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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,495 records · Page 83Linked to original sources

bioRxiv2015

Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce

Motivation: Public archives contain thousands of trillions of bases of valuable sequencing data. More than 40% of the Sequence Read Archive is human data protected by provisions such as dbGaP To analyze dbGaP-protected data, researchers must typically work with IT administrators and signing officials to ensure all levels of security are implemented at their institution. This is a major obstacle, impeding reproducibility and reducing the utility of archived data.\n\nResults: We present a protocol and software tool for analyzing protected data in a commercial cloud. The protocol, Rail-dbGaP, is applicable to any tool running on Amazon Web Services Elastic MapReduce. The tool, Rail-RNA v0.2, is a spliced aligner for RNA- seq data, which we demonstrate by running on 9,662 samples from the dbGaP-protected GTEx consortium dataset. The Rail-dbGaP protocol makes explicit for the first time the steps an investigator must take to develop Elastic MapReduce pipelines that analyze dbGaP-protected data in a manner compliant with NIH guidelines. Rail-RNA automates implementation of the protocol, making it easy for typical biomedical investigators to study protected RNA-seq data, regardless of their local IT resources or expertise.\n\nAvailability: Rail-RNA is available from http://rail.bio. Technical details on the Rail-dbGaP protocol as well as an implementation walkthrough are available at https://github.com/nellore/rail-dbgap. Detailed instructions on running Rail-RNA on dbGaP-protected data using Amazon Web Services are available at http://docs.rail.bio/dbgap/.\n\nContact: anellore@gmail.com, langmea@cs.jhu.edu

Bioinformatics↗

bioRxiv2015

Biological screens from linear codes: theory and tools

Molecular biology increasingly relies on large screens where enormous numbers of specimens are systematically assayed in the search for a particular, rare outcome. These screens include the systematic testing of small molecules for potential drugs and testing the association between genetic variation and a phenotype of interest. While these screens are \"hypothesis-free,\" they can be wasteful; pooling the specimens and then testing the pools is more efficient. We articulate in precise mathematical ways the type of structures useful in combinatorial pooling designs so as to eliminate waste, to provide light weight, flexible, and modular designs. We show that Reed-Solomon codes, and more generally linear codes, satisfy all of these mathematical properties. We further demonstrate the power of this technique with Reed-Solomonbased biological experiments. We provide general purpose tools for experimentalists to construct and carry out practical pooling designs with rigorous guarantees for large screens.

Bioinformatics↗

bioRxiv2015

solarius: an R interface to SOLAR for variance component analysis in pedigrees

SummaryThe open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for quantitative genetic analyses are stand-alone free programs developed by researchers in academia. SOLAR is the standard software program to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It also parses the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses, that makes the calculation of genome-wide scans more efficient.\n\nAvailability and implementationsolarius is available on CRAN https://cran.r-project.org/package=solarius and on GitHub https://github.com/ugcd/solarius. See http://solar.txbiomedgenetics.org/ for more information about SOLAR.\n\nContactaziyatdinov@santpau.cat

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bioRxiv2015

MPRAnator: a web-based tool for the design of Massively Parallel Reporter Assay experiments.

DNA regulatory elements contain short motifs where transcription factors (TFs) can bind to modulate gene expression. Although the broad principles of TF regulation are well understood, the rules that dictate how combinatorial TF binding translates into transcriptional activity remain largely unknown. With the rapid advances in DNA synthesis and sequencing technologies and the continuing decline in the associated costs, high-throughput experiments can be performed to investigate the regulatory role of thousands of oligonucleotide sequences simultaneously. Nevertheless, designing high-throughput reporter assay experiments such as Massively Parallel Reporter Assays (MPRAs) and similar methods remains challenging. We introduce MPRAnator, a set of tools that facilitate rapid design of MPRA experiments. With MPRA Motif design, a set of variables provides fine control of how motifs are placed into sequences therefore allowing the user to investigate the rules that govern TF occupancy. MPRA SNP design can be used to investigate the functional effects of single or combinations of SNPs at regulatory sequences. Finally, the Transmutation tool allows for the design of negative controls by permitting scrambling, reversing, complementing or introducing multiple random mutations in the input sequences or motifs.

Bioinformatics↗

bioRxiv2015

Establishing evidenced-based best practice for the de novo assembly and evaluation of transcriptomes from non-model organisms

Characterizing transcriptomes in both model and non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. Each step may be accomplished in one of several different ways, using different software packages, each producing different results. This analytical complexity begs the question - Which method(s) are optimal? Using reference and non-reference based evaluative methods, I propose a set of guidelines that aim to standardize and facilitate the process of transcriptome assembly. These recommendations include the generation of between 20 million and 40 million sequencing reads from single individual where possible, error correction of reads, gentle quality trimming, assembly filtering using Transrate and/or gene expression, annotation using dammit, and appropriate reporting. These recommendations have been extensively benchmarked and applied to publicly available transcriptomes, resulting in improvements in both content and contiguity. To facilitate the implementation of the proposed standardized methods, I have released a set of version controlled open-sourced code, The Oyster River Protocol for Transcriptome Assembly, available at http://oyster-river-protocol.rtfd.org/.

Bioinformatics↗

bioRxiv2015

MyGene.info and MyVariant.info: Gene and Variant Annotation Query Services

MyGene.info and MyVariant.info provide high-performance data APIs for querying gene and variant annotation information. They demonstrate a new model for organizing biological annotation information by utilizing a cloud-based scalable infrastructure. MyGene.info and MyVariant.info can be accessed at http://mygene.info and http://myvariant.info.

Bioinformatics↗

bioRxiv2015

A regression framework for the proportion of true null hypotheses

Modern scientific studies from many diverse areas of research abound with multiple hypothesis testing concerns. The false discovery rate is one of the most commonly used error rates for measuring and controlling rates of false discoveries when performing multiple tests. Adaptive false discovery rates rely on an estimate of the proportion of null hypotheses among all the hypotheses being tested. This proportion is typically estimated once for each collection of hypotheses. Here we propose a regression framework to estimate the proportion of null hypotheses conditional on observed covariates. This may then be used as a multiplication factor with the Benjamini-Hochberg adjusted p-values, leading to a plug-in false discovery rate estimator. Our case study concerns a genome-wise association meta-analysis which considers associations with body mass index. In our framework, we are able to use the sample sizes for the individual genomic loci and the minor allele frequencies as covariates. We further evaluate our approach via a number of simulation scenarios.

Bioinformatics↗

bioRxiv2016

ShapeCluster: Applying parametric regression to analyse time-series gene expression data

High-throughput technologies have made it possible to perform genome-scale analyses to investigate a variety of research areas. From these analyses, vast amounts of data are generated. However, these data can be noisy, which could obscure the underlying signal. Here, a high-throughput regression analysis approach was developed, where a variety of linear and nonlinear parametric models were fitted to gene expression profiles from time course experiments. These models include the logistic, Gompertz, exponential, critical exponential, linear+exponential, Gaussian and linear functions. The fitted parameters from these models reflect aspects of the model shape, and thus allowed for the interpretation of gene expression profiles in terms of the underlying biology, such as the time of initial gene expression. This provides a potentially more mechanistic ap-proach to studying the genetic responses to stimuli. Together with a cluster analysis, termed ShapeCluster, it was possible to group genes based on these aspects of the expression profiles. By investigating different combinations of parameters, this added flexibility to the analysis and allowed for the investigation of the data in multiple ways, including the identification of groups of genes that may be co-regulated, or participate in response to the biological stress in question. Clusters from these methods were assessed for significance through the use of over-represented annotation terms and motifs, and found to pro-duce biologically relevant sets of genes. The ShapeCluster package is available from https://sourceforge.net/projects/shapecluster/.

Bioinformatics↗

bioRxiv2016

The divisible load balance problem with shared cost and its application to phylogenetic inference

Motivated by load balance issues in parallel calculations of the phylogenetic likelihood function, we recently introduced an approximation algorithm for efficiently distributing partitioned alignment data to a given number of CPUs. The goal is to balance the accumulated number of sites per CPU, and, at the same time, to minimize the maximum number of unique partitions per CPU. The approximation algorithm assumes that likelihood calculations on individual alignment sites have identical runtimes and that likelihood calculation times on distinct sites are entirely independent from each other. However, a recently introduced optimization of the phylogenetic likelihood function, the so-called site repeats technique, violates both aforementioned assumptions. To this end, we modify our data distribution algorithm and explore 72 distinct heuristic strategies that take into account the additional restrictions induced by site repeats, to yield a good parallel load balance.\n\nOur best heuristic strategy yields a reduction in required arithmetic operations that ranges between 2% and 92% with an average of 62% for all test datasets using 2, 4, 8, 16, 32, and 64 CPUs compared to the original site-repeat-agnostic data distribution algorithm.

Bioinformatics↗

bioRxiv2016

Efficient detection of repeating sites to accelerate phylogenetic likelihood calculations

The phylogenetic likelihood function is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory saving attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 10-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the phylogenetic likelihood function currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation.

Bioinformatics↗

bioRxiv2016

Contrasting the genetic architecture of 30 complex traits from summary association data

Variance components methods that estimate the aggregate contribution of large sets of variants to the heritability of complex traits have yielded important insights into the disease architecture of common diseases. Here, we introduce new methods that estimate the total variance in trait explained by a single locus in the genome (local heritability) from summary GWAS data while accounting for linkage disequilibrium (LD) among variants. We apply our new estimator to ultra large-scale GWAS summary data of 30 common traits and diseases to gain insights into their local genetic architecture. First, we find that common SNPs have a high contribution to the heritability of all studied traits. Second, we identify traits for which the majority of the SNP heritability can be confined to a small percentage of the genome. Third, we identify GWAS risk loci where the entire locus explains significantly more variance in the trait than the GWAS reported variants. Finally, we identify 55 loci that explain a large proportion of heritability across multiple traits.

Bioinformatics↗

bioRxiv2016

fusionDB: assessing microbial diversity and environmental preferences via functional similarity networks

Microbial functional diversification is driven by environmental factors, i.e. microorganisms inhabiting the same environmental niche tend to be more functionally similar than those from different environments. In some cases, even closely phylogenetically related microbes differ more across environments than across taxa. While microbial similarities are often reported in terms of taxonomic relationships, no existing databases directly links microbial functions to the environment. We previously developed a method for comparing microbial functional similarities on the basis of proteins translated from the sequenced genomes. Here we describe fusionDB, a novel database that uses our functional data to represent 1,374 taxonomically distinct bacteria annotated with available metadata: habitat/niche, preferred temperature, and oxygen use. Each microbe is encoded as a set of functions represented by its proteome and individual microbes are connected via common functions. Users can search fusionDB via combinations of organism names and metadata. Moreover, the web interface allows mapping new microbial genomes to the functional spectrum of reference bacteria, rendering interactive similarity networks that highlight shared functionality. fusionDB provides a fast means of comparing microbes, identifying potential horizontal gene transfer events, and highlighting key environment-specific functionality.\n\nfusionDB is publicly available at http://services.bromberglab.org/fusiondb/.

Bioinformatics↗

bioRxiv2016

Regmex, Motif analysis in ranked lists of sequences

Motif analysis has long been an important method to characterize biological functionality and the current growth of sequencing-based genomics experiments further extends its potential. These diverse experiments often generate sequence lists ranked by some functional property. There is therefore a growing need for motif analysis methods that can exploit this coupled data structure and be tailored for specific biological questions. Here, we present a motif analysis tool, Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in a ranked list of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact probabilities for motif observations in sequences. Motif enrichment is optionally evaluated using random walks, Brownian bridges, or modified rank based statistics. These features make Regmex well suited for a range of biological sequence analysis problems related to motif discovery. We demonstrate different usage scenarios including rank correlation of microRNA binding sites co-occurring with a U-rich motif. The method is available as an R package.

Bioinformatics↗

bioRxiv2016

RTFBSDB: an integrated framework for transcription factor binding site analysis

Transcription factors (TFs) regulate complex programs of gene transcription by binding to short DNA sequence motifs. Here we introduce rtfbsdb, a unified framework that integrates a database of more than 65,000 TF binding motifs with tools to easily and efficiently scan target genome sequences. Rtfbsdb clusters motifs with similar DNA sequence specificities and optionally integrates RNA-seq or PRO-seq data to restrict analyses to motifs recognized by TFs expressed in the cell type of interest. Our package allows common analyses to be performed rapidly in an integrated environment.\n\nAvailability: rtfbsdb is available via github (https://github.com/Danko-Lab/rtfbsdb).

Bioinformatics↗

bioRxiv2016

RASLseqTools: open-source methods for designing and analyzing RNA-mediated oligonucleotide Annealing, Selection, and, Ligation sequencing (RASL-seq) experiments

RNA-mediated oligonucleotide Annealing, Selection, and Ligation (RASL-seq) is a method to measure the expression of hundreds of genes in thousands of samples for a fraction of the cost of competing methods. However, enzymatic inefficiencies of the original protocol and the lack of open source software to design and analyze RASL-seq experiments have limited its widespread adoption. We recently reported an Rnl2-based RASL-seq protocol (RRASL-seq) that offers improved ligation efficiency and a probe decoy strategy to optimize sequencing usage. Here, we describe an open source software package, RASLseqTools, that provides computational methods to design and analyze RASL-seq experiments. Furthermore, using data from a large RRASL-seq experiment, we demonstrate how normalization methods can be used for characterizing and correcting experimental, sequencing, and alignment error. We provide evidence that the three principal predictors of RRASL-seq reproducibility are barcode/probe sequence dissimilarity, sequencing read depth, and normalization strategy. Using dozens of technical and biological replicates across multiple 384-well plates, we find simple normalization strategies yield similar results to more statistically complex methods.

Bioinformatics↗

bioRxiv2016

Automating Assessment of the Undiscovered Biosynthetic Potential of Actinobacteria

Background. Biosynthetic potential of Actinobacteria has long been the subject of theoretical estimates. Such an estimate is indeed important as a test of further exploitability of a taxon or group of taxa for new therapeutics. As neither a set of available genomes nor a set of bacterial cultivation methods are static, it makes sense to simplify as much as possible and to improve reproducibility of biosynthetic gene clusters similarity, diversity, and abundance estimations.\n\nResults. We have developed a command-line computational pipeline (available at https://bitbucket.org/qmentis/clusterscluster/) that assists in performing empirical (genome-based) assessment of microbial secondary metabolite gene clusters similarity and abundance, and applied it to a set of 208 complete and de-duplicated Actinobacteria genomes. After a brief overview of Actinobacteria biosynthetic potential as compared to other bacterial taxa, we use similarity thresholds derived from 4 pairs of known similar gene clusters to identify up to 40-48% of 3247 gene clusters in our set of genomes as unique. There is no saturation of the cumulative unique gene clusters curve within the examined dataset, and Heaps alpha is 0.129, suggesting an open pan-clustome. We identify and highlight pitfalls and possible improvements of genome-based gene cluster similarity measurements.

Bioinformatics↗

bioRxiv2016

PEDLA: predicting enhancers with a deep learning-based algorithmic framework

Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers on a genome-wide scale remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from a limited number of training samples, a simplicity of features, class-imbalanced data, and inconsistent performance across diverse cell types/tissues. Here, we developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and we demonstrated that our PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to continuously learn from 22 training cell types/tissues, and the results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.

Bioinformatics↗

bioRxiv2016

Choosing panels of genomics assays using submodular optimization

Although the cost of high-throughput DNA sequencing continues to drop, extensively characterizing a given cell type using assays such as ChIP-seq and DNase-seq is still expensive. As a result, epigenomic characterization of a cell type is typically carried out using a small panel of assay types. Deciding a priori which assays to perform--e.g., a few complementary histone modification ChIP-seq experiments, perhaps an open chromatin assay, plus a few diverse transcription factor assays--is thus a critical step in many studies. Unfortunately, the field currently lacks a principled method for making these choices. We present submodular selection of assays (SSA), a method for choosing a diverse panel of genomic assays that leverages methods from the field of submodular optimization. We also describe a series of evaluation methods that allow us to measure the quality of a selected assay panel in the context of inference tasks such as data imputation, functional element prediction, and semi-automated genome annotation. Applying this evaluation framework to data from the ENCODE and Roadmap Epigenomics Consortia, we provide empirical evidence that SSA provides high quality panels of assays. The method is computationally efficient and is theoretically optimal under certain assumptions. SSA is extremely flexible, and can be employed to select assays for a new cell type or to select additional assays to be performed in a partially characterized cell type. More generally, this application serves as a model for how submodular optimization can be applied to other discrete problems in biology. SSA is available at http://melodi.ee.washington.edu/assay_panel_selection.html.

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