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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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Habitat variability does not generally promote metabolic network modularity in flies and mammals

The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.

Bioinformatics

Combating the scientific decline effect with confidence (intervals)

A symptom of the need for greater reproducibility in scientific practice is the \"decline effect,\" the fact that the size of many experimental effects decline with subsequent study or fail to replicate entirely. A simple way to combat this problem is for scientists to more routinely use confidence intervals (CIs) in their work. CIs provide frequentist bounds on the true size of an effect and can reveal when a statistically significant effect is possibly too small to be reliable or when a large effect might have been missed due to insufficient statistical power. CIs are often lacking in psychophysiological reports, likely due to the large number of dependent variables, which complicates deriving and visualizing CIs. In this article, I explain the value of CIs and show how to compute them for analyses involving multiple variables in various ways that adjust the intervals for the greater uncertainty induced by multiple statistical comparisons. The methods are illustrated using a basic visual oddball event-related potential (ERP) dataset and freely available Matlab software.

Bioinformatics

chromPlot: visualization of genomic data in chromosomal context

Summary: Visualizing genomic data in chromosomal context can help detecting errors in data generation or analysis and can suggest new hypotheses to be tested. Here we report a new tool for displaying large and diverse genomic data in idiograms of one or multiple chromosomes. The package is implemented in R so that visualization can be easily integrated with its numerous packages for processing genomic data. It supports simultaneous visualization of multiples tracks of data, each of potentially different nature. Large genomic regions such as QTLs or synteny tracts may be shown along histograms of number of genes, genetic variants, or any other type of genomic element. Tracks can also contain values for continuous or categorical variables and the user can choose among points, points connected by lines, line segments, barplots or histograms for representing data. chromPlot reads data from tables in BED format which are imported in R using its builtin functions. The information necessary to draw chromosomes for mouse and human is included with the package. Chromosomes for other organisms are downloaded automatically from the Ensembl website or can be provided by the user. We present common use cases here, and a full tutorial is included as the packages's vignette.\n\nAvailability: chromPlot is distributed under a GLP2 licence at Genomed Lab: http://genomed.med.uchile.cl.\n\nContact: raverdugo@u.uchile.cl

Bioinformatics

CrispRVariants: precisely charting the mutation spectrum in genome engineering experiments

CRISPR-Cas9 and related technologies efficiently alter genomic DNA at targeted positions and have far-reaching implications for functional screening and therapeutic gene editing. Understanding and unlocking this potential requires accurate evaluation of editing efficiency. We show that methodological decisions for analyzing sequencing data can significantly affect mutagenesis efficiency estimates and we provide a comprehensive R-based toolkit, CrispRVariants and accompanying web tool CrispRVariantsLite, that resolves and localizes individual mutant alleles with respect to the endonuclease cut site. CrispRVariants-enabled analyses of newly generated and existing genome editing datasets underscore how careful consideration of the full variant spectrum gives insight toward effective guide and amplicon design as well as the mutagenic process.

Bioinformatics

Data-driven hypothesis weighting increases detection power in multiple testing

Hypothesis weighting is a powerful approach for improving the power of data analyses that employ multiple testing. However, in general it is not evident how to choose the weights. We describe IHW, a method for data-driven hypothesis weighting that makes use of informative covariates that are independent of the test statistic under the null, but informative of each tests power or prior probability of the null hypothesis. Covariates can be continuous or categorical and need not fulfill any particular assumptions. The method increases statistical power in applications while controlling the false discovery rate (FDR) and produces additional insight by revealing the covariate-weight relationship. Independent hypothesis weighting is a practical approach to discovery of associations in large datasets.

Bioinformatics

Event Extraction from Biomedical Literature

The breadth and scope of the biomedical literature hinders a timely and thorough comprehension of its content. PubMed, the leading repository for biomedical literature, currently holds over 26 million records, and is growing at a rate of over 1.2 million records per year, with about 300 records added daily that mention cancer in the title or abstract. Natural language processing (NLP) can assist in accessing and interpreting this massive volume of literature, including its quality NLP approaches to the automatic extraction of biomedical entities and relationships may assist the development of explanatory models that can comprehensively scan and summarize biomedical articles for end users. Users can also formulate structured queries against these entities, and their interactions, to mine the latest developments in related areas of interest. In this article, we explore the latest advances in automated event extraction methods in the biomedical domain, focusing primarily on tools participated in the Biomedical NLP (BioNLP) Shared Task (ST) competitions. We review the leading BioNLP methods, summarize their results, and their innovative contributions in this field.

Bioinformatics

Gene expression inference with deep learning

MotivationLarge-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only 1,000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression, limiting its accuracy since it does not capture complex nonlinear relationship between expression of genes.\n\nResultsWe present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based GEO dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms linear regression with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than linear regression in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2,921 expression profiles. Deep learning still outperforms linear regression with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes.\n\nAvailabilityD-GEX is available at https://github.com/uci-cbcl/D-GEX.\n\nContactxhx@ics.uci.edu

Bioinformatics

Automatic genome segmentation with HMM-ANN hybrid models

We consider the problem of automatic genome segmentation (AGS) that aims to assign discrete labels to all genomic regions based on multiple ChIP-seq samples. We propose to use a hybrid model that combines a hidden Markov model (HMM) with an artificial neural network (ANN) to overcome the weaknesses of a standard HMM. Our contributions are threefold: first, we benchmark two approaches to generate targets for ANN training on an example dataset; second, we investigate many different ANN models to identify the ones with best predictions on chromatin states; third, we test different hyper-parameters and discuss how they affect the machine learning algorithms performance. We find our best performing models to beat two pervious state-of-the-art methods for AGS by large margins.

Bioinformatics

MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.\n\nMicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

Bioinformatics

Inferring protein-protein interaction networks from inter-protein sequence co-evolution

Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.

Bioinformatics

SPECtre: a spectral coherence-based classifier of actively translated transcripts from ribosome profiling sequence data

SummaryActive protein translation can be assessed and measured using ribosome profiling sequencing strategies. Existing analytical approaches applied to this technology make use of sequence fragment length or frame occupancy to differentiate between active translation and background noise, however they do not consider additional characteristics inherent to the technology which limits their overall accuracy. Here, we present an analytical tool that models the overall tri-nucleotide periodicity of ribosomal occupancy using a classifier based on spectral coherence. Our software, SPECtre, examines the relationship of normalized ribosome profiling read coverage over a rolling series of windows along a transcript against an idealized reference signal. A comparison of SPECtre against current methods on existing and new data shows a marked improvement in accuracy for detecting active translation and exhibits overall high sensitivity at a low false discovery rate.\n\nAvailability and ImplementationSPECtre source code is available for download at https://github.com/mills-lab/spectre.\n\nContact: remills@med.umich.edu

Bioinformatics

Practical Guidelines for Secure Cloud Computing using Genomic Data

Cloud security challenges Cloud security challenges Cloud security guidelines Security and Privacy Publications: Large scale genomics studies involving thousands of whole genome or exome sequences are underway1 on Cloud. While Cloud provides many conveniences for genomics research, it also raises concerns regarding large scale hacking, bad press, potential loss of patient privacy and the resulting loss of patient trust. Cloud providers argue that they have significant investments and expertise in security and, therefore, Cloud is equally secure, if not more so, compared to on-premise infrastructure. This gap in assessment of Cloud security is, in part, due to a fast evolving and largely unfamiliar technology stack for genomics data owners.\n\nWhat makes the Cloud security landscape discussion challenging is that security reco ...

Bioinformatics

MBPpred: Proteome-wide detection of membrane lipid-binding proteins using profile Hidden Markov Models

A large number of modular domains that exhibit specific lipid binding properties are present in many membrane proteins involved in trafficking and signal transduction. These domains are present in either eukaryotic peripheral membrane or transmembrane proteins and are responsible for the non-covalent interactions of these proteins with membrane lipids. Here we report a profile Hidden Markov Model based method capable of detecting Membrane Binding Proteins (MBPs) from information encoded in their amino acid sequence, called MBPpred. The method identifies MBPs that contain one or more of the Membrane Binding Domains (MBDs) that have been described to date, and further classifies these proteins based on their position in respect to the membrane, either as peripheral or transmembrane. MBPpred is available online at http://bioinformatics.biol.uoa.gr/MBPpred. This method was applied in selected eukaryotic proteomes, in order to examine the characteristics they exhibit in various eukaryotic kingdoms and phylums.

Bioinformatics

State aggregation for fast likelihood computations in molecular evolution

MotivationCodon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models.\n\nResultsWe show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analysed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuous-time Markov process-based model with large state space, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics.\n\nAvailabilityThe heuristic is implemented in the godon package (https://bitbucket.org/Davydov/godon) and in a version of FastCodeML (https://gitlab.isb-sib.ch/phylo/fastcodeml).

Bioinformatics

Bibliometric and Geographical Analysis of Cell Death Related Literature

Natural language processing continues to gain importance in a thriving scientific community that communicates its latest results in such a frequency that following up on the most recent developments even in a specific field cannot be managed by human readers alone. Here we summarize and compare the publishing activity of the previous years on a distinct topic across several countries, addressing not only publishing frequency and history, but also stylistic characteristics that are accessible by means of natural language processing. Though there are no profound differences in the sentence lengths or lexical diversity among different countries, writing styles approached by Part-Of-Speech tagging are similar among countries that share history or official language or those are spatially close.

Bioinformatics

De novo identification, differential analysis and functional annotation of SNPs from RNA-seq data in non-model species

SNPs (Single Nucleotide Polymorphisms) are genetic markers whose precise identification is a prerequisite for association studies. Methods to identify them are currently well developed for model species, but rely on the availability of a (good) reference genome, and therefore cannot be applied to non-model species. They are also mostly tailored for whole genome (re-)sequencing experiments, whereas in many cases, transcriptome sequencing can be used as a cheaper alternative which already enables to identify SNPs located in transcribed regions. In this paper, we propose a method that identifies, quantifies and annotates SNPs without any reference genome, using RNA-seq data only. Individuals can be pooled prior to sequencing, if not enough material is available for sequencing from one individual. Using human RNA-seq data, we first compared the performance of our method with GO_SCPLOWATKC_SCPLOW, a well established method that requires a reference genome. We showed that both methods predict SNPs with similar accuracy. We then validated experimentally the predictions of our method using RNA-seq data from two non-model species. The method can be used for any species to annotate SNPs and predict their impact on proteins. We further enable to test for the association of the identified SNPs with a phenotype of interest.

Bioinformatics

High-accuracy HLA type inference from whole-genome sequencing data

Extensive hyperpolymorphism and sequence similarity between the HLA genes make HLA type inference from whole-genome sequencing data a challenging problem. We address these by representing sequences from over 10,000 known alleles in a reference graph structure, enabling accurate read mapping. HLA*PRG, our algorithm, outperforms existing methods by a wide margin and for the first time consistently achieves the accuracy of gold-standard reference methods with one error across 158 alleles tested.

Bioinformatics

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