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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2015

Ensembler: Enabling high-throughput molecular simulations at the superfamily scale

The rapidly expanding body of available genomic and protein structural data provides a rich resource for understanding protein dynamics with biomolecular simulation. While computational infrastructure has grown rapidly, simulations on an omics scale are not yet widespread, primarily because software infrastructure to enable simulations at this scale has not kept pace. It should now be possible to study protein dynamics across entire (super)families, exploiting both available structural biology data and conformational similarities across homologous proteins. Here, we present a new tool for enabling high-throughput simulation in the genomics era. Ensembler takes any set of sequences--from a single sequence to an entire superfamily-- and shepherds them through various stages of modeling and refinement to produce simulation-ready structures. This includes comparative modeling to all relevant PDB structures (which may span multiple conformational states of interest), reconstruction of missing loops, addition of missing atoms, culling of nearly identical structures, assignment of appropriate protonation states, solvation in explicit solvent, and refinement and filtering with molecular simulation to ensure stable simulation. The output of this pipeline is an ensemble of structures ready for subsequent molecular simulations using computer clusters, supercomputers, or distributed computing projects like Folding@home. Ensembler thus automates much of the time consuming process of preparing protein models suitable for simulation, while allowing scalability up to entire superfamilies. A particular advantage of this approach can be found in the construction of kinetic models of conformational dynamics--such as Markov state models (MSMs)--which benefit from a diverse array of initial configurations that span the accessible conformational states to aid sampling. We demonstrate the power of this approach by constructing models for all catalytic domains in the human tyrosine kinase family, using all available kinase catalytic domain structures from any organism as structural templates.\n\nEnsembler is free and open source software licensed under the GNU General Public License (GPL) v2. It is compatible with Linux and OS X. The latest release can be installed via the conda package manager, and the latest source can be downloaded from https://github.com/choderalab/ensembler.

Bioinformatics↗

bioRxiv2015

A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu

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bioRxiv2015

Fast principal components analysis reveals convergent evolution of ADH1B gene in Europe and East Asia

Searching for genetic variants with unusual differentiation between subpopulations is an established approach for identifying signals of natural selection. However, existing methods generally require discrete subpopulations. We introduce a method that infers selection using principal components (PCs) by identifying variants whose differentiation along top PCs is significantly greater than the null distribution of genetic drift. To enable the application of this method to large data sets, we developed the FastPCA software, which employs recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using the PC-based test for natural selection, we replicate previously known selected loci and identify three new genome-wide significant signals of selection, including selection in Europeans at the ADH1B gene. The coding variant rs1229984*T has previously been associated to a decreased risk of alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents. We also detect new selection signals at IGFBP3 and IGH, which have also previously been associated to human disease.

Bioinformatics↗

bioRxiv2015

Measuring the Contribution of Genomic Predictors to Improving Estimator Precision in Randomized trials

The use of genomic data in the clinic has not been as widespread as was envisioned when sequencing and genomic analysis became common techniques. An underlying difficulty is the direct assessment of how much additional information genomic data are providing beyond standard clinical measurements. This is hard to quantify in the clinical setting where laboratory tests based on genomic signatures are fairly new and there are not sufficient data collected to determine how valuable these tests have been in practice. Here we focus on the potential precision gain from using the popular MammaPrint genomic signature in a covariate-adjusted, randomized clinical trial. We describe how adjustment of an estimator for the average treatment effect using baseline measurements can improve precision. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct a simulation study using genomic and clinical data gathered for breast cancer patients and find that adjusting for clinical factors alone provides a gain in precision of 5-6%, adjusting for genomic factors alone provides a similar gain (5%), and combining the two yields a 2-3% additional gain over only adjusting for clinical covariates.

Bioinformatics↗

bioRxiv2015

benchmarkR: an R package for benchmarking genome-scale methods

benchmarkR is an R package designed to assess and visualize the performance of statistical methods for datasets that have an independent truth (e.g., simulations or datasets with large-scale validation), in particular for methods that claim to control false discovery rates (FDR). We augment some of the standard performance plots (e.g., receiver operating characteristic, or ROC, curves) with information about how well the methods are calibrated (i.e., whether they achieve their expected FDR control). For example, performance plots are extended with a point to highlight the power or FDR at a user-set threshold (e.g., at a method's estimated 5% FDR). The package contains general containers to store simulation results (SimResults) and methods to create graphical summaries, such as receiver operating characteristic curves (rocX), false discovery plots (fdX) and power-to-achieved FDR plots (powerFDR); each plot is augmented with some form of calibration information. We find these plots to be an improved way to interpret relative performance of statistical methods for genomic datasets where many hypothesis tests are performed. The strategies, however, are general and will find applications in other domains.

Bioinformatics↗

bioRxiv2015

benchNGS : An approach to benchmark short reads alignment tools

In the last decade a number of algorithms and associated software were developed to align next generation sequencing (NGS) reads to relevant reference genomes. The results of these programs may vary significantly, especially when the NGS reads are contain mutations not found in the reference genome. Yet there is no standard way to compare these programs and assess their biological relevance.\n\nWe propose a benchmark to assess accuracy of the short reads mapping based on the pre-computed global alignment of closely related genome sequences. In this paper we outline the method and also present a short report of an experiment performed on five popular alignment tools.

Bioinformatics↗

bioRxiv2015

GO-PCA: An Unsupervised Method to Explore Biological Heterogeneity Based on Gene Expression and Prior Knowledge

Genome-wide expression profiling is a cost-efficient and widely used method to characterize heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such datasets typically relies on generic unsupervised methods, e.g. principal component analysis or hierarchical clustering. However, generic methods fail to exploit the significant amount of knowledge that exists about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that incorporates prior knowledge about gene functions in the form of gene ontology (GO) annotations. GO-PCA aims to discover and represent biological heterogeneity along all major axes of variation in a given dataset, while suppressing heterogeneity due to technical biases. To this end, GO-PCA combines principal component analysis (PCA) with nonparametric GO enrichment analysis, and uses the results to generate expression signatures based on small sets of functionally related genes. I first applied GO-PCA to expression data from diverse lineages of the human hematopoietic system, and obtained a small set of signatures that captured known cell characteristics for most lineages. I then applied the method to expression profiles of glioblastoma (GBM) tumor biopsies, and obtained signatures that were strongly associated with multiple previously described GBM subtypes. Surprisingly, GO-PCA discovered a cell cycle-related signature that exhibited significant differences between the Proneural and the prognostically favorable GBM CpG Island Methylator (G-CIMP) subtypes, suggesting that the G-CIMP subtype is characterized in part by lower mitotic activity. Previous expression-based classifications have failed to separate these subtypes, demonstrating that GO-PCA can detect heterogeneity that is missed by other methods. My results show that GO-PCA is a powerful and versatile expression-based method that facilitates exploration of large-scale expression data, without requiring additional types of experimental data. The low-dimensional representation generated by GO-PCA lends itself to interpretation, hypothesis generation, and further analysis.

Bioinformatics↗

bioRxiv2015

The "Gini index" in genetics: measuring genetic architecture complexity of quantitative traits

Genetic architecture is a general terminology used and discussed very often in complex traits genetics. It is related to the number of functional loci involved in explaining variation of a complex trait and the distribution of genetic effects across these loci. Understanding the complexity level of the genetic architecture of complex traits is essential for evaluating the potential power of mapping functional loci and prediction of complex traits. However, there has been no quantitative measurement of the genetic architecture complexity, which makes it difficult to link results from genetic data analysis to such terminology. Inspired by the \"Gini index\" for measuring income distribution in economics, I develop a genetic architecture score (\"GA score\") to measure genetic architecture complexity. Simulations indicate that the GA score is an effective measurement of the complexity level of complex traits genetic architecture.

Bioinformatics↗

bioRxiv2015

Controlling False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

We review existing methods for the analysis of RNA-Seq data and place them in a common framework of a sequence of tasks that are usually part of the process. We show that many existing methods produce large numbers of false positives in cases where the null hypothesis is true by construction and where actual data from RNA-Seq studies are used, as opposed to simulations that make specific assumptions about the nature of the data. We show that some of those mathematical assumptions about the data likely are one of the causes of the false positives, and define a general structure that is not apparently subject to these problems. The best performance was shown by limma-voom and by some simple methods composed of easily understandable steps.

Bioinformatics↗

bioRxiv2015

Fine-mapping cellular QTLs with RASQUAL and ATAC-seq

When cellular traits are measured using high-throughput DNA sequencing quantitative trait loci (QTLs) manifest at two levels: population level differences between individuals and allelic differences between cis-haplotypes within individuals. We present RASQUAL (Robust Allele Specific QUAntitation and quality controL), a novel statistical approach for association mapping that integrates genetic effects and robust modelling of biases in next generation sequencing (NGS) data within a single, probabilistic framework. RASQUAL substantially improves causal variant localisation and sensitivity of association detection over existing methods in RNA-seq, DNaseI-seq and ChIP-seq data. We illustrate how RASQUAL can be used to maximise association detection by generating the first map of chromatin accessibility QTLs (caQTLs) in a European population using ATAC-seq. Despite a modest sample size, we identified 2,706 independent caQTLs (FDR 10%) and illustrate how RASQUALs improved causal variant localisation provides powerful information for fine-mapping disease-associated variants. We also map \"multipeak\" caQTLs, identical genetic associations found across multiple, independent open chromatin regions and illustrate how genetic signals in ATAC-seq data can be used to link distal regulatory elements with gene promoters. Our results highlight how joint modelling of population and allele-specific genetic signals can improve functional interpretation of noncoding variation.

Bioinformatics↗

bioRxiv2015

Replication is Recursion; or, Lambda: the Biological Imperative

There is a striking visual symmetry between the 'paradoxical combinator' (which implements recursion in the mathematical theory of computation) and the biological replication fork (which implements reproduction in the living cell). Can this mean anything? Living organisms are information processors - the genome encodes instructions for the process of life much as computer programs encode instructions for a computer. Replication in biological systems is intuitively similar to recursion in computational systems. The following discussion will present enough of the mathematics to allow biologists to make sense of the symmetries in the logo. Mathematicians will learn nothing new about biology, but may be encouraged to look at biological processes from a new perspective.\n\nComputational biology: a flight of fancy Computational biology: a flight... Mathematics of computation Semant ...

Bioinformatics↗

bioRxiv2015

Using the Phenoscape Knowledgebase to relate genetic perturbations to phenotypic evolution

The abundance of phenotypic diversity among species can enrich our knowledge of development and genetics beyond the limits of variation that can be observed in model organisms. The Phenoscape Knowledgebase (KB) is designed to enable exploration and discovery of phenotypic variation among species. Because phenotypes in the KB are annotated using standard ontologies, evolutionary phenotypes can be compared with phenotypes from genetic perturbations in model organisms. To illustrate the power of this approach, we review the use of the KB to find taxa showing evolutionary variation similar to that of a query gene. Matches are made between the full set of phenotypes described for a gene and an evolutionary profile, the latter of which is defined as the set of phenotypes that are variable among the daughters of any node on the taxonomic tree. Phenoscapes semantic similarity interface allows the user to assess the statistical significance of each match and flags matches that may only result from differences in annotation coverage between genetic and evolutionary studies. Tools such as this will help meet the challenge of relating the growing volume of genetic knowledge in model organisms to the diversity of phenotypes in nature. The Phenoscape KB is available at http://kb.phenoscape.org.

Bioinformatics↗

bioRxiv2015

Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data

Due to their relatively low-cost per sample and broad, gene-centric coverage of CpGs across the human genome, Illuminas 450k arrays are widely used in large scale differential methylation studies. However, by their very nature, large studies are particularly susceptible to the effects of unwanted variation. The effects of unwanted variation have been extensively documented in gene expression array studies and numerous methods have been developed to mitigate these effects. However, there has been much less research focused on the appropriate methodology to use for accounting for unwanted variation in methylation array studies. Here we present a novel 2-stage approach using RUV-inverse in a differential methylation analysis of 450k data and show that it outperforms existing methods.

Bioinformatics↗

bioRxiv2015

Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 hours for US$0.91 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio.

Bioinformatics↗

bioRxiv2015

Improving the Power of Structural Variation Detection by Augmenting the Reference

The uses of the Genome Reference Consortiums human reference sequence can be roughly categorized into three related but distinct categories: as a representative species genome, as a coordinate system for identifying variants, and as an alignment reference for variation detection algorithms. However, the use of this reference sequence as simultaneously a representative species genome and as an alignment reference leads to unnecessary artifacts for structural variation detection algorithms and limits their accuracy. We show how decoupling these two references and developing a separate alignment reference can significantly improve the accuracy of structural variation detection, lead to improved genotyping of disease related genes, and decrease the cost of studying polymorphism in a population.

Bioinformatics↗

bioRxiv2015

ZIFA: Dimensionality reduction for zero-inflated single cell gene expression analysis

Single cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical dimensionality reduction methods because of the prevalence of dropout events leading to zero-inflated data. Here we develop a dimensionality reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modelling accuracy on simulated and biological datasets.

Bioinformatics↗

bioRxiv2015

ReproPhylo: An Environment for Reproducible Phylogenomics

The reproducibility of experiments is key to the scientific process, and particularly necessary for accurate reporting of analyses in data-rich fields such as phylogenomics. We present ReproPhylo, a phylogenomic analysis environment developed to ensure experimental reproducibility, to facilitate the handling of large-scale data, and to assist methodological experimentation. Reproducibility, and instantaneous repeatability, is built in to the ReproPhylo system, and does not require user intervention or configuration because it stores the experimental workflow as a single, serialized Python object containing explicit provenance and environment information. This single file approach ensures the persistence of provenance across iterations of the analysis, with changes automatically managed by the version control program Git. ReproPhylo produces an extensive human-readable report, and generates a comprehensive experimental archive file, both of which are suitable for submission with publications. The system facilitates thorough experimental exploration of both parameters and data. ReproPhylo is a platform independent CC0 python module, and is easily installed as a Docker image, with an Jupyter GUI, or as a slimmer version in a Galaxy distribution.

Bioinformatics↗

bioRxiv2015

Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION sequencing

The recently introduced Oxford Nanopore MinION platform generates DNA sequence data in real-time. This opens immense potential to shorten the sample-to-results time and is likely to lead to enormous benefits in rapid diagnosis of bacterial infection and identification of drug resistance. However, there are very few tools available for streaming analysis of real-time sequencing data. Here, we present a framework for streaming analysis of MinION real-time sequence data, together with probabilistic streaming algorithms for species typing, multi-locus strain typing, gene presence strain-typing and antibiotic resistance profile identification. Using three culture isolate samples as well as a mixed-species sample, we demonstrate that bacterial species and strain information can be obtained within 30 minutes of sequencing and using about 500 reads, initial drug-resistance profiles within two hours, and complete resistance profiles within 10 hours. Multi-locus strain typing required more than 15x coverage to generate confident assignments, whereas gene-presence typing could detect the presence of a known strain with 0.5x coverage. We also show that our pipeline can process over 100 times more data than the current throughput of the MinION on a desktop computer.

Bioinformatics↗