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Kleinstein Lab
300 George Street
Suite 505
New Haven, CT 06511

Bayesian Estimation of Antigen-Driven Selection in Immunoglobulin Sequences

BASELINe, a new computational framework for Bayesian estimation of Antigen-driven selection in Immunoglobulin sequences, provides a more intuitive means of analyzing selection by actually quantifying it.

Operating in log-odds ratio space, the approach also allows, for the first time, comparative analysis between groups of independent sequences. The results of the analysis are summarized in a table and various plots, all of which may be downloaded for further processing.

This website runs Baseline Version 1.3 (01/30/2014) . The source code can be downloaded here.

Enter Ig Sequence: ? OR Upload File:
Selection Statistic ?
SHM Targeting Model ?
Sequences are clonal ?
Remove terminal branch mutations ?
FWR/CDR Boundaries
(AA Positions)


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For further information on the methods, or if you use the results of this website, please cite the following papers:

Gur Yaari; Mohamed Uduman; Steven H. Kleinstein. Quantifying selection in high-throughput Immunoglobulin sequencing data sets. Nucleic Acids Res. 2012 May 27.

Mohamed Uduman; Gur Yaari; Uri Hershberg; Mark J. Shlomchik; Steven H. Kleinstein. Detecting selection in immunoglobulin sequences. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W499-504.

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