A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
TL;DR: The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.
About: This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.
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TL;DR: A review of integrative omics-based methods for natural product target discovery is presented in this paper, with a particular focus on the systematic integration of multi-omics strategies, including genomics, transcriptomics, proteomics, metabolomics and bioinformatics.
14 citations
TL;DR: The combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder, which expanded the pathways potentially involved in modifying HD onset and prioritized candidate therapeutics for future study.
Abstract: Huntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in HTT. The length of this repeat, however, only explains a proportion of the variability in age of onset in patients. Genome-wide association studies have identified modifiers that contribute toward a proportion of the observed variance. By incorporating tissue-specific transcriptomic information with these results, additional modifiers can be identified. We performed a transcriptome-wide association study assessing heritable differences in genetically determined expression in diverse tissues, with genome-wide data from over 4000 patients. Functional validation of prioritized genes was undertaken in isogenic HD stem cells and patient brains. Enrichment analyses were performed with biologically relevant gene sets to identify the core pathways. HD-associated gene coexpression modules were assessed for associations with neurological phenotypes in an independent cohort and to guide drug repurposing analyses. Transcriptomic analyses identified genes that were associated with age of HD onset and displayed colocalization with gene expression signals in brain tissue (FAN1, GPR161, PMS2, SUMF2), with supporting evidence from functional experiments. This included genes involved in DNA repair, as well as novel-candidate modifier genes that have been associated with other neurological conditions. Further, cortical coexpression modules were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder. Further, these analyses expanded the pathways potentially involved in modifying HD onset and prioritized candidate therapeutics for future study.
14 citations
TL;DR: Dbp, Crot and Mrpl4 are identified as potential targets for the treatment of hypertension and stroke and the expression of these genes may be reversed by the above miRNAs or drugs.
Abstract: The present study aimed to integrate the mRNA and microRNA (miRNA) expression profiles of spontaneously hypertensive rats (SHR rats) and stroke‑prone spontaneously hypertensive rats (SHRSP rats) to screen for potential therapeutic targets for hypertension and stroke. The datasets GSE41452, GSE31457, GSE41453 and GSE53363 were collected from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). The GSE53361 dataset was obtained to analyze differentially expressed miRNAs (DEMs). The DEGs and DEMs were identified between SHR (or SHRSP) rats and normotensive Wistar‑Kyoto (WKY) rats using the Linear Models for Microarray (limma) data method. Venn diagrams were used to show the SHR‑specific, SHRSP‑specific and SHR‑SHRSP shared DEGs and DEMs, and these were utilized to construct the protein‑protein interaction (PPI) and miRNA‑mRNA regulatory networks. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to explore the function of the genes. Subsequently, the connectivity Map (CMAP) database was searched to identify small‑molecule drugs. Comparisons between the GSE41452‑GSE31457‑GSE41453 merged and GSE53363 datasets identified 2 SHR‑specific, 8 SHRSP‑specific and 15 SHR‑SHRSP shared DEGs. Function enrichment analysis showed that SHRSP‑specific D‑box binding PAR bZIP transcription factor (Dbp) was associated with circadian rhythm, and SHR‑SHRSP shared carnitine O‑octanoyltransferase (Crot) was involved in fatty acid metabolic processes or the inflammatory response via interacting with epoxide hydrolase 2 (EPHX2). SHR‑SHRSP shared mitochondrial ribosomal protein L4 (Mrpl4) may exert roles by interacting with the threonine‑tRNA ligase, TARS2. The miRNA regulatory network predicted that upregulated Dbp could be regulated by rno‑miR‑126a‑5p, whereas downregulated Crot and Mrpl4 could be modulated by rno‑miR‑31a. The CMAP database predicted that small‑molecule drugs, including botulin, Gly‑His‑Lys, and podophyllotoxin, may possess therapeutic potential. In conclusion, the present study has identified Dbp, Crot and Mrpl4 as potential targets for the treatment of hypertension and stroke. Furthermore, the expression of these genes may be reversed by the above miRNAs or drugs.
14 citations
TL;DR: It is found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost, and can be used to add cell health annotations to Cell Painting perturbation datasets.
Abstract: Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays that use seven reagents to measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species (ROS), DNA damage, and aberrant cell cycle stage progression. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these trained models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1,500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts, and by confirming mitotic arrest and ROS phenotypes via PLK and proteasome inhibition, respectively. We provide an intuitive web app to browse all predictions at http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting perturbation datasets.
14 citations
TL;DR: In this article, a review of recent studies on machine learning models developed to infer various biological effects of molecules is presented, with particular attention paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model.
14 citations
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
10,968 citations
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
Abstract: Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences BLAT's speed stems from an index of all nonoverlapping K-mers in the genome This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly BLAT has several major stages It uses the index to find regions in the genome likely to be homologous to the query sequence It performs an alignment between homologous regions It stitches together these aligned regions (often exons) into larger alignments (typically genes) Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible This paper describes how BLAT was optimized Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications http://genomeucscedu hosts a web-based BLAT server for the human genome
8,326 citations
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
6,319 citations