Browsing by Author "Badr, Eman"

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    Computational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Data
    Badr, Eman; ElHefnawi, Mahmoud; Heath, Lenwood S. (PLOS, 2016-11-18)
    Alternative splicing is a vital process for regulating gene expression and promoting proteomic diversity. It plays a key role in tissue-specific expressed genes. This specificity is mainly regulated by splicing factors that bind to specific sequences called splicing regulatory elements (SREs). Here, we report a genome-wide analysis to study alternative splicing on multiple tissues, including brain, heart, liver, and muscle. We propose a pipeline to identify differential exons across tissues and hence tissue-specific SREs. In our pipeline, we utilize the DEXSeq package along with our previously reported algorithms. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, we identified 28,100 differentially used exons across the four tissues. We identified tissue-specific exonic splicing enhancers that overlap with various previously published experimental and computational databases. A complicated exonic enhancer regulatory network was revealed, where multiple exonic enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the exonic enhancers are found to be co-occurring with multiple exonic silencers and vice versa, which demonstrates a complicated relationship between tissue-specific exonic enhancers and silencers.
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    CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements
    Badr, Eman; Heath, Lenwood S. (2015-09-04)
    Background Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches. Results We identified 37 SRE sets that include both enhancer and silencer elements. We show that our results intersect with previous results, including some that are experimental. We also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. We applied CoSREM to RNA-Seq data for multiple tissues to identify combinatorial SREs which may be responsible for exon inclusion or exclusion across tissues. Conclusion The new algorithm can identify different combinations of splicing enhancers and silencers without assuming a predefined size or limiting the algorithm to find only pairs of SREs. Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.
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    Identifying Splicing Regulatory Elements with de Bruijn Graphs
    Badr, Eman (Virginia Tech, 2015-05-12)
    Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. This work tackles the limitations in the current approaches for identifying SREs. It addresses two major computational problems, identifying variable length SREs utilizing a graph-based model with de Bruijn graphs and discovering co-occurring sets of SREs (combinatorial SREs) utilizing graph mining techniques. In addition, I studied and analyzed the effect of alternative splicing on tissue specificity in human. First, I have used a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In my approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). Consequently, the predicted SREs are of different lengths. I identified 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. My model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation. Second, I developed CoSREM (Combinatorial SRE Miner), a graph mining algorithm for discovering combinatorial SREs. The goal is to identify sets of exonic splicing regulatory elements whether they are enhancers or silencers. Experimental evidence is incorporated through my graph-based model to increase the accuracy of the results. The identified SREs do not have a predefined length, and the algorithm is not limited to identifying only SRE pairs as are current approaches. I identified 37 SRE sets that include both enhancer and silencer elements in human genes. These results intersect with previous results, including some that are experimental. I also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. Further, I report a genome-wide analysis to study alternative splicing on multiple human tissues, including brain, heart, liver, and muscle. I developed a pipeline to identify tissue-specific exons and hence tissue-specific SREs. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, I identified 28,100 tissue-specific exons across the four tissues. I identified 1929 exonic splicing enhancers with 99% overlap with previously published experimental and computational databases. A complicated enhancer regulatory network was revealed, where multiple enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the enhancers are found to be co-occurring with multiple silencers and vice versa, which demonstrates a complicated relationship between tissue-specific enhancers and silencers.
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    A System to Automatically Classify and Name Any Individual Genome-Sequenced Organism Independently of Current Biological Classification and Nomenclature
    Marakeby, Haitham; Badr, Eman; Torkey, Hanaa; Song, Yuhyun; Leman, Scotland C.; Monteil, Caroline L.; Heath, Lenwood S.; Vinatzer, Boris A. (PLOS, 2014-02-21)
    A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity within species that is now being revealed by genome sequencing on a daily basis. To address this lack of a general intra-species classification and naming system adequate for today’s speed of discovery of new diversity, we propose a classification and naming system that is exclusively based on genome similarity and that is suitable for automatic assignment of codes to any genome-sequenced organism without requiring any phenotypic or phylogenetic analysis. We provide examples demonstrating that genome similarity-based codes largely align with current taxonomic groups at many different levels in bacteria, animals, humans, plants, and viruses. Importantly, the proposed approach is only slightly affected by the order of code assignment and can thus provide codes that reflect similarity between organisms and that do not need to be revised upon discovery of new diversity. We envision genome similarity-based codes to complement current biological nomenclature and to provide a universal means to communicate unambiguously about any genome-sequenced organism in fields as diverse as biodiversity research, infectious disease control, human and microbial forensics, animal breed and plant cultivar certification, and human ancestry research.