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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2013

Gappy TotalReCaller for RNASeq Base-Calling and Mapping

Understanding complex mammalian biology depends crucially on our ability to define a precise map of all the transcripts encoded in a genome, and to measure their relative abundances. A promising assay depends on RNASeq approaches, which builds on next generation sequencing pipelines capable of interrogating cDNAs extracted from a cell. The underlying pipeline starts with base-calling, collect the sequence reads and interpret the raw-read in terms of transcripts that are grouped with respect to different splice-variant isoforms of a messenger RNA. We address a very basic problem involved in all of these pipeline, namely accurate Bayesian base-calling, which could combine the analog intensity data with suitable underlying priors on base-composition in the transcripts. In the context of sequencing genomic DNA, a powerful approach for base-calling has been developed in the TotalReCaller pipeline. For these purposes, it uses a suitable reference whole-genome sequence in a compressed self-indexed format to derive its priors. However, TotalReCaller faces many new challenges in the transcriptomic domain, especially since we still lack a fully annotated library of all possible transcripts, and hence a sufficiently good prior. There are many possible solutions, similar to the ones developed for TotalReCaller, in applications addressing de novo sequencing and assembly, where partial contigs or string-graphs could be used to boot-strap the Bayesian priors on base-composition. A similar approach would be applicable here too, partial assembly of transcripts can be used to characterize the splicing junctions or organize them in incompatibility graphs and then used as priors for TotalReCaller. The key algorithmic techniques for this purpose have been addressed in a forthcoming paper on Stringomics. Here, we address a related but fundamental problem, by assuming that we only have a reference genome, with certain intervals marked as candidate regions for ORF (Open Reading Frames), but not necessarily complete annotations regarding the 5 or 3 termini of a gene or its exon-intron structure. The algorithms we describe find the most accurate base-calls of a cDNA with the best possible segmentation, all mapped to the genome appropriately.

Bioinformatics↗

bioRxiv2013

Unexpected links reflect the noise in networks

Gene regulatory networks are commonly used for modeling biological processes and revealing underlying molecular mechanisms. The reconstruction of gene regulatory networks from observational data is a challenging task, especially, considering the large number of involved players (e.g. genes) and much fewer biological replicates available for analysis. Herein, we proposed a new statistical method of estimating the number of erroneous edges that strongly enhances the commonly used inference approaches. This method is based on special relationship between correlation and causality, and allows to identify and to remove approximately half of erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we established a mathematical foundation for our method. Analyzing real biological datasets, we found a strong correlation between the results of our method and the commonly used false discovery rate (FDR) technique. Furthermore, the simulation analysis demonstrates that in large networks, our new method provides a more precise estimation of the proportion of erroneous links than FDR.

Bioinformatics↗

bioRxiv2013

Comment on “TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions” by Kim et al.

In the recent paper [1] (thereafter referred to as \"TopHat2paper\") the accuracy of TopHat2 was compared to other RNA-seq aligners. In this comment we re-examine most important analyses from the TopHat2paper and identify several deficiencies that significantly diminished performance of some of the aligners, including incorrect choice of mapping parameters, unfair comparison metrics, and unrealistic simulated data. Using STAR [2] as an exemplar, we demonstrate that correcting these deficiencies makes its accuracy equal or better than that of TopHat2. Furthermore, this exercise highlighted some serious issues with the TopHat2 algorithms, such as poor recall of alignments with a moderate (>3) number of mismatches, low sensitivity and high false discovery rate for splice junction detection, loss of precision for the realignment algorithm, and large number of false chimeric alignments. ...

Bioinformatics↗

bioRxiv2013

Inferring tree causal models of cancer progression with probability raising

Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models.

Bioinformatics↗

bioRxiv2013

A Bayesian Method to Incorporate Hundreds of Functional Characteristics with Association Evidence to Improve Variant Prioritization

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals (\"hits\") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.\n\nAuthor SummaryLarge-scale genetic studies have had success identifying genes that play a role in complex traits. Advanced statistical procedures suggest that there are still genetic variants to be discovered, but these variants are difficult to detect. Incorporating biological information that affect the amount of protein or other product produced can be used to prioritise the genetic variants in order to identify which are likely to be causal. The method proposed here uses such biological characteristics to predict which genetic variants are most likely to be causal for complex traits.

Bioinformatics↗

bioRxiv2013

Exploring DNA structures in real-time polymerase kinetics using Pacific Biosciences sequencer data

Pausing of DNA polymerase can indicate the presence of a DNA structure that differs from the canonical double-helix. Here we detail a method to investigate how polymerase pausing in the Pacific Biosciences sequencer reads can be related to DNA structure. The Pacific Biosciences sequencer uses optics to view a polymerase and its interaction with a single DNA molecule in real-time, offering a unique way to detect potential alternative DNA structures. We have developed a new way to examine polymerase kinetics and relate it to the DNA sequence by using a wavelet transform of read information from the sequencer. We use this method to examine how polymerase kinetics are related to nucleotide base composition. We then examine tandem repeat sequences known for their ability to form different DNA structures: (CGG)n and (CG)n repeats which can, respectively, form G-quadruplex DNA and Z-DNA. We find pausing around the (CGG)n repeat that may indicate the presence of G-quadruplexes in some of the sequencer reads. The (CG)n repeat does not appear to cause polymerase pausing, but its kinetics signature nevertheless suggests the possibility that alternative nucleotide conformations may sometimes be present. We discuss the implications of using our method to discover DNA sequences capable of forming alternative structures. The analyses presented here can be reproduced on any Pacific Biosciences kinetics data for any DNA pattern of interest using an R package that we have made publicly available.\n\nAuthor SummaryDNA can be found in various forms that differ from the double-helix first discovered by Watson and Crick in 1953. These alternative DNA structures depend on the DNA sequence, and researchers continue to explore which sequences have the potential to form alternative structures. Here we advance the use of Pacific Biosciences sequencer data to explore potential alternative DNA structures. The Pacific Bio-sciences sequencer provides an unprecedented way to examine the interaction between DNA polymerase and DNA by following a single polymerase in real time as it copies a DNA molecule. The pausing of DNA polymerase is a common method for exploring the DNA sequences that have the potential to form alternative DNA structures, and Pacific Biosciences data has previously been used to measure polymerase pausing at a slipped strand structure. DNA polymerase is known to pause at some of these alternative structures, such as the structure known as the G-quadruplex, a DNA structure that has potentially importing regulatory significance. We examine polymerase kinetics around a G-quadruplex, and find evidence of polymerase pausing in the Pacific Biosciences kinetics. We provide a method, with publicly available code, so that others can examine these polymerase kinetics for any sequence of interest.

Bioinformatics↗

bioRxiv2013

A null model for Pearson coexpression networks

Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent a simple but effective technique for discovering and interpreting linear gene relationships. In the last years several approach have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is mostly crucial when the number of samples is small, yielding a non negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The theoretical derivation of the new bound by geometrical methods is shown together with applications in onco- and neurogenomics.

Bioinformatics↗

bioRxiv2013

libRoadRunner: A High Performance SBML Compliant Simulator

SummaryWe describe libRoadRunner, a cross-platform, open-source, high performance C++ library for running and analyzing SBML-compliant models. libRoadRunner was created primarily to achieve high performance, ease of use, portability and an extensible architecture. libRoadRunner includes a comprehensive API, Plugin support, Python scripting and additional functionality such as stoichio-metric and metabolic control analysis.\n\nAccessibility and ImplementationTo maximize collaboration, we made libRoadRunner open source and released it under the Apache License, Version 2.0. To facilitate reuse, we have developed comprehensive Python bindings using SWIG (swig.org) and a C API. Li-bRoadRunner uses a number of statically linked third party libraries including: LLVM [4], libSBML [1], CVODE, NLEQ2, LAPACK and Poco. LibRoadRunner is supported on Windows, Mac OS X and Linux.\n\nSupplementary informationOnline documentation, build instructions and git source repository are available at http://www.libroadrunner.org

Bioinformatics↗

bioRxiv2013

Accurate detection of de novo and transmitted INDELs within exome-capture data using micro-assembly

We present a new open-source algorithm, Scalpel, for sensitive and specific discovery of INDELs in exome-capture data. By combining the power of mapping and assembly, Scalpel searches the de Bruijn graph for sequence paths (contigs) that span each exon. The algorithm creates a single path for exons with no INDEL, two paths for an exon with a heterozygous mutation, and multiple paths for more exotic variations. A detailed repeat composition analysis coupled with a self-tuning k-mer strategy allows Scalpel to outperform other state-of-the-art approaches for INDEL discovery. We extensively compared Scalpel with a battery of >10000 simulated and >1000 experimentally validated INDELs between 1 and 100bp against two recent algorithms for INDEL discovery: GATK HaplotypeCaller and SOAPindel. We report anomalies for these tools in their ability to detect INDELs, especially in regions containing near-perfect repeats which contribute to high false positive rates. In contrast, Scalpel demonstrates superior specificity while maintaining high sensitivity. We also present a large-scale application of Scalpel for detecting de novo and transmitted INDELs in 593 families with autistic children from the Simons Simplex Collection. Scalpel demonstrates enhanced power to detect long ([≥]20bp) transmitted events, and strengthens previous reports of enrichment for de novo likely gene-disrupting INDEL mutations in children with autism with many new candidate genes. The source code and documentation for the algorithm is available at http://scalpel.sourceforge.net.

Bioinformatics↗

bioRxiv2013

A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with Conly representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.

Bioinformatics↗

bioRxiv2013

Exploring community structure in biological networks with random graphs

BackgroundCommunity structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a systems functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge.\n\nResultsHere, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks.\n\nConclusionOur model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

Bioinformatics↗

bioRxiv2014

Algorithms in Stringomics (I): Pattern-Matching against "Stringomes"

This paper reports an initial design of new data-structures that generalizes the idea of pattern-matching in stringology, from its traditional usage in an (unstructured) set of strings to the arena of a well-structured family of strings. In particular, the object of interest is a family of strings composed of blocks/classes of highly similar \"stringlets,\" and thus mimic a population of genomes made by concatenating haplotype-blocks, further constrained by haplotype-phasing. Such a family of strings, which we dub \"stringomes,\" is formalized in terms of a multi-partite directed acyclic graph with a source and a sink. The most interesting property of stringomes is probably the fact that they can be represented efficiently with compression up to their k-th order empirical entropy, while ensuring that the compression does not hinder the pattern-matching counting and reporting queries - either internal to a block or spanning two (or a few constant) adjacent blocks. The solutions proposed here have immediate applications to next-generation sequencing technologies, base-calling, expression profiling, variant-calling, population studies, onco-genomics, cyber security trace analysis and text retrieval.

Bioinformatics↗

bioRxiv2014

SIANN: Strain Identification by Alignment to Near Neighbors

Next-generation sequencing is increasingly being used to study samples composed of mixtures of organisms, such as in clinical applications where the presence of a pathogen at very low abundance may be highly important. We present an analytical method (SIANN: Strain Identification by Alignment to Near Neighbors) specifically designed to rapidly detect a set of target organisms in mixed samples that achieves a high degree of species- and strain-specificity by aligning short sequence reads to the genomes of near neighbor organisms, as well as that of the target. Empirical benchmarking alongside the current state-of-the-art methods shows an extremely high Positive Predictive Value, even at very low abundances of the target organism in a mixed sample. SIANN is available as an Illumina BaseSpace app, as well as through Signature Science, LLC. SIANN results are presented in a streamlined report designed to be comprehensible to the non-specialist user, providing a powerful tool for rapid species detection in a mixed sample. By focusing on a set of (customizable) target organisms and their near neighbors, SIANN can operate quickly and with low computational requirements while delivering highly accurate results.

Bioinformatics↗

bioRxiv2014

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data

The analysis of whole-genome or exome sequencing data from trios and pedigrees has being successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyses data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of WGS data from a 17 individual, 3-generation CEPH pedigree sequenced to 50X average depth. Compared to singleton calling, our family caller produced more high quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance. We developed a ground truth dataset to further evaluate our calls by identifying recombination cross-overs in the pedigree and testing variants for consistency with the inferred phasing, and we show that our method significantly outperforms singleton and population variant calling in pedigrees. We identify all previously validated de novo mutations in NA12878, concurrent with a 7X precision improvement. Our results show that our method is scalable to large genomics and human disease studies and allows cost optimization by rational sequencing capacity distribution.

Bioinformatics↗

bioRxiv2014

An Improved Search Algorithm to Find G-Quadruplexes in Genome Sequences

A growing body of data suggests that the secondary structures adopted by G-rich polynucleotides may be more diverse than previously thought and that the definition of G-quadruplex-forming sequences should be broadened. We studied solution structures of a series of naturally occurring and model single-stranded DNA fragments defying the G3+NL1G3+NL2G3+NL3G3+ formula, which is used in most of the current GQ-search algorithms. The results confirm the GQ-forming potential of such sequences and suggest the existence of new types of GQs. We developed an improved (broadened) GQ-search algorithm (http://niifhm.ru/nauchnye-issledovanija/otdel-molekuljarnoj-biologii-i-genetiki/laboratorija-iskusstvennogo-antitelogeneza/497-2/) that accounts for the recently reported new types of GQs.

Bioinformatics↗

bioRxiv2014

Estimate of Within Population Incremental Selection Through Branch Imbalance in Lineage Trees

Incremental selection within a population, defined as a limited fitness change following a mutation, is an important aspect of many evolutionary processes and can significantly affect a large number of mutations through the genome. Strongly advantageous or deleterious mutations are detected through the fixation of mutations in the population, using the synonymous to non-synonymous mutations ratio in sequences. There are currently to precise methods to estimate incremental selection occurring over limited periods. We here provide for the first time such a detailed method and show its precision and its applicability to the genomic analysis of selection. A special case of evolution is rapid, short term micro-evolution, where organism are under constant adaptation, occurring for example in viruses infecting a new host, B cells mutating during a germinal center reactions or mitochondria evolving within a given host. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we pro-pose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. This method does not suffer from the need of a baseline model and is practically not affected by sampling biases. In order to show the wide applicability of this method, we apply it to multiple cases of micro-evolution, and show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens.

Bioinformatics↗

bioRxiv2014

A Powerful Approach for Identification of Differentially Transcribed mRNA Isoforms

Next generation sequencing is being increasingly used for transcriptome-wide analysis of differential gene expression. The primary goal in profiling expression is to identify genes or RNA isoforms differentially expressed between specific conditions. Yet, the next generation sequence-based count data are essentially different from the microarray data that are continuous type, therefore, the statistical methods developed well over the last decades cannot be applicable. For this reason, a variety of new statistical methods based on count data of transcript reads has been correspondingly developed. But currently the transcriptomic count data coming only from a few replicate libraries have high technical noise and small sample size bias, performances of these new methods are not desirable. We here developed a new statistical method specifically applicable to small sample count data called mBeta t-test for identifying differentially expressed gene or isoforms on the basis of the Beta t-test. The results obtained from simulated and real data showed that the mBeta t-test method significantly outperformed the existing statistical methods in all given scenarios. Findings of our method were validated by qRT-PCR experiments. The mBeta t-test method significantly reduced true false discoveries in differentially expressed genes or isoforms so that it had high work efficiencies in all given scenarios. In addition, the mBeta t-test method showed high stability in performance of statistical analysis and in estimation of FDR. These strongly suggests that our mBeta t-test method would offer us a creditable and reliable result of statistical analysis in practice.

Bioinformatics↗

bioRxiv2014

FALDO: A semantic standard for describing the location of nucleotide and protein feature annotation.

Background Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples.\n\nDescription We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned \"omics\" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations.\n\nConclusions Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.

Bioinformatics↗