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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01 Nov 2020
TL;DR: This work proposes a new cross-modal small molecule retrieval task, designed to force a model to learn to associate the structure of a small molecule with the transcriptional change it induces, developed formally as multi-view alignment problem, and presents a coordinated deep learning approach.
Abstract: Modeling the relationship between chemical structure and molecular activity is a key goal in drug development. Many benchmark tasks have been proposed for molecular property prediction, but these tasks are generally aimed at specific, isolated biomedical properties. In this work, we propose a new cross-modal small molecule retrieval task, designed to force a model to learn to associate the structure of a small molecule with the transcriptional change it induces. We develop this task formally as multi-view alignment problem, and present a coordinated deep learning approach that jointly optimizes representations of both chemical structure and perturbational gene expression profiles. We benchmark our results against oracle models and principled baselines, and find that cell line variability markedly influences performance in this domain. Our work establishes the feasibility of this new task, elucidates the limitations of current data and systems, and may serve to catalyze future research in small molecule representation learning.
3 citations
TL;DR: PP2, a known src-kinase inhibitor, is identified as a novel corrector of ΔF508-CFTR, which may represent a novel paradigm of multi-action therapeutics – corrector, anti-inflammatory, and anti-infective – in CF.
Abstract: Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. The most common mutation in CF, an in-frame deletion of phenylalanine 508, leads to a trafficking defect and endoplasmic reticulum retention of the protein where it becomes targeted for degradation. Successful clinical deployments of ivacaftor and ivacaftor/lumacaftor combination have been an exciting translational development in treating CF. However, their therapeutic effects are variable between subjects and remain insufficient. We used the Library of Integrated Network-based Cellular Signatures (LINCS) database as our chemical pool to screen for candidates. For in silico screening, we integrated connectivity mapping and CF systems biology to identify candidate therapeutic compounds for CF. Following in silico screening, we validated our candidate compounds with (i) an enteroid-based compound screening assay using CF (ΔF508/ΔF508-CFTR) patient-derived enteroids, (ii) short-circuit current analysis using polarized CF primary human airway epithelial cells and (iii) Western blots to measure F508-del-CFTR protein maturation. We identified 184 candidate compounds with in silico screening and tested 24 of them with enteroid-based forskolin-induced swelling (FIS) assay. The top hit compound was PP2, a known src-kinase inhibitor that induced swelling in enteroid comparable to known CF corrector (lumacaftor). Further validation with Western blot and short circuit current analysis showed that PP-2 could correct mutant CFTR mis-folding and restore CFTR-mediated transmembrane current. We have identified PP2, a known src-kinase inhibitor, as a novel corrector of ΔF508-CFTR. Based on our studies and previous reports, src kinase inhibition may represent a novel paradigm of multi-action therapeutics - corrector, anti-inflammatory, and anti-infective - in CF.
3 citations
Cites methods from "A Next Generation Connectivity Map:..."
...We used the LINCS cloud web tool [20] and gene set enrichment analysis (GSEA) to identify small molecules from the NIH’s LINCS library that could potentially reverse the CF gene expression profile....
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...The CF gene expression signature derived from rectal epithelia (RE) of human CF patients with ∆F508-CFTR mutation [18] was used to search the Library of Integrated Network-based Cellular Signatures (LINCS) database [19, 20] to identify compounds that are anti-correlated with the CF signature....
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TL;DR: In this paper, a network-based core gene identification method was proposed to identify the pathogenic genes of ASD, and two proposed drug repositioning methods were evaluated using gene network analysis.
Abstract: Identification of exact causative genes is important for in silico drug repositioning based on drug-gene-disease relationships. However, the complex polygenic etiology of the autism spectrum disorder (ASD) is a challenge in the identification of etiological genes. The network-based core gene identification method can effectively use the interactions between genes and accurately identify the pathogenic genes of ASD. We developed a novel network-based drug repositioning framework that contains three steps: network-specific core gene (NCG) identification, potential therapeutic drug repositioning, and candidate drug validation. First, through the analysis of transcriptome data for 178 brain tissues, gene network analysis identified 365 NCGs in 18 coexpression modules that were significantly correlated with ASD. Second, we evaluated two proposed drug repositioning methods. In one novel approach (dtGSEA), we used the NCGs to probe drug-gene interaction data and identified 35 candidate drugs. In another approach, we compared NCG expression patterns with drug-induced transcriptome data from the Connectivity Map database and found 46 candidate drugs. Third, we validated the candidate drugs using an in-house mental diseases and compounds knowledge graph (MCKG) that contained 7509 compounds, 505 mental diseases, and 123,890 edges. We found a total of 42 candidate drugs that were associated with mental illness, among which 10 drugs (baclofen, sulpiride, estradiol, entinostat, everolimus, fluvoxamine, curcumin, calcitriol, metronidazole, and zinc) were postulated to be associated with ASD. This study proposes a powerful network-based drug repositioning framework and also provides candidate drugs as well as potential drug targets for the subsequent development of ASD therapeutic drugs.
3 citations
TL;DR: A review of the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, highthroughput, high-content discipline across all biological scales can be found in this paper .
3 citations
01 Jan 2016
TL;DR: Ongoing clinical trials of biologics, small molecules, and alternative therapies are testing the effectiveness of these current approaches, and novel repositioning efforts will continue to integrate data-driven and experiential strategies to facilitate candidate identification.
Abstract: Systemic lupus erythematosus (SLE) is a complex and heterogeneous disease with few approved therapies. Drug development for SLE has been historically unsuccessful, with numerous failed clinical trials, and the approval of only a single new agent in the last sixty years. In efforts to introduce new therapies to the field, drug repurposing/repositioning, or the testing of drugs previously developed for one disease in a new disease, has emerged as a promising strategy. Indeed, most current lupus therapies have been “repurposed,” as many were developed for other indications such as rheumatoid arthritis, cancer, or transplant rejection before their use in or approval for lupus. Still, the recent failure of biologics and small molecules approved for other indications in lupus clinical trials demonstrates the complexity of repurposing. At present, literature mining, crowdsourcing, and many computational approaches incorporating both genomics and genetics have been employed to identify candidates for drug repositioning in lupus. Ongoing clinical trials of biologics, small molecules, and alternative therapies are testing the effectiveness of these current approaches. In the future, novel repositioning efforts will continue to integrate data-driven and experiential strategies to facilitate candidate identification.
3 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