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arXiv2009

Evolution of genomes in the hybridogenetic populations modelled by the Penna model

Background: Hybridogenesis is a very interesting example of reproduction which seems to integrate the sexual and clonal processes in one system. In a case of frogs, described in the paper, two parental species - Rana lessonae and Rana ridibunda can form fertile hybrid individuals - Rana esculenta. Hybrid individuals eliminate one parental haplotype from their germ line cells before meiosis (end before recombination) which implicates clonal reproduction of the haplotype transferred to the gametes. All three "species" are called "complex species". To study the evolution of genomes in the hybridogenetic fraction of this complex species we have used the Monte Carlo based model rendering the age structured populations. The model enables the analysis of distribution of defective alleles in the individual genomes as well as in the genetic pool of the whole populations. Results: We have shown that longer isolation of hybrids' populations leads to the speciation through emerging the specific sets of complementing haplotypes in their genetic pool. The fraction of defective alleles increases but the defects are complemented in the heterozygous loci. Nevertheless, even small supply of new hybrids generated by the two parental species or crossbreeding between hybrids and one of the parental species prevents the speciation and changes the strategy of the genome evolution from the complementing to the purifying Darwinian selection.

q-bio.GN↗

arXiv2009

PICS: Probabilistic Inference for ChIP-seq

ChIP-seq, which combines chromatin immunoprecipitation with massively parallel short-read sequencing, can profile in vivo genome-wide transcription factor-DNA association with higher sensitivity, specificity and spatial resolution than ChIP-chip. While it presents new opportunities for research, ChIP-seq poses new challenges for statistical analysis that derive from the complexity of the biological systems characterized and the variability and biases in its digital sequence data. We propose a method called PICS (Probabilistic Inference for ChIP-seq) for extracting information from ChIP-seq aligned-read data in order to identify regions bound by transcription factors. PICS identifies enriched regions by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. Its per-event fragment length estimates also allow it to remove from analysis regions that have atypical lengths. PICS uses pre-calculated, whole-genome read mappability profiles and a truncated t-distribution to adjust binding event models for reads that are missing due to local genome repetitiveness. It estimates uncertainties in model parameters that can be used to define confidence regions on binding event locations and to filter estimates. Finally, PICS calculates a per-event enrichment score relative to a control sample, and can use a control sample to estimate a false discovery rate. We compared PICS to the alternative methods MACS, QuEST, and CisGenome, using published GABP and FOXA1 data sets from human cell lines, and found that PICS' predicted binding sites were more consistent with computationally predicted binding motifs.

q-bio.GN↗

arXiv2009

Universal features of surname distribution in a subsample of a growing population

We examine the problem of family size statistics (the number of individuals carrying the same surname, or the same DNA sequence) in a given size subsample of an exponentially growing population. We approach the problem from two directions. In the first, we construct the family size distribution for the subsample from the stable distribution for the full population. This latter distribution is calculated for an arbitrary growth process in the limit of slow growth, and is seen to depend only on the average and variance of the number of children per individual, as well as the mutation rate. The distribution for the subsample is shifted left with respect to the original distribution, tending to eliminate the part of the original distribution reflecting the small families, and thus increasing the mean family size. From the subsample distribution, various bulk quantities such as the average family size and the percentage of singleton families are calculated. In the second approach, we study the past time development of these bulk quantities, deriving the statistics of the genealogical tree of the subsample. This approach reproduces that of the first when the current statistics of the subsample is considered. The surname distribution from th e 2000 U.S. Census is examined in light of these findings, and found to misrepresent the population growth rate by a factor of 1000.

q-bio.PE↗

arXiv2009

Reconstructing Spatiotemporal Gene Expression Data from Partial Observations

Developmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root; however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures. Given these partial observations, we present a novel method for reconstructing integrated high resolution spatiotemporal data. Our method is based on a new iterative algorithm for finding approximate roots to systems of bilinear equations.

q-bio.GN↗

arXiv2009

Genetic Code Table: A note on the three splittings into amino acid classes

This note represents the further progress in understanding the determination of the genetic code by Golden mean (Rakocevic, 1998). Three classes of amino acids that follow from this determination (the 7 "golden" amino acids, 7 of their complements, and 6 non-complements) are observed now together with two further possible splittings into 4 x 5 and 5 x 4 amino acids.

q-bio.BM↗

arXiv2009

Non-equilibrium thermodynamics of gene expression and transcriptional regulation

In recent times whole-genome gene expression analysis has turned out to be a highly important tool to study the coordinated function of a very large number of genes within their corresponding cellular environment, especially in relation to phenotypic diversity and disease. A wide variety of methods of quantitative analysis have been developed to cope with high throughput data sets generated by gene expression profiling experiments. Due to the complexity associated with transcriptomics, specially in the case of gene regulation phenomena, most of these methods are of a probabilistic or statistical nature. Even if these methods have reached a central status in the development of an integrative, systematic understanding of the associated biological processes, they very rarely constitute a concrete guide to the actual physicochemical mechanisms behind biological function and the role of these methods is more on a hypotheses generating line. An important improvement could be done with the development of a thermodynamic theory for gene expression and transcriptional regulation that will build the foundations for a proper integration of the vast amount of molecular biophysical data and could lead, in the future, to a systemic view of genetic transcription and regulation.

q-bio.GN↗

arXiv2009

KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis

Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.

cs.DB↗

arXiv2009

Extending the Recursive Jensen-Shannon Segmentation of Biological Sequences

In this paper, we extend a previously developed recursive entropic segmentation scheme for applications to biological sequences. Instead of Bernoulli chains, we model the statistically stationary segments in a biological sequence as Markov chains, and define a generalized Jensen-Shannon divergence for distinguishing between two Markov chains. We then undertake a mean-field analysis, based on which we identify pitfalls associated with the recursive Jensen-Shannon segmentation scheme. Following this, we explain the need for segmentation optimization, and describe two local optimization schemes for improving the positions of domain walls discovered at each recursion stage. We also develop a new termination criterion for recursive Jensen-Shannon segmentation based on the strength of statistical fluctuations up to a minimum statistically reliable segment length, avoiding the need for unrealistic null and alternative segment models of the target sequence. Finally, we compare the extended scheme against the original scheme by recursively segmenting the Escherichia coli K-12 MG1655 genome.

q-bio.GN↗

arXiv2009

The Context Sensitivity Problem in Biological Sequence Segmentation

In this paper, we describe the context sensitivity problem encountered in partitioning a heterogeneous biological sequence into statistically homogeneous segments. After showing signatures of the problem in the bacterial genomes of Escherichia coli K-12 MG1655 and Pseudomonas syringae DC3000, when these are segmented using two entropic segmentation schemes, we clarify the contextual origins of these signatures through mean-field analyses of the segmentation schemes. Finally, we explain why we believe all sequence segmentation schems are plagued by the context sensitivity problem.

q-bio.GN↗

arXiv2009

Perfect Phylogeny Haplotyping is Complete for Logspace

Haplotyping is the bioinformatics problem of predicting likely haplotypes based on given genotypes. It can be approached using Gusfield's perfect phylogeny haplotyping (PPH) method for which polynomial and linear time algorithms exist. These algorithm use sophisticated data structures or do a stepwise transformation of the genotype data into haplotype data and, therefore, need a linear amount of space. We are interested in the exact computational complexity of PPH and show that it can be solved space-efficiently by an algorithm that needs only a logarithmic amount of space. Together with the recently proved L-hardness of PPH, we establish L-completeness. Our algorithm relies on a new characterization for PPH in terms of bipartite graphs, which can be used both to decide and construct perfect phylogenies for genotypes efficiently.

cs.CC↗

arXiv2010

Identification and Query of Activated Gene Pathways in Disease Progression

Disease occurs due to aberrant expression of genes and modulation of the biological pathways along which they lie. Inference of activated gene pathways, using gene expression data during disease progression, is an important problem. In this work, we have developed a generalizable framework for the identification of interacting pathways while incorporating biological realism, using functional data analysis and manifold embedding techniques. Additionally, we have also developed a new method to query for the differential co-ordinated activity of any desired pathway during disease progression. The methods developed in this work can be generalized to any conditions of interest.

q-bio.GN↗

arXiv2009

Analyzing the Accuracy of the Fitch Method for Reconstructing Ancestral States on Ultrametric Phylogenies

Recurrence formulas are presented for studying the accuracy of the Fitch method for reconstructing the ancestral states in a given phylogenetic tree. As their applications, we analyze the convergence of the accuracy of reconstructing the root state in a complete binary tree of $2^n$ as $n$ goes to infinity and also give a lower bound on the accuracy of reconstructing the root state in an ultrametric tree.

q-bio.PE↗

arXiv2009

Open Regulatory Networks and Modularity

We study the dynamical properties of small regulatory networks treated as non autonomous dynamical systems called modules when working inside larger networks or, equivalently when subject to external signal inputs. Particular emphasis is put on the interplay between the internal properties of the open systems and the different possible inputs on them to deduce new functionalities of the modules. We use discrete-time, piecewise-affine and piecewise-contracting models with interactions of a regulatory nature to perform our study.

q-bio.MN↗

arXiv2009

Autoregressive Modeling of Coding Sequence Lengths in Bacterial Genome

Previous investigation of coding sequence lengths (CDS) in the bacterial circular chromosome revealed short range correlation in the series of these data. We have further analyzed the averaged periodograms of these series and we found that the organization of CDS can be well described by first order autoregressive processes. This involves interaction between the neighboring terms. The autoregressive analysis may have great potential in modeling various physical and biological processes like light emission of galaxies, protein organization, cell flickering, cognitive processes and perhaps others.

q-bio.GN↗

arXiv2009

A Molecular Mass Gradient is the Key Parameter of the Genetc Code Organization

The structure of the genetic code is discussed in formal terms. A rectangular table of the code ("the code matrix"), whose properties reveal its arithmetical content tagged with the information symbols in several notations. New parameters used to analyze of the code matrix, the serial numbers of the encoded products and coding elements, ordered by molecular mass. The structural similarity of the amino acid sequences corresponding to two aminoacyl tRNA synthetases classes is found. The code matrix shows how can be organized the so-called second genetic code. The symmetrical pattern of the matrix is supported with the other parameters; it also serves as a basis to construct a 3D model of the genetic code which follows the structure of the simplest Plato solid, tetrahedron. The reasons for this unusual structure of the genetic code remains unclear.

q-bio.OT↗

arXiv2009

N-tuple Zipf Analysis and Modeling for Language, Computer Program and DNA

n-tuple power law widely exists in language, computer program code, DNA and music. After a vast amount of Zipf analyses of n-tuple power law from empirical data, we propose a model to explain the n-tuple power law feature existed in these information translational carriers. Our model is a preferential selection approach inspired by Simon's model which explained scaling law of single symbol in a sequence Zipf analysis. The kernel mechanism is neat and simple in our model. It can be simply described as a randomly copy and paste process, that is, randomly select a random segment from current sequence and attach it to the end repeatedly. The simulation of our model shows that n-tuple power law exists in model generated data. Furthermore, two estimation equations: the Zipf exponent and the minimal length of n-tuple for power law appears all correspond to empirical data well. Our model can also reproduce the symmetry breaking process of ATGC number differences in DNA data.

physics.data-an↗

arXiv2009

Reasons underlying certain tendencies in the data on the frequency of codon usage

The tendencies described in this work were revealed in the course of examination of adenine and uracil distribution in the mRNA encoding sequence. The study also discusses the usage of codons occupied by the amino acid arginine in the table of the universal genetic code. All of the described tendencies are qualitative, so neither sophisticated methods nor cumbersome calculations are necessary to reveal and interpret them.

q-bio.OT↗

arXiv2009

A contact-waiting-time metric and RNA folding rates

Metrics for indirectly predicting the folding rates of RNA sequences are of interest. In this letter, we introduce a simple metric of RNA structural complexity, which accounts for differences in the energetic contributions of RNA base contacts toward RNA structure formation. We apply the metric to RNA sequences whose folding rates were previously determined experimentally. We find that the metric has good correlation (correlation coefficient: -0.95, p << 0.01) with the logarithmically transformed folding rates of those RNA sequences. This suggests that the metric can be useful for predicting RNA folding rates. We use the metric to predict the folding rates of bacterial and eukaryotic group II introns. Future applications of the metric (e.g., to predict structural RNAs) could prove fruitful.

q-bio.BM↗