POSA: Perl Objects for DNA Sequencing Data Analysis

BACKGROUND: Capillary DNA sequencing machines allow the generation of vast amounts of data with little hands-on time. With this expansion of data generation, there is a growing need for automated data processing. Most available software solutions, however, still require user intervention or provide modules that need advanced informatics skills to allow implementation in pipelines. RESULTS: Here we present POSA, a pair of new perl objects that describe DNA sequence traces and Phrap contig assemblies in detail. Methods included in POSA include basecalling with quality scores (by Phred), contig assembly (by Phrap), generation of primer3 input and automated SNP annotation (by PolyPhred). Although easily implemented by users with only limited programming experience, these objects considerabily reduce hands-on analysis time compared to using the Staden package for extracting sequence information from raw sequencing files and for SNP discovery. CONCLUSIONS: The POSA objects allow a flexible and easy design, implementation and usage of perl-based pipelines to handle and analyze DNA sequencing data, while requiring only minor programming skills

Investigating non-classical correlations between decision fused multi-modal documents

Correlation has been widely used to facilitate various information retrieval methods such as query expansion, relevance feedback, document clustering, and multi-modal fusion. Especially, correlation and independence are important issues when fusing different modalities that influence a multi-modal information retrieval process. The basic idea of correlation is that an observable can help predict or enhance another observable. In quantum mechanics, quantum correlation, called entanglement, is a sort of correlation between the observables measured in atomic-size particles when these particles are not necessarily collected in ensembles. In this paper, we examine a multimodal fusion scenario that might be similar to that encountered in physics by firstly measuring two observables (i.e., text-based relevance and image-based relevance) of a multi-modal document without counting on an ensemble of multi-modal documents already labeled in terms of these two variables. Then, we investigate the existence of non-classical correlations between pairs of multi-modal documents. Despite there are some basic differences between entanglement and classical correlation encountered in the macroscopic world, we investigate the existence of this kind of non-classical correlation through the Bell inequality violation. Here, we experimentally test several novel association methods in a small-scale experiment. However, in the current experiment we did not find any violation of the Bell inequality. Finally, we present a series of interesting discussions, which may provide theoretical and empirical insights and inspirations for future development of this direction

Environmental differences between sites control the diet and nutrition of the carnivorous plant Drosera rotundifolia

Background and aims: Carnivorous plants are sensitive to small changes in resource availability, but few previous studies have examined how differences in nutrient and prey availability affect investment in and the benefit of carnivory. We studied the impact of site-level differences in resource availability on ecophysiological traits of carnivory for Drosera rotundifolia L. Methods: We measured prey availability, investment in carnivory (leaf stickiness), prey capture and diet of plants growing in two bogs with differences in N deposition and plant available N: Cors Fochno (0.62 g m−2 yr.−1, 353 μg l−1), Whixall Moss (1.37 g m−2 yr.−1, 1505 μg l−1). The total N amount per plant and the contributions of prey/root N to the plants’ N budget were calculated using a single isotope natural abundance method. Results: Plants at Whixall Moss invested less in carnivory, were less likely to capture prey, and were less reliant on prey-derived N (25.5% compared with 49.4%). Actual prey capture did not differ between sites. Diet composition differed – Cors Fochno plants captured 62% greater proportions of Diptera. Conclusions: Our results show site-level differences in plant diet and nutrition consistent with differences in resource availability. Similarity in actual prey capture may be explained by differences in leaf stickiness and prey abundance

The influence of soil communities on the temperature sensitivity of soil respiration

Soil respiration represents a major carbon flux between terrestrial ecosystems and the atmosphere, and is expected to accelerate under climate warming. Despite its importance in climate change forecasts, however, our understanding of the effects of temperature on soil respiration (RS) is incomplete. Using a metabolic ecology approach we link soil biota metabolism, community composition and heterotrophic activity, to predict RS rates across five biomes. We find that accounting for the ecological mechanisms underpinning decomposition processes predicts climatological RS variations observed in an independent dataset (n = 312). The importance of community composition is evident because without it RS is substantially underestimated. With increasing temperature, we predict a latitudinal increase in RS temperature sensitivity, with Q10 values ranging between 2.33 ±0.01 in tropical forests to 2.72 ±0.03 in tundra. This global trend has been widely observed, but has not previously been linked to soil communities

Nitrogen transfer from forage legumes to nine neighbouring plants in a multi-species grassland

Legumes play a crucial role in nitrogen supply to grass-legume mixtures for ruminant fodder. To quantify N transfer from legumes to neighbouring plants in multi-species grasslands we established a grass-legume-herb mixture on a loamy-sandy site in Denmark. White clover (Trifolium repens L.), red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) were leaf-labelled with 15N enriched urea during one growing season. N transfer to grasses (Lolium perenne L. and xfestulolium), white clover, red clover, lucerne, birdsfoot trefoil (Lotus corniculatus L.), chicory (Cichorium intybus L.), plantain (Plantago lanceolata L.), salad burnet (Sanguisorba minor L.)and caraway (Carum carvi L.) was assessed. Neighbouring plants contained greater amounts of N derived from white clover (4.8 gm-2) compared with red clover (2.2 gm-2) and lucerne (1.1 gm-2). Grasses having fibrous roots received greater amounts of N from legumes than dicotyledonous plants which generally have taproots. Slurry application mainly increased N transfer from legumes to grasses. During the growing season the three legumes transferred approximately 40 kg N ha-1 to neighbouring plants. Below-ground N transfer from legumes to neighbouring plants differed among nitrogen donors and nitrogen receivers and may depend on root characteristics and regrowth strategies of plant species in the multi-species grassland

Wide-Scale Analysis of Human Functional Transcription Factor Binding Reveals a Strong Bias towards the Transcription Start Site

We introduce a novel method to screen the promoters of a set of genes with shared biological function, against a precompiled library of motifs, and find those motifs which are statistically over-represented in the gene set. The gene sets were obtained from the functional Gene Ontology (GO) classification; for each set and motif we optimized the sequence similarity score threshold, independently for every location window (measured with respect to the TSS), taking into account the location dependent nucleotide heterogeneity along the promoters of the target genes. We performed a high throughput analysis, searching the promoters (from 200bp downstream to 1000bp upstream the TSS), of more than 8000 human and 23,000 mouse genes, for 134 functional Gene Ontology classes and for 412 known DNA motifs. When combined with binding site and location conservation between human and mouse, the method identifies with high probability functional binding sites that regulate groups of biologically related genes. We found many location-sensitive functional binding events and showed that they clustered close to the TSS. Our method and findings were put to several experimental tests. By allowing a "flexible" threshold and combining our functional class and location specific search method with conservation between human and mouse, we are able to identify reliably functional TF binding sites. This is an essential step towards constructing regulatory networks and elucidating the design principles that govern transcriptional regulation of expression. The promoter region proximal to the TSS appears to be of central importance for regulation of transcription in human and mouse, just as it is in bacteria and yeast.Comment: 31 pages, including Supplementary Information and figure

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

<p>Abstract</p> <p>Background</p> <p>High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.</p> <p>Results</p> <p>We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called D<smcaps>A</smcaps>D<smcaps>A</smcaps>, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that D<smcaps>A</smcaps>D<smcaps>A</smcaps> outperforms existing methods in prioritizing candidate disease genes.</p> <p>Conclusions</p> <p>These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. D<smcaps>A</smcaps>D<smcaps>A</smcaps> is implemented in Matlab and is freely available at <url>http://compbio.case.edu/dada/</url>.</p

Fine-Tuning Enhancer Models to Predict Transcriptional Targets across Multiple Genomes

Networks of regulatory relations between transcription factors (TF) and their target genes (TG)- implemented through TF binding sites (TFBS)- are key features of biology. An idealized approach to solving such networks consists of starting from a consensus TFBS or a position weight matrix (PWM) to generate a high accuracy list of candidate TGs for biological validation. Developing and evaluating such approaches remains a formidable challenge in regulatory bioinformatics. We perform a benchmark study on 34 Drosophila TFs to assess existing TFBS and cis-regulatory module (CRM) detection methods, with a strong focus on the use of multiple genomes. Particularly, for CRM-modelling we investigate the addition of orthologous sites to a known PWM to construct phyloPWMs and we assess the added value of phylogenentic footprinting to predict contextual motifs around known TFBSs. For CRM-prediction, we compare motif conservation with network-level conservation approaches across multiple genomes. Choosing the optimal training and scoring strategies strongly enhances the performance of TG prediction for more than half of the tested TFs. Finally, we analyse a 35th TF, namely Eyeless, and find a significant overlap between predicted TGs and candidate TGs identified by microarray expression studies. In summary we identify several ways to optimize TF-specific TG predictions, some of which can be applied to all TFs, and others that can be applied only to particular TFs. The ability to model known TF-TG relations, together with the use of multiple genomes, results in a significant step forward in solving the architecture of gene regulatory networks

ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads

Copy number alterations are important contributors to many genetic diseases, including cancer. We present the readDepth package for R, which can detect these aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. In addition to achieving higher accuracy than existing packages, our tool runs much faster by utilizing multi-core architectures to parallelize the processing of these large data sets. In contrast to other published methods, readDepth does not require the sequencing of a reference sample, and uses a robust statistical model that accounts for overdispersed data. It includes a method for effectively increasing the resolution obtained from low-coverage experiments by utilizing breakpoint information from paired end sequencing to do positional refinement. We also demonstrate a method for inferring copy number using reads generated by whole-genome bisulfite sequencing, thus enabling integrative study of epigenomic and copy number alterations. Finally, we apply this tool to two genomes, showing that it performs well on genomes sequenced to both low and high coverage. The readDepth package runs on Linux and MacOSX, is released under the Apache 2.0 license, and is available at http://code.google.com/p/readdepth/

Integration of Two Diploid Potato Linkage Maps with the Potato Genome Sequence

To facilitate genome-guided breeding in potato, we developed an 8303 Single Nucleotide Polymorphism (SNP) marker array using potato genome and transcriptome resources. To validate the Infinium 8303 Potato Array, we developed linkage maps from two diploid populations (DRH and D84) and compared these maps with the assembled potato genome sequence. Both populations used the doubled monoploid reference genotype DM1-3 516 R44 as the female parent but had different heterozygous diploid male parents (RH89-039-16 and 84SD22). Over 4,400 markers were mapped (1,960 in DRH and 2,454 in D84, 787 in common) resulting in map sizes of 965 (DRH) and 792 (D84) cM, covering 87% (DRH) and 88% (D84) of genome sequence length. Of the mapped markers, 33.5% were in candidate genes selected for the array, 4.5% were markers from existing genetic maps, and 61% were selected based on distribution across the genome. Markers with distorted segregation ratios occurred in blocks in both linkage maps, accounting for 4% (DRH) and 9% (D84) of mapped markers. Markers with distorted segregation ratios were unique to each population with blocks on chromosomes 9 and 12 in DRH and 3, 4, 6 and 8 in D84. Chromosome assignment of markers based on linkage mapping differed from sequence alignment with the Potato Genome Sequencing Consortium (PGSC) pseudomolecules for 1% of the mapped markers with some disconcordant markers attributable to paralogs. In total, 126 (DRH) and 226 (D84) mapped markers were not anchored to the pseudomolecules and provide new scaffold anchoring data to improve the potato genome assembly. The high degree of concordance between the linkage maps and the pseudomolecules demonstrates both the quality of the potato genome sequence and the functionality of the Infinium 8303 Potato Array. The broad genome coverage of the Infinium 8303 Potato Array compared to other marker sets will enable numerous downstream applications