Title
Detecting Selection In Immunoglobulin Sequences
Abstract
The ability to detect selection by analyzing mutation patterns in experimentally derived immunoglobulin (Ig) sequences is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers and the diversification of pre-immune repertoires in certain species. Despite its importance, quantifying selection in experimentally derived sequences is fraught with difficulties. The necessary parameters for statistical tests (such as the expected frequency of replacement mutations in the absence of selection) are non-trivial to calculate, and results are not easily interpretable when analyzing more than a handful of sequences. We have developed a web server that implements our previously proposed Focused binomial test for detecting selection. Several features are integrated into the web site in order to facilitate analysis, including V(D)J germline segment identification with IMGT alignment, batch submission of sequences and integration of additional test statistics proposed by other groups. We also implement a Z-score-based statistic that increases the power of detecting selection while maintaining specificity, and further allows for the combined analysis of sequences from different germlines. The tool is freely available at http://clip.med.yale.edu/selection.
Year
DOI
Venue
2011
10.1093/nar/gkr413
NUCLEIC ACIDS RESEARCH
Field
DocType
Volume
Biology,Statistic,Genetics,Statistical hypothesis testing,Binomial test,Web server
Journal
39
Issue
ISSN
Citations 
Web Server issue
0305-1048
1
PageRank 
References 
Authors
0.40
5
6
Name
Order
Citations
PageRank
Mohamed Uduman1263.72
Gur Yaari2225.55
Uri Hershberg3202.97
Jacob A Stern410.40
Mark J Shlomchik510.40
Steven H Kleinstein69516.45