952289
DynGO: a tool for visualizing and mining of Gene Ontology and its associations
BackgroundA large volume of data and information about genes and gene products has been stored in various molecular biology databases. A major challenge for knowledge discovery using these databases is to identify related genes and gene products in disparate databases. The development of Gene Ontology (GO) as a common vocabulary for annotation allows integrated queries across multiple databases and identification of semantically related genes and gene products (i.e., genes and gene products that have similar GO annotations). Meanwhile, dozens of tools have been developed for browsing, mining or editing GO terms, their hierarchical relationships, or their "associated" genes and gene products (i.e., genes and gene products annotated with GO terms). Tools that allow users to directly search and inspect relations among all GO terms and their associated genes and gene products from multiple databases are needed.ResultsWe present a standalone package called DynGO, which provides several advanced functionalities in addition to the standard browsing capability of the official GO browsing tool (AmiGO). DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations. The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples.ConclusionWe have presented a standalone package DynGO that provides functionalities to search and browse GO and its association databases as well as several additional functions such as batch retrieval and semantic retrieval. The complete documentation and software are freely available for download from the website http://biocreative.ifsm.umbc.edu/dyngo.
471313
Dependence network modeling for biomarker identification
MOTIVATION
Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.


RESULTS
Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction.


AVAILABILITY
See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/
3641866
The iProClass integrated database for protein functional analysis
Increasingly, scientists have begun to tackle gene functions and other complex regulatory processes by studying organisms at the global scales for various levels of biological organization, ranging from genomes to metabolomes and physiomes. Meanwhile, new bioinformatics methods have been developed for inferring protein function using associative analysis of functional properties to complement the traditional sequence homology-based methods. To fully exploit the value of the high-throughput system biology data and to facilitate protein functional studies requires bioinformatics infrastructures that support both data integration and associative analysis. The iProClass database, designed to serve as a framework for data integration in a distributed networking environment, provides comprehensive descriptions of all proteins, with rich links to over 50 databases of protein family, function, pathway, interaction, modification, structure, genome, ontology, literature, and taxonomy. In particular, the database is organized with PIRSF family classification and maps to other family, function, and structure classification schemes. Coupled with the underlying taxonomic information for complete genomes, the iProClass system (http://pir.georgetown.edu/iproclass/) supports associative studies of protein family, domain, function, and structure. A case study of the phosphoglycerate mutases illustrates a systematic approach for protein family and phylogenetic analysis. Such studies may serve as a basis for further analysis of protein functional evolution, and its relationship to the co-evolution of metabolic pathways, cellular networks, and organisms.
12615323
PIRSF: family classification system at the Protein Information Resource
The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The system adopts a network structure for protein classification from superfamily to subfamily levels. Protein family members are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture). The PIRSF database consists of two data sets, preliminary clusters and curated families. The curated families include family name, protein membership, parent-child relationship, domain architecture, and optional description and bibliography. PIRSF is accessible from the website at http://pir.georgetown.edu/pirsf/ for report retrieval and sequence classification. The report presents family annotation, membership statistics, cross-references to other databases, graphical display of domain architecture, and links to multiple sequence alignments and phylogenetic trees for curated families. PIRSF can be utilized to analyze phylogenetic profiles, to reveal functional convergence and divergence, and to identify interesting relationships between homeomorphic families, domains and structural classes.
14776207
BioThesaurus: a web-based thesaurus of protein and gene names
UNLABELLED
BioThesaurus is a web-based system designed to map a comprehensive collection of protein and gene names to protein entries in the UniProt Knowledgebase. Currently covering more than two million proteins, BioThesaurus consists of over 2.8 million names extracted from multiple molecular biological databases according to the database cross-references in iProClass. The BioThesaurus web site allows the retrieval of synonymous names of given protein entries and the identification of protein entries sharing the same names.


AVAILABILITY
BioThesaurus is accessible for online searching at http://pir.georgetown.edu/iprolink/biothesaurus
197229
The PIR integrated protein databases and data retrieval system
The Protein Information Resource (PIR) provides many databases and tools to support genomic and proteomic research. PIR is a member of UniProt—Universal Protein Resource—the central repository of protein sequence and function, which maintains UniProt Knowledgebase with extensively curated annotation, UniProt Reference databases to speed sequence searches, and UniProt Archive to reflect sequence history. PIR also provides PIRSF family classification system based on evolutionary relationships of full-length proteins, and iProClass integrated database of protein family, function, and structure. These databases are easily accessible from PIR web site using a centralized data retrieval system for information retrieval and knowledge discovery.
17460651
A calculational fusion system HYLO
Fusion, one of the most useful transformation tactics for deriving efficient programs, is the process whereby separate pieces of programs are fused into a single one, leading to an efficient program without intermediate data structures produced. In this paper, we report our on-going investigation on the design and implementation of an automatic transformation system HYLO which performs fusion transformation in a more systematic and more general way than any other systems. The distinguished point of our system is its calculational feature based on simple application of transformation laws rather than traditional search-based transformations.
702761
Calculating a New Data Mining Algorithm for Market Basket Analysis
The general goal of data mining is to extract interesting correlated information from large collection of data. A key computationally-intensive subproblem of data mining involves finding frequent sets in order to help mine association rules for market basket analysis. Given a bag of sets and a probability, the frequent set problem is to determine which subsets occur in the bag with some minimum probability. This paper provides a convincing application of program calculation in the derivation of a completely new and fast algorithm for this practical problem. Beginning with a simple but inefficient specifocation expressed in a functional language, the new algorithm is calculated in a systematic manner from the specification by applying a sequence of known calculation techniques.
18834097
Utilization of two sample t-test statistics from redundant probe sets to evaluate different probe set algorithms in GeneChip studies
BackgroundThe choice of probe set algorithms for expression summary in a GeneChip study has a great impact on subsequent gene expression data analysis. Spiked-in cRNAs with known concentration are often used to assess the relative performance of probe set algorithms. Given the fact that the spiked-in cRNAs do not represent endogenously expressed genes in experiments, it becomes increasingly important to have methods to study whether a particular probe set algorithm is more appropriate for a specific dataset, without using such external reference data.ResultsWe propose the use of the probe set redundancy feature for evaluating the performance of probe set algorithms, and have presented three approaches for analyzing data variance and result bias using two sample t-test statistics from redundant probe sets. These approaches are as follows: 1) analyzing redundant probe set variance based on t-statistic rank order, 2) computing correlation of t-statistics between redundant probe sets, and 3) analyzing the co-occurrence of replicate redundant probe sets representing differentially expressed genes. We applied these approaches to expression summary data generated from three datasets utilizing individual probe set algorithms of MAS5.0, dChip, or RMA. We also utilized combinations of options from the three probe set algorithms. We found that results from the three approaches were similar within each individual expression summary dataset, and were also in good agreement with previously reported findings by others. We also demonstrate the validity of our findings by independent experimental methods.ConclusionAll three proposed approaches allowed us to assess the performance of probe set algorithms using the probe set redundancy feature. The analyses of redundant probe set variance based on t-statistic rank order and correlation of t-statistics between redundant probe sets provide useful tools for data variance analysis, and the co-occurrence of replicate redundant probe sets representing differentially expressed genes allows estimation of result bias. The results also suggest that individual probe set algorithms have dataset-specific performance.
190172
REDfly: a Regulatory Element Database for Drosophila
Bioinformatics studies of transcriptional regulation in the metazoa are significantly hindered by the absence of readily available data on large numbers of transcriptional cis-regulatory modules (CRMs). Even the richly annotated Drosophila melanogaster genome lacks extensive CRM information. We therefore present here a database of Drosophila CRMs curated from the literature complete with both DNA sequence and a searchable description of the gene expression pattern regulated by each CRM. This resource should greatly facilitate the development of computational approaches to CRM discovery as well as bioinformatics analyses of regulatory sequence properties and evolution.
9579545
An Injective Language for Reversible Computation
Erasure of information incurs an increase in entropy and dissipates heat. Therefore, information-preserving computation is essential for constructing computers that use energy more effectively. A more recent motivation to understand reversible transformations also comes from the design of editors where editing actions on a view need to be reflected back to the source data. In this paper we present a point-free functional language, with a relational semantics, in which the programmer is allowed to define injective functions only. Non-injective functions can be transformed into a program returning a history. The language is presented with many examples, and its relationship with Bennett’s reversible Turing machine is explained. The language serves as a good model for program construction and reasoning for reversible computers, and hopefully for modelling bi-directional updating in an editor.