A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
Aravind Subramanian,Rajiv Narayan,Steven M. Corsello,Steven M. Corsello,David Peck,Ted Natoli,Xiaodong Lu,Joshua Gould,John F. Davis,Andrew A. Tubelli,Jacob K. Asiedu,David L. Lahr,Jodi E. Hirschman,Zihan Liu,Melanie Donahue,Bina Julian,Mariya Khan,David Wadden,Ian Smith,Daniel D. Lam,Arthur Liberzon,Courtney Toder,Mukta Bagul,Marek Orzechowski,Oana M. Enache,Federica Piccioni,Sarah A. Johnson,Nicholas J. Lyons,Alice H. Berger,Alice H. Berger,Alykhan F. Shamji,Angela N. Brooks,Angela N. Brooks,Anita Vrcic,Corey Flynn,Jacqueline Rosains,David Y. Takeda,David Y. Takeda,Roger Hu,Desiree Davison,Justin Lamb,Kristin Ardlie,Larson Hogstrom,Peyton Greenside,Nathanael S. Gray,Nathanael S. Gray,Paul A. Clemons,Serena J. Silver,Xiaoyun Wu,Wen-Ning Zhao,Wen-Ning Zhao,Willis Read-Button,Xiaohua Wu,Stephen J. Haggarty,Stephen J. Haggarty,Lucienne Ronco,Jesse S. Boehm,Stuart L. Schreiber,Stuart L. Schreiber,Stuart L. Schreiber,John G. Doench,Joshua A. Bittker,David E. Root,Bang Wong,Todd R. Golub +64 more
TLDR
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.read more
Citations
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Appyters: Turning Jupyter Notebooks into data-driven web apps
Daniel J.B. Clarke,Minji Jeon,Dan J. Stein,Nicole Moiseyev,Eryk Kropiwnicki,Charles Dai,Zhuorui Xie,Megan L. Wojciechowicz,Skylar Litz,Jason Hom,John Erol Evangelista,Lucas Goldman,Serena Zhang,Christine Yoon,Tahmid Ahamed,Samantha Bhuiyan,Minxuan Cheng,Julie Karam,Kathleen M. Jagodnik,Ingrid Shu,Alexander Lachmann,Sam Ayling,Sherry L. Jenkins,Avi Ma'ayan +23 more
TL;DR: Appyters as mentioned in this paper enables the rapid development of interactive web-based bioinformatics applications by allowing users to upload their data and set various parameters for a multitude of data analysis workflows.
Journal ArticleDOI
EuRBPDB: a comprehensive resource for annotation, functional and oncological investigation of eukaryotic RNA binding proteins (RBPs)
Jian-You Liao,Bing Yang,Yu-Chan Zhang,Xiao-Juan Wang,Yushan Ye,Jing-Wen Peng,Zhi-Zhi Yang,Jie-Hua He,Yin Zhang,Kaishun Hu,De-Chen Lin,Dong Yin +11 more
TL;DR: A cancer web interface is designed to systematically and interactively display the biological features of RBPs in various types of cancers and establish a comprehensive eukaryotic RBP database, EuRBPDB, which provides detailed annotations for each RBP.
Journal ArticleDOI
Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions.
Joshua A. Harrill,Imran Shah,R. Woodrow Setzer,Derik E. Haggard,Scott S. Auerbach,Richard S. Judson,Russell S. Thomas +6 more
TL;DR: This work discusses study design considerations for HTTr concentration-response screening and presents a framework for the use of HTTr-based biological pathway-altering concentrations (BPACs) in a screening-level, risk-based chemical prioritization approach.
Journal ArticleDOI
CARM1 Is Essential for Myeloid Leukemogenesis but Dispensable for Normal Hematopoiesis
Sarah Greenblatt,Na Man,Pierre-Jacques Hamard,Takashi Asai,Daniel L. Karl,Concepción Martínez,Daniel Bilbao,Vasileios Stathias,Anna Jermakowicz,Stephanie Duffort,Madhavi Tadi,Ezra Blumenthal,Samantha Newman,Ly P. Vu,Ye Xu,Fan Liu,Stephan C. Schürer,Michael T. McCabe,Ryan G. Kruger,Mingjiang Xu,Feng Chun Yang,Daniel G. Tenen,Daniel G. Tenen,Justin M. Watts,Francisco Vega,Stephen D. Nimer +25 more
TL;DR: It is shown that knockout of Carm1 abrogates both the initiation and maintenance of acute myeloid leukemia (AML) driven by oncogenic transcription factors, and suggests that targeting CARM1 may be an effective therapeutic strategy for AML.
Journal ArticleDOI
Enhancing scientific discoveries in molecular biology with deep generative models
TL;DR: This review provides a brief overview of the technical notions behind generative models and their implementation with deep learning techniques and describes several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.
References
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
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.
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Visualizing Data using t-SNE
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.
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Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
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.
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BLAT—The BLAST-Like Alignment Tool
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.
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Adjusting batch effects in microarray expression data using empirical Bayes methods
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.
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