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arXiv2012

Protein function influences frequency of encoded regions containing VNTRs and number of unique interactions

Proteins encoded by genes containing regions of variable number tandem repeats (VNTRs) are known to be polymorphic within species but the influence of their instability in molecular interactions remains unclear. VNTRs are overrepresented in encoding sequence of particular functional groups where their presence could influence protein interactions. Using human consensus coding sequence, this work examines if genomic instability, determined by regions of VNTRs, influences the number of protein interactions. Findings reveal that, in relation to protein function, the frequency of unique interactions in human proteins increase with the number of repeated regions. This supports experimental evidence that repeat expansion may lead to an increase in molecular interactions. Genetic diversity, estimated by Ka/Ks, appeared to decrease as the number of protein-protein interactions increased. Additionally, G+C and CpG content were negatively correlated with increasing occurrence of VNTRs. This may indicate that nucleotide composition along with selective processes can increase genomic stability and thereby restrict the expansion of repeated regions. Proteins involved in acetylation are associated with a high number of repeated regions and interactions but a low G+C and CpG content. While in contrast, less interactive membrane proteins contain a lower number of repeated regions but higher levels of C+G and CpGs. This work provides further evidence that VNTRs may provide the genetic variability to generate unique interactions between proteins.

q-bio.GN↗

arXiv2012

Antibiotic resistant characteristics from 16S rRNA

Background: Microbiota have evolved to acclimate themselves to many environments. Humanity is become ever increasingly medicated and many of those medications are antibiotics. Sadly, Microbiota are adapting to medication and with each passing generation they become more difficult to subdue. The 16S small subunit of bacterial ribosomal rRNA provides a wealth of information for classifying the species level taxonomy of bacteria. Methodology/Principal Findings: Experiments were collected utilizing broad and narrow spectrum antibiotics, which act primarily on DNA. In each experiment a statistically significant, unique and predictable pattern of sequential and thermodynamic stability or instability was found to correlate to antibiotic resistance. Conclusions/Significance: Classification of antibiotic resistance is possible for some species and antibiotic combinations using the 16S rRNA sequential and thermodynamic properties.

q-bio.GN↗

arXiv2012

Gene silencing and large-scale domain structure of the E. coli genome

The H-NS chromosome-organizing protein in E. coli can stabilize genomic DNA loops, and form oligomeric structures connected to repression of gene expression. Motivated by the link between chromosome organization, protein binding and gene expression, we analyzed publicly available genomic data sets of various origins, from genome-wide protein binding profiles to evolutionary information, exploring the connections between chromosomal organization, genesilencing, pseudo-gene localization and horizontal gene transfer. We report the existence of transcriptionally silent contiguous areas corresponding to large regions of H-NS protein binding along the genome, their position indicates a possible relationship with the known large-scale features of chromosome organization.

q-bio.GN↗

arXiv2014

Using Ciliate Operations to construct Chromosome Phylogenies

We develop an algorithm based on three basic DNA editing operations suggested by a model for ciliate micronuclear decryption, to transform a given permutation into another. The number of ciliate operations performed by our algorithm during such a transformation is taken to be the distance between two such permutations. Applying well-known clustering methods to such distance functions enables one to determine phylogenies among the items to which the distance functions apply. As an application of these ideas we explore the relationships among the chromosomes of eight fruitfly (drosophila) species, using the well-known UPGMA algorithm on the distance function provided by our algorithm.

q-bio.GN↗

arXiv2012

Biospectrogram: a tool for spectral analysis of biological sequences

Summary: Biospectrogam is an open-source software for the spectral analysis of DNA and protein sequences. The software can fetch (from NCBI server), import and manage biological data. One can analyze the data using Digital Signal Processing (DSP) techniques since the software allows the user to convert the symbolic data into numerical data using 23 popular encodings and then apply popular transformations such as Fast Fourier Transform (FFT) etc. and export it. The ability of exporting (both encoding files and transform files) as a MATLAB .m file gives the user an option to apply variety of techniques of DSP. User can also do window analysis (both sliding in forward and backward directions and stagnant) with different size windows and search for meaningful spectral pattern with the help of exported MATLAB file in a dynamic manner by choosing time delay in the plot using Biospectrogram. Random encodings and user choice encoding allows software to search for many possibilities in spectral space. Availability: Biospectrogam is written in Java and is available to download freely from http://www.guptalab.org/biospectrogram. Software has been optimized to run on Windows, Mac OSX and Linux. User manual and you-tube (product demo) tutorial is also available on the website. We are in the process of acquiring open source license for it.

q-bio.QM↗

arXiv2014

LUMPY: A probabilistic framework for structural variant discovery

Comprehensive discovery of structural variation (SV) in human genomes from DNA sequencing requires the integration of multiple alignment signals including read-pair, split-read and read-depth. However, owing to inherent technical challenges, most existing SV discovery approaches utilize only one signal and consequently suffer from reduced sensitivity, especially at low sequence coverage and for smaller SVs. We present a novel and extremely flexible probabilistic SV discovery framework that is capable of integrating any number of SV detection signals including those generated from read alignments or prior evidence. We demonstrate improved sensitivity over extant methods by combining paired-end and split-read alignments and emphasize the utility of our framework for comprehensive studies of structural variation in heterogeneous tumor genomes. We further discuss the broader utility of this approach for probabilistic integration of diverse genomic interval datasets.

q-bio.GN↗

arXiv2012

A mixed model approach for joint genetic analysis of alternatively spliced transcript isoforms using RNA-Seq data

RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation due to external and genetic factors. Here, we present a mixed model approach that allows for (i) joint analysis and genetic mapping of multiple transcript isoforms and (ii) mapping of isoform-specific effects. Central to our approach is to comprehensively model the causes of variation and correlation between transcript isoforms, including the genomic background and technical quantification uncertainty. As a result, our method allows to accurately test for shared as well as transcript-specific genetic regulation of transcript isoforms and achieves substantially improved calibration of these statistical tests. Experiments on genotype and RNA-Seq data from 126 human HapMap individuals demonstrate that our model can help to obtain a more fine-grained picture of the genetic basis of gene expression variation.

q-bio.GN↗

arXiv2014

Bayesian Analysis for miRNA and mRNA Interactions Using Expression Data

MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt, which play important regulatory roles in post-transcriptional gene regulation by inhibiting the translation of the mRNA into proteins or otherwise cleaving the target mRNA. Inferring miRNA targets provides useful information for understanding the roles of miRNA in biological processes that are potentially involved in complex diseases. Statistical methodologies for point estimation, such as the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, have been proposed to identify the interactions of miRNA and mRNA based on sequence and expression data. In this paper, we propose using the Bayesian LASSO (BLASSO) and the non-negative Bayesian LASSO (nBLASSO) to analyse the interactions between miRNA and mRNA using expression data. The proposed Bayesian methods explore the posterior distributions for those parameters required to model the miRNA-mRNA interactions. These approaches can be used to observe the inferred effects of the miRNAs on the targets by plotting the posterior distributions of those parameters. For comparison purposes, the Least Squares Regression (LSR), Ridge Regression (RR), LASSO, non-negative LASSO (nLASSO), and the proposed Bayesian approaches were applied to four public datasets. We concluded that nLASSO and nBLASSO perform best in terms of sensitivity and specificity. Compared to the point estimate algorithms, which only provide single estimates for those parameters, the Bayesian methods are more meaningful and provide credible intervals, which take into account the uncertainty of the inferred interactions of the miRNA and mRNA. Furthermore, Bayesian methods naturally provide statistical significance to select convincing inferred interactions, while point estimate algorithms require a manually chosen threshold, which is less meaningful, to choose the possible interactions.

stat.AP↗

arXiv2012

A 454 survey of the community composition and core microbiome of the common bed bug, Cimex lectularius, reveals significant microbial community structure across an urban landscape

Elucidating the spatial dynamic and core constituents of the microbial communities found in association with arthropod hosts is of crucial importance for insects that may vector human or agricultural pathogens. The hematophagous Cimex lectularius, known as the common bed bug, has made a recent resurgence in North America, as well as worldwide, potentially owing to increased travel and resistance to insecticides. A comprehensive survey of the bed bug microbiome has not been performed to date, nor has an assessment of the spatial dynamics of its microbiome. Here we present a survey of bed bug microbial communities by amplifying the V4-V6 hypervariable region of the 16S rDNA gene region followed by 454 Titanium sequencing using 31 individuals from eight natural populations collected from residences in Cincinnati, OH. Across all samples, 97% of the microbial community is made up of two dominant OTUs identified as the α-proteobacterium Wolbachia and an unnamed γ-proteobacterium from the Enterobacteriaceae. Microbial communities varied among host populations for measures of community diversity and exhibited significant population structure. We also uncovered a strong negative correlation in the abundance of the two dominant OTUs, suggesting they may fulfill similar roles as nutritional mutualists. This broad survey represents the most comprehensive assessment, to date, of the microbes that associate with bed bugs, and uncovers evidence for potential antagonism between the two dominant members of the bed bug microbiome.

q-bio.GN↗

arXiv2015

Whole Genome Sequencing: Innovation Dream or Privacy Nightmare?

Over the past several years, DNA sequencing has emerged as one of the driving forces in life-sciences, paving the way for affordable and accurate whole genome sequencing. As genomes represent the entirety of an organism's hereditary information, the availability of complete human genomes prompts a wide range of revolutionary applications. The hope for improving modern healthcare and better understanding the human genome propels many interesting and challenging research frontiers. Unfortunately, however, the proliferation of human genomes amplifies worrisome privacy concerns, since a genome represents a treasure trove of highly personal and sensitive information. In this article, we provide an overview of positive results and biomedical advances in the field, and discuss privacy issues associated with human genomic information. Finally, we survey available privacy-enhancing technologies and list a number of open research challenges.

cs.CR↗

arXiv2012

Automated family-based naming of small RNAs for next generation sequencing data using a modified MD5-digest algorithm

We developed NameMyGene, a web tool and a stand alone program to easily generate putative family-based names for small RNA sequences so that laboratories can easily organize, analyze, and observe patterns from, the massive amount of data generated by next-generation sequencers. NameMyGene, also applicable to other emerging methods such as RNA-Seq, and Chip-Seq, solely uses the input small RNA sequence and does not require any additional data such as other sequence data sets. The web server and software is freely available (http://www.johnlab.org/NameMyGene) and is based on Java to ensure platform independency.

cs.CR↗

arXiv2013

The hourglass and the early conservation models - co-existing evolutionary patterns in vertebrate development

Developmental constraints have been postulated to limit the space of feasible phenotypes and thus shape animal evolution. These constraints have been suggested to be the strongest during either early or mid-embryogenesis, which corresponds to the early conservation model or the hourglass model, respectively. Conflicting results have been reported, but in recent studies of animal transcriptomes the hourglass model has been favored. Studies usually report descriptive statistics calculated for all genes over all developmental time points. This introduces dependencies between the sets of compared genes, and may lead to biased results. Here we overcome this problem using an alternative modular analysis. We used the Iterative Signature Algorithm to identify distinct modules of genes co-expressed specifically in consecutive stages of zebrafish development. We then performed a detailed comparison of several gene properties between modules, allowing for a less biased and more powerful analysis. Notably, our analysis corroborated the hourglass pattern only at the regulatory level, with sequences of regulatory regions being most conserved for genes expressed in mid-development, but not at the level of gene sequence, age or expression, in contrast to some previous studies. The early conservation model was supported with gene duplication and birth that were the most rare for genes expressed in early development. Finally, for all gene properties we observed the least conservation for genes expressed in late development or adult, consistent with both models. Overall, with the modular approach, we showed that different levels of molecular evolution follow different patterns of developmental constraints. Thus both models are valid, but with respect to different genomic features.

q-bio.PE↗

arXiv2012

Enhancing the functional content of protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, they face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we explore the use of the concept of common neighborhood similarity (CNS), which is a form of local structure in networks, to address these issues. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of S. cerevisiae interactions, and a set of 136 GO terms, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the $HC.cont$ measure proposed here performs particularly well for this task. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures, especially $HC.cont$, to prune out noisy edges and introduce new links between functionally related proteins.

q-bio.MN↗

arXiv2012

Solving Sequences of Generalized Least-Squares Problems on Multi-threaded Architectures

Generalized linear mixed-effects models in the context of genome-wide association studies (GWAS) represent a formidable computational challenge: the solution of millions of correlated generalized least-squares problems, and the processing of terabytes of data. We present high performance in-core and out-of-core shared-memory algorithms for GWAS: By taking advantage of domain-specific knowledge, exploiting multi-core parallelism, and handling data efficiently, our algorithms attain unequalled performance. When compared to GenABEL, one of the most widely used libraries for GWAS, on a 12-core processor we obtain 50-fold speedups. As a consequence, our routines enable genome studies of unprecedented size.

cs.MS↗

arXiv2012

Computing Petaflops over Terabytes of Data: The Case of Genome-Wide Association Studies

In many scientific and engineering applications, one has to solve not one but a sequence of instances of the same problem. Often times, the problems in the sequence are linked in a way that allows intermediate results to be reused. A characteristic example for this class of applications is given by the Genome-Wide Association Studies (GWAS), a widely spread tool in computational biology. GWAS entails the solution of up to trillions ($10^{12}$) of correlated generalized least-squares problems, posing a daunting challenge: the performance of petaflops ($10^{15}$ floating-point operations) over terabytes of data. In this paper, we design an algorithm for performing GWAS on multi-core architectures. This is accomplished in three steps. First, we show how to exploit the relation among successive problems, thus reducing the overall computational complexity. Then, through an analysis of the required data transfers, we identify how to eliminate any overhead due to input/output operations. Finally, we study how to decompose computation into tasks to be distributed among the available cores, to attain high performance and scalability. With our algorithm, a GWAS that currently requires the use of a supercomputer may now be performed in matter of hours on a single multi-core node. The discussion centers around the methodology to develop the algorithm rather than the specific application. We believe the paper contributes valuable guidelines of general applicability for computational scientists on how to develop and optimize numerical algorithms.

cs.MS↗

arXiv2013

The Lipid-RNA World

The simplest possible beginning of abiogenesis has been a riddle from the last century, which is most successfully solved by the Lipid World hypothesis. However, origin of the next stages of evolution starting form lipids is still in dark. We propose a 'Lipid-RNA World Scenario' based on the assumption that modern stable lipid-RNA interactions are molecular fossils of an ancient stage of evolution when RNA World originated from Lipid World. In accordance to the faint young sun conditions, we present an 'ice-covered hydrothermal vent' model of Hadean Ocean. Our hypothetical model suggests that faint young sun condition probably provided susceptible physical conditions for an evolutionary route from Lipid-World to Protein-RNA World, through an intermediate Lipid-RNA World. Ancient ribozymes were 'protected' by lipids assuring their survival in prebiotic ocean. The origin of natural selection ensures transition of Lipid-RNA World to Protein-RNA World after the origin of ribosome. Assuming the modern peptidyltransferase as the proto-ribosome structure, we have presented a hypothetical translation mechanism: proto-ribosome randomly polymerized amino acids being attached to the inner layer of a lipid-vesicle, using only physical energies available from our Hadean Ocean model. In accordance to the strategy of chemical evolution, we also have described the possible evolutionary behavior of this proto-ribosome, which explains the contemporary three-dimensional structure of 50S subunit and supports the predictions regarding the ancient regions of it. It also explains the origin of membrane-free 'minimal ribosome' in the time of LUCA.

q-bio.BM↗

arXiv2012

CGB: A UNIX shell program to create custom instances of the UCSC Genome Browser

The UCSC Genome Browser is a popular tool for the exploration and analysis of reference genomes. Mirrors of the UCSC Genome Browser and its contents exist at multiple geographic locations, and this mirror procedure has been modified to support genome sequences not maintained by UCSC and generated by individual researchers. While straightforward, this procedure is lengthy and tedious and would benefit from automation, especially when processing many genome sequences. We present a Unix shell program that facilitates the creation of custom instances of the UCSC Genome Browser for genome sequences not being maintained by UCSC. It automates many steps of the browser creation process, provides password protection for each browser instance, and automates the creation of basic annotation tracks. As an example we generate a custom UCSC Genome Browser for a bacterial genome obtained from a massively parallel sequencing platform.

q-bio.GN↗

arXiv2012

De novo genomic analyses for non-model organisms: an evaluation of methods across a multi-species data set

High-throughput sequencing (HTS) is revolutionizing biological research by enabling scientists to quickly and cheaply query variation at a genomic scale. Despite the increasing ease of obtaining such data, using these data effectively still poses notable challenges, especially for those working with organisms without a high-quality reference genome. For every stage of analysis - from assembly to annotation to variant discovery - researchers have to distinguish technical artifacts from the biological realities of their data before they can make inference. In this work, I explore these challenges by generating a large de novo comparative transcriptomic dataset data for a clade of lizards and constructing a pipeline to analyze these data. Then, using a combination of novel metrics and an externally validated variant data set, I test the efficacy of my approach, identify areas of improvement, and propose ways to minimize these errors. I find that with careful data curation, HTS can be a powerful tool for generating genomic data for non-model organisms.

q-bio.GN↗