The discovery of structural form.
Ontology highlight
ABSTRACT:
SUBMITTER: Kemp C
PROVIDER: S-EPMC2492756 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes. Adult earthworms (E. fetida) were exposed in soil spiked with TNT (0, 6, 12, 24, 48, or 96 mg/kg) or RDX (8, 16, 32, 64, or 128 mg/kg) for 0, 4 or 14 days. The 4-day treatment was repeated with RDX concentration being 2, 4, 8, 16 or 32 mg/kg soil. Each treatment originally had 10 replicate worms with 8-10 survivors at the end of exposure. Total RNA was isolated from the surviving worms. A total of 248 worm RNA samples were hybridized to a custom-designed oligo array using Agilentâs one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript (Gong et al. 2009). After hybridization and scanning, gene expression data were acquired using Agilentâs Feature Extraction Software (v.9.1.3). The 248-array dataset consists of three worm groups: 32 untreated controls, 96 TNT-treated, and 120 RDX-treated.
2010-10-08 | E-GEOD-18495 | biostudies-arrayexpress
Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes.
2010-10-08 | GSE18495 | GEO
Project description:Decoding post-transcriptional regulatory programs underlying gene expression is a crucial step toward a predictive dynamical understanding of cellular state transitions. Despite recent systematic efforts, the sequence determinants of such mechanisms remain largely uncharacterized. An important obstacle in revealing these elements stems from the contribution of local secondary structures in defining interaction partners in a variety of regulatory contexts, including but not limited to transcript stability, alternative splicing and localization. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (e.g. human cardiac troponin T) or affects other aspects of RNA biology. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence. We have developed a computational framework based on context-free grammars and mutual information that systematically explores the immense space of structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behavior. The application of this framework to genome-wide mammalian mRNA stability data revealed eight highly significant elements with substantial structural information, for the strongest of which we showed a major role in global mRNA regulation. Through biochemistry, mass-spectrometry, and in vivo binding studies, we identified HNRPA2B1 as the key regulator that binds this element and stabilizes a large number of its target genes. Ultimately, we created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach can also be employed to reveal the structural elements that modulate other aspects of RNA behavior. This SuperSeries is composed of the following subset Series: GSE35749: sRSM1 synthetic decoy vs. scrambled transfections in MDA-MB-231 cells GSE35753: HNRPA2B1 RIP-chip GSE35756: Whole-genome decay rate measurements in MDA-MB-231 cells transfected with HNRPA2B1 siRNAs versus controls GSE35757: siRNA-mediated HNRPA2B1 knock-down in MDA-MB-231 cells GSE35799: HNRPA2B1 HITS-CLIP Refer to individual Series
2012-02-28 | E-GEOD-35800 | biostudies-arrayexpress
Project description:Decoding post-transcriptional regulatory programs underlying gene expression is a crucial step toward a predictive dynamical understanding of cellular state transitions. Despite recent systematic efforts, the sequence determinants of such mechanisms remain largely uncharacterized. An important obstacle in revealing these elements stems from the contribution of local secondary structures in defining interaction partners in a variety of regulatory contexts, including but not limited to transcript stability, alternative splicing and localization. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (e.g. human cardiac troponin T) or affects other aspects of RNA biology. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence. We have developed a computational framework based on context-free grammars and mutual information that systematically explores the immense space of structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behavior. The application of this framework to genome-wide mammalian mRNA stability data revealed eight highly significant elements with substantial structural information, for the strongest of which we showed a major role in global mRNA regulation. Through biochemistry, mass-spectrometry, and in vivo binding studies, we identified HNRPA2B1 as the key regulator that binds this element and stabilizes a large number of its target genes. Ultimately, we created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach can also be employed to reveal the structural elements that modulate other aspects of RNA behavior. This SuperSeries is composed of the SubSeries listed below.
2012-02-28 | GSE35800 | GEO
Project description:Microarray analyses with cells/tissues overexpressing YAP have revealed many transcription targets of YAP (Dong et al, 2007; Zhao et al, 2008). However, as YAP induces transformation of non-cancerous cells, we thought many of known targets of YAP may be indirect consequence of transforming property of YAP. To identify the immediate transcription targets for YAP, we utilized immortalized mammary epithelial MCF-10A cells expressing a tamoxifen inducible, hyperactive (S127/381A) YAP mutant (MCF-10A ERT2-YAP 2SA). MCF-10A ERT2 and MCF-10A ERT2-YAP 2SA are generated. Each cell line was treated with 0.1% of ethanol (solvent) or 1uM of 4-hydroxytamoxifen for 2 or 6 hours. This makes 6 samples per set. The experiments were done in duplicate. The expression data from MCF-10A ERT2 and MCF-10A ERT2-YAP 2SA before tamoxifen treatment can serve as control.
2015-11-23 | E-GEOD-60579 | biostudies-arrayexpress
Project description:Microarray analyses with cells/tissues overexpressing YAP have revealed many transcription targets of YAP (Dong et al, 2007; Zhao et al, 2008). However, as YAP induces transformation of non-cancerous cells, we thought many of known targets of YAP may be indirect consequence of transforming property of YAP. To identify the immediate transcription targets for YAP, we utilized immortalized mammary epithelial MCF-10A cells expressing a tamoxifen inducible, hyperactive (S127/381A) YAP mutant (MCF-10A ERT2-YAP 2SA).
2015-11-23 | GSE60579 | GEO
Project description:RNase P RNA is an ancient, nearly universal feature of life. As part of the ribonucleoprotein RNase P complex, the RNA component catalyzes essential removal of 5' leaders in pre-tRNAs. In 2004, Li and Altman computationally identified the RNase P RNA gene in all but three sequenced microbes: Nanoarchaeum equitans, Pyrobaculum aerophilum, and Aquifex aeolicus (all hyperthermophiles) [Li Y, Altman S (2004) RNA 10:1533-1540]. A recent study concluded that N. equitans does not have or require RNase P activity because it lacks 5' tRNA leaders. The "missing" RNase P RNAs in the other two species is perplexing given evidence or predictions that tRNAs are trimmed in both, prompting speculation that they may have developed novel alternatives to 5' pre-tRNA processing. Using comparative genomics and improved computational methods, we have now identified a radically minimized form of the RNase P RNA in five Pyrobaculum species and the related crenarchaea Caldivirga maquilingensis and Vulcanisaeta distributa, all retaining a conventional catalytic domain, but lacking a recognizable specificity domain. We confirmed 5' tRNA processing activity by high-throughput RNA sequencing and in vitro biochemical assays. The Pyrobaculum and Caldivirga RNase P RNAs are the smallest naturally occurring form yet discovered to function as trans-acting precursor tRNA-processing ribozymes. Loss of the specificity domain in these RNAs suggests altered substrate specificity and could be a useful model for finding other potential roles of RNase P. This study illustrates an effective combination of next-generation RNA sequencing, computational genomics, and biochemistry to identify a divergent, formerly undetectable variant of an essential noncoding RNA gene.
| S-EPMC3012483 | biostudies-literature
Project description:Structural biology has emerged during the last thirty years as a powerful tool for rational drug discovery. Crystal structures of biological targets alone and in complex with ligands and inhibitors provide essential insights into the mechanisms of actions of enzymes, their conformational changes upon ligand binding, the architectures and interactions of binding pockets. Structure-based methods such as crystallographic fragment screening represent nowadays invaluable instruments for the identification of new biologically active compounds. In this context, three-dimensional protein structures have played essential roles for the understanding of the activity and for the design of novel antiviral agents against several different viruses. In this review, the evolution in the resolution of viral structures is analysed, along with the role of crystal structures in the discovery and optimisation of new antivirals.
| S-EPMC7185576 | biostudies-literature
Project description:Several efforts are under way to partition single-read expressed sequence tag (EST), as well as full-length transcript data, into large-scale gene indices, where transcripts are in common index classes if and only if they share a common progenitor gene. Accurate gene indexing facilitates gene expression studies, as well as inexpensive and early gene sequence discovery through assembly of ESTs that are derived from genes that have not been sequenced by classical methods. We extend, correct, and enhance the information obtained from index groups by splitting index classes into subclasses based on sequence dissimilarity (diversity). Two applications of this are highlighted in this report. First it is shown that our method can ameliorate the damage that artifacts, such as chimerism, inflict on index integrity. Additionally, we demonstrate how the organization imposed by an effective subpartition can greatly increase the sensitivity of gene expression studies by accounting for the existence and tissue- or pathology-specific regulation of novel gene isoforms and polymorphisms. We apply our subpartitioning treatment to the UniGene gene indexing project to measure a marked increase in information quality and abundance (in terms of assembly length and insertion/deletion error) after treatment and demonstrate cases where new levels of information concerning differential expression of alternate gene forms, such as regulated alternative splicing, are discovered. [Tables 2 and 3 can be viewed in their entirety as Online Supplements at http://www.genome.org.]
| S-EPMC310695 | biostudies-literature
Project description:Detecting genomic structural variants from high-throughput sequencing data is a complex and unresolved challenge. We have developed a statistical learning approach, based on Random Forests, that integrates prior knowledge about the characteristics of structural variants and leads to improved discovery in high-throughput sequencing data. The implementation of this technique, forestSV, offers high sensitivity and specificity coupled with the flexibility of a data-driven approach.
| S-EPMC3427657 | biostudies-literature