High Performance Computing
and Informatics Office, DCB
Resource, Condition, and Disease
Categorization (RCDC, with OPASI/OD): On a yearly
basis, NIH reports to the Congress and the public how much money is
allocated to approximately 360 research and disease categories such as
Parkinson's, mental health, diabetes, and cancer. Congress and the NIH
Office of the Director use this data to better understand NIH research
spending and priority areas. In the past, each institute and center
(IC) assigned their grants to these categories based on their own
interpretation of the category definition, and this led to inaccurate
and incomplete reporting. Congress, advocacy groups and the
public have recently requested that the agency improve its manner for
reporting NIH funding levels. As a result, the Research, Condition,
and Disease Categorization (RCDC) project was created to develop a
knowledge management infrastructure that standardizes and facilitates
budget reporting by research topic. Text mining techniques have been
implemented to classify NIH grant applications into proper research and
disease categories. HPCIO collaborates with RCDC project team to
identify and develop technologies and methodologies that can improve
the RCDC categorization tool and enhance internal NIH communication
processes. The end result of this project will improve
reliability and consistency of categorization across ICs.
The
Human Salivary Proteome Annotation Project is an effort supported
by NIDCR, aiming to generate a complete catalog of all salivary
secretory proteins using state-of-the-art, high-throughput proteomic
technologies. Several research centers are involved to
characterize about 1,700 proteins secreted by the salivary
glands. HPCIO is working with NIDCR to implement a wiki-based
platform that will allow scientists in this area to collaboratively
annotate the proteins with information such as their molecular
functions and associated pathways. Furthermore, the system will
leverage a wide range of semantic and ontological resources to
facilitate knowledge discovery from the annotations as well as
information mined from various authoritative databases. A working
prototype using the Semantic MediaWiki framework has been
implemented. More than 170,000 pages have been populated with
experimental data generated by the research groups. Additional
functionalities will be developed over the course of next year to
enhance the ease and accuracy of the annotations and to provide users
the ability to explore the knowledge space of every protein.
Genetic
Association in Bioinformatics: In collaboration with NIA,
HPCIO develops and enhances tools for the archival, retrieval, and
mining of genetic association study data. The Genetic Association
Database (GAD) is an archive of human genetic association studies
of complex diseases and disorders. GAD enables scientists to query
association data in a systematic manner and to integrate association
data with other molecular databases. Study data are recorded in the
context of official human gene nomenclature with additional molecular
reference numbers and links. The goal of this project is to collect all
published genetic association study data and allow users to rapidly
identify medically relevant polymorphism from the large volume of
polymorphism and mutational data, in the context of standardized
nomenclature. PubMatrix SE, is a Web-based text-mining
tool on MEDLINE citations. It applies natural language processing and
statistical methods on biomedical literature text to provide an
estimation of the strength of associations among various entities,
including genes and diseases. The results are represented in a matrix
format, facilitating more efficient interpretation of large amount of
text data to assist in microarray studies
The National Database for Autism Research (NDAR) is a collaborative biomedical informatics system being created by the National Institute of Health to provide a national resource to support and accelerate research in autism. HPCIO has taken the leading role in the development of a clinical assessment component of the NDAR. The clinical assessment component will enable researchers to design clinical study plans and will provide common measures for data-entry. With the centralized clinical assessment repository for autism research, NDAR can provide long-term subject histories that can be used to support clinical care and provide integration with genomic information for biomedical research.
Clinical Informatics and Management System
(CIMS, with NINDS and Brain IRB): HPCIO has been developing a
clinical care and research data management system to allow physicians
and clinicians to conduct clinical trials and researches. The Clinical
Informatics and Management System (CIMS) is a centralized clinical
data management and analysis system that will assist NIH clinical
investigators in managing protocols and patient and research data as
well as in integrating disparate data sources for analysis.
High Resolution Research Tomograph
(HRRT, with CC and NIMH): The Motion-compensation OSEM
(ordered subset expectation maximization) List-mode Algorithm for
Resolution-recovery Reconstruction (MOLAR) system is the result of an
ongoing collaboration between three organizations in the NIH Intramural
Research Program as well as Yale University, SUNY Buffalo, and CPS
Innovations, Knoxville, TN, USA. It is a complete system for
managing and performing high-resolution, iterative reconstructions of
positron emission tomography (PET) data. MOLAR has been designed
for use with the ECAT HRRT (High Resolution Research
Tomograph, CPS Innovations) operating in list-mode. Due to the
pluggable component design of the software, however, the MOLAR
reconstruction engine is readily adaptable to any PET scanner,
including frame-mode scanners. Reconstructions are performed on a
parallel cluster of commodity computers. One of the goals of the
project is to provide complete reference implementations (i.e., with
physical effects incorporated into the model) of common iterative
reconstruction algorithms such as OSEM. Another goal of the
project is to provide to the PET research community a general software
framework for performing list-mode or frame-mode reconstructions on the
HRRT or any other PET scanner. The framework has been designed to
allow collaborating groups or individuals the opportunity to contribute
their own components.
Electron
Paramagnetic Resonance (EPR) is a spectroscopic technique that
detects and characterizes molecules with unpaired electrons (i.e., free
radicals). Although it is closely related to nuclear magnetic
resonance (NMR) spectroscopy, EPR is still under development as an
imaging modality. Unlike other imaging modalities, EPR is able to
take direct measurements of tissue oxygen concentration in a manner
that is not dependent on complex biological processes such as ligand
binding specificity or tracer metabolism. The single-point
imaging (SPI) scheme is essentially a phase-encoding technique that
operates by acquiring a single data point in the free induction decay
(FID) after a fixed delay (the phase encoding time), in the presence of
static magnetic field gradients. SPI produces artifact-free
images because it does not measure the time evolution of the
magnetization. The goal of this collaboration with the
Radiation Biology Branch of NCI is to provide computational methodology
and resources that will advance the state of the science in EPR.
A particular focus of this collaboration is the development of
reconstruction methodology that will improve the quality of oximetric
images obtained using the SPI technique.
NHGRI
OPASI/OD
NIDCR
CIO/NIH
NIA
NINDS
Yale University
State University of New York
NCI