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: In this paper, the authors found that 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization, which is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro.
Abstract: The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.
6 citations
TL;DR: Several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing, RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization are reviewed.
Abstract: In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.
6 citations
TL;DR: In silico insight is provided into the role of PTTG1 in ccRCC, and the Rac1 inhibitor NSC23766 is repurposed for treating P TTG1-high expressingccRCC.
Abstract: The pituitary tumor-transforming gene 1 (PTTG1), also known as Securin, is considered an oncogene. This study aimed to investigate the role of PTTG1 in clear cell renal cell carcinoma (ccRCC) using in silico bioinformatics approaches. A pan-cancer analysis using The Cancer Genome Atlas (TCGA) data indicated that among all cancer types copy number amplification of PTTG1 gene was most frequently found in ccRCC. However, amplification of PTTG1 gene copy number did not correlate with the increase of mRNA level in ccRCC, and did not predict the patients' overall survival. Instead, ccRCC was correlated with overexpression of PTTG1 mRNA, and its expression level was stage-dependent increased in cancer patients. An outlier analysis using the Oncomine database suggested that PTTG1 mRNA expression served as a good biomarker for ccRCC. Pathway analysis for upregulated genes enriched in PTTG1-high expressing ccRCC patients found that PTTG1 overexpression was associated with mitotic defects. Mining drug sensitivity data using the Cancer Therapeutics Response Portal (CTRP) discovered that PTTG1-high expressing ccRCC cell lines were susceptible to a Rac1 (Ras-related C3 botulinum toxin substrate 1) inhibitor NSC23766. Therefore, this study provides an in silico insight into the role of PTTG1 in ccRCC, and repurposes the Rac1 inhibitor NSC23766 for treating PTTG1-high expressing ccRCC.
6 citations
27 Oct 2021
TL;DR: In this paper, a machine learning-based framework for predicting the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery is presented, combining two distinct datasets, drug-induced gene expression profiles from Open TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs.
Abstract: With the advancements in Artificial intelligence (AI) and the build up of health-related big data, it has become increasingly feasible and commonplace to leverage machine learning techniques to analyze clinical and omics data to assess the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery. Here, we have presented a novel machine learning-based framework for predicting the likelihood of ADRs; it combines two distinct datasets, drug-induced gene expression profiles from Open TG–GATEs (Toxicogenomics Project–Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs. Using this framework with Deep Neural Networks (DNN), we built a total of 14 predictive models; in the validation tests, our models achieved a mean accuracy of 89.94%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As examples, we have described the models in the context of Duodenal ulcer and Hepatitis fulminant, highlighting mechanistic insights into those ADRs. Our models should help to assess the likelihood of ADRs in testing novel pharmaceutical compounds, and will be useful for researchers in drug discovery.
6 citations
TL;DR: In this article, the authors explored the possibility of repurposing thalidomide for the treatment of SARS-coV-2 pneumonia by reanalyzing transcriptomes of infected tissues.
Abstract: Aim SARS-coV-2 pandemic continues to cause an unprecedented global destabilization requiring urgent attention towards drug and vaccine development. Thalidomide, a drug with known anti-inflammatory and immunomodulatory effects has been indicated to be effective in treating a SARS-coV-2 pneumonia patient. Here, we study the possible mechanisms through which thalidomide might affect Coronavirus Disease -19 (COVID-19). Methods The present study explores the possibility of repurposing thalidomide for the treatment of SARS-coV-2 pneumonia by reanalyzing transcriptomes of SARS-coV-2 infected tissues with thalidomide and lenalidomide induced transcriptomic changes in transformed lung and hematopoietic models as procured from databases, and further comparing them with the transcriptome of primary endothelial cells. Results Thalidomide and lenalidomide exhibited pleiotropic effects affecting a range of biological processes including inflammation, immune response, angiogenesis, MAPK signaling, NOD-like receptor signaling, Toll-like receptor (TLR) signaling, leukocyte differentiation and innate immunity, the processes which are aberrantly regulated in severe COVID-19 patients. Conclusion The present study indicates thalidomide analogs as a "better fit" for treating severe cases of novel viral infections, healing the damaged network by compensating the impairment caused by the COVID-19.
6 citations
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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