High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding

The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies

Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped k-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos

Nuclear export signals (NESs) in Arabidopsis thaliana : development and experimental validation of a prediction tool

Rubiano Castellanos CC. Nuclear export signals (NESs) in Arabidopsis thaliana : development and experimental validation of a prediction tool. Bielefeld (Germany): Bielefeld University; 2010.It is well established that nucleo-cytoplasmic shuttling regulates not only the localization but also the activity of many proteins like transcription factors, cell cycle regulators and tumor suppressor proteins just to mention some. Also in plants the nucleo-cytoplasmic partitioning of proteins emerges as an important regulation mechanism for many plant-specific processes. One requirement for a protein to shuttle between nucleus and cytoplasm lies in its nuclear export activity. The widely used mechanism for export of proteins from the nucleus involves the receptor Exportin 1 and the presence of a nuclear export signal (NES) in the cargo protein. Given the big amount of sequence data available nowadays the possibility to use a computational tool to predict the proteins potentially containing an NES would help to facilitate the screening and experimental characterization of NES-containing proteins. However, the computational prediction of NESs is a challenging task. Currently there is only one NES prediction tool and that is unfortunately not accurate for predicting these signals in proteins of plants. In that direction, this study aimed mainly at developing a prediction method for identifying NESs in proteins from Arabidopsis and to validate its usefulness experimentally. It included also the definition of the influence of the NES protein context in the nuclear export activity of specific proteins of Arabidopsis. Three machine-learning algorithms (i.e. k-NN, SVM and Random Forests) were trained with experimentally validated NES sequences from proteins of Arabidopsis and other organisms. Two kinds of features were included, the sequence of the NESs expressed as the score obtained from an HMM profile constructed with the NES sequences of proteins from Arabidopsis, and physicochemical properties of the amino acid residues expressed as amino acid index values. The Random Forest classifier was selected among the three classifiers after evaluation of the performance by different methods. It showed to be highly accurate (accuracy values over 85 percent, classification error around 10 percent, MCC around 0.7 and area under the ROC curve around 0.90) and performed better than the other two trained classifiers. Using the Random Forest classifier around 5000 proteins from the total of protein sequences from Arabidopsis were predicted as containing NESs. A group of these proteins was selected by using Gene Ontologies (GO) and from this last group, 13 proteins were experimentally tested for nuclear export activity. 11 out of those 13 proteins showed positive interaction with the receptor Exportin 1 (XPO1a) from Arabidopsis in yeast two-hybrid assays. The proteins showing nuclear export activity include 9 transcription factors and 2 DNA metabolism-related proteins. Furthermore, it was established that the amino acid residues located between the hydrophobic residues in the NES as well as the protein structure of the regions around the NES could modify the nuclear export activity of some proteins. In conclusion, this work presents a new prediction tool for NESs in proteins of Arabidopsis based on a Random Forest classifier. The experimental validation of the nuclear export activity in a selected group of proteins is an indicative of the usefulness of the tool. From the biological point of view, the nuclear export activity observed in those proteins strongly suggest that nucleo-cytoplasmic partitioning could be involved in the regulation of their functions. For the follow up research the further characterization of the proteins showing positive nuclear export activity as well as the validation of additional predicted NES-containing proteins is envisioned. In the near future, the developed tool is going to be available as a web application to facilitate and promote its further usage

Protein-DNA Recognition Models for the Homeodomain and C2H2 Zinc Finger Transcription Factor Families

Transcription factors: TFs) play a central role in the gene regulatory network of each cell. They can stimulate or inhibit transcription of their target genes by binding to short, degenerate DNA sequence motifs. The goal of this research is to build improved models of TF binding site recognition. This can facilitate the determination of regulatory networks and also allow for the prediction of binding site motifs based only on the TF protein sequence. Recent technological advances have rapidly expanded the amount of quantitative TF binding data available. PBMs: Protein Binding Microarrays) have recently been implemented in a format that allows all 10mers to be assayed in parallel. There is now PBM data available for hundreds of transcription factors. Another fairly recent technique for determining the binding preference of a TF is an in vivo bacterial one-hybrid assay: B1H). In this approach a TF is expressed in E. coli where it can be used to select strong binding sites from a library of randomized sites located upstream of a weak promoter, driving expression of a selectable gene. When coupled with high throughput sequencing and a newly developed analysis method, quantitative binding data can be obtained. In the last few years, the binding specificities of hundreds of TFs have been determined using B1H. The two largest eukaryotic transcription factor families are the zf-C2H2 and homeodomain TF families. Newly available PBM and B1H specificity models were used to develop recognition models for these two families, with the goal of being able to predict the binding specific of a TF from its protein sequence. We developed a feature selection method based on adjusted mutual information that automatically recovers nearly all of the known key residues for the homeodomain and zf-C2H2 families. Using those features we find that, for both families, random forest: RF) and support vector machine: SVM) based recognition models outperform the nearest neighbor method, which has previously been considered the best method

The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization.

Sorghum bicolor is a drought tolerant C4 grass used for the production of grain, forage, sugar, and lignocellulosic biomass and a genetic model for C4 grasses due to its relatively small genome (approximately 800 Mbp), diploid genetics, diverse germplasm, and colinearity with other C4 grass genomes. In this study, deep sequencing, genetic linkage analysis, and transcriptome data were used to produce and annotate a high-quality reference genome sequence. Reference genome sequence order was improved, 29.6 Mbp of additional sequence was incorporated, the number of genes annotated increased 24% to 34 211, average gene length and N50 increased, and error frequency was reduced 10-fold to 1 per 100 kbp. Subtelomeric repeats with characteristics of Tandem Repeats in Miniature (TRIM) elements were identified at the termini of most chromosomes. Nucleosome occupancy predictions identified nucleosomes positioned immediately downstream of transcription start sites and at different densities across chromosomes. Alignment of more than 50 resequenced genomes from diverse sorghum genotypes to the reference genome identified approximately 7.4 M single nucleotide polymorphisms (SNPs) and 1.9 M indels. Large-scale variant features in euchromatin were identified with periodicities of approximately 25 kbp. A transcriptome atlas of gene expression was constructed from 47 RNA-seq profiles of growing and developed tissues of the major plant organs (roots, leaves, stems, panicles, and seed) collected during the juvenile, vegetative and reproductive phases. Analysis of the transcriptome data indicated that tissue type and protein kinase expression had large influences on transcriptional profile clustering. The updated assembly, annotation, and transcriptome data represent a resource for C4 grass research and crop improvement

Prediction of Alternative Splice Sites in Human Genes

This thesis addresses the problem of predicting alternative splice sites in human genes. The most common way to identify alternative splice sites are the use of expressed sequence tags and microarray data. Since genes only produce alternative proteins under certain conditions, these methods are limited to detecting only alternative splice sites in genes whose alternative protein forms are expressed under the tested conditions. I have introduced three multiclass support vector machines that predict upstream and downstream alternative 3’ splice sites, upstream and downstream alternative 5’ splice sites, and the 3’ splice site of skipped and cryptic exons. On a test set extracted from the Alternative Splice Annotation Project database, I was able to correctly classify about 68% of the splice sites in the alternative 3’ set, about 62% of the splice sites in the alternative 5’ set, and about 66% in the exon skipping set

On the use of algorithms to discover motifs in DNA sequences

Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging for specialists nowadays due to the difficulties they find to deeply understand gene regulatory mechanisms, especially when analyzing binding sites in DNA. These sites or specific nucleotide sequences are known to be responsible for transcription processes. Thus, this work aims at providing an updated overview on strategies developed to discover meaningful motifs in DNA-related sequences, and, in particular, their attempts to find out relevant binding sites. From all existing approaches, this work is focused on dictionary, ensemble, and artificial intelligence-based algorithms since they represent the classical and the leading ones, respectively.Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00Junta de Andalucia P07-TIC- 02611

Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify high-quality (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data

Core promoters are predicted by their distinct physicochemical properties in the genome of Plasmodium falciparum

A method is presented to computationally identify core promoters in the Plasmodium falciparum genome using only DNA physicochemical properties