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An abstract of a study related to the bioinformatic analysis of gene regulation in Geobacter sulfurreducens, which was conducted by Julia Krushkal and colleagues, is presented.

Background: Parent of origin-specific allelic expression of imprinted genes is epigenetically controlled. In cancer, imprinted genes undergo both genomic and epigenomic alterations, including frequent copy number changes. We investigated whether copy number loss or gain of imprinted genes in cancer cell lines is associated with response to chemotherapy treatment. Results: We analyzed 198 human imprinted genes including protein-coding genes and noncoding RNA genes using data from tumor cell lines from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We examined whether copy number of the imprinted genes in 35 different genome locations was associated with response to cancer drug treatment. We also analyzed associations of pretreatment expression and DNA methylation of imprinted genes with drug response. Higher copy number of BLCAP, GNAS, NNAT, GNAS-AS1, HM13, MIR296, MIR298, and PSIMCT-1 in the chromosomal region 20q11-q13.32 was associated with resistance to multiple antitumor agents. Increased expression of BLCAP and HM13 was also associated with drug resistance, whereas higher methylation of gene regions of BLCAP, NNAT, SGK2, and GNAS was associated with drug sensitivity. While expression and methylation of imprinted genes in several other chromosomal regions was also associated with drug response and many imprinted genes in different chromosomal locations showed a considerable copy number variation, only imprinted genes at 20q11-q13.32 had a consistent association of their copy number with drug response. Copy number values among the imprinted genes in the 20q11-q13.32 region were strongly correlated. They were also correlated with the copy number of cancer-related non-imprinted genes MYBL2, AURKA, and ZNF217 in that chromosomal region. Expression of genes at 20q11-q13.32 was associated with ex vivo drug response in primary tumor samples from the Beat AML 1.0 acute myeloid leukemia patient cohort. Association of the increased copy number of the 20q11-q13.32 region with drug resistance may be complex and could involve multiple genes. Conclusions: Copy number of imprinted and non-imprinted genes in the chromosomal region 20q11-q13.32 was associated with cancer drug resistance. The genes in this chromosomal region may have a modulating effect on tumor response to chemotherapy. [ABSTRACT FROM AUTHOR]

Background: Indian natural products have been anecdotally used for cancer treatment but with limited efficacy. To better understand their mechanism, we examined the publicly available data for the activity of Indian natural products in the NCI-60 cell line panel.Methods: We examined associations of molecular genomic features in the well-characterized NCI-60 cancer cell line panel with in vitro response to treatment with 75 compounds derived from Indian plant-based natural products. We analyzed expression measures for annotated transcripts, lncRNAs, and miRNAs, and protein-changing single nucleotide variants in cancer-related genes. We also examined the similarities between cancer cell line response to Indian natural products and response to reference anti-tumor compounds recorded in a U.S. National Cancer Institute (NCI) Developmental Therapeutics Program database.Results: Hierarchical clustering based on cell line response measures identified clustering of Phyllanthus and cucurbitacin products with known anti-tumor agents with anti-mitotic mechanisms of action. Curcumin and curcuminoids mostly clustered together. We found associations of response to Indian natural products with expression of multiple genes, notably including SLC7A11 involved in solute transport and ATAD3A and ATAD3B encoding mitochondrial ATPase proteins, as well as significant associations with functional single nucleotide variants, including BRAF V600E.Conclusion: These findings suggest potential mechanisms of action and novel associations of in vitro response with gene expression and some cancer-related mutations that increase our understanding of these Indian natural products. [ABSTRACT FROM AUTHOR]

Abstracts from the UT-ORNL-KBRIN (University of Tennessee-Oak Ridge National Laboratory-Kentucky Bioinformatics Network) Bioinformatics Summit 2009 in Pikeville, Tennessee from March 20-22, 2009 are presented including "Prediction of peptide drift time in ion mobility-mass spectrometry," "Visualizing and sharing results in bioinformatics projects: GBrowse and GenBank exports," and "Molecular dissection of cardiac hypertrophy induced by interleukin-18."

Background: Altered DNA methylation patterns play important roles in cancer development and progression. We examined whether expression levels of genes directly or indirectly involved in DNA methylation and demethylation may be associated with response of cancer cell lines to chemotherapy treatment with a variety of antitumor agents. Results: We analyzed 72 genes encoding epigenetic factors directly or indirectly involved in DNA methylation and demethylation processes. We examined association of their pretreatment expression levels with methylation beta-values of individual DNA methylation probes, DNA methylation averaged within gene regions, and average epigenome-wide methylation levels. We analyzed data from 645 cancer cell lines and 23 cancer types from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We observed numerous correlations between expression of genes encoding epigenetic factors and response to chemotherapeutic agents. Expression of genes encoding a variety of epigenetic factors, including KDM2B, DNMT1, EHMT2, SETDB1, EZH2, APOBEC3G, and other genes, was correlated with response to multiple agents. DNA methylation of numerous target probes and gene regions was associated with expression of multiple genes encoding epigenetic factors, underscoring complex regulation of epigenome methylation by multiple intersecting molecular pathways. The genes whose expression was associated with methylation of multiple epigenome targets encode DNA methyltransferases, TET DNA methylcytosine dioxygenases, the methylated DNA-binding protein ZBTB38, KDM2B, SETDB1, and other molecular factors which are involved in diverse epigenetic processes affecting DNA methylation. While baseline DNA methylation of numerous epigenome targets was correlated with cell line response to antitumor agents, the complex relationships between the overlapping effects of each epigenetic factor on methylation of specific targets and the importance of such influences in tumor response to individual agents require further investigation. Conclusions: Expression of multiple genes encoding epigenetic factors is associated with drug response and with DNA methylation of numerous epigenome targets that may affect response to therapeutic agents. Our findings suggest complex and interconnected pathways regulating DNA methylation in the epigenome, which may both directly and indirectly affect response to chemotherapy. [ABSTRACT FROM AUTHOR]

Background: Small cell lung cancer (SCLC) is an aggressive neuroendocrine lung cancer. SCLC progression and treatment resistance involve epigenetic processes. However, links between SCLC DNA methylation and drug response remain unclear. We performed an epigenome-wide study of 66 human SCLC cell lines using the Illumina Infinium MethylationEPIC BeadChip array. Correlations of SCLC DNA methylation and gene expression with in vitro response to 526 antitumor agents were examined. Results: We found multiple significant correlations between DNA methylation and chemosensitivity. A potentially important association was observed for TREX1, which encodes the 3′ exonuclease I that serves as a STING antagonist in the regulation of a cytosolic DNA-sensing pathway. Increased methylation and low expression of TREX1 were associated with the sensitivity to Aurora kinase inhibitors AZD-1152, SCH-1473759, SNS-314, and TAK-901; the CDK inhibitor R-547; the Vertex ATR inhibitor Cpd 45; and the mitotic spindle disruptor vinorelbine. Compared with cell lines of other cancer types, TREX1 had low mRNA expression and increased upstream region methylation in SCLC, suggesting a possible relationship with SCLC sensitivity to Aurora kinase inhibitors. We also identified multiple additional correlations indicative of potential mechanisms of chemosensitivity. Methylation of the 3′UTR of CEP350 and MLPH, involved in centrosome machinery and microtubule tracking, respectively, was associated with response to Aurora kinase inhibitors and other agents. EPAS1 methylation was associated with response to Aurora kinase inhibitors, a PLK-1 inhibitor and a Bcl-2 inhibitor. KDM1A methylation was associated with PLK-1 inhibitors and a KSP inhibitor. Increased promoter methylation of SLFN11 was correlated with resistance to DNA damaging agents, as a result of low or no SLFN11 expression. The 5′ UTR of the epigenetic modifier EZH2 was associated with response to Aurora kinase inhibitors and a FGFR inhibitor. Methylation and expression of YAP1 were correlated with response to an mTOR inhibitor. Among non-neuroendocrine markers, EPHA2 was associated with response to Aurora kinase inhibitors and a PLK-1 inhibitor and CD151 with Bcl-2 inhibitors. Conclusions: Multiple associations indicate potential epigenetic mechanisms affecting SCLC response to chemotherapy and suggest targets for combination therapies. While many correlations were not specific to SCLC lineages, several lineage markers were associated with specific agents. [ABSTRACT FROM AUTHOR]

An abstract of the article "Bioinformatic analysis of gene regulation in the metal-reducing bacterial family Geobacteraceae," by Julia Krushkal, Ronald M. Adkins, and Jeanette Peeples is presented.

Background In microarray data analysis, we considered the problem of classifying genes based on ratio of the mean gene expression levels between the control and the treatment factors measured at t fixed times. In this setting, we assume that the control and treatment responses come from two independent normal populations, and the two treatment groups are significantly different only if the ratio of the two population means is less than r1 or greater than r2. We propose an approach based on the mapping of the T scores into C = {+1, 0, -1}, where +1 is the value when the t-score is greater than the upper critical point, -1 if it is less than the lower critical point, and 0 otherwise. Misclassification probability under small replications is given and the method is demonstrated using a microarray data. [ABSTRACT FROM AUTHOR]

Information about the Tenth Annual UT-ORNL-KBRIN Bioinformatics Summit 2011, held at the University of Memphis in Memphis, Tennessee from April 1 to 3, 2011 is presented. The conference featured workshops on various topics including Next-Generation Sequencing, the workflow platform Galaxy and GeneMANIA. Also, scientists participated include Eric Rouchka, Jon Armstrong and Josh Denny.

Information about the papers presented at the ninth annual University of Tennessee (UT)-Oak Ridge National Laboratory (ORNL)-Kentucky Biomedical Research Infrastructure Network (KBRIN) Bioinformatics Summit 2010 held in western Kentucky from March 15 to 21, 2010 is presented. Topics discussed include bioinformatics in Kentucky, DNA microarray analysis, and determination of differences in gene networks between two biological samples. Several professors and medical scientists attended the event.

Information about several topics discussed at the Bioinformatics Summit 2009 held in Pikeville, Tennessee from March 20-22, 2009 is presented. Topics include the discovery of single nucleotide polymorphisms, medical and translational informatics, epigenetic studies, and systems biology. The summit featured several researchers including Bruce Aronow, Michael Miles, and Robert Hanson.

Background: The role of the RNA polymerase sigma factor RpoN in regulation of gene expression in Geobacter sulfurreducens was investigated to better understand transcriptional regulatory networks as part of an effort to develop regulatory modules for genome-scale in silico models, which can predict the physiological responses of Geobacter species during groundwater bioremediation or electricity production. Results: An rpoN deletion mutant could not be obtained under all conditions tested. In order to investigate the regulon of the G. sulfurreducens RpoN, an RpoN over-expression strain was made in which an extra copy of the rpoN gene was under the control of a taclac promoter. Combining both the microarray transcriptome analysis and the computational prediction revealed that the G. sulfurreducens RpoN controls genes involved in a wide range of cellular functions. Most importantly, RpoN controls the expression of the dcuB gene encoding the fumarate/succinate exchanger, which is essential for cell growth with fumarate as the terminal electron acceptor in G. sulfurreducens. RpoN also controls genes, which encode enzymes for both pathways of ammonia assimilation that is predicted to be essential under all growth conditions in G. sulfurreducens. Other genes that were identified as part of the RpoN regulon using either the computational prediction or the microarray transcriptome analysis included genes involved in flagella biosynthesis, pili biosynthesis and genes involved in central metabolism enzymes and cytochromes involved in extracellular electron transfer to Fe(III), which are known to be important for growth in subsurface environment or electricity production in microbial fuel cells. The consensus sequence for the predicted RpoN-regulated promoter elements is TTGGCACGGTTTTTGCT. Conclusion: The G. sulfurreducens RpoN is an essential sigma factor and a global regulator involved in a complex transcriptional network controlling a variety of cellular processes. [ABSTRACT FROM AUTHOR]

Background Lysozyme is a 147 amino acid protein that can damage bacterial cell walls by destroying the glycosidic bond on the peptidoglycans molecule, particularly in gram-positive bacteria. Its native structure is composed of α and β domains and is cross-linked by four disulfides (Figure 1). Lysozyme can be aggregated by mutation of the lysozyme DNA sequence itself, or from the harsh environment of the cell inside (Figure 2). Rosetta++ is freely distributed software used to predict all possible protein conformations by calculating the folding energy of the protein sequences. An included docking function is used to simulate a protein binding as homoand hetero-dimers or trimers structures. This program is used to explore the possible conformations of one protein, which may not be easily found in the traditional approaches, such as NMR and crystallography. From these computer models, we can predict denatured and aggregate forms of proteins. Materials and methods 1) Import native lysozyme sequence (gi:45384212) from NCBI and structure (PDB 1E8L) from PDB websites 2) Run lysozyme in Ab initio mode to start folding independently from the original protein sequence. 3) Docking each fold as homodimers to define the lowest energy to form lysozyme fibril structures. 4) Analyze the results. Results We generated 1,000 Ab Initio structures, at a denaturing temperature, from the original lysozyme sequence and let each structure dock together as homodimers for 10,000 structures. We generated 10 million structures and calculated the RMSD value when compared with the native structure. Previous evidence suggests the diameter of the lysozyme fibril structure is around 20 to 30 nm. Homodimers with radius of gyration less than 20 nm were considered. 422,983 of the 10 million homodimers structures were within the range 18 to 19.99 nm (Figure 3). The six lowest radii of gyration (18.01 nm to 18.04 nm) structures have a score range from -42.48 to -40.61 and RMSD of 11.917 when compared to the native lysozyme structure (Figure 4). All six of the structures came from the single lysozyme Ab Initio fold (LLt515). In addition, the first six models with the lowest energy scores (-68.04 to -67.56) have radii of gyration range from 19.65 to 19.81 nm and RMSD from 12.757 to 16.261 (Figure 5). Five of these structures came from lysozyme Ab Initio fold code LLt687 and the other from LLt998. Conclusions and discussions Most of lowest radius of gyration come from the same Ab Initio fold (Figure 4b), but all the six lowest energy scores have many different and diverse form of structures (Figure 5b). Therefore, if we want to group or cluster all this potential fibril homodimers, we should sort by radius of gyration to maintain the structure within groups. For future study, we hope that the model of the fibrilar forms of denatured lysozyme will help us understand how to block fibril formation and model interactions with heat shock proteins or other chaperones during the dis-aggregation process (refolding mechanism) [2]. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the Seventh Annual (University of Tennessee (UT)-Oak Ridge National Laboratory (ORNL)-Kentucky Bioinformatics Network (KBRIN) Bioinformatics Summit held at Lake Barkley State Park in Cadiz, Kentucky from March 28 to 30, 2008. Wayne Matten of the National Center for Biotechnology Information (NCBI) gave a presentation on the association of a diseased gene with a corresponding change in phenotype. Jeremy Smith of ORNL Center for Molecular Physics talked about computer simulation of molecular machines.

Background The rapid growth of the biomedical literature and genomic information present a major challenge for determining the functional relationships among genes. Several bioinformatics tools have been developed to extract and identify gene relationships from various biological databases. In this study, we develop a Web-based bioinformatics tool called Feature Annotation Using Nonnegative matrix factorization (FAUN) to facilitate both the discovery and classification of functional relationships among genes. The algorithms of nonnegative matrix factorization (NMF) described in [1] are used. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. FAUN is first tested on a small manually constructed 50-gene (50TG) collection that we, as well as others, have previously used [2]. The screenshots of FAUN feature classification and gene-to-gene correlation for the 50TG collection are shown in Figures 1 and 2. We then apply FAUN to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g. MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation. FAUN tool is publicly available at https://shad.eecs.utk.edu/faun. Discussion For a preliminary assessment of FAUN feature classification, each gene in the 50TG collection was classified based on its most dominant annotated feature or based on some feature weight threshold. The FAUN classification using the strongest feature (per gene) yielded 90% accuracy. A FAUN-based analysis of a new cerebellum gene set has revealed new knowledge -- the gene set contains a large component of transcription factors. [ABSTRACT FROM AUTHOR]

Background: The APOBEC gene family of cytidine deaminases plays important roles in DNA repair and mRNA editing. In many cancers, APOBEC3B increases the mutation load, generating clusters of closely spaced, single-strand-specific DNA substitutions with a characteristic hypermutation signature. Some studies also suggested a possible involvement of APOBEC3A, REV1, UNG, and FHIT in molecular processes affecting APOBEC mutagenesis. It is important to understand how mutagenic processes linked to the activity of these genes may affect sensitivity of cancer cells to treatment. Results: We used information from the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer resources to examine associations of the prevalence of APOBEC-like motifs and mutational loads with expression of APOBEC3A, APOBEC3B, REV1, UNG, and FHIT and with cell line chemosensitivity to 255 antitumor drugs. Among the five genes, APOBEC3B expression levels were bimodally distributed, whereas expression of APOBEC3A, REV1, UNG, and FHIT was unimodally distributed. The majority of the cell lines had low levels of APOBEC3A expression. The strongest correlations of gene expression levels with mutational loads or with measures of prevalence of APOBEC-like motif counts and kataegis clusters were observed for REV1, UNG, and APOBEC3A. Sensitivity or resistance of cell lines to JQ1, palbociclib, bicalutamide, 17-AAG, TAE684, MEK inhibitors refametinib, PD-0325901, and trametinib and a number of other agents was correlated with candidate gene expression levels or with abundance of APOBEC-like motif clusters in specific cancers or across cancer types. Conclusions: We observed correlations of expression levels of the five candidate genes in cell line models with sensitivity to cancer drug treatment. We also noted suggestive correlations between measures of abundance of APOBEC-like sequence motifs with drug sensitivity in small samples of cell lines from individual cancer categories, which require further validation in larger datasets. Molecular mechanisms underlying the links between the activities of the products of each of the five genes, the resulting mutagenic processes, and sensitivity to each category of antitumor agents require further investigation. [ABSTRACT FROM AUTHOR]

An abstract of a study related to comparison of annotation terms between curated and automated E. coli K12 databases, which was conducted by ReddySailaja Marpuri and Claire A. Rinehart, is presented.

An abstract of a study related to the quantitative real-time polymerase chain reaction (PCR) validation and microarray analysis of gene expression during auditory hair cell regeneration in zebrafish, which was conducted by Julie B. Schuck and colleagues, is presented.

An abstract of a study related to bioinformatics determination of enterotoxigenic E. coli (ETEC) signature genes as potential targets for reverse vaccinology and molecular diagnosis, which was conducted by Heba M. Amin, Abdel-Gawad M. Hashem, and Ramy K. Aziz, is presented.

An abstract of a study related to parent-of-origin genetic effects on the fetal growth, which was conducted by Ronald M. Adkins and colleagues, is presented.

An abstract of a study related to prediction of gene regulatory information using Latent Semantic Indexing (LSI) based framework, which was conducted by Sujoy Roy, Lijing Xu, and Ramin Homayouni, is presented.

An abstract of a study related to visualization and sharing of results in bioinformatics projects through GBrowse software and GenBank exports, which was conducted by Elissaveta G. Arnaoudova and colleagues, is presented.

An abstract of a study related to reconstruction of the human pathogen Streptococcus pyogenes virulome by using annotation based on National Microbial Pathogen Data Resource of the U.S. National Institutes of Health, which was conducted by Ramy K. Aziz and Leslie K. Mcneil, is presented.

An abstract of a study related to parameters of latent semantic analysis model for mining biomedical literature, which was conducted by Mohammed Yeasin and colleagues, is presented.

An abstract of a study related to prediction of the drift time of peptides in the ion mobility-mass spectrometry, which was conducted by Bing Wang and colleagues, is presented.

Individuals infected with genetically identical group A streptococcal (GAS) strains develop starkly different disease progression and outcome [1]. We reported that HLA class II allelic variation contributes to differences in systemic disease severity by modulating host responses to streptococcal superantigens [2]. Inasmuch as the bacteria produce additional virulence factors, we sought to identify additional host gene networks modulating GAS sepsis. Accordingly, we used two parallel approaches to define these gene networks, quantitative trait loci (QTL) mapping and genome-wide transcriptome analyses. To map QTLs modulating response to severe GAS sepsis, we used advanced recombinant inbred (ARI) strains, which are genetically diverse strains that have common ancestral parents [3]. We chose to use BXD strains of ARI mice, as parental strains C57Bl/6J (B6) and DBA/2J (D2) show differential response to GAS sepsis and BXD strains are heavily genotyped at 13377 SNPs and microsatellite markers. BXD strains, derived from B6 and D2 parental strains, are homozygous inbred lines, each of which is genetically distinct. Using 30 different BXD strains (n = 5-26 mice per strain), we identified significant QTLs on chromosome 2 that strongly modulate disease severity [4]. To narrow down these mapped QTLs, we applied bioinformatics tools including: linkage, interval specific haplotype analyses, and gene ontology and we identified multiple candidate gene networks modulating immune response to sepsis. As a parallel approach, we performed genome-wide transcriptome analyses comparing resistant and susceptible strains. This comparison revealed 93 genes that were differentially regulated in mice spleens 36 h post-infection. These genes belonged to gene networks involving immune response to sepsis; particularly notable examples were prostaglandin (Ptges) and interleukin1 (IL-1) family pathways. Quantitative expression analyses, using real time PCR, of prostaglandin E synthase (Ptges), Ptges 2, Il1 and Il1 receptor antagonist (Il1rn) showed upregulation of these genes in spleens of susceptible strains post-infection. This upregulation in Il1 expression in susceptible strains was mirrored on protein levels as measured as plasma cytokines. Interestingly the gene networks that we identified using the two approaches share many common pathways. Therefore, integration of QTL mapping with differential gene expression uncovered multiple pathways modulating differential susceptibility to severe GAS sepsis, underscoring the complexity of traits modulating severe GAS sepsis. [ABSTRACT FROM AUTHOR]

Tissue bank background A bio-repository to collect, process, archive, and distribute human tissues and fluids for research has been established in the Department of Pathology and Laboratory Medicine, UTHSC. The tissue bank currently provides HIPPA-compliant, de-identified, high-quality specimens to facilitate researcher queries for translational research. The bank is archiving large numbers of paraffin-embedded tissues, and is also performing comprehensive management of bio-repositories for institutional projects. The tissue bank at Department of Pathology (see Figure 1) is an example of utilizing the "Scientific Laboratory Information Management (SLIM) System" to have a more organized system and improve the overall efficiency of the collecting process for Tissue Bank. The effective scientific data retriever will provide fast access to a wide range of scientific data via the Web or directly from an application, facilitating data sharing with other research teams and the general public. Research teams can download data files from a large data provider, augment this "mini-archive" with their own datasets, and make the whole heterogeneous data collection accessible via the Web. Users can interactively browse the contents of a file, examine a subset of the data, view a thumbnail image of the data, and download the desired subset. The SLIM system can be coupled with services for extracting, indexing, and searching metadata to create a complete repository service. Conclusion The effective scientific data retriever will enhance the effectiveness of scientific laboratory management. It will provide an application to collect laboratory materials, tools, and budgets for the scientific laboratory. The strategies for creating an application depend upon the nature of particular scientific research. [ABSTRACT FROM AUTHOR]

Background Analysis Tool for Proteomics Research (ATP) is our next generation approach for processing, storing and analyzing mass spectrometry-based proteomics data base on our previously developed bioinformatics graphical comparative analysis tools (BIGCAT) software [1]. This software is being developed for the efficient management, visualization, identification, characterization and comparison of peptides and proteins from tandem mass spectrometry experiments. The basic framework of the software suite consists of a back-end relational database to store primary LC-MS/MS data and the peptide/protein identification data from various database search algorithms. The interactive front-end software applications are interfaced to the relational database for managing, visual interpreting, and analyzing the LC-MS/MS results (Figure 1). As an open source and multi-functional analysis software suite, it enables users to 1) customize source code scheme to facilitate specialized experiment aims, 2) manage and load LC-MS/ MS data and protein identification, 3) graphically display and evaluate protein identifications using various views and threshold modifications, 4) compare, merge, and run cluster results from multiple LC-MS/MS experiments, 5) search for neutral losses that are indications of modified peptides, and 6) search immunological peptide motifs. The overall goal is to increase the efficiency and simplicity of managing proteomics data while providing means of publishing and analyzing data on the web in a biologically intuitive and robust way. [ABSTRACT FROM AUTHOR]

Overview We have developed the Proteome Discovery Pipeline, a stand-alone bioinformatics platform used for LC/MS data analysis and biomarker discovery. Data is processed in a series of self-contained analytical steps using modules that are controlled by a graphical user interface. The user interface was developed in Visual C++ 6.0 and provides a multi-threaded, tabbed user interface with each tab representing a step in the analysis process. Modules included are spectrum deconvolution, alignment, normalization, significance tests and pattern recognition. Modules consist of applications developed in C++ and the R scripting language, which are called as external processes from the GUI using inputted parameters. Molecular correlation analysis can be viewed interactively using SysNet. Figure 1 shows the architecture of the Proteome Discovery Pipeline. Spectrum deconvolution XMass [1] uses chemical noise filtering, charge state fitting and de-isotoping for improved analysis of complex peptide samples. Overlapping peptide signals in mass spectra were deconvoluted by correlation with modeled peptide isotopic peak profiles. Isotopic peak profiles for peptides were generated in silico from a protein database to produce reference model distributions. Peak alignment XAlign [2] is a two-step alignment algorithm. The first step is to detect significant peaks that are common to all samples. In the second step, all samples are aligned to the median sample using refined m/z and retention time variation values, where pattern recognition is applied as needed. Normalization Several normalization methods have been developed for proteomics, including auto-scaling, reference sample, log linear model, trimmed constant mean, and average intensity. Statistical significance tests Several different test methods (two-tailed t-test, one-way ANOVA, Kolmogorov-Smirnov test, the Mann-Whitney test) can be used to identify data elements that make large contributions to the protein profile of a sample or that distinguish groups of samples from others. Pattern recognition We have implemented principal component analysis (PCA), linear discriminate analysis (LDA), canonical discriminate analysis (CDA), and clustering objects on subset of attributes (COSA) [3] as clustering methods. Molecular correlation The software package, SysNet [4], is used to provide a dynamic visualization environment for molecular correlation of 'omics data. SysNet visualizes the 'omics expression data as a two-dimensional network. It features a circular layout, where molecular species are represented as nodes and all nodes are located on circles. The intermolecular correlations are represented as links, or edges, between nodes. [ABSTRACT FROM AUTHOR]

The rapid progress in bioinformatics and functional genomics lead to a vast amount of scientific literature. PubMed is one of the leading repositories and has approximately 20 million abstracts. Browsing such a huge repository to find relevant information as well as providing e-learning service requires new generation of interfaces. Methods such as GOPubmed are using keyword-based techniques. Keyword-based search methods often yield results that are not meaningful to the query. This problem can be partially addressed using a semantic enabled interface for browsing and information retrieval. A web interface called i-SEGOPubmed is being developed in CVPIA lab at the University of Memphis. i-SEGOPubmed enables semantically meaningful browsing of large volume of semi-structured data such as Pubmed using Gene Ontology. i-SEGOPubmed employs a client-server computing model. The SEGOPubmed engine [1] is used to process queries and web interface is using the Java Web Start, which uses Java Network Launching Protocol (JNLP) for downloading the application from the server to the client. Once the application is downloaded, Java Web Start initializes the application to check for the local resources. i-SEGOPubmed requires JRE (Java Runtime Environment) for its execution. It uses the Java Sockets to establish a data communication between the interface running on the client machine and the query processing done on the server machine. The server machine has a dedicated Server Socket listening to client requests. Once the server receives a request, it initializes a full duplex communication channel between the server and the client machine. Using this data channel, the interface sends the query to the server for further processing. Figure 1 shows the system architecture and the information flow of the proposed i-SEGOPubmed. [ABSTRACT FROM AUTHOR]

Background Fishes are capable of regenerating sensory hair cells in the inner ear after overexposure to sound [1]. However, the time course and gene expression of auditory hair cell regeneration has not been established for zebrafish (Danio rerio). This study establishes a time course of cell proliferation associated with hair cell regeneration in the zebrafish inner ear and examines gene expression patterns exhibited during this process. Materials and methods Adult zebrafish were exposed to a 100 Hz pure tone at 179 dB re 1 µPa RMS for 36 hours, and then allowed to recover for 0 to 14 days before morphological analysis. Saccular hair cells were quantified through visualization of stereocilia stained by phalloidin at 0, 2, 7, and 14 days postsound exposure. Cell proliferation was quantified through BrdU labeling at 0, 1, 2, 3, 7 and 10 days after the end of sound exposure. Groups of 18-20 fish were allowed to survive 0, 2 or 4 days post-sound exposure at which time the whole ears from each group were pooled and RNA extracted. Fluorescent cRNA from each treatment sample was hybridized to three duplicate Agilent Zebrafish oligonucleotide arrays (4 x 44 K, 60-mer oligonucleotides). Results Immediately following noise exposure, zebrafish saccules exhibited significant hair cell loss in the caudal region. Hair cell counts increased over the course of the experiment, reaching pretreatment levels by 14 days post-noise exposure. Cell proliferation peaked two days post-noise exposure in the caudal region, and to a lesser extent in the rostral region. Microarray analysis showed that a number of genes are significantly regulated in the sound-exposed zebrafish inner ear on days 2 and 4 following sound exposure (see Figure 1). Some of the most highly regulated genes included those encoding major histocompatibility complex, growth hormone, and cytoskeletal organization proteins. Conclusion The zebrafish ear is a good model for examining genes involved during the process of hair cell regeneration. A large number of genes were either up- or down-regulated at 2- and 4-days post-sound exposure. Putative biological processes for some of these genes have been identified, but more work will be needed to determine their function in the zebrafish inner ear. [ABSTRACT FROM AUTHOR]

Background In our previous work [1] we generalized Olshausen's algorithm [2] and designed a perceptual learning model using video sequences. In this study we propose to model conjunction search. Conjunction search (search for a unique combination of two features -- e.g., orientation and spatial frequency -- among distractions that share only one of these features) examines how the system combines features into perceptual wholes. We propose to improve the effectiveness of the decomposition algorithm by providing classification awareness. Attentional guidance does not depend solely on local visual features, but must also include the effects of interactions among features. The idea is to group together filters that will be responsible to extract similar features. It is well known that knowledge about which features define the target improves search performance and/or accuracy [3]. The nearest neighbors of the fixations will share a certain feature. Methods The main goal of this work is to use sequences of images and to design an attention model including conjunction search based on unsupervised self-learning. First, Independent Component Analysis algorithm is used to determine an initial set of basis functions from the first image (Figure 1). Second, unsupervised self-organizing learning is used to group together similar bases functions (Figure 2). Conclusion It is shown that performing sparse learning codes on video sequences of natural scenes produces results with qualitatively similar spatio-temporal properties of simple receptive field of neurons. The basic functions are similar to those obtained by sparse learning, but in our model they have a particular order (Figure 3). The proposed framework was tested using neurobiological (event related potentials ERP's) and behavioral (eye tracking) data. [ABSTRACT FROM AUTHOR]

Background SNP genotyping microarrays may be used to detect regions of loss-of-heterozygosity (LOH). Genotype array data are collected for tumor tissue and germline tissue samples from each subject. For each subject, an initial call of LOH or non-LOH is generated for each marker via straightforward comparison of the genotype call across each tissue sample pair [1]. The genotype calls are generated with some error. Therefore, statistical models are used to analyze the pattern of LOH calls to infer regions of LOH for each subject [1]. Materials and methods We propose call-based segmentation analysis (CBSA) as a permutation-based method to infer regions of LOH from this type of data. Chromosome endpoints and the positions of markers with initial LOH calls are used to divide the genomes of study subjects into a series of distinct segments that are indexed by subject and location. The size of each segment is measured by the number of non-LOH calls it contains. CBSA performs a permutation test to determine whether a segment has significantly fewer non-LOH calls than expected by chance. Permuting the assignment of initial LOH calls to subject and genomic position generates an empirical null distribution of segment size for computing p-values. In practice, p-values may be computed with a very accurate analytical approximation of the permutation distribution [2]. Next, the false discovery rate (FDR) is estimated with a robust method [3]. Finally, each segment defined by the observed positions of LOH calls has a size, p-value, and FDR estimate associated with it. Each segment with an FDR estimate below a selected threshold is inferred to be a segment of LOH. Mathematical proofs establish that the FDR estimate is conservative, i.e., the estimated FDR is expected to be greater than the actual FDR [3]. Results In our study of LOH in secondary leukemia [4], we applied CBSA with an estimated FDR of 10%. CBSA showed similar or greater sensitivity than dChip SNP [1] to detect LOH on each chromosome with one-copy loss according to cytogenetics [5]. Additionally, CBSA was robust against poor quality. After exclusion of two subjects with poor quality data, CBSA inferences were concordant with original CBSA inferences for the remaining eleven subjects at 99.6% of all markers. Conclusion CBSA is a practically useful method for detecting LOH. CBSA is conceptually simple, computationally efficient, statistically sound, and robust. Furthermore, CBSA may be a more powerful method than dChip SNP for some studies. [ABSTRACT FROM AUTHOR]

Background Recent advances in genotyping techniques and genomic knowledge via the Hap Map and Human Genome projects allow for true Genome-Wide Association (GWA) analysis for common complex diseases such as heart disease, diabetes, and Alzheimer's. A major obstacle in GWA analysis is the prohibitively high cost of genotyping the possibly thousands of individuals necessary to achieve statistical significance of results. One potential solution is to pool the DNA of case and control populations and to determine the genotype allele frequency differences in these populations by pooled allelotyping. While pooling can dramatically save time and money, it also adds sources of error. Our work has created a system process that allows for direct evaluation and comparison of pooled allelotyping to individual genotyping for GWA association analysis of complex disease. Materials and methods Complex disease penetrance functions were calculated for a 3 locus bi-allelic model with additive or multiplicative allelic spectrums using GenomeSIM software [1]. Penetrance probabilities were calculated for genotypes having from 0 to 6 disease-associated alleles. All probability functions used a base penetrance probability of 10% disease risk to account for environmental influences on disease risk. A total of 25,000 individual genotype files were created, each comprised of 10,000 SNPs with 3 disease-associated loci imbedded within. Custom MATLAB scripts were used to make in silico "pseudo-pools" for pooled allelotyping from the individual genotype files. HAPLOVIEW software was used to conduct individual genotyping association analysis [2]. A modified version of the Pooled DNA Analyzer (PDA) program was used for pooled association analysis [3]. Conclusion Power analysis was conducted for individual genotyping and pooled allelotyping with allele frequency estimation error from levels from 1% to 5% (see Figure 1). Our results show that pooling errors have a very large effect on the overall statistical significance of a pooled GWA study. Even a pooling error of 1% shifted the minimum resolvable relative risk (RR) with 80% power from (1.33-1.5) in individual genotyping to (1.5-1.67) in pooling. Pooling with 2% error had a minimum resolvable RR of (1.67-1.83). Pooling with 3% error resolved at RR (2.0-2.33). Further, pooling with 4% and 5% error of was not able to achieve 80% power at any of the levels of relative risk tested. Thus, pooled GWA studies may be limited to resolving complex disease associated variants with medium to high relative risks ratios. [ABSTRACT FROM AUTHOR]

An abstract of a study related to a web-based platform Motif Tool Manager used for deoxyribo-nucleic acid (DNA) motif discovery, which was conducted by Vinhthuy Phan, is presented.

An abstract of a study related to the comparison of lactating rat transcriptome against age-matched control virgins by implementing a mixed model approach, which was conducted by Antony T. Athippozhy and colleagues, is presented.

An abstract of a study related to molecular dissection of interleukin-18-induced cardiac hypertrophy, which was conducted by Sirshendu Majumdar and colleagues, is presented.

An abstract of a study related to an integrated data system Slim-Prim developed for the clinical and translational research, which was conducted by Teeradache Viangteeravat and colleagues, is presented.

An abstract of a study related to unsupervised approach on gene saliency based on the fuzzy rule, which was conducted by Nishchal K. Verma, Pooja Agrawal, and Yan Cui, is presented.

Background and solution MedSurv is a system designed for the rapid creation and maintenance of research surveys and questionnaires that does not require programmer intervention. MedSurv is built with medical surveys in mind and utilizes a group-based permission control with additional security features to help ensure compliance with applicable healthcare regulations. MedSurv is designed as a module for DotNet- Nuke [1], an open source portal and content management system built with ASP.Net technology, and therefore can be deployed and managed as intranet, extranet, and web sites. At the same time, all data is stored at the researcher's institution to guarantee the required data privacy. Thanks to its built-in support for user authentication and user roles, there is no need to create such functionality from scratch. However, a group-based permissions system is added to MedSurv to support sufficient granularity for access control. Although from the data access point of view data storage acts as a relational table, MedSurv uses a solution that we call virtual tables. The premise behind such a solution is that the structure of the tables is itself stored in a set of relational tables within the database, essentially creating a miniature database within the database. This additional layer is transparent to the user and removes the need for any programming or database knowledge. At the same time it gives the user the flexibility of changing the survey at runtime. Unlike a traditional structure that may require database developer's involvement each time a survey is added or changed, with virtual tables there is very low developer and database administration need after launch. MedSurv allows for creating complex medical surveys and is, in particular, used to develop questionnaires for research driven data collection in the Department of Gastroenterology. [ABSTRACT FROM AUTHOR]

Objective To test the association between haplotypes in the insulin -- insulin-like growth factor 2 (IGF2) locus and both risk of small for gestational age birth and umbilical cord IGFII levels. Subjects 207 pairs of healthy African-American full-term, newborn and mothers from Memphis, Tennessee and Jackson, Mississippi. Methods Associations of individual SNPs and inferred haplotypes with risk of small for gestational age (SGA) birth were tested using logistic regression, and mean umbilical cord IGF-II levels were compared by ANOVA. The risk of SGA and differences in cord IGF-II were also compared according to the parental origin of haplotypes. Results In newborns three INS SNPs exhibited significant (p < 0.01) association with reduced SGA risk. Two of these SNPs were significantly associated with umbilical cord IGF-II levels. In the mothers, the alternate SNP alleles were associated with reduced risk of SGA. No maternal SNPs were associated with umbilical cord IGF-II levels. When analyzed according to parental origin of haplotypes, paternally transmitted haplotypes were significantly associated with risk of SGA and umbilical cord IGFII levels, but maternally transmitted haplotypes were not significantly associated. Conclusion Newborn genotypes for polymorphisms near the 5' end of the insulin gene are significantly associated with size for gestational age and umbilical cord IGF-II levels, with a major effect due to the paternally inherited allele, which is preferentially expressed due to imprinting. There is some evidence that complementary haplotypes confer reduced risk of SGA in mothers and newborns. [ABSTRACT FROM AUTHOR]

Background Codon usage is well established for a number of different species. Multiple models have been proposed to show codon bias as a balance between mutation and selection. Most of these models emphasize controlling the speed of protein translation from the mRNA and increasing the accuracy where this bias is dependent on the abundance of the available tRNA. We show codon usage bias from a different angle based on a new hypothesis where selection is expected to act in a direction to favor codons that are more buffered, or protected, from mutation than those sensitive to mutation. It is anticipated that the more buffered the original coding sequence, the higher the survival chance for the whole organism since the resulting protein sequence remains unchanged. Two different complementary measures are developed to compute the average buffering capacity in a given sequence. We show that the buffering capacity of coding sequences is in general higher than that of randomly generated sequences and that of shifted reading frames. Highly expressed genes are shown to have an even higher buffering capacity than non-housekeeping genes. [ABSTRACT FROM AUTHOR]

Securin is a proto-oncogene that plays a pivotal role in cell division, sister chromatids separation and pancreatic beta cell proliferation. Over-expression of securin in normal cells results in cellular transformation and development on tumor in nude mice. On the other hand, depletion of securin from cancer cells results in suppression of tumor and reversal of cancer phenotype in vitro and in vivo. In many cases securin acts as a transcription factor and regulates the expression of important target genes. However, only a few target genes have been identified. Therefore, we used a quantitative microarray assay to identify the genes that are regulated by securin in a human embryonic kidney cell line (HEK293) that do not express securin or express at a very low level. We infected HEK293 cells with empty adenovirus, adenovirus expressing GFP or adenovirus expressing securin. Infection of cells with adenovirus expressing securin for 48 h resulted in alterations in the levels of expression of genes that ranged in magnitude from 1.1- to 42-fold, with a total of 313 genes exhibiting a p value < 0.05 compared to control adenovirus or adenovirus expressing GFP protein. Infection of HEK293 with adenovirus expressing securin was found to affect the expression of a diverse range of genes, including oncogenes and those that encode transcription factors, ion channel proteins, and cytoskeletal proteins as well as other proteins that are involved in signal transduction, the cell cycle, cell proliferation and apoptosis. The altered expression of some of these genes that were found by microarray analysis to be regulated by securin was confirmed by semiquantitative reverse transcriptase-polymerase chain reaction. The application of the microarray technique should prove to be a powerful tool for future analysis of the mechanisms by which securin regulates tumorigenesis. [ABSTRACT FROM AUTHOR]

Background An important aspect of bioinformatics is the infrastructure for storage and display of high throughput sequence data generated by biologists. The construction of databases and applications to display the results in a meaningful and useful way is not a trivial task. A well-designed and executed system can vastly improve the results obtained from an experiment. We present a system for storing and viewing EST sequences based on a set generated for a geranium genome. Results A series of over 4,000 expressed-sequence tags (ESTs) originating from the trichome of the geranium, Pelagonium xhortorum, were generated in a wet lab. EST sequences were organized into clusters of overlapping sequences using CAP3. The resulting clusters, including single EST clusters, were analyzed and annotated by hand in order to classify the sequences into families and subfamilies of genes. Further refinement using Blast2GO classifies these sequences into annotated groups based on biological process, cellular component, and molecular function. Presentation of the results were accomplished through a MySQL database and java servlet web pages used to analyze, group, and display the resulting data. Queries into the data can be performed by using blast or by searching by known gene families. [ABSTRACT FROM AUTHOR]

With multiple metazoan genomes in each family being sequenced promoter analysis is becoming a useful tool in genomic analysis. Aligning the promoter regions in the DNA of C. elegans and C. briggsae identifies conserved promoter elements. While not all promoter elements are conserved and not all conserved regions are promoter elements, we find that conservation is a useful method for determining promoter complexity. Promoter complexity identifies which genes have particularly interesting regulation, identifying gene groups with a strong promoter complexity signal and cases where a gene's promoter complexity differs from the group's promoter complexity. We identify potential promoter sequence by several local sequence alignment methods. Instead of studying individual promoter elements we are looking at patterns of promoter complexity; the total conserved sequence for each gene gives us a measure for promoter complexity. Monte Carlo random sampling is used to identify Gene Ontology and KEGG Pathway annotated gene groups that appear to have significantly low or high complexity. Developmental genes were found to have low complexity while growth genes have high complexity. Other groups that we expected to have high significance show none at all or had low promoter complexity. Genes contributing to the extracellular region scored high in promoter complexity while basal transcription factors often scored low in complexity. Genes annotated with GO terms transcription factors, signalling genes, genes with multiple alternative splice products, and developmental genes had significant promoter scores. We examined gene expression in the published C. elegans microarray experiments and found a strong positive correlation between gene group expression variation and promoter complexity. Promoter complexity tends to be an accurate predictor of the complexity of a gene's pattern of expression and also gives us another tool to find anomalous genes. [ABSTRACT FROM AUTHOR]