SoDA2: a Hidden Markov Model approach for identification of immunoglobulin rearrangements.
BACKGROUND
The inference of pre-mutation immunoglobulin (Ig) rearrangements is essential in the study of the antibody repertoires produced in response to infection, in B-cell neoplasms and in autoimmune disease. Often, there are several rearrangements that are nearly equivalent as candidates for a given Ig gene, but have different consequences in an analysis. Our aim in this article is to develop a probabilistic model of the rearrangement process and a Bayesian method for estimating posterior probabilities for the comparison of multiple plausible rearrangements.


RESULTS
We have developed SoDA2, which is based on a Hidden Markov Model and used to compute the posterior probabilities of candidate rearrangements and to find those with the highest values among them. We validated the software on a set of simulated data, a set of clonally related sequences, and a group of randomly selected Ig heavy chains from Genbank. In most tests, SoDA2 performed better than other available software for the task. Furthermore, the output format has been redesigned, in part, to facilitate comparison of multiple solutions.


BACKGROUND
SoDA2 is available online at https://hippocrates.duhs.duke.edu/soda. Simulated sequences are available upon request.
Munshaw
Supriya
S
Center for Computational Immunology, Computational Biology and Bioinformatics Program, Duke University, Durham, NC 27705, USA.
Kepler
Thomas B
TB
D000595:Amino Acid Sequence; D001402:B-Lymphocytes / Q000276:immunology; D001483:Base Sequence; D015322:Gene Rearrangement, B-Lymphocyte; D005803:Genes, Immunoglobulin; D007136:Immunoglobulins / Q000235:genetics*; D008390:Markov Chains; D008969:Molecular Sequence Data; D016415:Sequence Alignment; D012984:Software
D007136:Immunoglobulins