Parametric inference of recombination in HIV genomes

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/.Comment: 20 pages, 5 figure

Computational Molecular Biology

Computational Biology is a fairly new subject that arose in response to the computational problems posed by the analysis and the processing of biomolecular sequence and structure data. The field was initiated in the late 60's and early 70's largely by pioneers working in the life sciences. Physicists and mathematicians entered the field in the 70's and 80's, while Computer Science became involved with the new biological problems in the late 1980's. Computational problems have gained further importance in molecular biology through the various genome projects which produce enormous amounts of data. For this bibliography we focus on those areas of computational molecular biology that involve discrete algorithms or discrete optimization. We thus neglect several other areas of computational molecular biology, like most of the literature on the protein folding problem, as well as databases for molecular and genetic data, and genetic mapping algorithms. Due to the availability of review papers and a bibliography this bibliography

Genome-wide inference of ancestral recombination graphs

The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of n chromosomes conditional on an ARG of n-1 chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the true posterior distribution and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. Preliminary results also indicate that our methods can be used to gain insight into complex features of human population structure, even with a noninformative prior distribution.Comment: 88 pages, 7 main figures, 22 supplementary figures. This version contains a substantially expanded genomic data analysi

Analysis of recombination in molecular sequence data

We present the new and fast method Recco for analyzing a multiple alignment regarding recombination. Recco is based on a dynamic program that explains one sequence in the alignment with the other sequences using mutation and recombination. The dynamic program allows for an intuitive visualization of the optimal solution and also introduces a parameter α controlling the number of recombinations in the solution. Recco performs a parametric analysis regarding α and orders all pareto-optimal solutions by increasing number of recombinations. α is also directly related to the Savings value, a quantitative and intuitive measure for the preference of recombination in the solution. The Savings value and the solutions have a simple interpretation regarding the ancestry of the sequences in the alignment and it is usually easy to understand the output of the method. The distribution of the Savings value for non-recombining alignments is estimated by processing column permutations of the alignment and p-values are provided for recombination in the alignment, in a sequence and at a breakpoint position. Recco also uses the p-values to suggest a single solution, or recombinant structure, for the explained sequence. Recco is validated on a large set of simulated alignments and has a recombination detection performance superior to all current methods. The analysis of real alignments confirmed that Recco is among the best methods for recombination analysis and further supported that Recco is very intuitive compared to other methods.Wir präsentieren Recco, eine neue und schnelle Methode zur Analyse von Rekombinationen in multiplen Alignments. Recco basiert auf einem dynamischen Programm, welches eine Sequenz im Alignment durch die anderen Sequenzen im Alignment rekonstruiert, wobei die Operatoren Mutation und Rekombination erlaubt sind. Das dynamische Programm ermöglicht eine intuitive Visualisierung der optimalen Lösung und besitzt einen Parameter α, welcher die Anzahl der Rekombinationsereignisse in der optimalen Lösung steuert. Recco führt eine parametrische Analyse bezüglich des Parameters α durch, so dass alle pareto-optimalen Lösungen nach der Anzahl ihrer Rekombinationsereignisse sortiert werden können. α steht auch direkt in Beziehung mit dem sogenannten Savings-Wert, der die Neigung zum Einfügen von Rekombinationsereignissen in die optimale Lösung quantitativ und intuitiv bemisst. Der Savings-Wert und die optimalen Lösungen haben eine einfache Interpretation bezüglich der Historie der Sequenzen im Alignment, so dass es in der Regel leicht fällt, die Ausgabe von Recco zu verstehen. Recco schätzt die Verteilung des Savings-Werts für Alignments ohne Rekombinationen durch einen Permutationstest, der auf Spaltenpermutationen basiert. Dieses Verfahren resultiert in p-Werten für Rekombination im Alignment, in einer Sequenz und an jeder Position im Alignment. Basierend auf diesen p-Werten schlägt Recco eine optimale Lösung vor, als Schätzer für die rekombinante Struktur der erklärten Sequenz. Recco wurde auf einem großen Datensatz simulierter Alignments getestet und erzielte auf diesem Datensatz eine bessere Vorhersagegüte in Bezug auf das Erkennen von Alignments mit Rekombination als alle anderen aktuellen Verfahren. Die Analyse von realen Datensätzen bestätigte, dass Recco zu den besten Methoden für die Rekombinationsanalyse gehört und im Vergleich zu anderen Methoden oft leichter verständliche Resultate liefert

Reconstructing a History of Recombinations From a Set of Sequences

One of the classic problems in computational biology is the reconstruction of evolutionary history. A recent trend in the area is to increase the explanatory power of the models that are considered by incorporating higher-order evolutionary events that more accurately reflect the mechanisms of mutation at the level of the chromosome. We take a step in this direction by considering the problem of reconstructing an evolutionary history for a set of genetic sequences that have evolved by recombination. Recombination is a non-tree-like event that produces a child sequence by crossing two parent sequences. We present polynomial-time algorithms for reconstructing a parsimonious history of such events for several models of recombination when all sequences, including those of ancestors, are present in the input. We also show that these models appear to be near the limit of what can be solved in polynomial time, in that several natural generalizations are NP-complete. Keywords Computational bio..