis ; Demography ; Statistical distributions ; Statistical inference
英文摘要:	Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0098011&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/18726
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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File Name/ File Size	Content Type	Version	Access	License
10.1371journal.pone.0098011.PDF（802KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden;Department of Zoology, Stockholm University, Stockholm, Sweden;Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden;Department of Zoology, Stockholm University, Stockholm, Sweden;Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden

Recommended Citation:

Edson Sandoval-Castellanos,Eleftheria Palkopoulou,Love Dalén. Back to BaySICS: A User-Friendly Program for Bayesian Statistical Inference from Coalescent Simulations[J]. PLOS ONE,2014-01-01,9(5)

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