Optimal Computation of Avoided Words

The deviation of the observed frequency of a word $w$ from its expected frequency in a given sequence $x$ is used to determine whether or not the word is avoided. This concept is particularly useful in DNA linguistic analysis. The value of the standard deviation of $w$, denoted by $std(w)$, effectively characterises the extent of a word by its edge contrast in the context in which it occurs. A word $w$ of length $k>2$ is a $\rho$-avoided word in $x$ if $std(w) \leq \rho$, for a given threshold $\rho < 0$. Notice that such a word may be completely absent from $x$. Hence computing all such words na\"{\i}vely can be a very time-consuming procedure, in particular for large $k$. In this article, we propose an $O(n)$-time and $O(n)$-space algorithm to compute all $\rho$-avoided words of length $k$ in a given sequence $x$ of length $n$ over a fixed-sized alphabet. We also present a time-optimal $O(\sigma n)$-time and $O(\sigma n)$-space algorithm to compute all $\rho$-avoided words (of any length) in a sequence of length $n$ over an alphabet of size $\sigma$. Furthermore, we provide a tight asymptotic upper bound for the number of $\rho$-avoided words and the expected length of the longest one. We make available an open-source implementation of our algorithm. Experimental results, using both real and synthetic data, show the efficiency of our implementation

Developing and applying heterogeneous phylogenetic models with XRate

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog

Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands

Genoviz Software Development Kit: Java tool kit for building genomics visualization applications

<p>Abstract</p> <p>Background</p> <p>Visualization software can expose previously undiscovered patterns in genomic data and advance biological science.</p> <p>Results</p> <p>The Genoviz Software Development Kit (SDK) is an open source, Java-based framework designed for rapid assembly of visualization software applications for genomics. The Genoviz SDK framework provides a mechanism for incorporating adaptive, dynamic zooming into applications, a desirable feature of genome viewers. Visualization capabilities of the Genoviz SDK include automated layout of features along genetic or genomic axes; support for user interactions with graphical elements (Glyphs) in a map; a variety of Glyph sub-classes that promote experimentation with new ways of representing data in graphical formats; and support for adaptive, semantic zooming, whereby objects change their appearance depending on zoom level and zooming rate adapts to the current scale. Freely available demonstration and production quality applications, including the Integrated Genome Browser, illustrate Genoviz SDK capabilities.</p> <p>Conclusion</p> <p>Separation between graphics components and genomic data models makes it easy for developers to add visualization capability to pre-existing applications or build new applications using third-party data models. Source code, documentation, sample applications, and tutorials are available at <url>http://genoviz.sourceforge.net/</url>.</p

The Quantitative Methods Boot Camp:Teaching Quantitative Thinking and Computing Skills to Graduate Students in the Life Sciences

<div><p>The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a “boot camp” in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students’ engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others.</p></div

Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology

IgTM: An algorithm to predict transmembrane domains and topology in proteins

<p>Abstract</p> <p>Background</p> <p>Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.</p> <p>Results</p> <p>We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: <url>http://www.dsic.upv.es/users/tlcc/bio/bio.html</url></p> <p>Conclusion</p> <p>We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.</p

Lessons from the CAGI-4 Hopkins clinical panel challenge

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state of the art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of fourteen possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other groups. We discuss the causal variant predictions by the different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false positive rate of DNA-guided analysis in the absence of prior phenotypic indication. This article is protected by copyright. All rights reserved

Context-driven discovery of gene cassettes in mobile integrons using a computational grammar

<p>Abstract</p> <p>Background</p> <p>Gene discovery algorithms typically examine sequence data for low level patterns. A novel method to computationally discover higher order DNA structures is presented, using a context sensitive grammar. The algorithm was applied to the discovery of gene cassettes associated with integrons. The discovery and annotation of antibiotic resistance genes in such cassettes is essential for effective monitoring of antibiotic resistance patterns and formulation of public health antibiotic prescription policies.</p> <p>Results</p> <p>We discovered two new putative gene cassettes using the method, from 276 integron features and 978 GenBank sequences. The system achieved <it>κ </it>= 0.972 annotation agreement with an expert gold standard of 300 sequences. In rediscovery experiments, we deleted 789,196 cassette instances over 2030 experiments and correctly relabelled 85.6% (<it>α </it>≥ 95%, <it>E </it>≤ 1%, mean sensitivity = 0.86, specificity = 1, F-score = 0.93), with no false positives.</p> <p>Error analysis demonstrated that for 72,338 missed deletions, two adjacent deleted cassettes were labeled as a single cassette, increasing performance to 94.8% (mean sensitivity = 0.92, specificity = 1, F-score = 0.96).</p> <p>Conclusion</p> <p>Using grammars we were able to represent heuristic background knowledge about large and complex structures in DNA. Importantly, we were also able to use the context embedded in the model to discover new putative antibiotic resistance gene cassettes. The method is complementary to existing automatic annotation systems which operate at the sequence level.</p