Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1121319 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:High-throughput microbial culturomics using automation and machine learning
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Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.
2013-08-06 | E-GEOD-47184 | biostudies-arrayexpress
Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.
2013-08-06 | E-GEOD-47183 | biostudies-arrayexpress
Project description:Functional Optimization of Designer Cardiac Organoids Enabled by Machine Learning Techniques
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Project description:High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntington’s disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds’ structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts.
2020-01-22 | GSE129141 | GEO
Project description:We describe XmaI-RRBS method for rapid and affordable genome-wide DNA methylation analysis, with library preparation taking only four days and sequencing possible within four hours. Small sizes of the XmaI-RRBS libraries allow their multiplexing and sequencing on the benchtop high-throughput machines. Described here is the first RRBS protocol validated for the Ion Torrent Personal Genome Machine. DNA from MCF7 cell line and 6 normal breast samples (total 7 samples) were subjected to reduced representation bisulfite sequencing analysis (XmaI-RRBS) by using Ion Torrent platform.
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Project description:To identify senescence-regulation of miRNAs in Arabidopsis thaliana, eight small RNA libraries were constructed and sequenced at four different stages of development and senescence from both leaves and siliques. Parallel Analysis of RNA Ends (PARE) libraries were also constructed and sequenced to enable the large-scale examination of miRNA-guided cleavage products. Genome-wide small RNA profiling was done by Illumina TruSeq sample preparation followed by high-throughput sequencing with Illumina HiSeq 2000 platform.
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