title: Can We Predict Gene Expression by Understanding Proximal Promoter Architecture?
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.
authors.affiliation: Institute of Genetics and Animal Breeding, Polish Academy of Sciences, ul. Postępu 36A, Jastrzębiec, 05-552 Magdalenka, Poland. Electronic address: l.huminiecki@ighz.pl.
authors.forename: Łukasz
authors.initials: Ł
authors.lastname: Huminiecki
authors.affiliation: Institute of Genetics and Animal Breeding, Polish Academy of Sciences, ul. Postępu 36A, Jastrzębiec, 05-552 Magdalenka, Poland.
authors.forename: Jarosław
authors.initials: J
authors.lastname: Horbańczuk
mesh_terms: D000818:Animals; D057225:Data Mining / Q000379:methods*; D030561:Databases, Nucleic Acid; D005786:Gene Expression Regulation; D015894:Genome, Human; D006801:Humans; D011237:Predictive Value of Tests; D011401:Promoter Regions, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*