Parametric inference of recombination in HIV genomes
Niko Beerenwinkel, Colin N. Dewey, Kevin M. Woods
Abstract
Recombination is an important event in the evolution of HIV. It affects the global spread of the pandemic as well as evolutionary escape from host immune response and from drug therapy within single patients. Comprehensive computational methods are needed for detecting recombinant sequences in large databases, and for inferring the parental sequences. We present a hidden Markov model to annotate a query sequence as a recombinant of a given set of aligned sequences. Parametric inference is used to determine all optimal annotations for all parameters of the model. We show that the inferred annotations recover most features of established hand-curated annotations. Thus, parametric analysis of the hidden Markov model is feasible for HIV full-length genomes, and it improves the detection and annotation of recombinant forms. All computational results, reference alignments, and C++ source code are available at http://bio.math.berkeley.edu/recombination/.
Create a lesson
Related papers
Learning Interpretable Tumor Microenvironment Representations by Fitting Pan-Cancer Cell State-Niche Correlation
Xiao Xiao, Jiashu He, Shiyang Zhang et al.
Optimizing RNA yield using deep neural networks coupled to massively parallel screening
Dinghai Zheng, Justin Hong, Jun Wang et al.
A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1Ψ-Modified RNA 5' UTRs
Narges Zarnaghinaghsh, Ahmadreza Mofayezi, Byung-Jun Yoon
mLS-GKM: Efficient Multi-class Regulatory Sequence Classification with Gapped k-mer SVMs
Kieran Howard, Nathan Harmston
DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification
Jana T. Winterstein, Lukas Heinlein, Günter Raddatz et al.
Hepatitis C Virus Genotyping with a Transformer Neural Network
Ariella Aro, Taimá Furuyama, Marcelo R. S. Briones et al.