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At least 253 records · Page 14Linked to original sources
arXiv2011

An Experimental Comparison of PMSPrune and Other Algorithms for Motif Search

Extracting meaningful patterns from voluminous amount of biological data is a very big challenge. Motifs are biological patterns of great interest to biologists. Many different versions of the motif finding problem have been identified by researchers. Examples include the Planted $(l, d)$ Motif version, those based on position-specific score matrices, etc. A comparative study of the various motif search algorithms is very important for several reasons. For example, we could identify the strengths and weaknesses of each. As a result, we might be able to devise hybrids that will perform better than the individual components. In this paper we (either directly or indirectly) compare the performance of PMSprune (an algorithm based on the $(l, d)$ motif model) and several other algorithms in terms of seven measures and using well established benchmarks In this paper, we (directly or indirectly) compare the quality of motifs predicted by PMSprune and 14 other algorithms. We have employed several benchmark datasets including the one used by Tompa, et.al. These comparisons show that the performance of PMSprune is competitive when compared to the other 14 algorithms tested. We have compared (directly or indirectly) the performance of PMSprune and 14 other algorithms using the Benchmark dataset provided by Tompa, et.al. It is observed that both PMSprune and DME (an algorithm based on position-specific score matrices) in general perform better than the 13 algorithms reported in Tompa et. al.. Subsequently we have compared PMSprune and DME on other benchmark data sets including ChIP-Chip, ChIP-seq, and ABS. Between PMSprune and DME, PMSprune performs better than DME on six measures. DME performs better than PMSprune on one measure (namely, specificity).

q-bio.QM↗
arXiv2011

Rare coding SNP in DZIP1 gene associated with late-onset sporadic Parkinson's disease

We present the first application of the hypothesis-rich mathematical theory to genome-wide association data. The Hamza et al. late-onset sporadic Parkinson's disease genome-wide association study dataset was analyzed. We found a rare, coding, non-synonymous SNP variant in the gene DZIP1 that confers increased susceptibility to Parkinson's disease. The association of DZIP1 with Parkinson's disease is consistent with a Parkinson's disease stem-cell ageing theory.

q-bio.GN↗
arXiv2011

High-order chromatin architecture determines the landscape of chromosomal alterations in cancer

The rapid growth of cancer genome structural information provides an opportunity for a better understanding of the mutational mechanisms of genomic alterations in cancer and the forces of selection that act upon them. Here we test the evidence for two major forces, spatial chromosome structure and purifying (or negative) selection, that shape the landscape of somatic copy-number alterations (SCNAs) in cancer1. Using a maximum likelihood framework we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule (FG) model2. This analysis provides evidence that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and additionally suggests that purifying selection as well as positive selection shapes the landscape of SCNAs during somatic evolution of cancer cells.

q-bio.GN↗
arXiv2013

Slow evolution of vertebrates with large genomes

Darwin introduced the concept of the "living fossil" to describe species belonging to lineages that have experienced little evolutionary change, and suggested that species in more slowly evolving lineages are more prone to extinction (1). Recent studies revealed that some living fossils such as the lungfish are indeed evolving more slowly than other vertebrates (2, 3). The reason for the slower rate of evolution in these lineages remains unclear, but the same observations suggest a possible genome size effect on rates of evolution. Genome size (C-value) in vertebrates varies over 200 fold ranging from pufferfish (0.4 pg) to lungfish (132.8 pg) (4). Variation in genome size and architecture is a fundamental cellular adaptation that remains poorly understood (5). C-value is correlated with several allometric traits such as body size and developmental rates in many, but not all, organisms (6, 7). To date, no consensus exists concerning the mechanisms driving genome size evolution or the effect that genome size has on species traits such as evolutionary rates (8-12). In the following we show that: 1) within the same range of divergence times, genetic diversity decreases as genome size increases and 2) average rates of molecular evolution decline with increasing genome size in vertebrates. Together, these observations indicate that genome size is an important factor influencing rates of speciation and extinction.

q-bio.GN↗
arXiv2011

Some inverse problems in biophysics

During the past few years the development of experimental techniques has allowed the quantitative analysis of biological systems ranging from neurobiology and molecular biology. This work focuses on the quantitative description of these systems by means of theoretical and numerical tools ranging from statistical physics to probability theory. This dissertation is divided in three parts, each of which has a different biological system as its focus. The first such system is Infotaxis, an olfactory search algorithm proposed by Vergassola et al. in 2007: we give a continuous formulation and we characterize its performances. Secondly we will focus on single-molecule experiments, especially unzipping of DNA molecules, whose experimental traces depend strongly on the DNA sequence: we develop a detailed model of the dynamics for this kind of experiments and then we propose several inference algorithm aiming at the characterization of the genetic sequence. The last section is devoted to the description of an algorithm that allows the inference of interactions between neurons given the recording of neural activity from multi-electrode experiments; we propose an integrated software that will allow the analysis of these data.

q-bio.BM↗
arXiv2012

Quantifying uniformity of mapped reads

Summary: We describe a tool for quantifying the uniformity of mapped reads in high-throughput sequencing experiments. Our statistic directly measures the uniformity of both read position and fragment length, and we explain how to compute a p-value that can be used to quantify biases arising from experimental protocols and mapping procedures. Our method is useful for comparing different protocols in experiments such as RNA-Seq. Availability and Implementation: We provide a freely available and open source python script that can be used to analyze raw read data or reads mapped to transcripts in BAM format at http://www.math.miami.edu/~vhower/ReadSpy.html . Contact: lpachter@math.berkeley.edu

q-bio.GN↗
arXiv2011

Genetic Testing for Complex Diseases: a Simulation Study Perspective

It is widely recognized nowadays that complex diseases are caused by, amongst the others, multiple genetic factors. The recent advent of genome-wide association study (GWA) has triggered a wave of research aimed at discovering genetic factors underlying common complex diseases. While the number of reported susceptible genetic variants is increasing steadily, the application of such findings into diseases prognosis for the general population is still unclear, and there are doubts about whether the size of the contribution by such factors is significant. In this respect, some recent simulation-based studies have shed more light to the prospect of genetic tests. In this report, we discuss several aspects of simulation-based studies: their parameters, their assumptions, and the information they provide.

stat.AP↗
arXiv2011

A Minimum Principle in Codon-Anticodon Interaction

Imposing a minimum principle in the framework of the so called crystal basis model of the genetic code, we determine the structure of the minimum set of anticodons which allows the translational-transcription for animal mitochondrial code. The results are in very good agreement with the observed anticodons.

q-bio.QM↗
arXiv2011

Codon Capture and Ambiguous Intermediate Scenarios of Genetic Code Evolution

Using the shape space of codons and tRNAs we give a physical description of the genetic code evolution on the basis of the codon capture and ambiguous intermediate scenarios in a consistent manner. In the lowest dimensional version of our description, a physical quantity, codon level is introduced. In terms of the codon levels two scenarios are typically classified into two different routes of the evolutional process. In the case of the ambiguous intermediate scenario we perform an evolutional simulation implemented cost selection of amino acids and confirm a rapid transition of the code change. Such rapidness reduces uncomfortableness of the non-unique translation of the code at intermediate state that is the weakness of the scenario. In the case of the codon capture scenario the survival against mutations under the mutational pressure minimizing GC content in genomes is simulated and it is demonstrated that cells which experience only neutral mutations survive.

q-bio.GN↗
arXiv2011

On Programs and Genomes

We outline the global control architecture of genomes. A theory of genomic control information is presented. The concept of a developmental control network called a cene (for control gene) is introduced. We distinguish parts-genes from control genes or cenes. Cenes are interpreted and executed by the cell and, thereby, direct cell actions including communication, growth, division, differentiation and multi-cellular development. The cenome is the global developmental control network in the genome. The cenome is also a cene that consists of interlinked sub-cenes that guide the ontogeny of the organism. The complexity of organisms is linked to the complexity of the cenome. The relevance to ontogeny and evolution is mentioned. We introduce the concept of a universal cell and a universal genome.

q-bio.OT↗
arXiv2011

Cancer Networks: A general theoretical and computational framework for understanding cancer

We present a general computational theory of cancer and its developmental dynamics. The theory is based on a theory of the architecture and function of developmental control networks which guide the formation of multicellular organisms. Cancer networks are special cases of developmental control networks. Cancer results from transformations of normal developmental networks. Our theory generates a natural classification of all possible cancers based on their network architecture. Each cancer network has a unique topology and semantics and developmental dynamics that result in distinct clinical tumor phenotypes. We apply this new theory with a series of proof of concept cases for all the basic cancer types. These cases have been computationally modeled, their behavior simulated and mathematically described using a multicellular systems biology approach. There are fascinating correspondences between the dynamic developmental phenotype of computationally modeled {\em in silico} cancers and natural {\em in vivo} cancers. The theory lays the foundation for a new research paradigm for understanding and investigating cancer. The theory of cancer networks implies that new diagnostic methods and new treatments to cure cancer will become possible.

q-bio.MN↗
arXiv2011

SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding

Scaffolding is an important subproblem in "de novo" genome assembly in which mate pair data are used to construct a linear sequence of contigs separated by gaps. Here we present SLIQ, a set of simple linear inequalities derived from the geometry of contigs on the line that can be used to predict the relative positions and orientations of contigs from individual mate pair reads and thus produce a contig digraph. The SLIQ inequalities can also filter out unreliable mate pairs and can be used as a preprocessing step for any scaffolding algorithm. We tested the SLIQ inequalities on five real data sets ranging in complexity from simple bacterial genomes to complex mammalian genomes and compared the results to the majority voting procedure used by many other scaffolding algorithms. SLIQ predicted the relative positions and orientations of the contigs with high accuracy in all cases and gave more accurate position predictions than majority voting for complex genomes, in particular the human genome. Finally, we present a simple scaffolding algorithm that produces linear scaffolds given a contig digraph. We show that our algorithm is very efficient compared to other scaffolding algorithms while maintaining high accuracy in predicting both contig positions and orientations for real data sets.

q-bio.GN↗
arXiv2011

Faster and More Accurate Sequence Alignment with SNAP

We present the Scalable Nucleotide Alignment Program (SNAP), a new short and long read aligner that is both more accurate (i.e., aligns more reads with fewer errors) and 10-100x faster than state-of-the-art tools such as BWA. Unlike recent aligners based on the Burrows-Wheeler transform, SNAP uses a simple hash index of short seed sequences from the genome, similar to BLAST's. However, SNAP greatly reduces the number and cost of local alignment checks performed through several measures: it uses longer seeds to reduce the false positive locations considered, leverages larger memory capacities to speed index lookup, and excludes most candidate locations without fully computing their edit distance to the read. The result is an algorithm that scales well for reads from one hundred to thousands of bases long and provides a rich error model that can match classes of mutations (e.g., longer indels) that today's fast aligners ignore. We calculate that SNAP can align a dataset with 30x coverage of a human genome in less than an hour for a cost of $2 on Amazon EC2, with higher accuracy than BWA. Finally, we describe ongoing work to further improve SNAP.

cs.DS↗
arXiv2011

Polymorphic gene conferring susceptibility to insulin-dependent diabetes mellitus typed by ps-resolved FRET on nonamplified genomic DNA

This work concerns the identification of the allelic sequences of the DQB1 gene of the human leukocyte antigen system conferring susceptibility to the development of insulin-dependent diabetes mellitus (IDDM) in DNA samples with no need of PCR amplification. Our method is based on the time-resolved analysis of a Förster energy-transfer mechanism that occurs in a dual-labeled fluorescent probe specific for the base sequence of the allelic variant of interest. Such an oligonucleotide probe is labeled, at the two ends, by a pair of chromophores that operate as donor and acceptor in a Förster resonant energy-transfer. The donor fluorescence is quenched with an efficiency that is strongly dependent on the donor-to-acceptor distance, hence on the configuration of the probe after hybridization with the DNA containing or not the selected allelic sequence. By time-correlated single-photon counting, performed with an excitation/detection system endowed with 30-ps resolution, we measure the time-resolved fluorescence decay of the donor and discriminate, by means of the decay time value, the DNA bearing the allele conferring susceptibility to IDDM from the DNAs bearing any other sequence in the same region of the DQB1 gene.

q-bio.GN↗
arXiv2011

Barcoding-free BAC Pooling Enables Combinatorial Selective Sequencing of the Barley Gene Space

We propose a new sequencing protocol that combines recent advances in combinatorial pooling design and second-generation sequencing technology to efficiently approach de novo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when dealing with hundreds or thousands of DNA samples, such as genome-tiling gene-rich BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundreds of million of short reads and assign them to the correct BAC clones so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is extremely accurate (99.57% of the deconvoluted reads are assigned to the correct BAC), and the resulting BAC assemblies have very high quality (BACs are covered by contigs over about 77% of their length, on average). Experimental results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate (almost 70% of left/right pairs in paired-end reads are assigned to the same BAC, despite being processed independently) and the BAC assemblies have good quality (the average sum of all assembled contigs is about 88% of the estimated BAC length).

q-bio.GN↗
arXiv2011

Direct genetic effects and their estimation from matched case-control data

In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker has independent effects on all phenotypes. In this article, we address this question by assessing whether there is a direct genetic effect on one phenotype that is not mediated through the other phenotypes. In particular, we investigate how to identify and estimate such direct genetic effects on the basis of (matched) case-control data. We discuss conditions under which such effects are identifiable from the available (matched) case-control data. We find that direct genetic effects are sometimes estimable via standard regression methods, and sometimes via a more general G-estimation method, which has previously been proposed for random samples and unmatched case-control studies (Vansteelandt, 2009) and is here extended to matched case-control studies. The results are used to assess whether the FTO gene is associated with myocardial infarction other than via an effect on obesity.

stat.ME↗
arXiv2011

Homo denisova, Correspondence Spectral Analysis, Finite Sites Reticulate Hierarchical Coalescent Models and the Ron Jeremy Hypothesis

This article shows how to fit reticulate finite and infinite sites sequence spectra to aligned data from five modern human genomes (San, Yoruba, French, Han and Papuan) plus two archaic humans (Denisovan and Neanderthal), to better infer demographic parameters. These include interbreeding between distinct lineages. Major improvements in the fit of the sequence spectrum are made with successively more complicated models. Findings include some evidence of a male biased gene flow from the Denisova lineage to Papuan ancestors and possibly even more archaic gene flow. It is unclear if there is evidence for more than one Neanderthal interbreeding, as the evidence suggesting this largely disappears when a finite sites model is fitted.

q-bio.PE↗