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arXiv2012

Forensic Identification: Database likelihood ratios and familial DNA searching

Familial Searching is the process of searching in a DNA database for relatives of a certain individual. It is well known that in order to evaluate the genetic evidence in favour of a certain given form of relatedness between two individuals, one needs to calculate the appropriate likelihood ratio, which is in this context called a Kinship Index. Suppose that the database contains, for a given type of relative, at most one related individual. Given prior probabilities for being the relative for all persons in the database, we derive the likelihood ratio for each database member in favour of being that relative. This likelihood ratio takes all the Kinship Indices between the target individual and the members of the database into account. We also compute the corresponding posterior probabilities. We then discuss two methods to select a subset from the database that contains the relative with a known probability, or at least a useful lower bound thereof. One method needs prior probabilities and yields posterior probabilities, the other does not. We discuss the relation between the approaches, and illustrate the methods with familial searching carried out in the Dutch National DNA Database.

stat.AP↗

arXiv2012

Effective Clustering Algorithms for Gene Expression Data

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in Bioinformatics research. In this paper, K-Means algorithm hybridised with Cluster Centre Initialization Algorithm (CCIA) is proposed Gene Expression Data. The proposed algorithm overcomes the drawbacks of specifying the number of clusters in the K-Means methods. Experimental analysis shows that the proposed method performs well on gene Expression Data when compare with the traditional K- Means clustering and Silhouette Coefficients cluster measure.

cs.CE↗

arXiv2012

Alternative Methods for H1 Simulations in Genome Wide Association Studies

Assessing the statistical power to detect susceptibility variants plays a critical role in GWA studies both from the prospective and retrospective points of view. Power is empirically estimated by simulating phenotypes under a disease model H1. For this purpose, the "gold" standard consists in simulating genotypes given the phenotypes (e.g. Hapgen). We introduce here an alternative approach for simulating phenotypes under H1 that does not require generating new genotypes for each simulation. In order to simulate phenotypes with a fixed total number of cases and under a given disease model, we suggest three algorithms: i) a simple rejection algorithm; ii) a numerical Markov Chain Monte-Carlo (MCMC) approach; iii) and an exact and efficient backward sampling algorithm. In our study, we validated the three algorithms both on a toy-dataset and by comparing them with Hapgen on a more realistic dataset. As an application, we then conducted a simulation study on a 1000 Genomes Project dataset consisting of 629 individuals (314 cases) and 8,048 SNPs from Chromosome X. We arbitrarily defined an additive disease model with two susceptibility SNPs and an epistatic effect. The three algorithms are consistent, but backward sampling is dramatically faster than the other two. Our approach also gives consistent results with Hapgen. Using our application data, we showed that our limited design requires a biological a priori to limit the investigated region. We also proved that epistatic effects can play a significant role even when simple marker statistics (e.g. trend) are used. We finally showed that the overall performance of a GWA study strongly depends on the prevalence of the disease: the larger the prevalence, the better the power.

stat.AP↗

arXiv2012

TAPIR enables high-throughput estimation and comparison of phylogenetic informativeness using locus-specific substitution models

Massively parallel DNA sequencing techniques are rapidly changing the dynamics of phylogenetic study design by exponentially increasing the discovery of phylogenetically useful loci. This increase in the number of phylogenetic markers potentially provides researchers the opportunity to select subsets of loci best-addressing particular phylogenetic hypotheses based on objective measures of performance over different time scales. Investigators may also want to determine the power of particular phylogenetic markers relative to each other. However, currently available tools are designed to evaluate a small number of markers and are not well-suited to screening hundreds or thousands of candidate loci across the genome. TAPIR is an alternative implementation of Townsend's estimate of phylogenetic informativeness (PI) that enables rapid estimation and summary of PI when applied to data sets containing hundreds to thousands of candidate, phylogenetically informative loci.

q-bio.PE↗

arXiv2013

Segmenting DNA sequence into `words'

This paper presents a novel method to segment/decode DNA sequences based on n-grams statistical language model. Firstly, we find the length of most DNA 'words' is 12 to 15 bps by analyzing the genomes of 12 model species. Then we design an unsupervised probability based approach to segment the DNA sequences. The benchmark of segmenting method is also proposed.

q-bio.GN↗

arXiv2012

DNA bending and "structural" waters in major and minor grooves of A-tracts. Monte Carlo computer simulations

To elucidate the possible role of structural waters in stabilizing bent DNA, various conformations of AT-containing decamers, (A5T5)2 and A10:T10, were studied by Monte Carlo simulations. The duplexes were constrained to reproduce the NMR inter-proton distances for the A-tracts at two temperatures: 5 and 35C. Analysis of the water shell structures revealed a strong correlation between the groove widths on the one hand, and the types of hydration patterns and their probabilities on the other hand. Depending on the minor groove width, the following patterns were observed in this groove: either an interstrand "hydration spine", or interstrand two-water bridges, or a double ribbon of intrastrand sugar-base "water strings". Hydration shell in the major groove is less regular than in the minor groove, which agrees with crystallographic data. As in previous studies, energetically advantageous hydration is found for the A-tract conformations with narrow minor groove and wide major groove (B'-like DNA), known to be correlated with DNA bending and curvature. In addition, our calculations indicate that the advantage of such DNA conformations is coupled with the increase in adenine N7 hydration in the major groove. As the major groove is opened wider, its hydration shell is enriched by energetically favorable "trident waters" hydrogen bonded to three hydrophilic centers belonging to two adjacent adenines: two N7 atoms and one amino-proton H(N6). Based on these results, we suggest that formation of the novel hydration pattern in the major groove is one of the factors responsible for stabilization of the B'-like conformations of A-tracts at low temperature in the absense of dehydrating agents, leading in turn to the strong intrinsic curvature of DNA containing alternating A-tracts and "mixed" GC-rich sequences.

q-bio.BM↗

arXiv2012

Bayesian hierarchical reconstruction of protein profiles including a digestion model

Introduction : Mass spectrometry approaches are very attractive to detect protein panels in a sensitive and high speed way. MS can be coupled to many proteomic separation techniques. However, controlling technological variability on these analytical chains is a critical point. Adequate information processing is mandatory for data analysis to take into account the complexity of the analysed mixture, to improve the measurement reliability and to make the technology user friendly. Therefore we develop a hierarchical parametric probabilistic model of the LC-MS analytical chain including the technological variability. We introduce a Bayesian reconstruction methodology to recover the protein biomarkers content in a robust way. We will focus on the digestion step since it brings a major contribution to technological variability. Method : In this communication, we introduce a hierarchical model of the LC-MS analytical chain. Such a chain is a cascade of molecular events depicted by a graph structure, each node being associated to a molecular state such as protein, peptide and ion and each branch to a molecular processing such as digestion, ionisation and LC-MS separation. This molecular graph defines a hierarchical mixture model. We extend the Bayesian statistical framework we have introduced previously [1] to this hierarchical description. As an example, we will consider the digestion step. We describe the digestion process on a pair of peptides within the targeted protein as a Bernoulli random process associated with a cleavage probability controlled by the digestion kinetic law.

q-bio.GN↗

arXiv2012

In Silico Genome-Genome Hybridization Values Accurately and Precisely Predict Empirical DNA-DNA Hybridization Values for Classifying Prokaryotes

For nearly 50 years microbiologists have been determining prokaryotic genome relatedness by means of nucleic acid reassociation kinetics. These methods, however, are technically challenging, difficult to reproduce, and - given the time and resources it takes to generate a single data-point - not cost effective. In the post genomic era, with the cost of sequencing whole prokaryotic genomes no longer a limiting factor, we believed that computationally predicting the output value from a traditional DNA-DNA hybridization experiment using pair-wise comparisons of whole genome sequences to be of value. While other computational whole-genome classification methods exist, they predict values on widely different scales than DNA-DNA hybridization, introducing yet another metric into the polyphasic approach of defining microbial species. Our goal was to develop an in silico BLAST based pipeline that would predict with a high level of certainty the value of the wet lab-based DNA-DNA hybridization values. Here we report on one such method that produces estimates that are both accurate and precise with respect to the DNA-DNA hybridization values they are designed to emulate.

q-bio.GN↗

arXiv2012

CLEVER: Clique-Enumerating Variant Finder

Next-generation sequencing techniques have facilitated a large scale analysis of human genetic variation. Despite the advances in sequencing speeds, the computational discovery of structural variants is not yet standard. It is likely that many variants have remained undiscovered in most sequenced individuals. Here we present a novel internal segment size based approach, which organizes all, including also concordant reads into a read alignment graph where max-cliques represent maximal contradiction-free groups of alignments. A specifically engineered algorithm then enumerates all max-cliques and statistically evaluates them for their potential to reflect insertions or deletions (indels). For the first time in the literature, we compare a large range of state-of-the-art approaches using simulated Illumina reads from a fully annotated genome and present various relevant performance statistics. We achieve superior performance rates in particular on indels of sizes 20--100, which have been exposed as a current major challenge in the SV discovery literature and where prior insert size based approaches have limitations. In that size range, we outperform even split read aligners. We achieve good results also on real data where we make a substantial amount of correct predictions as the only tool, which complement the predictions of split-read aligners. CLEVER is open source (GPL) and available from http://clever-sv.googlecode.com.

q-bio.GN↗

arXiv2012

Evaluation of the Genome Mixture Contents by Means of the Compositional Spectra Method

In this research, we consider a mixture of genome fragments of a certain bacteria set. The problem of mixture separation is studied under the assumption that all the genomes present in the mixture are completely sequenced or are close to those already sequenced. Such assumption is relevant, e.g., in regular observations of ecological or biomedical objects, where the possible set of microorganisms is known and it is only necessary to follow their concentrations.

q-bio.GN↗

arXiv2012

GC3 Biology in Eukaryotes and Prokaryotes

We describe the distribution of Guanine and Cytosine (GC) content in the third codon position (GC3) distributions in different species, analyze evolutionary trends and discuss differences between genes and organisms with distinct GC3 levels. We scrutinize previously published theoretical frameworks and construct a unified view of GC3 biology in eukaryotes and prokaryotes.

q-bio.GN↗

arXiv2012

A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data

Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified single-cell genomes, and metagenomes has enabled investigation of a wide range of organisms and ecosystems. However, sampling variation in short-read data sets and high sequencing error rates of modern sequencers present many new computational challenges in data interpretation. These challenges have led to the development of new classes of mapping tools and {\em de novo} assemblers. These algorithms are challenged by the continued improvement in sequencing throughput. We here describe digital normalization, a single-pass computational algorithm that systematizes coverage in shotgun sequencing data sets, thereby decreasing sampling variation, discarding redundant data, and removing the majority of errors. Digital normalization substantially reduces the size of shotgun data sets and decreases the memory and time requirements for {\em de novo} sequence assembly, all without significantly impacting content of the generated contigs. We apply digital normalization to the assembly of microbial genomic data, amplified single-cell genomic data, and transcriptomic data. Our implementation is freely available for use and modification.

q-bio.GN↗

arXiv2013

Information Theory of DNA Shotgun Sequencing

DNA sequencing is the basic workhorse of modern day biology and medicine. Shotgun sequencing is the dominant technique used: many randomly located short fragments called reads are extracted from the DNA sequence, and these reads are assembled to reconstruct the original sequence. A basic question is: given a sequencing technology and the statistics of the DNA sequence, what is the minimum number of reads required for reliable reconstruction? This number provides a fundamental limit to the performance of {\em any} assembly algorithm. For a simple statistical model of the DNA sequence and the read process, we show that the answer admits a critical phenomena in the asymptotic limit of long DNA sequences: if the read length is below a threshold, reconstruction is impossible no matter how many reads are observed, and if the read length is above the threshold, having enough reads to cover the DNA sequence is sufficient to reconstruct. The threshold is computed in terms of the Renyi entropy rate of the DNA sequence. We also study the impact of noise in the read process on the performance.

cs.IT↗

arXiv2012

Phenomenon of irreducible genetic markers for TATAAA motifs in human chromosome 1

It is well known that the general transcription factors (GTF) specifically recognize correct TATA boxes, distinguishing them from many others. Employing the principles of determinacy analysis (mathematical theory of rules) we analyzed a fragment of human chromosome 1 DNA sequence and identified specific genetic markers (IG-markers = Irreducible Genetic markers) in the nearest proximity to TATAAA motifs. The IG-markers enable determining the exact location of any TATAAA motif within the investigated DNA fragment. Based on our data we hypothesize that the GTF recognize the «true» transcriptional start TATA box by means of IG-markers. The math method described here is universal and can be used to find IG-markers that will provide, like a global navigation satellite system, for the specific location of any distinct sequence motif within larger DNA sequence content.

q-bio.GN↗

arXiv2012

Detecting lateral genetic material transfer

The bioinformatical methods to detect lateral gene transfer events are mainly based on functional coding DNA characteristics. In this paper, we propose the use of DNA traits not depending on protein coding requirements. We introduce several semilocal variables that depend on DNA primary sequence and that reflect thermodynamic as well as physico-chemical magnitudes that are able to tell apart the genome of different organisms. After combining these variables in a neural classificator, we obtain results whose power of resolution go as far as to detect the exchange of genomic material between bacteria that are phylogenetically close.

cs.NE↗

arXiv2012

A semi-automatic method to guide the choice of ridge parameter in ridge regression

We consider the application of a popular penalised regression method, Ridge Regression, to data with very high dimensions and many more covariates than observations. Our motivation is the problem of out-of-sample prediction and the setting is high-density genotype data from a genome-wide association or resequencing study. Ridge regression has previously been shown to offer improved performance for prediction when compared with other penalised regression methods. One problem with ridge regression is the choice of an appropriate parameter for controlling the amount of shrinkage of the coefficient estimates. Here we propose a method for choosing the ridge parameter based on controlling the variance of the predicted observations in the model. Using simulated data, we demonstrate that our method outperforms subset selection based on univariate tests of association and another penalised regression method, HyperLasso regression, in terms of improved prediction error. We extend our approach to regression problems when the outcomes are binary (representing cases and controls, as is typically the setting for genome-wide association studies) and demonstrate the method on a real data example consisting of case-control and genotype data on Bipolar Disorder, taken from the Wellcome Trust Case Control Consortium and the Genetic Association Information Network.

stat.AP↗

arXiv2012

Structured Input-Output Lasso, with Application to eQTL Mapping, and a Thresholding Algorithm for Fast Estimation

We consider the problem of learning a high-dimensional multi-task regression model, under sparsity constraints induced by presence of grouping structures on the input covariates and on the output predictors. This problem is primarily motivated by expression quantitative trait locus (eQTL) mapping, of which the goal is to discover genetic variations in the genome (inputs) that influence the expression levels of multiple co-expressed genes (outputs), either epistatically, or pleiotropically, or both. A structured input-output lasso (SIOL) model based on an intricate l1/l2-norm penalty over the regression coefficient matrix is employed to enable discovery of complex sparse input/output relationships; and a highly efficient new optimization algorithm called hierarchical group thresholding (HiGT) is developed to solve the resultant non-differentiable, non-separable, and ultra high-dimensional optimization problem. We show on both simulation and on a yeast eQTL dataset that our model leads to significantly better recovery of the structured sparse relationships between the inputs and the outputs, and our algorithm significantly outperforms other optimization techniques under the same model. Additionally, we propose a novel approach for efficiently and effectively detecting input interactions by exploiting the prior knowledge available from biological experiments.

stat.ML↗

arXiv2012

Analytical Study of Hexapod miRNAs using Phylogenetic Methods

MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene expression. Identification of total number of miRNAs even in completely sequenced organisms is still an open problem. However, researchers have been using techniques that can predict limited number of miRNA in an organism. In this paper, we have used homology based approach for comparative analysis of miRNA of hexapoda group .We have used Apis mellifera, Bombyx mori, Anopholes gambiae and Drosophila melanogaster miRNA datasets from miRBase repository. We have done pair wise as well as multiple alignments for the available miRNAs in the repository to identify and analyse conserved regions among related species. Unfortunately, to the best of our knowledge, miRNA related literature does not provide in depth analysis of hexapods. We have made an attempt to derive the commonality among the miRNAs and to identify the conserved regions which are still not available in miRNA repositories. The results are good approximation with a small number of mismatches. However, they are encouraging and may facilitate miRNA biogenesis for

cs.CE↗