Mehmet M. Dalkilic

Information dependencies

This paper uses the tools of information theory to examine and reason about the information content of the attributes within a relation instance. For two sets of attributes <italic>X</italic> and <italic>Y</italic>, an <italic>information dependency measure</italic> (InD measure) characterizes the uncertainty remaining about the values for the set <italic>Y</italic> when the values for the set <italic>X</italic> are known. A variety of arithmetic inequalities (InD <italic>inequalities</italic>) are shown to hold among InD measures; InD inequalities hold in any relation instance. Numeric constraints (InD <italic>constraints</italic>) on InD measures, consistent with the InD inequalities, can be applied to relation instances. Remarkably, functional and multivalued dependencies correspond to setting certain constraints to zero, with Armstrongs axioms shown to be consequences of the arithmetic inequalities applied to constraints. As an analog of completeness, for any set of constraints consistent with the inequalities, we may construct a relation instance that approximates these constraints within any positive <italic>ε</italic>. InD measures suggest many valuable applications in areas such as data mining.

Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function

BackgroundDiscovering the functions of all genes is a central goal of contemporary biomedical research. Despite considerable effort, we are still far from achieving this goal in any metazoan organism. Collectively, the growing body of high-throughput functional genomics data provides evidence of gene function, but remains difficult to interpret.ResultsWe constructed the first network of functional relationships for Drosophila melanogaster by integrating most of the available, comprehensive sets of genetic interaction, protein-protein interaction, and microarray expression data. The complete integrated network covers 85% of the currently known genes, which we refined to a high confidence network that includes 20,000 functional relationships among 5,021 genes. An analysis of the network revealed a remarkable concordance with prior knowledge. Using the network, we were able to infer a set of high-confidence Gene Ontology biological process annotations on 483 of the roughly 5,000 previously unannotated genes. We also show that this approach is a means of inferring annotations on a class of genes that cannot be annotated based solely on sequence similarity. Lastly, we demonstrate the utility of the network through reanalyzing gene expression data to both discover clusters of coregulated genes and compile a list of candidate genes related to specific biological processes.ConclusionsHere we present the the first genome-wide functional gene network in D. melanogaster. The network enables the exploration, mining, and reanalysis of experimental data, as well as the interpretation of new data. The inferred annotations provide testable hypotheses of previously uncharacterized genes.

A new method for stranded whole transcriptome RNA-seq

This report describes an improved protocol to generate stranded, barcoded RNA-seq libraries to capture the whole transcriptome. By optimizing the use of duplex specific nuclease (DSN) to remove ribosomal RNA reads from stranded barcoded libraries, we demonstrate improved efficiency of multiplexed next generation sequencing (NGS). This approach detects expression profiles of all RNA types, including miRNA (microRNA), piRNA (Piwi-interacting RNA), snoRNA (small nucleolar RNA), lincRNA (long non-coding RNA), mtRNA (mitochondrial RNA) and mRNA (messenger RNA) without the use of gel electrophoresis. The improved protocol generates high quality data that can be used to identify differential expression in known and novel coding and non-coding transcripts, splice variants, mitochondrial genes and SNPs (single nucleotide polymorphisms).

Data pushing: a fly-centric guide to bioinformatics tools.

Bioinformatics tools can be invaluable resources to Drosophila researchers; however, the sheer number of applications and databases can be overwhelming. We present a broad overview of common bioinformatics tasks and the resources used to do them, with a specific focus on resources for Drosophila. The topics covered include: Genome Databases, Sequence Analysis, Comparative Genomics, Gene Expression Databases and Analysis Tools, Function-Based Data and Analysis, Pathways, Networks, and Interactions; and finally, tools to stay current with resources and literature. We also present a compilation of URLs and short descriptions that correspond to the topics and resources mentioned in this review. Supplementary Material can be found at: http://www.landesbioscience.com/supplement/CostelloFLY2-1-sup.pdf

A Measurement Ontology Generalizable for Emerging Domain Applications on the Semantic Web

This article introduces a measurement ontology for applications to Semantic Web applications, specifically for emerging domains such as microarray analysis. The Semantic Web is the next generation Web of structured data that are automatically shared by software agents, which apply definitions and constraints organized in ontologies to correctly process data from disparate sources. One facet needed to develop Semantic Web ontologies of emerging domains is creating ontologies of concepts that are common to these domains. These general “common-sense†ontologies can be used as building blocks to develop more domain-specific ontologies. However most measurement ontologies concentrate on representing units of measurement and quantities, and not on other measurement concepts such as sampling, mean values, and evaluations of quality based on measurements. In this article, we elaborate on a measurement ontology that represents all these concepts. We present the generality of the ontology, and describe how it is developed, used for analysis and validated.

Guiding motif discovery by iterative pattern refinement

In this paper, we demonstrate that the performance of a motif discovery algorithm can be significantly improved by embedding it into a novel framework that effectively guides the motif discovery process. The framework is also general enough to allow any statistical motif discovery algorithm to be used. Motivation for this research comes from the fact that the statistical significance of patterns depends on the background probability which is largely determined by input sequences. Our framework guides motif discovery by inputting subsequences to an existing motif discovery algorithm, rather than using entire sequences. Subsequences are determined by motifs discovered using existing motif discovery and search algorithms. Then this technique is iteratively applied until convergence. A starting set of patterns is discovered by a simple, but effective pattern set generation algorithm. Our framework was implemented using MEME and MAST and tested with 108 PROSITE patterns. The result demonstrates that our framework significantly improves the performance of MEME.

SUPCRTBL: A revised and extended thermodynamic dataset and software package of SUPCRT92

Abstract The computer-enabled thermodynamic database associated with SUPCRT92 ( Johnson et al., 1992 ) enables the calculation of the standard molal thermodynamic properties of minerals, gases, aqueous species, and reactions for a wide range of temperatures and pressures. However, new data on the thermodynamic properties of both aqueous species and minerals have become available since the database’s initial release in 1992 and its subsequent updates. In light of these developments, we have expanded SUPCRT92’s thermodynamic dataset and have modified the accompanying computer code for thermodynamic calculations by using newly available properties. The modifications in our new version include: (1) updating the standard state thermodynamic properties for mineral end-members with properties from Holland and Powell (2011) to improve the study of metamorphic petrology and economic geology; (2) adding As-acid, As-metal aqueous species, and As-bearing minerals to improve the study of environmental geology; (3) updating properties for Al-bearing species, SiO 2 ° (aq) and HSiO 3 - , boehmite, gibbsite, and dawsonite for modeling geological carbon sequestration. The new thermodynamic dataset and the modified SUPCRT92 program were implemented in a software package called SUPCRTBL, which is available online at www.indiana.edu/~hydrogeo/supcrtbl.html .

An approximate de bruijn graph approach to multiple local alignment and motif discovery in protein sequences

Motif discovery is an important problem in protein sequence analysis. Computationally, it can be viewed as an application of the more general multiple local alignment problem, which often encounters the difficulty of computer time when aligning many sequences. We introduce a new algorithm for multiple local alignment for protein sequences, based on the de Bruijn graph approach first proposed by Zhang and Waterman for aligning DNA sequence. We generalize their approach to aligning protein sequences by building an approximate de Bruijn graph to allow gluing similar but not identical amino acids. We implement this algorithm and test it on motif discovery of 100 sets of protein sequences. The results show that our method achieved comparable results as other popular motif discovery programs, while offering advantages in terms of speed.

Improving Query Evaluation with Approximate Functional Dependency Based Decompositions

We investigate how relational restructuring may be used to improve query performance. Our approach parallels recent research extending semantic query optimization (SQO), which uses knowledge about the instance to achieve more efficient query processing. Our approach differs, however, in that the instance does not govern whether the optimization may be applied; rather, the instance governs whether the optimization yields more efficient query processing. It also differs in that it involves an explicit decomposition of the relation instance. We use approximate functional dependencies as the conceptual basis for this decomposition and develop query rewriting techniques to exploit it. We present experimental results leading to a characterization of a well-defined class of queries for which improved processing time is observed.

iGibbs: Improving Gibbs motif sampler for proteins by sequence clustering and iterative pattern sampling

The motif prediction problem is to predict short, conserved subsequences that are part of a family of sequences, and it is a very important biological problem. Gibbs is one of the first successful motif algorithms and it runs very fast compared with other algorithms, and its search behavior is based on the well‐studied Gibbs random sampling. However, motif prediction is a very difficult problem and Gibbs may not predict true motifs in some cases. Thus, the authors explored a possibility of improving the prediction accuracy of Gibbs while retaining its fast runtime performance. In this paper, the authors considered Gibbs only for proteins, not for DNA binding sites. The authors have developed iGibbs, an integrated motif search framework for proteins that employs two previous techniques of their own: one for guiding motif search by clustering sequences and another by pattern refinement. These two techniques are combined to a new double clustering approach to guiding motif search. The unique feature of their framework is that users do not have to specify the number of motifs to be predicted when motifs occur in different subsets of the input sequences since it automatically clusters input sequences into clusters and predict motifs from the clusters. Tests on the PROSITE database show that their framework improved the prediction accuracy of Gibbs significantly. Compared with more exhaustive search methods like MEME, iGibbs predicted motifs more accurately and runs one order of magnitude faster. Proteins 2007.