Bioinformatics is a field that melds information technology and biology, chiefly through the development of software that seeks out patterns and regularities in different biological data sources. The 2004 database issue of Nucleic Acids Research contains descriptions of 548 databases of value to biologists. The 2003 Web Server Issue of the same journal reported on 131 web servers offering an array of useful bioinformatics services from simple pattern prediction to three-dimensional protein structure prediction. The Bioinformatics Core was created in March 2003 to support the UMass DERC researchers in selecting and applying these computational tools and educating them to integrate the extensive information contained in the biological databases into their research activities. Specifically, this Core provides bioinformatics services in the following areas: l)Integrate bioinformatics resources. 2) Perform bioinformatics analyses. 3) Microarray data analysis. 4) Support research projects with computational tools and implement these tools. This Core has two single processor workstations running the linux operating system and two workstations running Windows. These machines are located in the office space occupied by the Core. Two two-processor server type machines running linux and using RAID for data storage are dedicated database and web servers. One of these server-type computers is located in the IS Department of UMMS, is monitored by the IS Department, and is backed up daily. The Bioinformatics Core is predominantly used for microarray analysis and a database was designed and implemented to store microarray data from several UMass DERC members' laboratories. The data can be accessed through a www browser and allows limited data mining. The database is constantly being improved according to specifications of the researchers. This Core enhances the productivity of diabetes research by a) providing support in the use of standard bioinformatics tools, b) facilitating access to biological data, c) automating bioinformatics analyses, and d) supporting analysis of microarray data sets.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Center Core Grants (P30)
Project #
5P30DK032520-23
Application #
7311476
Study Section
Special Emphasis Panel (ZDK1)
Project Start
Project End
Budget Start
2006-04-01
Budget End
2007-03-31
Support Year
23
Fiscal Year
2006
Total Cost
$221,800
Indirect Cost
Name
University of Massachusetts Medical School Worcester
Department
Type
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
01655
Desai, Paurav B; San Agustin, Jovenal T; Stuck, Michael W et al. (2018) Ift25 is not a cystic kidney disease gene but is required for early steps of kidney development. Mech Dev 151:10-17
Ly, Socheata; Navaroli, Deanna M; Didiot, Marie-Cécile et al. (2017) Visualization of self-delivering hydrophobically modified siRNA cellular internalization. Nucleic Acids Res 45:15-25
Wang, Feng; McCannell, Kurtis N; Boškovi?, Ana et al. (2017) Rlim-Dependent and -Independent Pathways for X Chromosome Inactivation in Female ESCs. Cell Rep 21:3691-3699
Watkin, Levi B; Mishra, Rabinarayan; Gil, Anna et al. (2017) Unique influenza A cross-reactive memory CD8 T-cell receptor repertoire has a potential to protect against EBV seroconversion. J Allergy Clin Immunol 140:1206-1210
LeBlanc, Scott E; Wu, Qiong; Lamba, Pallavi et al. (2016) Promoter-enhancer looping at the PPAR?2 locus during adipogenic differentiation requires the Prmt5 methyltransferase. Nucleic Acids Res 44:5133-47
Wang, Feng; Shin, JongDae; Shea, Jeremy M et al. (2016) Regulation of X-linked gene expression during early mouse development by Rlim. Elife 5:
Kincaid, Eleanor Z; Murata, Shigeo; Tanaka, Keiji et al. (2016) Specialized proteasome subunits have an essential role in the thymic selection of CD8(+) T cells. Nat Immunol 17:938-45
Townsley, E; O'Connor, G; Cosgrove, C et al. (2016) Interaction of a dengue virus NS1-derived peptide with the inhibitory receptor KIR3DL1 on natural killer cells. Clin Exp Immunol 183:419-30
Wyss, Lena; Stadinski, Brian D; King, Carolyn G et al. (2016) Affinity for self antigen selects Treg cells with distinct functional properties. Nat Immunol 17:1093-101
Delong, Thomas; Wiles, Timothy A; Baker, Rocky L et al. (2016) Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion. Science 351:711-4

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