Robert M. Flight

Proceedings of the ninth annual UT-ORNL-KBRIN Bioinformatics Summit 2010

The University of Tennessee (UT), the Oak Ridge National Laboratory (ORNL), and the Kentucky Biomedical Research Infrastructure Network (KBRIN), have collaborated over the past eleven years to share research and educational expertise in bioinformatics. One result of this collaboration is the joint sponsorship of an annual regional summit to bring together researchers, educators and students who are interested in bioinformatics from a variety of research and educational institutions. This summit provides unique opportunities for collaboration and forging links between members of the various institutions. This year, the Eleventh Annual UT-ORNL-KBRIN Bioinformatics Summit was held at the Seelbach Hilton Hotel in Louisville, Kentucky from March 30-April 1, 2012. A total of 232 participants pre-registered for the summit, with 126 from various Kentucky institutions and 80 from various Tennessee institutions. A number of additional participants came from universities and research institutions from other states and countries, e.g. University of Arkansas Medical Sciences, Michigan State University, University of Cincinnati, Iowa State University, etc. Eighty-four registrants were faculty, with an additional 68 students, 37 staff, and 32 postdoctoral participants (with 12 undeclared). The conference program consisted of three days of presentations. The first day included a pre-summit of talks by Kentucky researchers and a workshop on Next-Generation Sequencing technologies. The next two days were dedicated to scientific presentations divided into four plenary sessions on Next-Generation Sequencing, Medical Informatics, Metagenomics, and Behavioral and Comparative Genomics. The Medical Informatics session was followed by four short talks, selected from 47 submitted poster abstracts.

Genome-Wide Profiling of PARP1 Reveals an Interplay with Gene Regulatory Regions and DNA Methylation

Poly (ADP-ribose) polymerase-1 (PARP1) is a nuclear enzyme involved in DNA repair, chromatin remodeling and gene expression. PARP1 interactions with chromatin architectural multi-protein complexes (i.e. nucleosomes) alter chromatin structure resulting in changes in gene expression. Chromatin structure impacts gene regulatory processes including transcription, splicing, DNA repair, replication and recombination. It is important to delineate whether PARP1 randomly associates with nucleosomes or is present at specific nucleosome regions throughout the cell genome. We performed genome-wide association studies in breast cancer cell lines to address these questions. Our studies show that PARP1 associates with epigenetic regulatory elements genome-wide, such as active histone marks, CTCF and DNase hypersensitive sites. Additionally, the binding of PARP1 to chromatin genome-wide is mutually exclusive with DNA methylation pattern suggesting a functional interplay between PARP1 and DNA methylation. Indeed, inhibition of PARylation results in genome-wide changes in DNA methylation patterns. Our results suggest that PARP1 controls the fidelity of gene transcription and marks actively transcribed gene regions by selectively binding to transcriptionally active chromatin. These studies provide a platform for developing our understanding of PARP1’s role in gene regulation.

categoryCompare, an analytical tool based on feature annotations.

Assessment of high-throughput—omics data initially focuses on relative or raw levels of a particular feature, such as an expression value for a transcript, protein, or metabolite. At a second level, analyses of annotations including known or predicted functions and associations of each individual feature, attempt to distill biological context. Most currently available comparative- and meta-analyses methods are dependent on the availability of identical features across data sets, and concentrate on determining features that are differentially expressed across experiments, some of which may be considered “biomarkers.” The heterogeneity of measurement platforms and inherent variability of biological systems confounds the search for robust biomarkers indicative of a particular condition. In many instances, however, multiple data sets show involvement of common biological processes or signaling pathways, even though individual features are not commonly measured or differentially expressed between them. We developed a methodology, categoryCompare, for cross-platform and cross-sample comparison of high-throughput data at the annotation level. We assessed the utility of the approach using hypothetical data, as well as determining similarities and differences in the set of processes in two instances: (1) denervated skin vs. denervated muscle, and (2) colon from Crohns disease vs. colon from ulcerative colitis (UC). The hypothetical data showed that in many cases comparing annotations gave superior results to comparing only at the gene level. Improved analytical results depended as well on the number of genes included in the annotation term, the amount of noise in relation to the number of genes expressing in unenriched annotation categories, and the specific method in which samples are combined. In the skin vs. muscle denervation comparison, the tissues demonstrated markedly different responses. The Crohns vs. UC comparison showed gross similarities in inflammatory response in the two diseases, with particular processes specific to each disease.

A Computational Framework for High-Throughput Isotopic Natural Abundance Correction of Omics-Level Ultra-High Resolution FT-MS Datasets

New metabolomics applications of ultra-high resolution and accuracy mass spectrometry can provide thousands of detectable isotopologues, with the number of potentially detectable isotopologues increasing exponentially with the number of stable isotopes used in newer isotope tracing methods like stable isotope-resolved metabolomics (SIRM) experiments. This huge increase in usable data requires software capable of correcting the large number of isotopologue peaks resulting from SIRM experiments in a timely manner. We describe the design of a new algorithm and software system capable of handling these high volumes of data, while including quality control methods for maintaining data quality. We validate this new algorithm against a previous single isotope correction algorithm in a two-step cross-validation. Next, we demonstrate the algorithm and correct for the effects of natural abundance for both 13C and 15N isotopes on a set of raw isotopologue intensities of UDP-N-acetyl-D-glucosamine derived from a 13C/15N-tracing experiment. Finally, we demonstrate the algorithm on a full omics-level dataset.

IB4-binding sensory neurons in the adult rat express a novel 3' UTR-extended isoform of CaMK4 that is associated with its localization to axons.

Calcium/calmodulin‐dependent protein kinase 4 (gene and transcript: CaMK4; protein: CaMKIV) is the nuclear effector of the Ca2+/calmodulin kinase (CaMK) pathway where it coordinates transcriptional responses. However, CaMKIV is present in the cytoplasm and axons of subpopulations of neurons, including some sensory neurons of the dorsal root ganglia (DRG), suggesting an extranuclear role for this protein. We observed that CaMKIV was expressed strongly in the cytoplasm and axons of a subpopulation of small‐diameter DRG neurons, most likely cutaneous nociceptors by virtue of their binding the isolectin IB4. In IB4+ spinal nerve axons, 20% of CaMKIV was colocalized with the endocytic marker Rab7 in axons that highly expressed CAM‐kinase‐kinase (CAMKK), an upstream activator of CaMKIV, suggesting a role for CaMKIV in signaling though signaling endosomes. Using fluorescent in situ hybridization (FISH) with riboprobes, we also observed that small‐diameter neurons expressed high levels of a novel 3′ untranslated region (UTR) variant of CaMK4 mRNA. Using rapid amplification of cDNA ends (RACE), reverse‐transcription polymerase chain reaction (RT‐PCR) with gene‐specific primers, and cDNA sequencing analyses we determined that the novel transcript contains an additional 10 kb beyond the annotated gene terminus to a highly conserved alternate polyadenylation site. Quantitative PCR (qPCR) analyses of fluorescent‐activated cell sorted (FACS) DRG neurons confirmed that this 3′‐UTR‐extended variant was preferentially expressed in IB4‐binding neurons. Computational analyses of the 3′‐UTR sequence predict that UTR‐extension introduces consensus sites for RNA‐binding proteins (RBPs) including the embryonic lethal abnormal vision (ELAV)/Hu family proteins. We consider the possible implications of axonal CaMKIV in the context of the unique properties of IB4‐binding DRG neurons. J. Comp. Neurol. 522:308–336, 2014.

UTR extension and alternate polyadenylation in neuroplasticity: an emerging paradigm?

Results Computational analyses of the novel UTR sequences, focusing on RNA-binding protein (RNAbp) interaction motifs revealed strongly over-represented RNAbps with known roles in nervous system pathologies. We consider the implications of 3’UTR transcript extension and protein interaction in the context of axonal plasticity and the consequences of mis-regulation of this process during neurological disease.

Potential Bias in Go::TermFinder

The increased need for multiple statistical comparisons under conditions of non-independence in bioinformatics applications, such as DNA microarray data analysis, has led to the development of alternatives to the conventional Bonferroni correction for adjusting P-values. The use of the false discovery rate (FDR), in particular, has grown considerably. However, the calculation of the FDR frequently depends on drawing random samples from a population, and inappropriate sampling will result in a bias in the calculated FDR. In this work, we demonstrate a bias due to incorrect random sampling in the widely used GO::TermFinder package. Both T(2) and permutation tests are used to confirm the bias for a test set of data, which leads to an overestimation of the FDR of about 10%. A simple fix to the random sampling method is proposed to remove the bias.

AbsIDconvert: An absolute approach for converting genetic identifiers at different granularities

BackgroundHigh-throughput molecular biology techniques yield vast amounts of data, often by detecting small portions of ribonucleotides corresponding to specific identifiers. Existing bioinformatic methodologies categorize and compare these elements using inferred descriptive annotation given this sequence information irrespective of the fact that it may not be representative of the identifier as a whole.ResultsAll annotations, no matter the granularity, can be aligned to genomic sequences and therefore annotated by genomic intervals. We have developed AbsIDconvert, a methodology for converting between genomic identifiers by first mapping them onto a common universal coordinate system using an interval tree which is subsequently queried for overlapping identifiers. AbsIDconvert has many potential uses, including gene identifier conversion, identification of features within a genomic region, and cross-species comparisons. The utility is demonstrated in three case studies: 1) comparative genomic study mapping plasmodium gene sequences to corresponding human and mosquito transcriptional regions; 2) cross-species study of Incyte clone sequences; and 3) analysis of human Ensembl transcripts mapped by Affymetrix®; and Agilent microarray probes. AbsIDconvert currently supports ID conversion of 53 species for a given list of input identifiers, genomic sequence, or genome intervals.ConclusionAbsIDconvert provides an efficient and reliable mechanism for conversion between identifier domains of interest. The flexibility of this tool allows for custom definition identifier domains contingent upon the availability and determination of a genomic mapping interval. As the genomes and the sequences for genetic elements are further refined, this tool will become increasingly useful and accurate. AbsIDconvert is freely available as a web application or downloadable as a virtual machine at:http://bioinformatics.louisville.edu/abid/.

Transcriptional Profile of Immediate Response to Ionizing Radiation Exposure

Astronauts participating in long duration space missions are likely to be exposed to ionizing radiation associated with highly energetic and charged heavy particles. Previously proposed gene biomarkers for radiation exposure include phosphorylated H2A Histone Family, Member X (γH2AX), Tumor Protein 53 (TP53), and Cyclin-Dependent Kinase Inhibitor 1A (CDKN1A). However, transcripts of these genes may not be the most suitable biomarkers for radiation exposure due to a lack of sensitivity or specificity. As part of a larger effort to develop lab-on-a-chip methods for detecting radiation exposure events using blood samples, we designed a dose–course microarray study in order to determine coding and non-coding RNA transcripts undergoing differential expression immediately following radiation exposure. The main goal was to elicit a small set of sensitive and specific radiation exposure biomarkers at low, medium, and high levels of ionizing radiation exposure. Four separate levels of radiation were considered: 0 Gray (Gy) control; 0.3 Gy; 1.5 Gy; and 3.0 Gy with four replicates at each radiation level. This report includes raw gene expression data files from the resulting microarray experiments from all three radiation levels ranging from a lower, typical exposure than an astronaut might see (0.3 Gy) to high, potentially lethal, levels of radiation (3.0 Gy). The data described here is available in NCBIs Gene Expression Omnibus (GEO), accession GSE64375.

Preliminary exploration of time course DNA microarray data with correlation maps.

In the analysis of data from high-throughput experiments, information regarding the underlying data structure provides the researcher with confidence in the appropriateness of various analysis methods. One extremely simple but powerful data visualization method is the correlation heat map, whereby correlations between experiments/conditions are calculated and represented using color. In this work, the use of correlation maps to shed light on transcription patterns from DNA microarray time course data prior to gene-level analysis is described. Using three different time course studies from the literature, it is shown how the patterns observed at the array level provide insights into the dynamics of the system under study and the experimental design.