Identification of cis-regulatory sequence variations in individual genome sequences

Abstract

Functional contributions of cis-regulatory sequence variations to human genetic disease are numerous. For instance, disrupting variations in a HNF4A transcription factor binding site upstream of the Factor IX gene contributes causally to hemophilia B Leyden. Although clinical genome sequence analysis currently focuses on the identification of protein-altering variation, the impact of cis-regulatory mutations can be similarly strong. New technologies are now enabling genome sequencing beyond exomes, revealing variation across the non-coding 98% of the genome responsible for developmental and physiological patterns of gene activity. The capacity to identify causal regulatory mutations is improving, but predicting functional changes in regulatory DNA sequences remains a great challenge. Here we explore the existing methods and software for prediction of functional variation situated in the cis-regulatory sequences governing gene transcription and RNA processing.

Cistrome plasticity and mechanisms of cistrome reprogramming

“…SNPs are DNA sequence variations that are suspected to be one of the major determinants of phenotypic diversity among individuals and/or population groups, including gene expression variations, differences in susceptibility to disease and response to pharmacologic treatment. [107][108][109][110] The human genome contains roughly 1-2 SNPs per kb, and when these variations occur at DNA target sites for TFs, they may affect the recruiting properties of these elements, hence their potential of TF occupation. For example, the cistrome of the NFκB component, p65, shows 7.5% binding sites differentially occupied in lymphoblastoid cells of 10 different individuals.…”

QTL mapping and loci dissection for leaf epicuticular wax load and canopy temperature depression and their association with QTL for staygreen in Sorghum bicolor under stress

“…In the case where no flanking markers were available or where a peak marker did not directly correspond to an exome region, the search was zoomed out to 1 kb immediately upstream and 1 kb immediately downstream of the peak marker sequences. The 1 kb region is within many of the new chromatin immunoprecipitation, ChIP-derived data of DNA segments containing nonexome functional elements (Worsley-Hunt et al 2011). These were used in the first ab initio gene prediction in Genefinder in the FGENESH suit (Solovyev 2001;Solovyev et al 2006;Yao et al 2005) which applies the HMM (hidden Markov model) algorithm (Stratonovich 1960;Baum and Petrie 1966).…”

Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions

“…For example, the characterization of the ''evenskipped'' stripe II enhancer, with binding sites of KR, GT, HB, and BCD, has revealed how a striped pattern of gene expression in the Drosophila embryo emerges from the combination of TF concentrations, on the one hand, and the genome sequence, on the other hand (Small et al 1993;Davidson 2001;Carroll et al 2009). A better knowledge of CRMs can also contribute to the understanding of disease processes, for example, by providing an interpretation of polymorphisms and mutations in the noncoding genome found to be whole-genome sequencing or GWAS studies (Worsley-Hunt et al 2011). Finally, CRMs can provide insight into evolutionary processes because they account for a large fraction of morphological divergence in the animal kingdom (Wray 2007;Wittkopp and Kalay 2012).…”