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arXiv2010

Towards a theoretical understanding of false positives in DNA motif finding

Detection of false-positive motifs is one of the main causes of low performance in motif finding methods. It is generally assumed that false-positives are mostly due to algorithmic weakness of motif-finders. Here, however, we derive the theoretical dependence of false positives on dataset size and find that false positives can arise as a result of large dataset size, irrespective of the algorithm used. Interestingly, the false-positive strength depends more on the number of sequences in the dataset than it does on the sequence length. As expected, false-positives can be reduced by decreasing the sequence length or by adding more sequences to the dataset. The dependence on number of sequences, however, diminishes and reaches a plateau after which adding more sequences to the dataset does not reduce the false-positive rate significantly. Based on the theoretical results presented here, we provide a number of intuitive rules of thumb that may be used to enhance motif-finding results in practice.

q-bio.GN↗

arXiv2010

What use are Exponential Weights for flexi-Weighted Least Squares Phylogenetic Trees?

The method of flexi-Weighted Least Squares on evolutionary trees uses simple polynomial or exponential functions of the evolutionary distance in place of model-based variances. This has the advantage that unexpected deviations from additivity can be modeled in a more flexible way. At present, only polynomial weights have been used. However, a general family of exponential weights is desirable to compare with polynomial weights and to potentially exploit recent insights into fast least squares edge length estimation on trees. Here describe families of weights that are multiplicative on trees, along with measures of fit of data to tree. It is shown that polynomial, but also multiplicative weights can approximate model-based variance of evolutionary distances well. Both models are fitted to evolutionary data from yeast genomes and while the polynomial weights model fits better, the exponential weights model can fit a lot better than ordinary least squares. Iterated least squares is evaluated and is seen to converge quickly and with minimal change in the fit statistics when the data are in the range expected for the useful evolutionary distances and simple Markov models of character change. In summary, both polynomial and exponential weighted least squares work well and justify further investment into developing the fastest possible algorithms for evaluating evolutionary trees.

q-bio.PE↗

arXiv2010

A Unified Framework for Trees, Multi-Dimensional Scaling and Planar Graphs

Least squares trees, multi-dimensional scaling and Neighbor Nets are all different and popular ways of visualizing multi-dimensional data. The method of flexi-Weighted Least Squares (fWLS) is a powerful method of fitting phylogenetic trees, when the exact form of errors is unknown. Here, both polynomial and exponential weights are used to model errors. The exact same models are implemented for multi-dimensional scaling to yield flexi-Weighted MDS, including as special cases methods such as the Sammon Stress function. Here we apply all these methods to population genetic data looking at the relationships of "Abrahams Children" encompassing Arabs and now widely dispersed populations of Jews, in relation to an African outgroup and a variety of European populations. Trees, MDS and Neighbor Nets of this data are compared within a common likelihood framework and the strengths and weaknesses of each method are explored. Because the errors in this type of data can be complex, for example, due to unexpected genetic transfer, we use a residual resampling method to assess the robustness of trees and the Neighbor Net. Despite the Neighbor Net fitting best by all criteria except BIC, its structure is ill defined following residual resampling. In contrast, fWLS trees are favored by BIC and retain considerable strong internal structure following residual resampling. This structure clearly separates various European and Middle Eastern populations, yet it is clear all of the models have errors much larger than expected by sampling variance alone.

q-bio.PE↗

arXiv2010

Is the unfoldome widespread in proteomes?

The term unfoldome has been recently used to indicate the universe of intrinsically disordered proteins. These proteins are characterized by an ensemble of high-flexible interchangeable conformations and therefore they can interact with many targets without requiring pre-existing stereo-chemical complementarity. It has been suggested that intrinsically disordered proteins are frequent in proteomes and disorder is widespread also in structured proteins. However, several studies raise some doubt about these views. It this paper we estimate the frequency of intrinsically disordered proteins in several living organisms by using the ratio S between the likelihood, for a protein sequence, of being composed mainly by order-promoting or disorder-promoting residues. We scan several proteomes from Archaea, Bacteria and Eukarya. We find the following figures: 1.63% for Archaea, 3.91% for Bacteria, 16.35% for Eukarya. The frequencies we found can be considered an upper bound to the real frequency of intrinsically disordered proteins in proteomes. Our estimates are lower than those previously reported in several studies. A scanning of proteins in the Protein Data Bank (PDB) searching for segments of non-observed residues reveals that segments of non-observed residues longer than 30 amino acids, are rare. Our observations support the idea that the spread of the unfoldome has been often overestimated. If we exclude some exceptions, the structure-function paradigm is generally valid and pre-existing stereo-chemical complementarity among structures remains an important requisite for interactions between biological macromolecules.

q-bio.GN↗

arXiv2012

Amino acid composition and thermal stability of protein structures: the free energy geography of the Protein Data Bank

We study the combined influence of amino acid composition and chain length on the thermal stability of protein structures. A new parameterization of the internal free energy is considered, as the sum of hydrophobic effect, hydrogen-bond and de-hydration energy terms. We divided a non-redundant selection of protein structures from the Protein Data Bank into three groups: i) rich in order-promoting residues (OPR proteins); ii) rich in disorder-promoting residues (DPR proteins); iii) belonging to a twilight zone (TZ proteins). We observe a partition of PDB in several groups with different internal free energies, amino acid compositions and protein lengths. Internal free energy of 96% of the proteins analyzed ranges from -2 to -6.5 kJ/mol/res. We found many DPR and OPR proteins with the same relative thermal stability. Only OPR proteins with internal energy between -4 and -6.5 kJ/mol/res are observed to have chains longer than 200 residues, with a high de-hydration energy compensated by the hydrophobic effect. DPR and TZ proteins are shorter than 200 residues and they have an internal energy above -4 kJ/mol/res, with a few exceptions among TZ proteins. Hydrogen-bonds play an important role in the stabilization of these DPR folds, often higher than contact energy. The new parameterization of internal free energy let emerge a geography of thermal stabilities of PDB structures. Amino acid composition per se is not sufficient to determine the stability of protein folds, since. DPR and TZ proteins generally have a relatively high internal free energy, and they are stabilized by hydrogen-bonds. Long DPR proteins are not observed in the PDB, because their low hydrophobicity cannot compensate the high de-hydration energy necessary to accommodate residues within a highly packed globular fold.

q-bio.GN↗

arXiv2010

Resampling Residuals on Phylogenetic Trees: Extended Results

In this article the results of Waddell and Azad (2009) are extended. In particular, the geometric percentage mean standard deviation measure of the fit of distances to a phylogenetic tree is adjusted for the number of parameters fitted to the model. The formulae are also presented in their general form for any weight that is a function of the distance. The cell line gene expression data set of Ross et al. (2000) is reanalyzed. It is shown that ordinary least squares (OLS) is a much better fit to the data than a Neighbor Joining or BME tree. Residual resampling shows that cancer cell lines do indeed fit a tree fairly well and that the tree does have strong internal structure. Simulations show that least squares tree building methods, including OLS, are strong competitors with BME type methods for fitting model data, while real world examples often suggest the same conclusion.

q-bio.PE↗

arXiv2010

Relationships among the nucleotide content of human genome sequence, gene structure, and gene expression features (PhD synopsis)

The Dissertation is focused on the studies of associations between functional elements in human genome and their nucleotide structure. The asymmetry in nucleotide content (skew, bias) was chosen as the main feature for nucleotide structure. A significant difference in nucleotide content asymmetry was found for human exons vs. introns. Specifically, exon sequences display bias for purines (i.e., excess of A and G over C and T), while introns exhibit keto-amino skew (i.e. excess of G and T over A and C). The extents of these biases depend upon gene expression patterns. The highest intronic keto-amino skew is found in the introns of housekeeping genes. In the case of introns, whose sequences are under weak repair system, the AT->GC and CG->TA substitutions are preferentially accumulated. A comparative analysis of gene sequences encoding cytochrome P450 2E1 of Homo sapiens and representative mammals was done. The cladistic tree on the basis of coding sequences similarity of the gene Cyp2e1 was constructed. A new programming tools of NCBI database sequence mining and analysis was developed, resulting in construction of a own database.

q-bio.GN↗

arXiv2011

Construction and Functional Analysis of Human Genetic Interaction Networks with Genome-wide Association Data

Genetic interaction measures how different genes collectively contribute to a phenotype, and can reveal functional compensation and buffering between pathways under genetic perturbations. Recently, genome-wide screening for genetic interactions has revealed genetic interaction networks that provide novel insights either when analyzed by themselves or when integrated with other functional genomic datasets. For higher eukaryotes such as human, the above reverse-genetics approaches are not straightforward since the phenotypes of interest for higher eukaryotes are difficult to study in a cell based assay. We propose a general framework for constructing and analyzing human genetic interaction networks from genome-wide single nucleotide polymorphism (SNP) data used for case-control studies on complex diseases. Specifically, the approach contains three major steps: (1) estimating SNP-SNP genetic interactions, (2) identifying linkage disequilibrium (LD) blocks and mapping SNP-SNP interactions to block-block interactions, and (3) functional mapping for LD blocks. We performed two sets of functional analyses for each of the six datasets used in the paper, and demonstrated that (i) the constructed genetic interaction networks are supported by functional evidence from independent biological databases, and (ii) the network can be used to discover pairs of compensatory gene modules (between-pathway models) in their joint association with a disease phenotype. The proposed framework should provide novel insights beyond existing approaches that either ignore interactions between SNPs or model different SNP-SNP pairs with genetic interactions separately. Furthermore, our study provides evidence that some of the core properties of genetic interaction networks based on reverse genetics in model organisms like yeast are also present in genetic interactions revealed by natural variation in human populations.

q-bio.MN↗

arXiv2011

Integration of Differential Gene-combination Search and Gene Set Enrichment Analysis: A General Approach

Gene Set Enrichment Analysis (GSEA) and its variations aim to discover collections of genes that show moderate but coordinated differences in expression. However, such techniques may be ineffective if many individual genes in a phenotype-related gene set have weak discriminative power. A potential solution is to search for combinations of genes that are highly differentiating even when individual genes are not. Although such techniques have been developed, these approaches have not been used with GSEA to any significant degree because of the large number of potential gene combinations and the heterogeneity of measures that assess the differentiation provided by gene groups of different sizes. To integrate the search for differentiating gene combinations and GSEA, we propose a general framework with two key components: (A) a procedure that reduces the number of scores to be handled by GSEA to the number of genes by summarizing the scores of the gene combinations involving a particular gene in a single score, and (B) a procedure to integrate the heterogeneous scores from combinations of different sizes and from different gene combination measures by mapping the scores to p-values. Experiments on four gene expression data sets demonstrate that the integration of GSEA and gene combination search can enhance the power of traditional GSEA by discovering gene sets that include genes with weak individual differentiation but strong joint discriminative power. Also, gene sets discovered by the integrative framework share several common biological processes and improve the consistency of the results among three lung cancer data sets.

q-bio.GN↗

arXiv2011

Beyond the consensus: dissecting within-host viral population diversity of foot-and-mouth disease virus using next-generation genome sequencing

The sequence diversity of viral populations within individual hosts is the starting material for selection and subsequent evolution of RNA viruses such as foot-and-mouth disease virus (FMDV). Using next-generation sequencing (NGS) performed on a Genome Analyzer platform (Illumina), this study compared the viral populations within two bovine epithelial samples (foot lesions) from a single animal with the Inoculum used to initiate experimental infection. Genomic sequences were determined in duplicate sequencing runs, and the consensus sequence determined by NGS, for the Inoculum, was identical to that previously determined using the Sanger method. However, NGS reveals the fine polymorphic sub-structure of the viral population, from nucleotide variants present at just below 50% frequency to those present at fractions of 1%. Some of the higher frequency polymorphisms identified encoded changes within codons associated with heparan sulphate binding and were present in both feet lesions revealing intermediate stages in the evolution of a tissue-culture adapted virus replicating within a mammalian host. We identified 2,622, 1,434 and 1,703 polymorphisms in the Inoculum, and in the two foot lesions respectively: most of the substitutions occurred only in a small fraction of the population and represent the progeny from recent cellular replication prior to onset of any selective pressures. We estimated an upper limit for the genome-wide mutation rate of the virus within a cell to be 7.8 x 10-4 per nt. The greater depth of detection, achieved by NGS, demonstrates that this method is a powerful and valuable tool for the dissection of FMDV populations within-hosts.

q-bio.GN↗

arXiv2012

An exploratory analysis of combined genome-wide SNP data from several recent studies

The usefulness of a `total-evidence' approach to human population genetics was assessed through a clustering analysis of combined genome-wide SNP datasets. The combination contained only 3146 SNPs. Detailed examination of the results nonetheless enables the extraction of relevant clues about the history of human populations, some pertaining to events as ancient as the first migration out of Africa. The results are mostly coherent with what is known from history, linguistics, and previous genetic analyses. These promising results suggest that cross-studies data confrontation have the potential to yield interesting new hypotheses about human population history.

q-bio.PE↗

arXiv2011

The pattern and process of gene family evolution

Large scale databases are available that contain homologous gene families constructed from hundreds of complete genome sequences from across the three domains of Life. Here we discuss approches of increasing complexity aimed at extracting information on the pattern and process of gene family evolution from such datasets. In particular, we consider models that invoke processes of gene birth (duplication and transfer) and death (loss) to explain the evolution of gene families. First, we review birth-and-death models of family size evolution and their implications in light of the universal features of family size distribution observed across different species and the three domains of life. Subsequently, we proceed to recent developments on models capable of more completely considering information in the sequences of homologous gene families through the probabilistic reconciliation of the phylogenetic histories of individual genes with the phylogenetic history of the genomes in which they have resided. To illustrate the methods and results presented, we use data from the HOGENOM database, demonstrating that the distribution of homologous gene family sizes in the genomes of the Eukaryota, Archaea and Bacteria exhibit remarkably similar shapes. We shown that these distributions are best described by models of gene family size evolution where for individual genes the death (loss) rate is larger than the birth (duplication and transfer) rate, but new families are continually supplied to the genome by a process of origination. Finally, we use probabilistic reconciliation methods to take into consideration additional information from gene phylogenies, and find that, for prokaryotes, the majority of birth events are the result of transfer.

q-bio.GN↗

arXiv2011

Low-pass Genomewide Sequencing and Variant Imputation Using Identity-by-descent in an Isolated Human Population

Whole-genome sequencing in an isolated population with few founders directly ascertains variants from the population bottleneck that may be rare elsewhere. In such populations, shared haplotypes allow imputation of variants in unsequenced samples without resorting to statistical methods, as in studies of outbred cohorts. We focus on an isolated population cohort from the Pacific Island of Kosrae, Micronesia, where we previously collected SNP array and rich phenotype data for the majority of the population. We report identification of long regions with haplotypes co-inherited between pairs of individuals and methodology to leverage such shared genetic content for imputation. Our estimates show that sequencing as few as 40 personal genomes allows for imputation in up to 60% of the 3,000-person cohort at the average locus. We ascertained a pilot data-set of whole-genome sequences from seven Kosraean individuals, with average 5X coverage. This dataset identified 5,735,306 unique sites of which 1,212,831 were previously unknown. Additionally, these Kosraen variants are unusually enriched for alleles that are rare in other populations when compared to geographic neighbors. We were able to use the presence of shared haplotypes between the seven individuals to estimate imputation accuracy of known and novel variants and achieved levels of 99.6% and 97.3%, respectively. This study presents the first whole-genome analysis of a homogenous isolate population with emphasis on rare variant inference.

q-bio.GN↗

arXiv2011

Inferring Disease and Gene Set Associations with Rank Coherence in Networks

A computational challenge to validate the candidate disease genes identified in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease causative genes are incomplete. We propose a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. Assuming coherent associations between the genes ranked by their relevance to the query gene set, and the disease phenotypes ranked by their relevance to the hidden target disease phenotypes of the query gene set, we formulate a learning framework maximizing the rank coherence with respect to the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation, and two variants are introduced to find the optimal solution of the framework. We evaluated the rcNet algorithms and existing baseline methods with both leave-one-out cross-validation and a task of predicting recently discovered disease-gene associations in OMIM. The experiments demonstrated that the rcNet algorithms achieved the best overall rankings compared to the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of GWAS, DNA copy number variation analysis, and gene expression profiling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies. The rcNet algorithms are available as a webtool for disease and gene set association analysis at http://compbio.cs.umn.edu/dgsa_rcNet.

q-bio.GN↗

arXiv2011

dMotifGreedy: a novel tool for de novo discovery of DNA motifs with enhanced power of reporting distinct motifs

De novo discovery of over-represented DNA motifs is one of the major challenges in computational biology. Although numerous tools have been available for de novo motif discovery, many of these tools are subject to local optima phenomena, which may hinder detection of multiple distinct motifs. A greedy algorithm based tool named dMotifGreedy was developed. dMotifGreedy begins by searching for candidate motifs from pair-wise local alignments of input sequences and then computes an optimal global solution for each candidate motif through a greedy algorithm. dMotifGreedy has competitive performance in detecting a true motif and greatly enhanced performance in detecting multiple distinct true motifs. dMotifGreedy is freely available via a stand-alone program at http://lambchop.ads.uga.edu/dmotifgreedy/download.php.

q-bio.GN↗

arXiv2011

Characterizing Discriminative Patterns

Discriminative patterns are association patterns that occur with disproportionate frequency in some classes versus others, and have been studied under names such as emerging patterns and contrast sets. Such patterns have demonstrated considerable value for classification and subgroup discovery, but a detailed understanding of the types of interactions among items in a discriminative pattern is lacking. To address this issue, we propose to categorize discriminative patterns according to four types of item interaction: (i) driver-passenger, (ii) coherent, (iii) independent additive and (iv) synergistic beyond independent additive. Either of the last three is of practical importance, with the latter two representing a gain in the discriminative power of a pattern over its subsets. Synergistic patterns are most restrictive, but perhaps the most interesting since they capture a cooperative effect. For domains such as genetic research, differentiating among these types of patterns is critical since each yields very different biological interpretations. For general domains, the characterization provides a novel view of the nature of the discriminative patterns in a dataset, which yields insights beyond those provided by current approaches that focus mostly on pattern-based classification and subgroup discovery. This paper presents a comprehensive discussion that defines these four pattern types and investigates their properties and their relationship to one another. In addition, these ideas are explored for a variety of datasets (ten UCI datasets, one gene expression dataset and two genetic-variation datasets). The results demonstrate the existence, characteristics and statistical significance of the different types of patterns. They also illustrate how pattern characterization can provide novel insights into discriminative pattern mining and the discriminative structure of different datasets.

cs.DB↗

arXiv2011

Probabilistic analysis of the human transcriptome with side information

Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new views to the study of living organisms and the structure of life. In this thesis, novel computational strategies have been developed to investigate a key functional layer of genetic information, the human transcriptome, which regulates the function of living cells through protein synthesis. The key contributions of the thesis are general exploratory tools for high-throughput data analysis that have provided new insights to cell-biological networks, cancer mechanisms and other aspects of genome function. A central challenge in functional genomics is that high-dimensional genomic observations are associated with high levels of complex and largely unknown sources of variation. By combining statistical evidence across multiple measurement sources and the wealth of background information in genomic data repositories it has been possible to solve some the uncertainties associated with individual observations and to identify functional mechanisms that could not be detected based on individual measurement sources. Statistical learning and probabilistic models provide a natural framework for such modeling tasks. Open source implementations of the key methodological contributions have been released to facilitate further adoption of the developed methods by the research community.

stat.ML↗

arXiv2011

Exponential wealth distribution: a new approach from functional iteration theory

Exponential distribution is ubiquitous in the framework of multi-agent systems. Usually, it appears as an equilibrium state in the asymptotic time evolution of statistical systems. It has been explained from very different perspectives. In statistical physics, it is obtained from the principle of maximum entropy. In the same context, it can also be derived without any consideration about information theory, only from geometrical arguments under the hypothesis of equiprobability in phase space. Also, several multi-agent economic models based on mappings, with random, deterministic or chaotic interactions, can give rise to the asymptotic appearance of the exponential wealth distribution. An alternative approach to this problem in the framework of iterations in the space of distributions has been recently presented. Concretely, the new iteration given by $ f_{n+1}(x) = \int\int_{u+v>x}{f_n(u)f_n(v)\over u+v} dudv.$. It is found that the exponential distribution is a stable fixed point of the former functional iteration equation. From this point of view, it is easily understood why the exponential wealth distribution (or by extension, other kind of distributions) is asymptotically obtained in different multi-agent economic models.

nlin.AO↗