HapTree-X: An Integrative Bayesian Framework for Haplotype Reconstruction from Transcriptome and Genome Sequencing Data

By running standard genotype calling tools, it is possible to accurately identify the number of wild type and mutant alleles for each single-nucleotide polymorphism (SNP) site. However, in the case of two heterozygous SNP sites, genotype calling tools cannot determine whether mutant alleles from different SNP loci are on the same chromosome or on different homologous chromosomes (i.e. compound heterozygote)

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the struct ure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains. Keywords: interactome analysis; network integration; heterogeneous networks; dimensionality reduction; network diffusion; gene function prediction; genetic interaction prediction; gene ontology reconstruction; drug response predictionNational Institutes of Health (U.S.) (Grant R01GM081871

RCK: accurate and efficient inference of sequence- and structure-based protein–RNA binding models from RNAcompete data

Motivation: Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset. Results: We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale.National Institutes of Health (U.S.) (Grant R01GM081871

Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or pro- teins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusion-based “guilt-by-association” and graph- theoretic methods have had success in inferring gene functions [1, 2, 3]. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. A type of diffusion algorithm, also known as random walk with restart (RWR), has been extensively studied in the context of biological networks and effectively applied to protein function prediction (e.g., [1]). The key idea is to propagate information along the network, in order to exploit both direct and indirect linkages between genes. Typically, a distribution of topological similar- ity is computed for each gene, in relation to other genes in the network, so that researchers can select the most related genes in the resulting distribution or, rather, select genes that share the most similar distributions. Though successful, these approaches are susceptible to noise in the input networks due to the high dimensionality of the computed distributions

Computational biology in the 21st century

Computational biologists answer biological and biomedical questions by using computation in support of—or in place of—laboratory procedures, hoping to obtain more accurate answers at a greatly reduced cost. The past two decades have seen unprecedented technological progress with regard to generating biological data; next-generation sequencing, mass spectrometry, microarrays, cryo-electron microscopy, and other highthroughput approaches have led to an explosion of data. However, this explosion is a mixed blessing. On the one hand, the scale and scope of data should allow new insights into genetic and infectious diseases, cancer, basic biology, and even human migration patterns. On the other hand, researchers are generating datasets so massive that it has become difficult to analyze them to discover patterns that give clues to the underlying biological processes.National Institutes of Health. (U.S.) ( grant GM108348)Hertz Foundatio

Traversing the k-mer Landscape of NGS Read Datasets for Quality Score Sparsification

It is becoming increasingly impractical to indefinitely store raw sequencing data for later processing in an uncompressed state. In this paper, we describe a scalable compressive framework, Read-Quality-Sparsifier (RQS), which substantially outperforms the compression ratio and speed of other de novo quality score compression methods while maintaining SNP-calling accuracy. Surprisingly, RQS also improves the SNP-calling accuracy on a gold-standard, real-life sequencing dataset (NA12878) using a k-mer density profile constructed from 77 other individuals from the 1000 Genomes Project. This improvement in downstream accuracy emerges from the observation that quality score values within NGS datasets are inherently encoded in the k-mer landscape of the genomic sequences. To our knowledge, RQS is the first scalable sequence-based quality compression method that can efficiently compress quality scores of terabyte-sized and larger sequencing datasets. Availability: An implementation of our method, RQS, is available for download at: http://rqs.csail.mit.edu/. © 2014 Springer International Publishing Switzerland. Keywords: RQS; quality score; sparsification; compression; accuracy; variant callingHertz FoundationNational Institutes of Health (U.S.) (R01GM108348

Enabling Privacy-Preserving GWASs in Heterogeneous Human Populations

The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWASs). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWASs that adapts principles of differential privacy-a cryptographic theory that facilitates secure analysis of sensitive data-to both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.National Institutes of Health (U.S.) (Grant GM108348

An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models

Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations.National Institutes of Health (U.S.) (Grant GM108348)Howard Hughes Medical Institut

Fast genotyping of known SNPs through approximate

Motivation: As the volume of next-generation sequencing (NGS) data increases, faster algorithms become necessary. Although speeding up individual components of a sequence analysis pipeline (e.g. read mapping) can reduce the computational cost of analysis, such approaches do not take full advantage of the particulars of a given problem. One problem of great interest, genotyping a known set of variants (e.g. dbSNP or Affymetrix SNPs), is important for characterization of known genetic traits and causative disease variants within an individual, as well as the initial stage of many ancestral and population genomic pipelines (e.g. GWAS). Results: We introduce lightweight assignment of variant alleles (LAVA), an NGS-based genotyping algorithm for a given set of SNP loci, which takes advantage of the fact that approximate matching of mid-size k-mers (with k = 32) can typically uniquely ide ntify loci in the human genome without full read alignment. LAVA accurately calls the vast majority of SNPs in dbSNP and Affymetrix's Genome-Wide Human SNP Array 6.0 up to about an order of magnitude faster than standard NGS genotyping pipelines. For Affymetrix SNPs, LAVA has significantly higher SNP calling accuracy than existing pipelines while using as low as ∼5 GB of RAM. As such, LAVA represents a scalable computational method for population-level genotyping studies as well as a flexible NGS-based replacement for SNP arrays. Availability and Implementation: LAVA software is available at http://lava.csail.mit.edu

Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping

Sequence libraries that cover all k-mers enable universal, unbiased measurements of binding to both oligonucleotides and peptides. While the number of k-mers grows exponentially in k, space on all experimental platforms is limited. Here, we shrink k-mer library sizes by using joker characters, which represent all characters in the alphabet simultaneously. We present the JokerCAKE (joker covering all k-mers) algorithm for generating a short sequence such that each k-mer appears at least p times with at most one joker character per k-mer. By running our algorithm on a range of parameters and alphabets, we show that JokerCAKE produces near-optimal sequences. Moreover, through comparison with data from hundreds of DNA-protein binding experiments and with new experimental results for both standard and JokerCAKE libraries, we establish that accurate binding scores can be inferred for high-affinity k-mers using JokerCAKE libraries. JokerCAKE libraries allow researchers to search a significantly larger sequence space using the same number of experimental measurements and at the same cost. We present a new compact sequence design that covers all k-mers utilizing joker characters and develop an efficient algorithm to generate such designs. We show through simulations and experimental validation that these sequence designs are useful for identifying high-affinity binding sites at significantly reduced cost and space. Keywords: sequence libraries; microarray design; de Bruijn graphNational Institutes of Health (U.S.) (Grant R01GM081871