Gene expression measurement using microarrays or next-generation sequencing techniques, is a popular and useful technology for genomic analysis. Challenging problems result from the large volume of data generated in these experiments. Quality control and experimental design remain important fundamental issues. Analytical techniques which account for complex experimental designs and minimizing artifacts are required. Bioinformaticians are required to be able to handle large scale data projects while also being to process data into a format where statistical procedures can be applied. There are different statistical and bioinformatics issues that remain and this project attempts to address some of these. Next generation sequencing techniques are now a popular means for RNA expression measurement (RNAseq). As with microarrays, a host of technical and quality control issues remain as challenges, in addition to the new statistical problems implied by change of scale from continuous (microarray fluorescence) to discrete (read counts). Affordable, high-quality software availability has been one of the bottlenecks in analysis of microarray data. We have further developed the MSCL Analyst's Toolbox written in the JMP software package to address this need. This toolbox allows investigators to download Affymetrix microarray data from a central database, normalize and transform the data, inspect it for a variety of outliers or defects, perform a variety of statistical tests to select relevant genes affected in the experiment, and then visualize and classify various patterns of gene expression. In collaboration with over forty investigators in NCI, CC, NHLBI, NINDS, NIAID, NHGRI, NICHD, NIA, NIDDK, NIDA , this tool has been applied to dozens of microarray studies. The Analyst's Toolbox has been extended to now handle analysis of RNAseq data, with inclusion of new data transformations, and utility functions. In addition, the capability to link data from the user's workstation to online databases has been a nice feature that has been recently added to the Toolbox. In a collaboration with NHGRI, we are conducting an RNA-seq investigation of transcriptomic differences using a case-control design, of coronary artery calcification, based on ClinSeq study samples. We integrated RNA-seq and microarray data from the same individuals, and found consistent changes across the two methodologies, which are now candidates for follow-up studies. That same experiment has been extended to a possible novel transcript finding in coronary artery calcification patients. This finding is being further researched within our lab and the NHGRI. In a collaboration with NEI, we are analyzing the transcriptome of mouse photoreceptor from embryonic, through neonatal to later adult stages. This extensive time series, using both the Affymetrix Exon array and RNA-seq in parallel, allows for high resolution analysis at the gene and exon levels, and is providing an unparalleled view of transcriptomic changes accompanying important developmental events (e.g. differentiation, eye opening, aging).
The aim i s to identify genes involved in mammalian aging and which may be relevant to age-related diseases of the eye in human. This manuscript has recently been accepted by the journal Cell Reports after two minor revisions are made. In a collaboration with NCI, we are analyzing the transcriptome of miRNAs of MEN1 gene mutation pancreatic and other tumors from nearby tissues. Clonality of these tumors was assessed using the Human v2 miRNA Nanostring chip (NanoString Technologies, Seattle, WA). The purpose of this work is to assess and analyze clonality and miRNA profiling of neuroendocrine tumors from four MEN1 patients. These patients have the tumor present in at least two separate sites including the pancreas, duodenum and the retroduodenal or peri-pancreatic lymph nodes. The manuscript for this work is in preparation An analytical pipeline was developed and published in Plos One in February 2016 utilizing 16s rRNA sequencing data from the Ion Metagenomics Kit (Life Technologies). This publication analyzed and evaluated the results from four different mock samples from BEI Laboratories. The results of this work will be used in other collaborations. In a collaboration with the Clinical Center, we are analyzing metagenomic data from 16S rRNA sequencing of severe aplastic anemia (SAA) patients. This is a longitudinal study where samples are collected at baseline, three months after treatment and 6 months after treatment. Pilot data from two patients at baseline and follow up have been studied so far. The study had an enrollment N of 24 patients. The goal of this study is to identify a core microbiome in humans regardless of disease state while also being able to identify changes in the microbiome in SAA patients before treatment and after treatment. This work will be presented at the NIH Research Festival and a manuscript is in preparation. In addition to the above collaboration with the Clinical Center, a collaboration with NIAAA has begun aimed at studying the gut microbiome changes during the alcoholic detoxification process. The pipeline published in February 2016 will be used to analyze this data generated from the 16s Ion Metagenomics Kit (Life Technologies). Libraries from this kit include amplicons that target 7 hypervariable regions of the 16s gene.

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Kim, Jung-Woong; Yang, Hyun-Jin; Brooks, Matthew John et al. (2016) NRL-Regulated Transcriptome Dynamics of Developing Rod Photoreceptors. Cell Rep 17:2460-2473
Barb, Jennifer J; Oler, Andrew J; Kim, Hyung-Suk et al. (2016) Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples. PLoS One 11:e0148047
Moutsopoulos, Niki M; Chalmers, Natalia I; Barb, Jennifer J et al. (2015) Subgingival microbial communities in Leukocyte Adhesion Deficiency and their relationship with local immunopathology. PLoS Pathog 11:e1004698
Akula, N; Barb, J; Jiang, X et al. (2014) RNA-sequencing of the brain transcriptome implicates dysregulation of neuroplasticity, circadian rhythms and GTPase binding in bipolar disorder. Mol Psychiatry 19:1179-85
Sen, Shurjo K; Boelte, Kimberly C; Barb, Jennifer J et al. (2014) Integrative DNA, RNA, and protein evidence connects TREML4 to coronary artery calcification. Am J Hum Genet 95:66-76
Raghavachari, Nalini; Liu, Poching; Barb, Jennifer J et al. (2014) Integrated analysis of miRNA and mRNA during differentiation of human CD34+ cells delineates the regulatory roles of microRNA in hematopoiesis. Exp Hematol 42:14-27.e1-2
Sen, Shurjo K; Barb, Jennifer J; Cherukuri, Praveen F et al. (2014) Identification of candidate genes involved in coronary artery calcification by transcriptome sequencing of cell lines. BMC Genomics 15:198
Shankavaram, Uma; Fliedner, Stephanie M J; Elkahloun, Abdel G et al. (2013) Genotype and tumor locus determine expression profile of pseudohypoxic pheochromocytomas and paragangliomas. Neoplasia 15:435-47