Fast Association Tests for Genes with FAST

Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data. Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home     

RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing

RNA-binding proteins (RBPs) regulate splicing according to position-dependent principles, which can be exploited for analysis of regulatory motifs. Here we present RNAmotifs, a method that evaluates the sequence around differentially regulated alternative exons to identify clusters of short and degenerate sequences, referred to as multivalent RNA motifs. We show that diverse RBPs share basic positional principles, but differ in their propensity to enhance or repress exon inclusion. We assess exons differentially spliced between brain and heart, identifying known and new regulatory motifs, and predict the expression pattern of RBPs that bind these motifs. RNAmotifs is available at https://bitbucket.org/rogrro/rna_motifs.     

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.     

Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.     

DynaMIT: the dynamic motif integration toolkit

De-novo motif search is a frequently applied bioinformatics procedure to identify and prioritize recurrent elements in sequences sets for biological investigation, such as the ones derived from high-throughput differential expression experiments. Several algorithms have been developed to perform motif search, employing widely different approaches and often giving divergent results. In order to maximize the power of these investigations and ultimately be able to draft solid biological hypotheses, there is the need for applying multiple tools on the same sequences and merge the obtained results. However, motif reporting formats and statistical evaluation methods currently make such an integration task difficult to perform and mostly restricted to specific scenarios. We thus introduce here the Dynamic Motif Integration Toolkit (DynaMIT), an extremely flexible platform allowing to identify motifs employing multiple algorithms, integrate them by means of a user-selected strategy and visualize results in several ways; furthermore, the platform is user-extendible in all its aspects. DynaMIT is freely available at http://cibioltg.bitbucket.org.     

dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units

dadi is a popular but computationally intensive program for inferring models of demographic history and natural selection from population genetic data. I show that running dadi on a Graphics Processing Unit can dramatically speed computation compared with the CPU implementation, with minimal user burden. Motivated by this speed increase, I also extended dadi to four- and five-population models. This functionality is available in dadi version 2.1.0, https://bitbucket.org/gutenkunstlab/dadi/.     

Polyphony: superposition independent methods for ensemble-based drug discovery

Background

Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates.

Results

Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered.

Conclusions

New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].     

Reverse-Phase Protein Array: Technology,Application, Data Processing,and Integration

Reverse-phase protein array (RPPA) is a high-throughput antibody-based targeted proteomics platform that can quantify hundreds of proteins in thousands of samples derived from tissue or cell lysates, serum, plasma, or other body fluids. Protein samples are robotically arrayed as microspots on nitrocellulose-coated glass slides. Each slide is probed with a specific antibody that can detect levels of total protein expression or post-translational modifications, such as phosphorylation as a measure of protein activity. Here we describe workflow protocols and software tools that we have developed and optimized for RPPA in a core facility setting that includes sample preparation, microarray mapping and printing of protein samples, antibody labeling, slide scanning, image analysis, data normalization and quality control, data reporting, statistical analysis, and management of data. Our RPPA platform currently analyzes ∼240 validated antibodies that primarily detect proteins in signaling pathways and cellular processes that are important in cancer biology. This is a robust technology that has proven to be of value for both validation and discovery proteomic research and integration with other omics data sets.     

Inferring Correlation Networks from Genomic Survey Data

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world''s oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.     

The Jaw of the Worm: GTPase-activating Protein EAT-17 Regulates Grinder Formation in Caenorhabditis elegans

Constitutive transport of cellular materials is essential for cell survival. Although multiple small GTPase Rab proteins are required for the process, few regulators of Rabs are known. Here we report that EAT-17, a novel GTPase-activating protein (GAP), regulates RAB-6.2 function in grinder formation in Caenorhabditis elegans. We identified EAT-17 as a novel RabGAP that interacts with RAB-6.2, a protein that presumably regulates vesicle trafficking between Golgi, the endoplasmic reticulum, and plasma membrane to form a functional grinder. EAT-17 has a canonical GAP domain that is critical for its function. RNA interference against 25 confirmed and/or predicted RABs in C. elegans shows that RNAi against rab-6.2 produces a phenotype identical to eat-17. A directed yeast two-hybrid screen using EAT-17 as bait and each of the 25 RAB proteins as prey identifies RAB-6.2 as the interacting partner of EAT-17, confirming that RAB-6.2 is a specific substrate of EAT-17. Additionally, deletion mutants of rab-6.2 show grinder defects identical to those of eat-17 loss-of-function mutants, and both RAB-6.2 and EAT-17 are expressed in the terminal bulb of the pharynx where the grinder is located. Collectively, these results suggest that EAT-17 is a specific GTPase-activating protein for RAB-6.2. Based on the conserved function of Rab6 in vesicular transport, we propose that EAT-17 regulates the turnover rate of RAB-6.2 activity in cargo trafficking for grinder formation.     

Structural Integrity of Centromeric Chromatin and Faithful Chromosome Segregation Requires Pat1

The kinetochore (centromeric DNA and associated protein complex) is essential for faithful chromosome segregation and maintenance of genome stability. Here we report that an evolutionarily conserved protein Pat1 is a structural component of Saccharomyces cerevisiae kinetochore and associates with centromeres in a NDC10-dependent manner. Consistent with a role for Pat1 in kinetochore structure and function, a deletion of PAT1 results in delay in sister chromatid separation, errors in chromosome segregation, and defects in structural integrity of centromeric chromatin. Pat1 is involved in topological regulation of minichromosomes as altered patterns of DNA supercoiling were observed in pat1Δ cells. Studies with pat1 alleles uncovered an evolutionarily conserved region within the central domain of Pat1 that is required for its association with centromeres, sister chromatid separation, and faithful chromosome segregation. Taken together, our data have uncovered a novel role for Pat1 in maintaining the structural integrity of centromeric chromatin to facilitate faithful chromosome segregation and proper kinetochore function.     

The Role of Dbf4-Dependent Protein Kinase in DNA Polymerase ζ-Dependent Mutagenesis in Saccharomyces cerevisiae

The yeast Dbf4-dependent kinase (DDK) (composed of Dbf4 and Cdc7 subunits) is an essential, conserved Ser/Thr protein kinase that regulates multiple processes in the cell, including DNA replication, recombination and induced mutagenesis. Only DDK substrates important for replication and recombination have been identified. Consequently, the mechanism by which DDK regulates mutagenesis is unknown. The yeast mcm5-bob1 mutation that bypasses DDK’s essential role in DNA replication was used here to examine whether loss of DDK affects spontaneous as well as induced mutagenesis. Using the sensitive lys2ΔA746 frameshift reversion assay, we show DDK is required to generate “complex” spontaneous mutations, which are a hallmark of the Polζ translesion synthesis DNA polymerase. DDK co-immunoprecipitated with the Rev7 regulatory, but not with the Rev3 polymerase subunit of Polζ. Conversely, Rev7 bound mainly to the Cdc7 kinase subunit and not to Dbf4. The Rev7 subunit of Polζ may be regulated by DDK phosphorylation as immunoprecipitates of yeast Cdc7 and also recombinant Xenopus DDK phosphorylated GST-Rev7 in vitro. In addition to promoting Polζ-dependent mutagenesis, DDK was also important for generating Polζ-independent large deletions that revert the lys2ΔA746 allele. The decrease in large deletions observed in the absence of DDK likely results from an increase in the rate of replication fork restart after an encounter with spontaneous DNA damage. Finally, nonepistatic, additive/synergistic UV sensitivity was observed in cdc7Δ pol32Δ and cdc7Δ pol30-K127R,K164R double mutants, suggesting that DDK may regulate Rev7 protein during postreplication “gap filling” rather than during “polymerase switching” by ubiquitinated and sumoylated modified Pol30 (PCNA) and Pol32.     

Variation in Crossover Frequencies Perturb Crossover Assurance Without Affecting Meiotic Chromosome Segregation in Saccharomyces cerevisiae

The segregation of homologous chromosomes during the Meiosis I division requires an obligate crossover per homolog pair (crossover assurance). In Saccharomyces cerevisiae and mammals, Msh4 and Msh5 proteins stabilize Holliday junctions and its progenitors to facilitate crossing over. S. cerevisiae msh4/5 hypomorphs that reduce crossover levels up to twofold at specific loci on chromosomes VII, VIII, and XV without affecting homolog segregation were identified recently. We use the msh4–R676W hypomorph to ask if the obligate crossover is insulated from variation in crossover frequencies, using a S. cerevisiae S288c/YJM789 hybrid to map recombination genome-wide. The msh4–R676W hypomorph made on average 64 crossovers per meiosis compared to 94 made in wild type and 49 in the msh4Δ mutant confirming the defect seen at individual loci on a genome-wide scale. Crossover reductions in msh4–R676W and msh4Δ were significant across chromosomes regardless of size, unlike previous observations made at specific loci. The msh4–R676W hypomorph showed reduced crossover interference. Although crossover reduction in msh4–R676W is modest, 42% of the four viable spore tetrads showed nonexchange chromosomes. These results, along with modeling of crossover distribution, suggest the significant reduction in crossovers across chromosomes and the loss of interference compromises the obligate crossover in the msh4 hypomorph. The high spore viability of the msh4 hypomorph is maintained by efficient segregation of the natural nonexchange chromosomes. Our results suggest that variation in crossover frequencies can compromise the obligate crossover and also support a mechanistic role for interference in obligate crossover formation.     

Caenorhabditis elegans PIG-1/MELK Acts in a Conserved PAR-4/LKB1 Polarity Pathway to Promote Asymmetric Neuroblast Divisions

Asymmetric cell divisions produce daughter cells with distinct sizes and fates, a process important for generating cell diversity during development. Many Caenorhabditis elegans neuroblasts, including the posterior daughter of the Q cell (Q.p), divide to produce a larger neuron or neuronal precursor and a smaller cell that dies. These size and fate asymmetries require the gene pig-1, which encodes a protein orthologous to vertebrate MELK and belongs to the AMPK-related family of kinases. Members of this family can be phosphorylated and activated by the tumor suppressor kinase LKB1, a conserved polarity regulator of epithelial cells and neurons. In this study, we present evidence that the C. elegans orthologs of LKB1 (PAR-4) and its partners STRAD (STRD-1) and MO25 (MOP-25.2) regulate the asymmetry of the Q.p neuroblast division. We show that PAR-4 and STRD-1 act in the Q lineage and function genetically in the same pathway as PIG-1. A conserved threonine residue (T169) in the PIG-1 activation loop is essential for PIG-1 activity, consistent with the model that PAR-4 (or another PAR-4-regulated kinase) phosphorylates and activates PIG-1. We also demonstrate that PIG-1 localizes to centrosomes during cell divisions of the Q lineage, but this localization does not depend on T169 or PAR-4. We propose that a PAR-4-STRD-1 complex stimulates PIG-1 kinase activity to promote asymmetric neuroblast divisions and the generation of daughter cells with distinct fates. Changes in cell fate may underlie many of the abnormal behaviors exhibited by cells after loss of PAR-4 or LKB1.