A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available

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

A clone-free, single molecule map of the domestic cow (Bos taurus) genome.

BackgroundThe cattle (Bos taurus) genome was originally selected for sequencing due to its economic importance and unique biology as a model organism for understanding other ruminants, or mammals. Currently, there are two cattle genome sequence assemblies (UMD3.1 and Btau4.6) from groups using dissimilar assembly algorithms, which were complemented by genetic and physical map resources. However, past comparisons between these assemblies revealed substantial differences. Consequently, such discordances have engendered ambiguities when using reference sequence data, impacting genomic studies in cattle and motivating construction of a new optical map resource--BtOM1.0--to guide comparisons and improvements to the current sequence builds. Accordingly, our comprehensive comparisons of BtOM1.0 against the UMD3.1 and Btau4.6 sequence builds tabulate large-to-immediate scale discordances requiring mediation.ResultsThe optical map, BtOM1.0, spanning the B. taurus genome (Hereford breed, L1 Dominette 01449) was assembled from an optical map dataset consisting of 2,973,315 (439 X; raw dataset size before assembly) single molecule optical maps (Rmaps; 1 Rmap = 1 restriction mapped DNA molecule) generated by the Optical Mapping System. The BamHI map spans 2,575.30 Mb and comprises 78 optical contigs assembled by a combination of iterative (using the reference sequence: UMD3.1) and de novo assembly techniques. BtOM1.0 is a high-resolution physical map featuring an average restriction fragment size of 8.91 Kb. Comparisons of BtOM1.0 vs. UMD3.1, or Btau4.6, revealed that Btau4.6 presented far more discordances (7,463) vs. UMD3.1 (4,754). Overall, we found that Btau4.6 presented almost double the number of discordances than UMD3.1 across most of the 6 categories of sequence vs. map discrepancies, which are: COMPLEX (misassembly), DELs (extraneous sequences), INSs (missing sequences), ITs (Inverted/Translocated sequences), ECs (extra restriction cuts) and MCs (missing restriction cuts).ConclusionAlignments of UMD3.1 and Btau4.6 to BtOM1.0 reveal discordances commensurate with previous reports, and affirm the NCBI's current designation of UMD3.1 sequence assembly as the "reference assembly" and the Btau4.6 as the "alternate assembly." The cattle genome optical map, BtOM1.0, when used as a comprehensive and largely independent guide, will greatly assist improvements to existing sequence builds, and later serve as an accurate physical scaffold for studies concerning the comparative genomics of cattle breeds

A rapid and scalable method for multilocus species delimitation using Bayesian model comparison and rooted triplets

Multilocus sequence data provide far greater power to resolve species limits than the single locus data typically used for broad surveys of clades. However, current statistical methods based on a multispecies coalescent framework are computationally demanding, because of the number of possible delimitations that must be compared and time-consuming likelihood calculations. New methods are therefore needed to open up the power of multilocus approaches to larger systematic surveys. Here, we present a rapid and scalable method that introduces two new innovations. First, the method reduces the complexity of likelihood calculations by decomposing the tree into rooted triplets. The distribution of topologies for a triplet across multiple loci has a uniform trinomial distribution when the 3 individuals belong to the same species, but a skewed distribution if they belong to separate species with a form that is specified by the multispecies coalescent. A Bayesian model comparison framework was developed and the best delimitation found by comparing the product of posterior probabilities of all triplets. The second innovation is a new dynamic programming algorithm for finding the optimum delimitation from all those compatible with a guide tree by successively analyzing subtrees defined by each node. This algorithm removes the need for heuristic searches used by current methods, and guarantees that the best solution is found and potentially could be used in other systematic applications. We assessed the performance of the method with simulated, published and newly generated data. Analyses of simulated data demonstrate that the combined method has favourable statistical properties and scalability with increasing sample sizes. Analyses of empirical data from both eukaryotes and prokaryotes demonstrate its potential for delimiting species in real cases

An overview of the role of context-sensitive HMMs in the prediction of ncRNA genes

Non-coding RNAs (ncRNA) are RNA molecules that function in the cells without being translated into proteins. In recent years, much evidence has been found that ncRNAs play a crucial role in various biological processes. As a result, there has been an increasing interest in the prediction of ncRNA genes. Due to the conserved secondary structure in ncRNAs, there exist pairwise dependencies between distant bases. These dependencies cannot be effectively modeled using traditional HMMs, and we need a more complex model such as the context-sensitive HMM (csHMM). In this paper, we overview the role of csHMMs in the RNA secondary structure analysis and the prediction of ncRNA genes. It is demonstrated that the context-sensitive HMMs can serve as an efficient framework for these purposes

Gcn4p and novel upstream activating sequences regulate targets of the unfolded protein response.

Eukaryotic cells respond to accumulation of unfolded proteins in the endoplasmic reticulum (ER) by activating the unfolded protein response (UPR), a signal transduction pathway that communicates between the ER and the nucleus. In yeast, a large set of UPR target genes has been experimentally determined, but the previously characterized unfolded protein response element (UPRE), an upstream activating sequence (UAS) found in the promoter of the UPR target gene KAR2, cannot account for the transcriptional regulation of most genes in this set. To address this puzzle, we analyzed the promoters of UPR target genes computationally, identifying as candidate UASs short sequences that are statistically overrepresented. We tested the most promising of these candidate UASs for biological activity, and identified two novel UPREs, which are necessary and sufficient for UPR activation of promoters. A genetic screen for activators of the novel motifs revealed that the transcription factor Gcn4p plays an essential and previously unrecognized role in the UPR: Gcn4p and its activator Gcn2p are required for induction of a majority of UPR target genes during ER stress. Both Hac1p and Gcn4p bind target gene promoters to stimulate transcriptional induction. Regulation of Gcn4p levels in response to changing physiological conditions may function as an additional means to modulate the UPR. The discovery of a role for Gcn4p in the yeast UPR reveals an additional level of complexity and demonstrates a surprising conservation of the signaling circuit between yeast and metazoan cells