SEARCH · Search bioRxiv

Results for “Bioinformatics”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,513 records · Page 84Linked to original sources

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.

Bioinformatics↗

bioRxiv2016

Systematic identification of cooperation between DNA binding proteins in 3D space

Cooperation between DNA-binding proteins (DBPs) such as transcription factors and chromatin remodeling enzymes plays a pivotal role in regulating gene expression and other biological processes. Such cooperation is often via interaction between DBPs that bind to loci located distal in the linear genome but close in the 3D space, referred as trans-cooperation. Due to the lack of 3D chromosomal structure, identification of DBP cooperation has been limited to those binding to neighbor regions in the linear genome, referred as cis-cooperation. Here we present the first study that integrates protein ChIP-seq and Hi-C data to systematically identify both cis- and trans-cooperation between DBPs. We developed a new network model that allows identification of cooperation between multiple DBPs and reveals cell type specific or independent regulations. Particularly interesting, we have retrieved many known and previously unknown trans-cooperation between DBPs in the chromosomal loops that may be a key factor for influencing 3D chromosomal structure. The software is available at http://wanglab.ucsd.edu/star/DBPnet/index.html.

Bioinformatics↗

bioRxiv2016

Computational prediction shines light on type III secretion origins

Type III secretion system is a key bacterial symbiosis and pathogenicity mechanism responsible for a variety of infectious diseases, ranging from food-borne illnesses to the bubonic plague. In many Gram-negative bacteria, the type III secretion system transports effector proteins into host cells, converting resources to bacterial advantage. Here we introduce a computational method that identifies type III effectors by combining homology based inference with de novo predictions, reaching up to 3-fold higher performance than existing tools. Our work reveals that signals for recognition and transport of effectors are distributed over the entire protein sequence instead of being confined to the N-terminus, as was previously thought. Our scan of hundreds of prokaryotic genomes identified previously unknown effectors, suggesting that type III secretion may have evolved prior to the archaea/bacteria split. Crucially, our method performs well for short sequence fragments, facilitating evaluation of microbial communities and rapid identification of bacterial pathogenicity - no genome assembly required. pEffect and its data sets are available at http://services.bromberglab.org/peffect.

Bioinformatics↗

bioRxiv2016

High-quality thermodynamic data on the stability changes of proteins upon single-site mutations

We have set up and manually curated a dataset containing experimental information on the impact of amino acid substitutions in a protein on its thermal stability. It consists of a repository of experimentally measured melting temperatures (Tm) and their changes upon point mutations ({Delta}Tm) for proteins having a well-resolved X-ray structure. This high-quality dataset is designed for being used for the training or benchmarking of in silico thermal stability prediction methods. It also reports other experimentally measured thermodynamic quantities when available, i.e. the folding enthalpy ({Delta}H) and heat capacity ({Delta}CP) of the wild type proteins and their changes upon mutations ({Delta}{Delta}H and {Delta}{Delta}CP), as well as the change in folding free energy ({Delta}{Delta}G) at a reference temperature. These data are analyzed in view of improving our insights into the correlation between thermal and thermodynamic stabilities, the asymmetry between the number of stabilizing and destabilizing mutations, and the difference in stabilization potential of thermostable versus mesostable proteins.

Bioinformatics↗

bioRxiv2016

ARGON: fast, whole-genome simulation of the discrete time Wright-Fisher process

MotivationSimulation under the coalescent model is ubiquitous in the analysis of genetic data. The rapid growth of real data sets from multiple human populations led to increasing interest in simulating very large sample sizes at whole-chromosome scales. When the sample size is large, the coalescent model becomes an increasingly inaccurate approximation of the discrete time Wright-Fisher model (DTWF). Analytical and computational treatment of the DTWF, however, is generally harder.\n\nResultsWe present a simulator (ARGON) for the DTWF process that scales up to hundreds of thousands of samples and whole-chromosome lengths, with a time/memory performance comparable or superior to currently available methods for coalescent simulation. The simulator supports arbitrary demographic history, migration, Newick tree output, variable mutation/recombination rates and gene conversion, and efficiently outputs pairwise identical-bydescent (IBD) sharing data.\n\nAvailabilityARGON (version 0.1) is written in Java, open source, and freely available at https://github.com/pierpal/ARGON.\n\nContactppalama@hsph.harvard.edu\n\nSupplementary informationSupplementary data are available online.

Bioinformatics↗

bioRxiv2016

Long single-molecule reads can resolve the complexity of the Influenza virus composed of rare, closely related mutant variants

As a result of a high rate of mutations and recombination events, an RNA-virus exists as a heterogeneous \"swarm\". The ability of next-generation sequencing to produce massive quantities of genomic data inexpensively has allowed virologists to study the structure of viral populations from an infected host at an unprecedented resolution. However, high similarity and low frequency of the viral variants impose a huge challenge to assembly of individual full-length genomes. The long read length offered by a single-molecule sequencing technologies allows each mutant variant to be sequenced in a single pass. However, high error rate limits the ability to reconstruct heterogeneous viral population composed of rare, related mutant variants. In this paper, we present 2SNV, a method able to tolerate the high error-rate of the single-molecule protocol and reconstruct mutant variants. The proposed protocol is able to eliminate sequencing errors and reconstruct closely related viral mutant variants. 2SNV uses linkage between single nucleotide variations to efficiently distinguish them from read errors. To benchmark the sensitivity of 2SNV, we performed a single-molecule sequencing experiment on a sample containing a titrated level of known viral mutant variants.\n\nOur method is able to accurately reconstruct clone with frequency of 0.2% and distinguish clones that differed in only two nucleotides distantly located on the genome. 2SNV outperforms existing methods for full-length viral mutant reconstruction. With a high sensitivity and accuracy, 2SNV is anticipated to facilitate not only viral quasispecies reconstruction, but also other biological questions that require detection of rare haplotypes such as genetic diversity in cancer cell population, and monitoring B-cell and T-cell receptor repertoire. The open source implementation of 2SNV is freely available for download at http://alan.cs.gsu.edu/NGS/?q=content/2snv

Bioinformatics↗

bioRxiv2016

SC3 - consensus clustering of single-cell RNA-Seq data

Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.

Bioinformatics↗

bioRxiv2016

Principal component of explained variance: an efficient and optimal data dimension reduction framework for association studies

The genomics era has led to an increase in the dimensionality of the data collected to investigate biological questions. In this context, dimension-reduction techniques can be used to summarize high-dimensional signals into low-dimensional ones, to further test for association with one or more covariates of interest. This paper revisits one such approach, previously known as Principal Component of Heritability and renamed here as Principal Component of Explained Variance (PCEV). As its name suggests, the PCEV seeks a linear combination of outcomes in an optimal manner, by maximising the proportion of variance explained by one or several covariates of interest. By construction, this method optimises power but limited by its computational complexity, it has unfortunately received little attention in the past. Here, we propose a general analytical PCEV framework that builds on the assets of the original method, i.e. conceptually simple and free of tuning parameters. Moreover, our framework extends the range of applications of the original procedure by providing a computationally simple strategy for high-dimensional outcomes, along with exact and asymptotic testing procedures that drastically reduce its computational cost. We investigate the merits of the PCEV using an extensive set of simulations. Furthermore, the use of the PCEV approach will be illustrated using three examples taken from the epigenetics and brain imaging areas.

Bioinformatics↗

bioRxiv2016

Searching more genomic sequence with less memory for fast and accurate metagenomic profiling

Software for rapid, accurate, and comprehensive microbial profiling of metagenomic sequence data on a desktop will play an important role in large scale clinical use of metagenomic data. Here we describe LMAT-ML (Livermore Metagenomics Analysis Toolkit-Marker Library) which can be run with 24 GB of DRAM memory, an amount available on many clusters, or with 16 GB DRAM plus a 24 GB low cost commodity flash drive (NVRAM), a cost effective alternative for desktop or laptop users. We compared results from LMAT with five other rapid, low-memory tools for metagenome analysis for 131 Human Microbiome Project samples, and assessed discordant calls with BLAST. All the tools except LMAT-ML reported overly specific or incorrect species and strain resolution of reads that were in fact much more widely conserved across species, genera, and even families. Several of the tools misclassified reads from synthetic or vector sequence as microbial or human reads as viral. We attribute the high numbers of false positive and false negative calls to a limited reference database with inadequate representation of known diversity. Our comparisons with real world samples show that LMAT-ML is the only tool tested that classifies the majority of reads, and does so with high accuracy.

Bioinformatics↗

bioRxiv2016

Structural variation detection with read pair information --- An improved null-hypothesis reduces bias

Reads from paired-end and mate-pair libraries are often utilized to find structural variation in genomes, and one common approach is to use their fragment length for detection. After aligning read-pairs to the reference, read-pair distances are analyzed for statistically significant deviations. However, previously proposed methods are based on a simplified model of observed fragment lengths that does not agree with data. We show how this model limits statistical analysis of identifying variants and propose a new model, by adapting a model we have previously introduced for contig scaffolding, which agrees with data. From this model we derive an improved improved null hypothesis that, when applied in the variant caller CLEVER, reduces the number of false positives and corrects a bias that contributes to more deletion calls than insertion calls. A reference implementation is freely available at https://github.com/ksahlin/GetDistr.

Bioinformatics↗

bioRxiv2016

Structural features of the fly chromatin colors revealed by automatic three-dimensional modeling.

The sequence of a genome is insufficient to understand all genomic processes carried out in the cell nucleus. To achieve this, the knowledge of its three- dimensional architecture is necessary. Advances in genomic technologies and the development of new analytical methods, such as Chromosome Conformation Capture (3C) and its derivatives, now permit to investigate the spatial organization of genomes. However, inferring structures from raw contact data is a tedious process for shortage of available tools. Here we present TADbit, a computational framework to analyze and model the chromatin fiber in three dimensions. To illustrate the use of TADbit, we automatically modeled 50 genomic domains from the fly genome revealing differential structural features of the previously defined chromatin colors, establishing a link between the conformation of the genome and the local chromatin composition. More generally, TADbit allows to obtain three-dimensional models ready for visualization from 3C-based experiments and to characterize their relation to gene expression and epigenetic states. TADbit is open-source and available for download from http://www.3DGenomes.org.

Bioinformatics↗

bioRxiv2016

pileup.js: a JavaScript library for interactive and in-browser visualization of genomic data

pileup.js is a new browser-based genome viewer. It is designed to facilitate the investigation of evidence for genomic variants within larger web applications. It takes advantage of recent developments in the JavaScript ecosystem to provide a modular, reliable and easily embedded library.\n\nAvailabilityThe code and documentation for pileup.js is publicly available at https://github.com/hammerlab/pileup.js under the Apache 2.0 license.\n\nContactcorrespondence@hammerlab.org

Bioinformatics↗

bioRxiv2016

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. Finding actionable knowledge is becoming more important, but also more challenging as datasets grow in size and complexity. We present Logic Optimization for Binary Input to Continuous Output (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a binarized continuous output variable. Although the continuous output variable is binarized prior to optimization, the continuous information is retained to find the optimal logic model. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO is formulated as an integer programming problem, which enables rapid computation on large datasets. Moreover, LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

Bioinformatics↗