The Experts below are selected from a list of 121812 Experts worldwide ranked by ideXlab platform
Uwe Ohler - One of the best experts on this subject based on the ideXlab platform.
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ribo seqc comprehensive analysis of cytoplasmic and organellar ribosome Profiling Data
bioRxiv, 2019Co-Authors: Lorenzo Calviello, Uwe Ohler, Dominique Sydow, Dermot HarnettAbstract:Abstract Summary Ribosome Profiling enables genome-wide analysis of translation with unprecedented resolution. We present Ribo-seQC, a versatile tool for the comprehensive analysis of Ribo-seq Data, providing in-depth insights on Data quality and translational profiles for cytoplasmic and organelle ribosomes. Ribo-seQC automatically generates platform-independent HTML reports, offering a detailed and easy-to-share basis for collaborative Ribo-seq projects. Availability Ribo-seQC is available at https://github.com/ohlerlab/RiboseQC and submitted to Bioconductor. Contact uwe.ohler@mdc-berlin.de
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detecting actively translated open reading frames in ribosome Profiling Data
Nature Methods, 2016Co-Authors: Lorenzo Calviello, Neelanjan Mukherjee, Emanuel Wyler, Henrik Zauber, Antje Hirsekorn, Matthias Selbach, Markus Landthaler, Benedikt Obermayer, Uwe OhlerAbstract:RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome Profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/), a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq Data. We used RiboTaper with deep Ribo-seq Data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry Data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq Data.
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a spectral analysis approach to detect actively translated open reading frames in high resolution ribosome Profiling Data
bioRxiv, 2015Co-Authors: Lorenzo Calviello, Neelanjan Mukherjee, Emanuel Wyler, Henrik Zauber, Antje Hirsekorn, Matthias Selbach, Markus Landthaler, Benedikt Obermayer, Uwe OhlerAbstract:RNA sequencing protocols allow for quantifying gene expression regulation at each individual step, from transcription to protein synthesis. Ribosome Profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. Despite its great potential, a rigorous statistical approach to identify translated regions by means of the characteristic three-nucleotide periodicity of Ribo-seq Data is not yet available. To fill this gap, we developed RiboTaper, which quantifies the significance of periodic Ribo-seq reads via spectral analysis methods. We applied RiboTaper on newly generated, deep Ribo-seq Data in HEK293 cells, to derive an extensive map of translation that covers Open Reading Frame (ORF) annotations for more than 11,000 protein- coding genes. We also find distinct ribosomal signatures for several hundred detected upstream ORFs and ORFs in annotated non-coding genes (ncORFs). Mass spectrometry Data confirms that RiboTaper achieves excellent coverage of the cellular proteome and validates dozens of novel peptide products. Collectively, RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/ ) is a powerful method for comprehensive de novo identification of actively used ORFs in the human genome.
Olga G Troyanskaya - One of the best experts on this subject based on the ideXlab platform.
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predicting effects of noncoding variants with deep learning based sequence model
Nature Methods, 2015Co-Authors: Jian Zhou, Olga G TroyanskayaAbstract:Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-Profiling Data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.
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predicting effects of noncoding variants with deep learning based sequence model
Nature Methods, 2015Co-Authors: Jian Zhou, Olga G TroyanskayaAbstract:DeepSEA, a deep-learning algorithm trained on large-scale chromatin-Profiling Data, predicts chromatin effects from sequence alone, has single-nucleotide sensitivity and can predict effects of noncoding variants.
Xing Wang Deng - One of the best experts on this subject based on the ideXlab platform.
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evaluation of light regulatory potential of calvin cycle steps based on large scale gene expression Profiling Data
Plant Molecular Biology, 2003Co-Authors: Hongyu Zhao, Xing Wang DengAbstract:Although large-scale gene expression Data have been studied from many perspectives, they have not been systematically integrated to infer the regulatory potentials of individual genes in specific pathways. Here we report the analysis of expression patterns of genes in the Calvin cycle from 95 Arabidopsis microarray experiments, which revealed a consistent gene regulation pattern in most experiments. This identified pattern, likely due to gene regulation by light rather than feedback regulations of the metabolite fluxes in the Calvin cycle, is remarkably consistent with the rate-limiting roles of the enzymes encoded by these genes reported from both experimental and modeling approaches. Therefore, the regulatory potential of the genes in a pathway may be inferred from their expression patterns. Furthermore, gene expression analysis in the context of a known pathway helps to categorize various biological perturbations that would not be recognized with the prevailing methods.
Lorenzo Calviello - One of the best experts on this subject based on the ideXlab platform.
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ribo seqc comprehensive analysis of cytoplasmic and organellar ribosome Profiling Data
bioRxiv, 2019Co-Authors: Lorenzo Calviello, Uwe Ohler, Dominique Sydow, Dermot HarnettAbstract:Abstract Summary Ribosome Profiling enables genome-wide analysis of translation with unprecedented resolution. We present Ribo-seQC, a versatile tool for the comprehensive analysis of Ribo-seq Data, providing in-depth insights on Data quality and translational profiles for cytoplasmic and organelle ribosomes. Ribo-seQC automatically generates platform-independent HTML reports, offering a detailed and easy-to-share basis for collaborative Ribo-seq projects. Availability Ribo-seQC is available at https://github.com/ohlerlab/RiboseQC and submitted to Bioconductor. Contact uwe.ohler@mdc-berlin.de
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detecting actively translated open reading frames in ribosome Profiling Data
Nature Methods, 2016Co-Authors: Lorenzo Calviello, Neelanjan Mukherjee, Emanuel Wyler, Henrik Zauber, Antje Hirsekorn, Matthias Selbach, Markus Landthaler, Benedikt Obermayer, Uwe OhlerAbstract:RNA-sequencing protocols can quantify gene expression regulation from transcription to protein synthesis. Ribosome Profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. We have developed RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/), a rigorous statistical approach that identifies translated regions on the basis of the characteristic three-nucleotide periodicity of Ribo-seq Data. We used RiboTaper with deep Ribo-seq Data from HEK293 cells to derive an extensive map of translation that covered open reading frame (ORF) annotations for more than 11,000 protein-coding genes. We also found distinct ribosomal signatures for several hundred upstream ORFs and ORFs in annotated noncoding genes (ncORFs). Mass spectrometry Data confirmed that RiboTaper achieved excellent coverage of the cellular proteome. Although dozens of novel peptide products were validated in this manner, few of the currently annotated long noncoding RNAs appeared to encode stable polypeptides. RiboTaper is a powerful method for comprehensive de novo identification of actively used ORFs from Ribo-seq Data.
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a spectral analysis approach to detect actively translated open reading frames in high resolution ribosome Profiling Data
bioRxiv, 2015Co-Authors: Lorenzo Calviello, Neelanjan Mukherjee, Emanuel Wyler, Henrik Zauber, Antje Hirsekorn, Matthias Selbach, Markus Landthaler, Benedikt Obermayer, Uwe OhlerAbstract:RNA sequencing protocols allow for quantifying gene expression regulation at each individual step, from transcription to protein synthesis. Ribosome Profiling (Ribo-seq) maps the positions of translating ribosomes over the entire transcriptome. Despite its great potential, a rigorous statistical approach to identify translated regions by means of the characteristic three-nucleotide periodicity of Ribo-seq Data is not yet available. To fill this gap, we developed RiboTaper, which quantifies the significance of periodic Ribo-seq reads via spectral analysis methods. We applied RiboTaper on newly generated, deep Ribo-seq Data in HEK293 cells, to derive an extensive map of translation that covers Open Reading Frame (ORF) annotations for more than 11,000 protein- coding genes. We also find distinct ribosomal signatures for several hundred detected upstream ORFs and ORFs in annotated non-coding genes (ncORFs). Mass spectrometry Data confirms that RiboTaper achieves excellent coverage of the cellular proteome and validates dozens of novel peptide products. Collectively, RiboTaper (available at https://ohlerlab.mdc-berlin.de/software/ ) is a powerful method for comprehensive de novo identification of actively used ORFs in the human genome.
Andreas Bender - One of the best experts on this subject based on the ideXlab platform.
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current trends in drug sensitivity prediction
Current Pharmaceutical Design, 2017Co-Authors: Isidro Cortesciriano, Lewis H Mervin, Andreas BenderAbstract:Abstract Cancer cell line panels have proved useful disease models to, among others, identify genomic markers of drug sensitivity and to develop new anticancer drugs. The increasing availability of in vitro sensitivity and cell line Profiling Data sets raises the question of whether this information could be used, and to which extent, to predict the activity of drugs in cancer cell lines and, ultimately, in patients tumors. Drug sensitivity prediction embraces those approaches aiming at predicting in vitro drug activity on cancer cell lines by integrating genomic and/or chemical information using machine learning models. In this review, we summarize the cytotoxicity assays generally used to determine in vitro activity on cultured cell lines, and revisit the drug sensitivity prediction studies that have leveraged chemical and cell line Profiling Data from the NCI60, Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) projects. A section outlining current limitations and future perspectives in the field closes the review.
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Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data
ChemMedChem, 2007Co-Authors: Kamal Azzaoui, Steve Whitebread, Jacques Hamon, Bernard Faller, Edgar Jacoby, Jeremy L. Jenkins, Andreas Bender, Laszlo UrbanAbstract:: This study describes a method for mining and modeling binding Data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naive Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug Databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening Data or for lead optimization.
