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At least 289 records · Page 16Linked to original sources
arXiv2012

The Chromatin Organization of an Eukaryotic Genome : Sequence Specific+ Statistical=Combinatorial (Extended Abstract)

Nucleosome organization in eukaryotic genomes has a deep impact on gene function. Although progress has been recently made in the identification of various concurring factors influencing nucleosome positioning, it is still unclear whether nucleosome positions are sequence dictated or determined by a random process. It has been postulated for a long time that,in the proximity of TSS, a barrier determines the position of the +1 nucleosome and then geometric constraints alter the random positioning process determining nucleosomal phasing. Such a pattern fades out as one moves away from the barrier to become again a random positioning process. Although this statistical model is widely accepted,the molecular nature of the barrier is still unknown. Moreover,we are far from the identification of a set of sequence rules able:to account for the genome-wide nucleosome organization;to explain the nature of the barriers on which the statistical mechanism hinges;to allow for a smooth transition from sequence-dictated to statistical positioning and back. We show that sequence complexity,quantified via various methods, can be the rule able to at least partially account for all the above.In particular, we have conducted our analyses on 4 high resolution nucleosomal maps of the model eukaryotes and found that nucleosome depleted regions can be well distinguished from nucleosome enriched regions by sequence complexity measures.In particular, (a) the depleted regions are less complex than the enriched ones, (b) around TSS complexity measures alone are in striking agreement with in vivo nucleosome occupancy,in particular precisely indicating the positions of the +1 and -1 nucleosomes. Those findings indicate that the intrinsic richness of subsequences within sequences plays a role in nucleosomal formation in genomes, and that sequence complexity constitutes the molecular nature of nucleosome barrier.

q-bio.GN↗
arXiv2012

Towards a Mathematical Foundation of Immunology and Amino Acid Chains

We attempt to set a mathematical foundation of immunology and amino acid chains. To measure the similarities of these chains, a kernel on strings is defined using only the sequence of the chains and a good amino acid substitution matrix (e.g. BLOSUM62). The kernel is used in learning machines to predict binding affinities of peptides to human leukocyte antigens DR (HLA-DR) molecules. On both fixed allele (Nielsen and Lund 2009) and pan-allele (Nielsen et.al. 2010) benchmark databases, our algorithm achieves the state-of-the-art performance. The kernel is also used to define a distance on an HLA-DR allele set based on which a clustering analysis precisely recovers the serotype classifications assigned by WHO (Nielsen and Lund 2009, and Marsh et.al. 2010). These results suggest that our kernel relates well the chain structure of both peptides and HLA-DR molecules to their biological functions, and that it offers a simple, powerful and promising methodology to immunology and amino acid chain studies.

stat.ML↗
arXiv2012

LMM-Lasso: A Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction

Exploring the genetic basis of heritable traits remains one of the central challenges in biomedical research. In simple cases, single polymorphic loci explain a significant fraction of the phenotype variability. However, many traits of interest appear to be subject to multifactorial control by groups of genetic loci instead. Accurate detection of such multivariate associations is nontrivial and often hindered by limited power. At the same time, confounding influences such as population structure cause spurious association signals that result in false positive findings if they are not accounted for in the model. Here, we propose LMM-Lasso, a mixed model that allows for both, multi-locus mapping and correction for confounding effects. Our approach is simple and free of tuning parameters, effectively controls for population structure and scales to genome-wide datasets. We show practical use in genome-wide association studies and linkage mapping through retrospective analyses. In data from Arabidopsis thaliana and mouse, our method is able to find a genetic cause for significantly greater fractions of phenotype variation in 91% of the phenotypes considered. At the same time, our model dissects this variability into components that result from individual SNP effects and population structure. In addition to this increase of genetic heritability, enrichment of known candidate genes suggests that the associations retrieved by LMM-Lasso are more likely to be genuine.

q-bio.PE↗
arXiv2012

What does the Allen Gene Expression Atlas tell us about mouse brain evolution?

We use the Allen Gene Expression Atlas (AGEA) and the OMA ortholog dataset to investigate the evolution of mouse-brain neuroanatomy from the standpoint of the molecular evolution of brain-specific genes. For each such gene, using the phylogenetic tree for all fully sequenced species and the presence of orthologs of the gene in these species, we construct and assign a discrete measure of evolutionary age. The gene expression profile of all gene of similar age, relative to the average gene expression profile, distinguish regions of the brain that are over-represented in the corresponding evolutionary timescale. We argue that the conclusions one can draw on evolution of twelve major brain regions from such a molecular level analysis supplements existing knowledge of mouse brain evolution and introduces new quantitative tools, especially for comparative studies, when AGEA-like data sets for other species become available. Using the functional role of the genes representational of a certain evolutionary timescale and brain region we compare and contrast, wherever possible, our observations with existing knowledge in evolutionary neuroanatomy.

q-bio.QM↗
arXiv2012

Extraction of Deep Phylogenetic Signal and Improved Resolution of Evolutionary Events within the recA/RAD51 Phylogeny

The recA/RAD51 gene family encodes a diverse set of recombinase proteins that effect homologous recombination, DNA-repair, and genome stability. The recA gene family is expressed in almost all species of Eubacteria, Archaea, and Eukaryotes, and even in some viruses. To date, efforts to resolve the deep evolutionary origins of this ancient protein family have been hindered, in part, by the high sequence divergence between families (i.e. ~30% identity between paralogous groups). Through (i) large taxon sampling, (ii) the use of a phylogenetic algorithm designed for measuring highly divergent paralogs, and (iii) novel Evolutionary Spatial Dynamics simulation and analytical tools, we obtained a robust, parsimonious and more refined phylogenetic history of the recA/RAD51 superfamily. Taken together, our model for the evolution of recA/RAD51 family provides a better understanding of ancient origin of recA proteins and multiple events leading to the diversification of recA homologs in eukaryotes, including the discovery of additional RAD51 sub-families.

q-bio.GN↗
arXiv2012

SeeSite: Efficiently Finding Co-occurring Splice Sites and Exon Splicing Enhancers

The problem of identifying splice sites consists of two sub-problems: finding their boundaries, and characterizing their sequence markers. Other splicing elements---including, enhancers and silencers---that occur in the intronic and exonic regions play an important role in splicing activity. Existing methods for detecting splicing elements are limited to finding either splice sites or enhancers and silencers, even though these elements are well-known to co-occur. We introduce SeeSite, an efficient and accurate tool for detecting splice sites and their complementary exon splicing enhancers (ESEs). SeeSite has three stages: graph construction, finding dense subgraphs, and recovering splice sites and ESEs along with their consensus. The third step involves solving Consensus Sequence with Outliers, an NP-complete string clustering problem. We prove that our algorithm for this problem outputs near-optimal solutions in polynomial time. Using SeeSite we demonstrate that ESEs are preferentially associated with weaker splice sites, and splice sites of a certain canonical form co-occur with specific ESEs.

q-bio.QM↗
arXiv2012

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information, and sequences, effectively collapsing very large datasets to less than 15% of their original size with no loss of information. Availability: Quip is freely available under the BSD license from http://cs.washington.edu/homes/dcjones/quip.

q-bio.QM↗
arXiv2012

Haplotype-based variant detection from short-read sequencing

The direct detection of haplotypes from short-read DNA sequencing data requires changes to existing small-variant detection methods. Here, we develop a Bayesian statistical framework which is capable of modeling multiallelic loci in sets of individuals with non-uniform copy number. We then describe our implementation of this framework in a haplotype-based variant detector, FreeBayes.

q-bio.GN↗
arXiv2012

Joint discovery of haplotype blocks and complex trait associations from SNP sequences

Haplotypes, the global patterns of DNA sequence variation, have important implications for identifying complex traits. Recently, blocks of limited haplotype diversity have been discovered in human chromosomes, intensifying the research on modelling the block structure as well as the transitions or co-occurrence of the alleles in these blocks as a way to compress the variability and infer the associations more robustly. The haplotype block structure analysis is typically complicated by the fact that the phase information for each SNP is missing, i.e., the observed allele pairs are not given in a consistent order across the sequence. The techniques for circumventing this require additional information, such as family data, or a more complex sequencing procedure. In this paper we present a hierarchical statistical model and the associated learning and inference algorithms that simultaneously deal with the allele ambiguity per locus, missing data, block estimation, and the complex trait association. While the blo structure may differ from the structures inferred by other methods, which use the pedigree information or previously known alleles, the parameters we estimate, including the learned block structure and the estimated block transitions per locus, define a good model of variability in the set. The method is completely datadriven and can detect Chron's disease from the SNP data taken from the human chromosome 5q31 with the detection rate of 80% and a small error variance.

q-bio.GN↗
arXiv2012

Power-Laws and the Conservation of Information in discrete token systems: Part 1 General Theory

The Conservation of Energy plays a pivotal part in the development of the physical sciences. With the growth of computation and the study of other discrete token based systems such as the genome, it is useful to ask if there are conservation principles which apply to such systems and what kind of functional behaviour they imply for such systems. Here I propose that the Conservation of Hartley-Shannon Information plays the same over-arching role in discrete token based systems as the Conservation of Energy does in physical systems. I will go on to prove that this implies power-law behaviour in component sizes in software systems no matter what they do or how they were built, and also implies the constancy of average gene length in biological systems as reported for example by Lin Xu et al (10.1093/molbev/msk019). These propositions are supported by very large amounts of experimental data extending the first presentation of these ideas in Hatton (2011, IFIP / SIAM / NIST Working Conference on Uncertainty Quantification in Scientific Computing, Boulder, August 2011).

cs.IT↗
arXiv2012

Lossy Compression of Quality Values via Rate Distortion Theory

Motivation: Next Generation Sequencing technologies revolutionized many fields in biology by enabling the fast and cheap sequencing of large amounts of genomic data. The ever increasing sequencing capacities enabled by current sequencing machines hold a lot of promise as for the future applications of these technologies, but also create increasing computational challenges related to the analysis and storage of these data. A typical sequencing data file may occupy tens or even hundreds of gigabytes of disk space, prohibitively large for many users. Raw sequencing data consists of both the DNA sequences (reads) and per-base quality values that indicate the level of confidence in the readout of these sequences. Quality values account for about half of the required disk space in the commonly used FASTQ format and therefore their compression can significantly reduce storage requirements and speed up analysis and transmission of these data. Results: In this paper we present a framework for the lossy compression of the quality value sequences of genomic read files. Numerical experiments with reference based alignment using these quality values suggest that we can achieve significant compression with little compromise in performance for several downstream applications of interest, as is consistent with our theoretical analysis. Our framework also allows compression in a regime - below one bit per quality value - for which there are no existing compressors.

q-bio.GN↗
arXiv2013

A Quantitative Understanding of Human Sex Chromosomal Genes

In the last few decades, the human allosomes are engrossed in an intensive attention among researchers. The allosomes are now already been sequenced and found there are about 2000 and 78 genes in human X and Y chromosomes respectively. The hemizygosity of the human X chromosome in males exposes recessive disease alleles, and this phenomenon has prompted decades of intensive study of X-linked disorders. By contrast, the small size of the human Y chromosome, and its prominent long-arm heterochromatic region suggested absence of function beyond sex determination. But the present problem is to accomplish whether a given sequence of nucleotides i.e. a DNA is a Human X or Y chromosomal genes or not, without any biological experimental support. In our perspective, a proper quantitative understanding of these genes is required to justify or nullify whether a given sequence is a Human X or Y chromosomal gene. In this paper, some of the X and Y chromosomal genes have been quantified in genomic and proteomic level through Fractal Geometric and Mathematical Morphometric analysis. Using the proposed quantitative model, one can easily make probable justification or deterministic nullification whether a given sequence of nucleotides is a probable Human X or Y chromosomal gene or not, without seeking any biological experiment. Of course, a further biological experiment is essential to validate it as the probable Human X or Y chromosomal gene homologue. This study would enable Biologists to understand these genes in more quantitative manner instead of their qualitative features.

q-bio.GN↗
arXiv2012

Hidden breakpoints in genome alignments

During the course of evolution, an organism's genome can undergo changes that affect the large-scale structure of the genome. These changes include gene gain, loss, duplication, chromosome fusion, fission, and rearrangement. When gene gain and loss occurs in addition to other types of rearrangement, breakpoints of rearrangement can exist that are only detectable by comparison of three or more genomes. An arbitrarily large number of these "hidden" breakpoints can exist among genomes that exhibit no rearrangements in pairwise comparisons. We present an extension of the multichromosomal breakpoint median problem to genomes that have undergone gene gain and loss. We then demonstrate that the median distance among three genomes can be used to calculate a lower bound on the number of hidden breakpoints present. We provide an implementation of this calculation including the median distance, along with some practical improvements on the time complexity of the underlying algorithm. We apply our approach to measure the abundance of hidden breakpoints in simulated data sets under a wide range of evolutionary scenarios. We demonstrate that in simulations the hidden breakpoint counts depend strongly on relative rates of inversion and gene gain/loss. Finally we apply current multiple genome aligners to the simulated genomes, and show that all aligners introduce a high degree of error in hidden breakpoint counts, and that this error grows with evolutionary distance in the simulation. Our results suggest that hidden breakpoint error may be pervasive in genome alignments.

q-bio.GN↗
arXiv2012

ARISA data from the human gut microbiome can detect individual differences observed by 454 sequencing regardless of binning strategy

ARISA (Automated Ribosomal Intergenic Spacer Analysis) is a low-cost technique that allows for the rapid comparison of different microbial environments. In this study, we asked if a set of ARISA profiles can distinguish human microbial environments from one another with the same accuracy as results generated from 454 high throughput DNA sequencing. Using a set of human microbial communities where the sequencing results cluster by subject, we tested how choices made during ARISA data processing influence clustering. We found that choice of clustering methods had a profound effect with Ward's clustering generating profiles the most similar to 454 sequencing. Factors such as bin size, using presence or absence calls and technical replicate manipulation had a negligible effect on clustering. In fact, no established bin sizing method reported in the literature performed significantly different results than simply picking bin intervals at random. We conclude that in an analysis of ARISA data from an ecosystem of sufficient complexity to saturate bins, a careful choice of clustering algorithm is essential whereas differing strategies for choosing bins are likely to have a much less pronounced effect on the outcome of the analysis. As a tool for distinguishing complex microbial communities, ARISA closely approximates the results obtained from DNA sequencing at a fraction of the cost; however ARISA fails to reproduce the sequencing results perfectly.

q-bio.GN↗
arXiv2012

Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests

Transcriptional regulatory network inference methods have been studied for years. Most of them relie on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which assumes that transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. This method is conceptually simple and easy to implement and integrate in any statistical software environment. Benchmarking on standard E. coli and yeast reference datasets showed that it performs equally well as the best existing methods. Analysis of the predicted interactions suggested that it works best to infer context-specific TF-target interactions which only co-express locally. We confirmed this hypothesis on a dataset of more than 1,000 normal human tissue samples, where we found that our method predicts highly tissue-specific and functionally relevant interactions, whereas a global co-expression method only associates general TFs to non-specific biological processes.

q-bio.GN↗
arXiv2012

A New Approach to Understanding Ontogenesis and The Theory of Aging

This paper proposes an original theory of aging of multicellular organisms. The cells of multicellular organisms, in contrast to unicellular organisms, are burdened with a two- part genome: housekeeping and specialized (multicellular), responsible for ontogenesis and terminal differentiation. The two parts of the genome compete for limited adaptive resources thereby interfering with the ability of the house-keeping part of the genome to adequately perform reparative and adaptive functions in post mitotic cells. The necessity to complete the ontgenesis program, leads to increased activity of the multicellular components of the genome. As a result, the allocation of cellular resources to specialized genome con-tinuously increases with time. This leads to a deficit of reparative and adaptive capacity in post mitotic cells. Suggestions for future research focus on identifying groups of genes responsible for regulation of growth rate of specialized genome and suppressing ability of the cell division. A better understanding of the relationship between the two parts of the genome will not only help us to manipulate ontogenesis and aging, but will also improve our understanding of cancer development and ontogenesis.

q-bio.CB↗