Search

How do instantaneous rate models of insertion-deletion processes relate to distributions over pairwise sequence alignments? The only exactly-solved model is the 1991 Thorne....., We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and the alignment distribution can be represented by pair hidden Markov models (HMMs). In general, combining HMMs in this way induces a multiplication of their state spaces; to control this, we introduce a coarse-graining operation to keep the state space at a constant size. This leads naturally to ordinary differential equations for the evolution of the transition probabilities of the approximating pair HMM. The TKF91 model emerges as an exact solution to these equations for the special case of single-residue indels. For the more general case of multiple-residue indels, the equations can be solved by numerical integration. Using simulated data, we show that the resulting distribution over alignments, when compared to previous approximations, is a better fit over a broader range of parameters. We also propose a related approach to develop differential equations for sufficient statistics to estimate the underlying instantaneous indel rates by expectation maximization. Our code and data are available at https://github.com/ihh/trajectory-likelihood.

As one of the US Department of Agriculture—Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov

International audience; Following the emerging bluetongue virus transmission in European temperate regions, we question the vector competence of the abundant Culicoides austropalpalis Lee and Reye in South-East temperate Australia. Field collected Culicoides midges were membrane fed with a bluetongue virus serotype 1 (BTV-1). The average feeding rate was 50%. After 13 days, survival rate was 25% and virus RNA presence was checked by quantitative PCR targeting viral genome segment 10. Virus RNA was found in 7.4% of individually tested females with relative viral RNA load values lower than freshly fed females, indicating that viral replication was low or null. A second qPCR targeting viral genome segment 1 confirmed the presence of virus RNA in only four out of 29 previously positive specimens. After 10 days culture on Culicoides cells, none of these four confimed positive samples did show subsequent cytopathogenic effect on Vero cells or BTV antigen detection by ELISA. As control for this virus activity detection, 12 days after microinjection of BTV-1, Culex annulirostris mosquitoes showed, after culture on Kc cells, cytopathogenic effect on Vero cells, with ELISA-confirmed infection. Despite its abundance in farm environment of the temperate Australian regions, the results of this study make C. austropalpalis of unlikely epidemiological importance in the transmission of BTV in Australia.

For the first time, nanofluid boiling was applied as a process for the creation of a semiconductor TiO

Background Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole-genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth. Findings Herein, we present an extension of CNVnator developed in Python—CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency likelihood information from single-nucleotide polymorphisms and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number–neutral losses of heterozygosity. Conclusions CNVpytor is significantly faster than CNVnator—particularly for parsing alignment files (2–20 times faster)—and has (20–50 times) smaller intermediate files. CNV calls can be filtered using several criteria, annotated, and merged over multiple samples. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

Detecting copy number variations (CNVs) and copy number alterations (CNAs) based on whole genome sequencing data is important for personalized genomics and treatment. CNVnator is one of the most popular tools for CNV/CNA discovery and analysis based on read depth (RD). Herein, we present an extension of CNVnator developed in Python -- CNVpytor. CNVpytor inherits the reimplemented core engine of its predecessor and extends visualization, modularization, performance, and functionality. Additionally, CNVpytor uses B-allele frequency (BAF) likelihood information from single nucleotide polymorphism and small indels data as additional evidence for CNVs/CNAs and as primary information for copy number neutral losses of heterozygosity. CNVpytor is significantly faster than CNVnator—particularly for parsing alignment files (2 to 20 times faster)—and has (20-50 times) smaller intermediate files. CNV calls can be filtered using several criteria and annotated. Modular architecture allows it to be used in shared and cloud environments such as Google Colab and Jupyter notebook. Data can be exported into JBrowse, while a lightweight plugin version of CNVpytor for JBrowse enables nearly instant and GUI-assisted analysis of CNVs by any user. CNVpytor release and the source code are available on GitHub at https://github.com/abyzovlab/CNVpytor under the MIT license.

Understanding population connectivity within a species as well as potential interactions with its close relatives is crucial to define management units and to derive efficient management actions. However, although genetics can reveal mismatches between biological and management units and other relevant but hidden information such as species misidentification or hybridization, the uptake of genetic methods by the fisheries management process is far from having been consolidated. Here, we have assessed the power of genetics to better understand the population connectivity of white (Lophius piscatorius) and its interaction with its sister species, the black anglerfish (Lophius budegassa). Our analyses, based on thousands of genome-wide single nucleotide polymorphisms, show three findings that are crucial for white anglerfish management. We found (i) that white anglerfish is likely composed of a single panmictic population throughout the Northeast Atlantic, challenging the three-stock based management, (ii) that a fraction of specimens classified as white anglerfish using morphological characteristics are genetically identified as black anglerfish (L. budegassa), and iii) that the two Lophius species naturally hybridize leading to a population of hybrids of up to 20% in certain areas. Our results set the basics for a genetics-informed white anglerfish assessment framework that accounts for stock connectivity, revises and establishes new diagnostic characters for Lophius species identification, and evaluates the effect of hybrids in the current and future assessments of the white anglerfish. Furthermore, our study contributes to provide additional evidence of the potentially negative consequences of ignoring genetic data for assessing fisheries resources., SI

We introduce a systematic method of approximating finite-time transition probabilities for continuous-time insertion-deletion models on sequences. The method uses automata theory to describe the action of an infinitesimal evolutionary generator on a probability distribution over alignments, where both the generator and the alignment distribution can be represented by Pair Hidden Markov Models (Pair HMMs). In general, combining HMMs in this way induces a multiplication of their state spaces; to control this, we introduce a coarse-graining operation to keep the state space at a constant size. This leads naturally to ordinary differential equations for the evolution of the transition probabilities of the approximating Pair HMM. The TKF model emerges as an exact solution to these equations for the special case of single-residue indels. For the general case, the equations can be solved by numerical integration. Using simulated data we show that the resulting distribution over alignments, when compared to previous approximations, is a better fit over a broader range of parameters. We also propose a related approach to develop differential equations for sufficient statistics to estimate the underlying instantaneous indel rates by Expectation-Maximization. Our code and data are available athttps://github.com/ihh/trajectory-likelihood.

JBrowse and Apollo: Tools for Genome Visualization and Annotation https://github.com/GMOD/jbrowse https://github.com/GMOD/Apollo JBrowse 2: https://github.com/GMOD/jbrowse-components

Genome browsers are essential tools for integrating, exploring, and visualizing data from large cancer genomics datasets. JBrowse 2 is a JavaScript genome browser with novel features for visualizing structural variants, syntenic alignments, and multiple types of genomic data. Although JBrowse 2 is available as a web-based or desktop application, the capabilities and features are not fixed and can be expanded through a comprehensive plugin system. To help address users' needs, we launched the new JBrowse 2 Plugin Store (https://jbrowse.org/jb2/plugin_store/). The overarching goals of the Plugin Store are to showcase the crucial features plugins add to JBrowse 2, to enable researchers to search for plugins they need, and for developers to highlight their plugins. The plugin system spans all aspects of the JBrowse 2 application and enables new track types (e.g. Manhattan plots, Hi-C data, ideograms), data adapters (e.g. API endpoint adapters for NCI Genomic Data Commons and the International Cancer Genome Consortium), and views (e.g. dot plots and multiple sequence alignments). Here, we present the capabilities of JBrowse 2 plugins and describe usage scenarios for cancer biologists and bioinformaticians, and software developers. Citation Format: Robin Andrew Haw, Colin Diesh, Caroline Bridge, Rob Buels, Garrett Stevens, Peter Xie, Teresa Martinez, Elliot Hershberg, Junjun Zhang, Shihab Dider, Scott Cain, Lincoln Stein, Ian Holmes. Using JBrowse 2 plugins to visualize cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB503.

Introduction: Genome browsers are useful tools for research in human genetics, able to display and integrate biological information such as long and short read sequencing data, variant calls, and annotations. While traditional linear genome browsers have demonstrated broad utility for many applications, there is an increasing need for visualizing complex structural variants (SVs) beyond the linear view. To meet these challenges, we created JBrowse 2: an extensible open-source platform for visualizing and integrating genomic data. Results: JBrowse 2 is a flexible platform that provides the foundation for the development of applications and web dashboards that combine novel views and representations extending beyond linear displays in genomic reference coordinates. For example, a flagship JBrowse 2 application is the SV Inspector, which lets users open a list of structural variants in a data table and see the results in a whole-genome circular view. Clicking on a given variant in the table or circular view opens up a linear view that displays the read evidence supporting the SV, even across complex breakpoints like translocations. Dotplot and synteny views are built-in, enabling “long read vs reference” dotplot visualizations, or alignments of de-novo assembled contigs to the genome. These views are integrated with each other, so that (for example) a long read is just a few clicks away from the corresponding dotplot. In addition to including these new views, the JBrowse 2 platform is designed from the ground up to enable third-party plugins to add new views, data adapters, and track types, which has facilitated the creation of new plugins that address some very specific use cases. These include our MSA view plugin for viewing multiple sequence alignments, data adapters that download data from UCSC and CIVIC APIs, and the Quantseq plugin for viewing quantitative motif scores as a genome browser track. JBrowse 2 is available as both a web app or a local desktop app. We also offer re-usable components on NPM, and users of R can programmatically create an instance of JBrowse 2 with the JBrowseR package on CRAN. JBrowse 2 can also generate high quality SVG snapshots from inside the app, and we also created a CLI tool called jb2export to perform automated or bulk exports of JBrowse 2 visualizations. We anticipate that JBrowse 2 will better serve genome scientists with its structural variant visualization capabilities, and will provide the flexibility to adapt to new visualization and analysis challenges as they emerge in the coming years. Citation Format: Scott Cain, Robin Haw, Caroline Bridge, Junjun Zhang, Robert Buels, Colin Diesh, Garrett Stevens, Teresa Martinez, Peter K. Xie, Elliot Hershberg, Shihab Dider, Lincoln Stein, Ian Holmes. JBrowse 2: An extensible open-source platform for modern genome analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6400.

Background: Treatment approaches that engage both the innate and adaptive immune response have the potential to transform anti-cancer therapy, especially in settings of checkpoint inhibitor insensitivity or acquired resistance. Toll-like receptors (TLRs) are a class of targets that mediate the initial cellular response to external pathogens or endogenous alarmins, activating downstream pro-inflammatory cascades and leading to the activation and recruitment of key innate subsets. TLR2 is a cell surface receptor, expressed predominantly on macrophages, dendritic cells (DC), neutrophils and subsets of NK and T cells. TLR2 signaling plays important role in NK and T cell cytolytic cell functions, as well as DC and macrophage activation. AXA-042 is a novel synthetic TLR2/6 agonist designed for systemic delivery to re-engage the innate immune response to help overcome tumour immune escape. Methods: In vitro potency and selectivity of AXA-042 was assessed across a panel of human and mouse HEK-blue TLR reporter assays. AXA-042 activity was profiled relative to other TLR agonists in human PBMC cytokine release assays. AXA-042 in vivo efficacy was evaluated in the EMT6 and CT26 syngeneic tumour models. The Nanostring nCounter™ Mouse PanCancer Immune Profiling Panel was used to identify AXA-042 tumour-localized and peripheral response signatures. Results: AXA-042 demonstrated potent and selective activity in murine and human TLR2/6 reporter assays and a differential PBMC cytokine release profile relative to other TLR agonists in vitro. Systemic delivery (QW, IV or IP) of AXA-042 significantly inhibited EMT6 and CT26 tumour growth, which was accompanied by an increase in circulating plasma levels of IL6, IL12 and CXCL10. Combination with anti-PD-1 Ab further potentiated anti-tumour response, resulting in approximately 30-40% tumour regressions. AXA-042 anti-tumour effects were accompanied by increased activation of the tumour-infiltrating myeloid cells, including DCs and macrophages, and a reduction in the M2 TAM subset. Nanostring analysis confirmed TLR and IFNγ pathway engagement and revealed an induction of gene signatures associated with macrophage, DC and NK functions. Longitudinal studies to characterize whole blood biomarkers associated with response are currently in progress. Conclusion: AXA-042 was well tolerated in vivo and demonstrated innate response engagement and anti-tumour efficacy as monotherapy and in combination with anti-PD-1 immunotherapy. AXA-042 has completed GLP toxicology studies and initiation of Phase 1/1b trial is planned for early 2022. Citation Format: Anna V. Galkin, Francesca Mercuri, Nicholas West, Ping Zhang, Weiguang Zeng, Grant McLachlan, Michael Bettess, Ian Holmes, David Jackson, Chris Smith, Christophe Demaison, Tobias Bald, Phil Kearney. AXA-042 - a novel systemic TLR2/6 agonist for anti-tumor therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3502.

Video Video 1 EGD, which showed obstructed distal flange of the gastrojejunal lumen-apposing metal stent (LAMS) by the contralateral jejunal wall. Two double-pigtail stents were placed across the LAMS to relieve the obstruction.

Motivation Genome browsers are an essential tool in genome analysis. Modern genome browsers enable complex and interactive visualization of a wide variety of genomic data modalities. While such browsers are very powerful, they can be challenging to configure and program for bioinformaticians lacking expertise in web development. Results We have developed an R package that provides an interface to the JBrowse 2 genome browser. The package can be used to configure and customize the browser entirely with R code. The browser can be deployed from the R console, or embedded in Shiny applications or R Markdown documents. Availability and implementation JBrowseR is available for download from CRAN, and the source code is openly available from the Github repository at https://github.com/GMOD/JBrowseR/.

Nanopore technology allows for direct sequencing of individual DNA duplexes. However, its higher error rate compared to other sequencing methods has limited its application in situations where deep coverage is unavailable, such as detection of rare variants or characterization of highly polymorphic samples. In principle, 2X coverage is available even for single duplexes, using Oxford Nanopore Technologies’ 1D2protocol or related methods which sequence both strands of the duplex consecutively. Using both strands should improve accuracy; however, most neural network basecaller architectures are designed to operate on single strands. We have developed a general approach for improving accuracy of 1D2and related protocols by finding the consensus of two neural network basecallers, by combining a constrained profile-profile alignment with a heuristic variant of beam search. When run on a basecalling neural network we trained, our consensus algorithm improves median basecall accuracy from 86.2% (for single-read decoding) to 92.1% (for pair decoding). Our software can readily be adapted to work with the output of other basecallers, such as the recently released Bonito basecaller. Although Bonito operates only on individual strands and was not designed to leverage the 1D2protocol, our method lifts its median accuracy from 93.3% to 97.7%, more than halving the median error rate. This surpasses the maximum accuracy achievable with Guppy, an alternate basecaller which was designed to include pair decoding of 1D2reads. Our software PoreOver, including both our neural network basecaller and our consensus pair decoder (which can be separably applied to improve other basecallers), is implemented in Python 3 and C++11 and is freely available athttps://github.com/jordisr/poreover.

Motivation Many software libraries for using Hidden Markov Models in bioinformatics focus on inference tasks, such as likelihood calculation, parameter-fitting and alignment. However, construction of the state machines can be a laborious task, automation of which would be time-saving and less error-prone. Results We present Machine Boss, a software tool implementing not just inference and parameter-fitting algorithms, but also a set of operations for manipulating and combining automata. The aim is to make prototyping of bioinformatics HMMs as quick and easy as the construction of regular expressions, with one-line ‘recipes’ for many common applications. We report data from several illustrative examples involving protein-to-DNA alignment, DNA data storage and nanopore sequence analysis. Availability and implementation Machine Boss is released under the BSD-3 open source license and is available from http://machineboss.org/. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation The Unix ‘make’ program is widely used in bioinformatics pipelines, but suffers from problems that limit its application to large analysis datasets. These include reliance on file modification times to determine whether a target is stale, lack of support for parallel execution on clusters, and restricted flexibility to extend the underlying logic program. Results We present BioMake, a make-like utility that is compatible with most features of GNU Make and adds support for popular cluster-based job-queue engines, MD5 signatures as an alternative to timestamps, and logic programming extensions in Prolog. Availability and implementation BioMake is available for MacOSX and Linux systems from https://github.com/evoldoers/biomake under the BSD3 license. The only dependency is SWI-Prolog (version 7), available from http://www.swi-prolog.org/. Supplementary information Feature table comparing BioMake to similar tools. Supplementary data are available at Bioinformatics online.

Genome annotation is the process of identifying the location and function of a genome’s encoded features. Improving the biological accuracy of annotation is a complex and iterative process requiring researchers to review and incorporate multiple sources of information such as transcriptome alignments, predictive models based on sequence profiles, and comparisons to features found in related organisms. Because rapidly decreasing costs are enabling an ever-growing number of scientists to incorporate sequencing as a routine laboratory technique, there is widespread demand for tools that can assist in the deliberative analytical review of genomic information. To this end, Apollo is an open source software package that enables researchers to efficiently inspect and refine the precise structure and role of genomic features in a graphical browser-based platform.In this paper we first outline some of Apollo’s newer user interface features, which were driven by the needs of this expanding genomics community. These include support for real-time collaboration, allowing distributed users to simultaneously edit the same encoded features while also instantly seeing the updates made by other researchers on the same region in a manner similar to Google Docs. Its technical architecture enables Apollo to be integrated into multiple existing genomic analysis pipelines and heterogeneous laboratory workflow platforms. Finally, we consider the implications that Apollo and related applications may have on how the results of genome research are published and made accessible. Source: https://github.com/GMOD/ApolloLicense (BSD-3): https://github.com/GMOD/Apollo/blob/master/LICENSE.mdDocker: https://hub.docker.com/r/gmod/apollo/tags/, https://github.com/GMOD/docker-apolloRequirements: JDK 1.8, Node v6.0+User guide: http://genomearchitect.org; technical guide: http://genomearchitect.readthedocs.io/en/latest/Mailing list: apollo@lists.lbl.gov

This thesis highlights the ways in which the Muslim brotherhood politicizes Islam, depicted in the group’s official documents. The study theorizes a social-constructivist approach to politicized religion and argues the concept as an effort to manipulate an understanding of religion with political intentions. Through a descriptive content analysis, this thesis analyzes three official political documents from the Muslim brotherhood’s English website, ‘Ikhwanweb’. The analysis identifies the religious ideology of the group as all-encompassing Islamic, as well as concludes the Muslim brotherhood as utilizing politicized religion extensively in the chosen material.

Motivation The CRAM format addresses rising DNA storage costs for short-read sequencing by aligning reads to a reference genome and encoding the resulting alignment with Huffman, subexponential, Elias gamma, rANS, gzip and other codes. The CRAM codec is complex, and until now, there has been no JavaScript implementation. Results We have developed a JavaScript library, Cram-JS, that natively reads and decompresses the CRAM format on-the-fly. The library is used in the JBrowse and IGV-JS genome browsers and can readily be used by other JavaScript applications, in the web browser or in Node. Availability and implementation Cram-JS is written to the ES-6 standard and is available from the GitHub repository at https://github.com/GMOD/cram-js.

© 2017 The Author(s). Background: Despite the long-anticipated possibility of putting sequence alignment on the same footing as statistical phylogenetics, theorists have struggled to develop time-dependent evolutionary models for indels that are as tractable as the analogous models for substitution events. Main text: This paper discusses progress in the area of insertion-deletion models, in view of recent work by Ezawa (BMC Bioinformatics 17:304, 2016); (BMC Bioinformatics 17:397, 2016); (BMC Bioinformatics 17:457, 2016) on the calculation of time-dependent gap length distributions in pairwise alignments, and current approaches for extending these approaches from ancestor-descendant pairs to phylogenetic trees. Conclusions: While approximations that use finite-state machines (Pair HMMs and transducers) currently represent the most practical approach to problems such as sequence alignment and phylogeny, more rigorous approaches that work directly with the matrix exponential of the underlying continuous-time Markov chain also show promise, especially in view of recent advances.

Neutral models which assume ecological equivalence between species provide null models for community assembly. In Hubbell's Unified Neutral Theory of Biodiversity (UNTB), many local communities are connected to a single metacommunity through differing immigration rates. Our ability to fit the full multi-site UNTB has hitherto been limited by the lack of a computationally tractable and accurate algorithm. We show that a large class of neutral models with this mainland-island structure but differing local community dynamics converge in the large population limit to the hierarchical Dirichlet process. Using this approximation we developed an efficient Bayesian fitting strategy for the multi-site UNTB. We can also use this approach to distinguish between neutral local community assembly given a non-neutral metacommunity distribution and the full UNTB where the metacommunity too assembles neutrally. We applied this fitting strategy to both tropical trees and a data set comprising 570,851 sequences from 278 human gut microbiomes. The tropical tree data set was consistent with the UNTB but for the human gut neutrality was rejected at the whole community level. However, when we applied the algorithm to gut microbial species within the same taxon at different levels of taxonomic resolution, we found that species abundances within some genera were almost consistent with local community assembly. This was not true at higher taxonomic ranks. This suggests that the gut microbiota is more strongly niche constrained than macroscopic organisms, with different groups adopting different functional roles, but within those groups diversity may at least partially be maintained by neutrality.We also observed a negative correlation between body mass index and immigration rates within the family Ruminococcaceae., Comment: 50 pages, 5 figures, 2 tables and submitted to Proceedings of the IEEE

We describe a strategy for constructing codes for DNA-based information storage by serial composition of weighted finite-state transducers. The resulting state machines can integrate correction of substitution errors; synchronization by interleaving watermark and periodic marker signals; conversion from binary to ternary, quaternary or mixed-radix sequences via an efficient block code; encoding into a DNA sequence that avoids homopolymer, dinucleotide, or trinucleotide runs and other short local repeats; and detection/correction of errors (including local duplications, burst deletions, and substitutions) that are characteristic of DNA sequencing technologies. We present software implementing these codes, available at github.com/ihh/dnastore, with simulation results demonstrating that the generated DNA is free of short repeats and can be accurately decoded even in the presence of substitutions, short duplications and deletions., Submitted to IEEE Transactions on Information Theory

One strategy for building ontologies covering domains such as disease or anatomy is to weave together existing knowledge sources (databases, vocabularies and ontologies) into single cohesive whole. A first step in this process is to generate mappings between the elements of these different sources. There are a number of well-known techniques for generating mappings (also known as ontology alignemnt), both manual and automatic[7]. Sometimes mappings are seen as an end in themselves, with the sources remaining in a loosely connected state. However, if we want to take the next step and use the mappings to integrate the different sources into a cohesive reference ontology, then we need to translate the mappings into precise logical relationships. This will allow us to safely merge equivalent concepts, creating a unified ontology. This translation is a non-trivial step, as each mapping can be interpreted as multiple different logical relationships, with each interpretation affecting the likelihood of the others. There is a lack of automated methods to assist with this last step; this resolution is typically performed by expert ontologists.Here we describe an ontology construction technique that takes two or more ontologies linked by hypothetical axioms, and estimates the most likely unified logical ontology. Hypothetical axioms can themselves be derived from semantically loose mappings. The method combines deductive reasoning and probabilistic inference and is called Bayesian OWL Ontology Merging (BOOM). We describe a special form k-BOOM that works by factorizing the probabilistic ontology into k submodules. We also briefly describe a supplemental lexical and knowledge-based technique for generating a set of hypothetical axioms from loose mappings.We are currently using this technique to build a merged disease ontology (Monarch Disease Ontology; MonDO) that unifies a broad range of vocabularies into a consistent and coherent whole.

JBrowse is a web-based genome browser, allowing many sources of data to be visualized, interpreted and navigated in a coherent visual framework. JBrowse uses efficient data structures, pre-generation of image tiles and client-side rendering to provide a fast, interactive browsing experience. Many of JBrowse's design features make it well suited for visualizing high-volume data, such as aligned next-generation sequencing reads.

Motivation: Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. Results: We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. Availability: The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Contact: soer@ruc.dk Supplementary information: Supplementary data are available at Bioinformatics online.