A phylogenetic approach to inferring the order in which mutations arise during cancer progression.

Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.

RBPBind: Quantitative Prediction of Protein-RNA Interactions.

There are hundreds of RNA binding proteins in the human genome alone and their interactions with messenger and other RNAs in a cell regulate every step in an RNA's life cycle. To understand this interplay of proteins and RNA it is important to be able to know which protein binds which RNA how strongly and where. Here, we introduce RBPBind, a web-based tool for the quantitative prediction of the interaction of single-stranded RNA binding proteins with target RNAs that fully takes into account the effect of RNA secondary structure on binding affinity. Given a user-specified RNA and a protein selected from a set of several RNA-binding proteins, RBPBind computes their binding curve and effective binding constant. The server also computes the probability that, at a given protein concentration, a protein molecule will bind to any particular nucleotide along the RNA. The sequence specificity of the protein-RNA interaction is parameterized from public RNAcompete experiments and integrated into the recursions of the Vienna RNA package to simultaneously take into account protein binding and RNA secondary structure. We validate our approach by comparison to experimentally determined binding affinities of the HuR protein for several RNAs of different sequence contexts from the literature, showing that integration of raw sequence affinities into RNA secondary structure prediction significantly improves the agreement between computationally predicted and experimentally measured binding affinities. Our resource thus provides a quick and easy way to obtain reliable predicted binding affinities and locations for single-stranded RNA binding proteins based on RNA sequence alone.

Hypothesis tests for phylogenetic quartets, with applications to coalescent-based species tree inference.

Numerous statistical methods have been developed to estimate evolutionary relationships among a collection of present-day species, typically represented by a phylogenetic tree, using the information contained in the DNA sequences sampled from representatives of each species. In the current era of high-throughput genome sequencing, the models underlying such methods have become increasingly sophisticated, and the resulting computations are often prohibitive. Here we consider the problem of rigorously testing the phylogenetic relationships among collections of four species under the multispecies coalescent model that accommodates both multi-locus datasets and SNP data. Our test employs a new statistic - the summed absolute differences between certain columns in flattened phylogenetic matrices - as well as a previously used statistic that measures the distance of a flattened matrix from the space of rank-10 matrices. We derive distributional results for both statistics and study the performance of the corresponding hypothesis tests using both simulated and empirical data. We discuss how these tests may be used to improve inference of phylogenetic relationships for larger samples of species under the multispecies coalescent model, a problem that has until recently been computationally intractable.