Computational prediction of the global functional genomic landscape: Applications, methods, and challenges

Weiqiang Zhou, Ben Sherwood, Hongkai Ji

Research output: Contribution to journalReview articlepeer-review

Abstract

Technological advances have led to an explosive growth of high-throughput functional genomic data. Exploiting the correlation among different data types, it is possible to predict one functional genomic data type from other data types. Prediction tools are valuable in understanding the relationship among different functional genomic signals. They also provide a cost-efficient solution to inferring the unknown functional genomic profiles when experimental data are unavailable due to resource or technological constraints. The predicted data may be used for generating hypotheses, prioritizing targets, interpreting disease variants, facilitating data integration, quality control, and many other purposes. This article reviews various applications of prediction methods in functional genomics, discusses analytical challenges, and highlights some common and effective strategies used to develop prediction methods for functional genomic data.

Original languageEnglish (US)
Pages (from-to)88-105
Number of pages18
JournalHuman Heredity
Volume81
Issue number2
DOIs
StatePublished - Jan 1 2017

Keywords

  • Epigenome
  • Genomics
  • High-dimensional data
  • High-throughput sequencing
  • Statistical methods

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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