Can We Predict Gene Expression by Understanding Proximal Promoter Architecture?. Issue 6 (June 2017)

• Record Type: Journal Article
• Authors: Huminiecki, Łukasz; Horbańczuk, Jarosław
• Is Part Of: Trends in biotechnology. Volume 35:Issue 6(2017)
• Page Start: 530
• Page End: 546
• Publication Date: 2017-06
• Subjects: Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2017.03.007
• Languages: English
• ISSNs: 0167-7799

Abstract

Abstract : We review computational predictions of expression from the promoter architecture – the set of transcription factors that can bind the proximal promoter. We focus on spatial expression patterns in animals with complex body plans and many distinct tissue types. This field is ripe for change as functional genomics datasets accumulate for both expression and protein–DNA interactions. While there has been some success in predicting the breadth of expression (i.e., the fraction of tissue types a gene is expressed in), predicting tissue specificity remains challenging. We discuss how progress can be achieved through either machine learning or complementary combinatorial data mining. The likely impact of single-cell expression data is considered. Finally, we discuss the design of artificial promoters as a practical application. Trends: Integrative data mining of functional genomics datasets is an increasingly attractive research strategy that does not require investment in reagents or experimental facilities, but it does require qualified bioinformatics staff with expertise in applied statistics. A wave of experimental data on promoter architectures is redefining how we model gene expression. Thus, we need new mathematical formalism to represent promoter architectures, implement computations on them, and facilitate combinatorial data mining. Practical applications of the data mining of promoter architectures could include automatic genome annotation or the design of artificial promoters. We can predict breadth of expression, but predicting tissue specificity remains a challenge. Moreover, the concepts of breadth of expression and tissue specificity will need to be redefined in view of single-cell expression data.