Browsing by Subject "SVM"
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Item Open Access A Semi-Supervised Predictive Model to Link Regulatory Regions to Their Target Genes(2015) Hafez, Dina MohamedNext generation sequencing technologies have provided us with a wealth of data profiling a diverse range of biological processes. In an effort to better understand the process of gene regulation, two predictive machine learning models specifically tailored for analyzing gene transcription and polyadenylation are presented.
Transcriptional enhancers are specific DNA sequences that act as ``information integration hubs" to confer regulatory requirements on a given cell. These non-coding DNA sequences can regulate genes from long distances, or across chromosomes, and their relationships with their target genes are not limited to one-to-one. With thousands of putative enhancers and less than 14,000 protein-coding genes, detecting enhancer-gene pairs becomes a very complex machine learning and data analysis challenge.
In order to predict these specific-sequences and link them to genes they regulate, we developed McEnhancer. Using DNAseI sensitivity data and annotated in-situ hybridization gene expression clusters, McEnhancer builds interpolated Markov models to learn enriched sequence content of known enhancer-gene pairs and predicts unknown interactions in a semi-supervised learning algorithm. Classification of predicted relationships were 73-98% accurate for gene sets with varying levels of initial known examples. Predicted interactions showed a great overlap when compared to Hi-C identified interactions. Enrichment of known functionally related TF binding motifs, enhancer-associated histone modification marks, along with corresponding developmental time point was highly evident.
On the other hand, pre-mRNA cleavage and polyadenylation is an essential step for 3'-end maturation and subsequent stability and degradation of mRNAs. This process is highly controlled by cis-regulatory elements surrounding the cleavage site (polyA site), which are frequently constrained by sequence content and position. More than 50\% of human transcripts have multiple functional polyA sites, and the specific use of alternative polyA sites (APA) results in isoforms with variable 3'-UTRs, thus potentially affecting gene regulation. Elucidating the regulatory mechanisms underlying differential polyA preferences in multiple cell types has been hindered by the lack of appropriate tests for determining APAs with significant differences across multiple libraries.
We specified a linear effects regression model to identify tissue-specific biases indicating regulated APA; the significance of differences between tissue types was assessed by an appropriately designed permutation test. This combination allowed us to identify highly specific subsets of APA events in the individual tissue types. Predictive kernel-based SVM models successfully classified constitutive polyA sites from a biologically relevant background (auROC = 99.6%), as well as tissue-specific regulated sets from each other. The main cis-regulatory elements described for polyadenylation were found to be a strong, and highly informative, hallmark for constitutive sites only. Tissue-specific regulated sites were found to contain other regulatory motifs, with the canonical PAS signal being nearly absent at brain-specific sites. We applied this model on SRp20 data, an RNA binding protein that might be involved in oncogene activation and obtained interesting insights.
Together, these two models contribute to the understanding of enhancers and the key role they play in regulating tissue-specific expression patterns during development, as well as provide a better understanding of the diversity of post-transcriptional gene regulation in multiple tissue types.
Item Open Access Predictive Modeling of Loss-of-Heterozygosity Events in Yeast(2015) Yin, YiDuring mitotic cell cycles, DNA experiences many types of endogenous and exogenous damaging agents that could potentially cause double strand breaks (DSB). In S. cerevisiae, DSBs are primarily repaired by mitotic recombination and as a result, could lead to loss-of-heterozygosity (LOH). Genetic recombination can happen in both meiosis and mitosis. While genome-wide distribution of meiotic recombination events has been intensively studied, mitotic recombination events have not been mapped unbiasedly throughout the genome until recently. Methods for selecting mitotic crossovers and mapping the positions of crossovers have recently been developed in our lab. Our current approach uses a diploid yeast strain that is heterozygous for about 55,000 SNPs, and employs SNP-Microarrays to map LOH events throughout the genome. These methods allow us to examine selected crossovers and unselected mitotic recombination events (crossover, noncrossover and BIR) at about 1 kb resolution across the genome. Using this method, we generated maps of spontaneous and UV-induced LOH events. In this study, we explore machine learning and variable selection techniques to build a predictive model for where the LOH events occur in the genome.
Randomly from the yeast genome, we simulated control tracts resembling the LOH tracts in terms of tract lengths and locations with respect to single-nucleotide-polymorphism positions. We then extracted roughly 1,100 features such as base compositions, histone modifications, presence of tandem repeats etc. and train classifiers to distinguish control tracts and LOH tracts. We found interesting features of good predictive values. We also found that with the current repertoire of features, the prediction is generally better for spontaneous LOH events than UV-induced LOH events.