Computational Genomics Tools for Copy-Number Fluctuations in Prostate Cancer

Abstract

The goal of this research was to produce a large, useful, open-access database of lesions in prostate cancer, organized in terms of segments of aberrant copy numbers for subsequent automated statistical analysis. The authors aimed to make this data base easily searched, so that users could find those genes likely to be causally related to the disease. They hoped to maximize the utility of the data base by optimizing the following factors: cost, availability, efficiency, quality, and ease of use. In summary, the authors have made significant and visible progress towards the following three goals: (1) developed new statistical methods to improve the detection of abnormal lesions, define confidence in the detected lesions, and localize putative genes involved in the cancer; (2) created a data base with improved statistical significance and with an enhanced human-computer interface so that users can effortlessly maneuver through the data to draw conclusions; and (3) created the foundations to build two important "bridges" to future work. The first bridge is a novel statistical algorithm to combine the genomic data with whole genome data for SNP and other markers (for instance, indicating LOH). The second bridge is better low-level background correction software that makes the genomic data usable without too many expensive biological replicates. They now have software based on "redescription" that allows one to easily combine the genomic data with gene-expression data.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2005
Accession Number
ADA455105

Entities

People

  • Bhubaneswar Mishra

Organizations

  • New York University

Tags

DTIC Thesaurus Topics

  • Biology
  • Computational Biology
  • Computer Science
  • Computers
  • Databases
  • Diseases And Disorders
  • Genomics
  • Information Science
  • Mathematical Analysis
  • Neoplasms
  • New York
  • Prostate
  • Prostate Cancer
  • Statistical Algorithms
  • Statistical Analysis
  • Synthetic Biology
  • Systems Biology

Readers

  • Distributed Systems and Data Platform Development
  • Molecular and genetic basis of cancer.
  • Regression Analysis.