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Articles 1 - 10 of 10
Full-Text Articles in Computational Biology
Spatiotemporal Transcriptome Diversity And Responses To Salinity Tolerance In The Extremophyte Schrenkiella Parvula, Chathura Wijesinghege
Spatiotemporal Transcriptome Diversity And Responses To Salinity Tolerance In The Extremophyte Schrenkiella Parvula, Chathura Wijesinghege
LSU Doctoral Dissertations
Schrenkiella parvula is an extremophyte model related to the most widely studied plant model, Arabidopsis thaliana and Brassica crops in the mustard family (Brassicaceae). It can thrive in highly saline environments where the soil is enriched in Na+, K+, Li+, borates, and chlorides. Understanding how this extremophyte can survive high salinity with genomic adaptations can provide insight into developing stress resilient crops in the future. Gene expression of S. parvula in response to salt has been investigated using shoot and root tissue from mature vegetative-phase plants. However, prior studies have not examined the transcript …
Transcription Factor Binding Site Clusters Identify Target Genes With Similar Tissue-Wide Expression And Buffer Against Mutations., Peter Rogan, Ruipeng Lu
Transcription Factor Binding Site Clusters Identify Target Genes With Similar Tissue-Wide Expression And Buffer Against Mutations., Peter Rogan, Ruipeng Lu
Biochemistry Publications
Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were …
Loregic: A Method To Characterize The Cooperative Logic Of Regulatory Factors, Daifeng Wang, Koon-Kiu Yan, Cristina Sisu, Chao Cheng, Joel Rozowsky, William Meyerson, Mark B. Gerstein
Loregic: A Method To Characterize The Cooperative Logic Of Regulatory Factors, Daifeng Wang, Koon-Kiu Yan, Cristina Sisu, Chao Cheng, Joel Rozowsky, William Meyerson, Mark B. Gerstein
Dartmouth Scholarship
The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. …
Transcription Factor Binding Profiles Reveal Cyclic Expression Of Human Protein-Coding Genes And Non-Coding Rnas, Chao Cheng, Matthew Ung, Gavin D. Grant, Michael L. Whitfield
Transcription Factor Binding Profiles Reveal Cyclic Expression Of Human Protein-Coding Genes And Non-Coding Rnas, Chao Cheng, Matthew Ung, Gavin D. Grant, Michael L. Whitfield
Dartmouth Scholarship
Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and …
Transcriptomic Characterization Of A Synergistic Genetic Interaction During Carpel Margin Meristem Development In Arabidopsis Thaliana, April N. Wynn, Elizabeth E. Rueschhoff, Robert G. Franks
Transcriptomic Characterization Of A Synergistic Genetic Interaction During Carpel Margin Meristem Development In Arabidopsis Thaliana, April N. Wynn, Elizabeth E. Rueschhoff, Robert G. Franks
Biological Sciences Research
In flowering plants the gynoecium is the female reproductive structure. In Arabidopsis thalianaovules initiate within the developing gynoecium from meristematic tissue located along the margins of the floral carpels. When fertilized the ovules will develop into seeds. SEUSS (SEU) and AINTEGUMENTA (ANT) encode transcriptional regulators that are critical for the proper formation of ovules from the carpel margin meristem (CMM). The synergistic loss of ovule initiation observed in the seu ant double mutant suggests that SEU and ANT share overlapping functions during CMM development. However the molecular mechanism underlying this synergistic interaction is unknown. Using …
Genome Sequence Of The Model Mushroom Schizophyllum Commune, Robin A. Ohm, Jan F. De Jong, Luis G. Lugones, Andrea Aerts, Erika Kothe, Jason E. Stajich, Ronald P. De Vries, Eric Record, Anthony Levasseur, Scott E. Baker, Kirk A. Bartholomew, Pedro M. Coutinho, Susann Erdmann, Thomas J. Fowler, Allen C. Gathmen, Vincent Lombard, Bernard Henrissat, Nicole Knabe, Ursula Kues, Walt W. Lily
Genome Sequence Of The Model Mushroom Schizophyllum Commune, Robin A. Ohm, Jan F. De Jong, Luis G. Lugones, Andrea Aerts, Erika Kothe, Jason E. Stajich, Ronald P. De Vries, Eric Record, Anthony Levasseur, Scott E. Baker, Kirk A. Bartholomew, Pedro M. Coutinho, Susann Erdmann, Thomas J. Fowler, Allen C. Gathmen, Vincent Lombard, Bernard Henrissat, Nicole Knabe, Ursula Kues, Walt W. Lily
Biology Faculty Publications
Much remains to be learned about the biology of mushroom-forming fungi, which are an important source of food, secondary metabolites and industrial enzymes. The wood-degrading fungus Schizophyllum commune is both a genetically tractable model for studying mushroom development and a likely source of enzymes capable of efficient degradation of lignocellulosic biomass. Comparative analyses of its 38.5-megabase genome, which encodes 13,210 predicted genes, reveal the species's unique wood-degrading machinery. One-third of the 471 genes predicted to encode transcription factors are differentially expressed during sexual development of S. commune. Whereas inactivation of one of these, fst4, prevented mushroom formation, inactivation of another, …
Minimum Criteria For Dna Damage-Induced Phase Advances In Circadian Rhythms, Christian I. Hong, Judit Zámborszky, Attila Csikász-Nagy
Minimum Criteria For Dna Damage-Induced Phase Advances In Circadian Rhythms, Christian I. Hong, Judit Zámborszky, Attila Csikász-Nagy
Dartmouth Scholarship
Robust oscillatory behaviors are common features of circadian and cell cycle rhythms. These cyclic processes, however, behave distinctively in terms of their periods and phases in response to external influences such as light, temperature, nutrients, etc. Nevertheless, several links have been found between these two oscillators. Cell division cycles gated by the circadian clock have been observed since the late 1950s. On the other hand, ionizing radiation (IR) treatments cause cells to undergo a DNA damage response, which leads to phase shifts (mostly advances) in circadian rhythms. Circadian gating of the cell cycle can be attributed to the cell cycle …
A Novel Ensemble Learning Method For De Novo Computational Identification Of Dna Binding Sites, Arijit Chakravarty, Jonathan M. Carlson, Radhika S. Khetani, Robert H H. Gross
A Novel Ensemble Learning Method For De Novo Computational Identification Of Dna Binding Sites, Arijit Chakravarty, Jonathan M. Carlson, Radhika S. Khetani, Robert H H. Gross
Dartmouth Scholarship
Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. …
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross
Dartmouth Scholarship
The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota
Dartmouth Scholarship
The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …