Optimization and Data Analysis in Biomedical Informatics
This volume covers some of the topics that are related to the rapidly growing field of biomedical informatics. In June 11–12, 2010 a workshop entitled ‘Optimization and Data Analysis in Biomedical Informatics’ was organized at The Fields Institute. Following this event, invited contributions were gathered based on the talks presented at the workshop, and additional invited chapters were solicited from leading experts. In this publication, the authors share their expertise in the form of state-of-the-art research and review chapters, bringing together researchers from different disciplines and emphasizing the value of mathematical methods in the areas of clinical sciences.
The paper formulates the problem of constructing a broadly applicable and flexible Conceptual Metagrammar (CM). It is to be a collection of the rules enabling us to construct step by step a semantic representation (or text meaning representation) of practically arbitrary sentence or discourse pertaining to mass spheres of human’s professional activity. The opinion is grounded that the first version of broadly applicable and flexible CM is already available in the scientific literature. It is conjectured that the definition of the class of SK-languages (standard knowledge languages) provided by the theory of K-representations (knowledge representations) can be interpreted as the first version of broadly applicable and flexible CM. The current version of the latter theory is stated in the author’s monograph published by Springer in 2010. The final part of the paper describes the connections with the related approaches, in particular, with the studies on developing a Multilingual Semantic Web.
The paper describes the structure and possible applications of the theory of K-representations (knowledge representations) in bioinformatics and in the development of a Semantic Web of a new generation. It is an original theory of designing semantic-syntactic analyzers of natural language (NL) texts with the broad use of formal means for representing input, intermediary, and output data. The current version of the theory is set forth in a monograph by V. Fomichov (Springer, 2010). The first part of the theory is a formal model describing a system consisting of ten operations on conceptual structures. This model defines a new class of formal languages – the class of SK-languages. The broad possibilities of constructing semantic representations of complex discourses pertaining to biology are shown. A new formal approach to developing multilingual algorithms of semantic-syntactic analysis of NL-texts is outlined. This approach is realized by means of a program in the language PYTHON.
A comprehensive theoretical framework for the development of a Semantic Web of a new generation, or of a Multilingual Semantic Web, is outlined. Firstly, the paper grounds the possibility of using a mathematical model being the kernel of the theory of K-representations and describing a system of 10 partial operations on conceptual structures for building semantic representations (or text meaning representations) of, likely, arbitrary sentences and discourses in English, Russian, French, German, and other languages. The possibilities of using SK-languages defined by the theory of K-representations for building semantic annotations of informational sources and for constructing semantic representations of discourses pertaining to biology and medicine are illustrated. Secondly, an original strategy of transforming the existing Web into a Semantic Web of a new generation with the well-developed mechanisms of understanding natural language texts is described. The third subject of this paper is a description of the correspondence between the inputs and outputs of the elaborated algorithm of semantic-syntactic analysis and of its advantages; the semantic representations of the input texts are the expressions of SK-languages (standard knowledge languages). The input texts can be the statements, questions, and commands from the sublanguages of English, Russian, and German. The algorithm has been implemented by means of the programming language PYTHON.
The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V−SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software.
Error correction of sequenced reads remains a difficult task, especially in single-cell sequencing projects with extremely non-uniform coverage. While existing error correction tools designed for standard (multi-cell) sequencing data usually come up short in single-cell sequencing projects, algorithms actually used for single-cell error correction have been so far very simplistic.
We introduce several novel algorithms based on Hamming graphs and Bayesian subclustering in our new error correction tool BAYESHAMMER. While BAYESHAMMER was designed for single-cell sequencing, we demonstrate that it also improves on existing error correction tools for multi-cell sequencing data while working much faster on real-life datasets. We benchmark BAYESHAMMER on both k-mer counts and actual assembly results with the SPADES genome assembler.
Abstracts of the Ninth International Conference on Bioinformatics of Genome Regulation and Structure\Systems Biology. Printed without editing