Derivation of Context-free Stochastic L-Grammar Rules for Promoter Sequence Modeling Using Support Vector Machine

Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modeling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognized by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L- grammar rules are analyzed and compared with natural promoter sequences

Recognizing speculative language in research texts

This thesis studies the use of sequential supervised learning methods on two tasks related to the detection of hedging in scientific articles: those of hedge cue identification and hedge cue scope detection. Both tasks are addressed using a learning methodology that proposes the use of an iterative, error-based approach to improve classification performance, suggesting the incorporation of expert knowledge into the learning process through the use of knowledge rules. Results are promising: for the first task, we improved baseline results by 2.5 points in terms of F-score by incorporating cue cooccurence information, while for scope detection, the incorporation of syntax information and rules for syntax scope pruning allowed us to improve classification performance from an F-score of 0.712 to a final number of 0.835. Compared with state-of-the-art methods, the results are very competitive, suggesting that the approach to improving classifiers based only on the errors commited on a held out corpus could be successfully used in other, similar tasks. Additionaly, this thesis presents a class schema for representing sentence analysis in a unique structure, including the results of different linguistic analysis. This allows us to better manage the iterative process of classifier improvement, where different attribute sets for learning are used in each iteration. We also propose to store attributes in a relational model, instead of the traditional text-based structures, to facilitate learning data analysis and manipulation

Unraveling the transcriptional Cis-regulatory code

It is nowadays accepted that eukaryotic complexity is not dictated by the number of protein-coding genes of the genome, but rather achieved through the combinatorics of gene expression programs. Distinct aspects of the expression pattern of a gene are mediated by discrete regulatory sequences, known as cis-regulatory elements. The work described in this thesis was aimed at developing computational and statistical methods to guide the search and characterization of novel cis-regulatory elements

Sequence determinants of human gene regulatory elements

Analysis of massively parallel reporter assays measuring the transcriptional activity of DNA sequences indicates that most transcription factor (TF) activity is additive and does not rely on specific TF-TF interactions. Individual TFs can have different gene regulatory activities. DNA can determine where and when genes are expressed, but the full set of sequence determinants that control gene expression is unknown. Here, we measured the transcriptional activity of DNA sequences that represent an similar to 100 times larger sequence space than the human genome using massively parallel reporter assays (MPRAs). Machine learning models revealed that transcription factors (TFs) generally act in an additive manner with weak grammar and that most enhancers increase expression from a promoter by a mechanism that does not appear to involve specific TF-TF interactions. The enhancers themselves can be classified into three types: classical, closed chromatin and chromatin dependent. We also show that few TFs are strongly active in a cell, with most activities being similar between cell types. Individual TFs can have multiple gene regulatory activities, including chromatin opening and enhancing, promoting and determining transcription start site (TSS) activity, consistent with the view that the TF binding motif is the key atomic unit of gene expression.Peer reviewe

Mapping and Functional Analysis of cis-Regulatory Elements in Mouse Photoreceptors

Photoreceptors are light-sensitive neurons that mediate vision, and they are the most commonly affected cell type in genetic forms of blindness. In mice, there are two basic types of photoreceptors, rods and cones, which mediate vision in dim and bright environments, respectively. The transcription factors (TFs) that control rod and cone development have been studied in detail, but the cis-regulatory elements (CREs) through which these TFs act are less well understood. To comprehensively identify photoreceptor CREs in mice and to understand their relationship with gene expression, we performed open chromatin (ATAC-seq) and transcriptome (RNA-seq) profiling of FACS-purified rods and cones. We find that rods have significantly fewer regions of open chromatin than cones (as well as \u3e60 additional cell types and tissues), and we demonstrate that this uniquely closed chromatin architecture depends on the rod master regulator Nrl. Finally, we find that regions of rod- and cone-specific open chromatin are enriched for distinct sets of TF binding sites, providing insight into the cis-regulatory grammar of these cell types. We also sought to understand how the regulatory activity of rod and cone open chromatin regions is encoded in DNA sequence. Cone-rod homeobox (CRX) is a paired-like homeodomain TF and master regulator of both rod and cone development, and CRX binding sites are by far the most enriched TF binding sites in photoreceptor CREs. The in vitro DNA binding preferences of CRX have been extensively characterized, but how well in vitro models of TF binding site affinity predict in vivo regulatory activity is not known. In addition, paired-class homeodomain TFs bind DNA as both monomers and dimers, but whether monomeric and dimeric CRX binding sites have distinct regulatory activities is not known. To address these questions, we used a massively parallel reporter assay to quantify the activity of thousands native and mutant CRX binding sites in explanted mouse retinas. These data reveal that dimeric CRX binding sites encode stronger enhancers than monomeric CRX binding sites. Moreover, the activity of half-sites within dimeric CRX binding sites is cooperative and spacing-dependent. In addition, saturating mutagenesis of 195 CRX binding sites reveals that, while TF binding site affinity and activity are moderately correlated across mutations within individual CREs, they are poorly correlated across mutations from distinct CREs. Accordingly, we show that accounting for baseline CRE activity improves the prediction of the effects of mutations in regulatory DNA from sequence-based models. Taken together, these data demonstrate that the activity of CRX binding sites depends on multiple layers of sequence context, providing insight into photoreceptor gene regulation and illustrating functional principles of homeodomain TF binding sites