DSigDB: drug signatures database for gene set analysis

Yoo, Minjae; Shin, Jaehyun; Kim, Jihye; Ryall, Karen A.; Lee, Kyubum; Lee, Sunwon; Jeon, Minji; Kang, Jaewoo; Tan, Aik Choon

doi:10.1093/bioinformatics/btv313

Cited by 325 publications

(253 citation statements)

References 10 publications

(10 reference statements)

Supporting

Mentioning

253

Contrasting

Order By: Relevance

“…EFNB2, a receptor tyrosine kinase (TK), also cycled strongly and may have prognostic significance in both small cell lung cancer and nonpulmonary cancers (25). We used the Drug Signatures Database to identify rhythms in drug targets (Dataset S2) (26). Several drug target classes in asthma treatment were rhythmic, including β-adrenergic receptors (targeted by β-agonists) and glucocorticoid receptors (targeted by inhaled and systemic steroids).…”

Section: Resultsmentioning

confidence: 99%

CYCLOPS reveals human transcriptional rhythms in health and disease

Anafi

Francey

Hogenesch

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

198

247

View full text Add to dashboard Cite

Circadian rhythms modulate many aspects of physiology. Knowledge of the molecular basis of these rhythms has exploded in the last 20 years. However, most of these data are from model organisms, and translation to clinical practice has been limited. Here, we present an approach to identify molecular rhythms in humans from thousands of unordered expression measurements. Our algorithm, cyclic ordering by periodic structure (CYCLOPS), uses evolutionary conservation and machine learning to identify elliptical structure in high-dimensional data. From this structure, CYCLOPS estimates the phase of each sample. We validated CYCLOPS using temporally ordered mouse and human data and demonstrated its consistency on human data from two independent research sites. We used this approach to identify rhythmic transcripts in human liver and lung, including hundreds of drug targets and disease genes. Importantly, for many genes, the circadian variation in expression exceeded variation from genetic and other environmental factors. We also analyzed hepatocellular carcinoma samples and show these solid tumors maintain circadian function but with aberrant output. Finally, to show how this method can catalyze medical translation, we show that dosage time can temporally segregate efficacy from dose-limiting toxicity of streptozocin, a chemotherapeutic drug. In sum, these data show the power of CYCLOPS and temporal reconstruction in bridging basic circadian research and clinical medicine.gene expression | biological rhythms | machine learning | autoencoder | circadian rhythms C ircadian rhythms are nearly ubiquitous in nature. In animals, much of physiology and behavior is under circadian control. Body temperature, hormonal rhythms, blood pressure, and locomotor activity are just a few of the processes displaying daily rhythms. In circadian model systems (e.g., cyanobacteria, Neurospora, Arabidopsis, Drosophila, and mice), high-resolution time sampling is straightforward, and experiments show that a substantial fraction of the transcriptome is under clock control. For example, in mice, a majority of genes are clock regulated in at least 1 of 12 different organs (1).Circadian rhythms are also critical for humans. Shift work-induced circadian misalignment is associated with higher rates of metabolic, cardiovascular, and neoplastic disease. Clinical experience suggests time of day can have a marked effect on disease severity (2-4). Indeed, the majority of the best-selling prescription drugs and World Health Organization essential medicines target molecules that oscillate in mice (1). However, translation of these findings to clinical medicine remains slow. How does human molecular physiology change with circadian time? In mice, and presumably humans, circadian output genes are markedly different in each tissue. Obviously, repeated sampling from most human organs is not possible. As a result, we have limited ability to study human molecular rhythms and relate them to either normal or disease physiology.One approach is to analyze temporally ...

show abstract

Section: Resultsmentioning

confidence: 99%

CYCLOPS reveals human transcriptional rhythms in health and disease

Anafi

Francey

Hogenesch

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

198

247

View full text Add to dashboard Cite

show abstract

“…By collecting both the variants and the clinical significance related to these variants, ClinVar offers a database for researchers to explore the significance of sequencing findings in the clinical setting [74]. Pharmacological databases such as PharmGKB [75], DrugBank [76], and DSigDB [77] provide the link between drug and drug targets (variants). For example, by querying a list of variants to one of these databases, it allows users to identify actionable targets via enrichment analysis for the repurposing of drugs.…”

Section: Computational Methods For Beyond Vcf Analysesmentioning

confidence: 99%

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

Hintzsche

Robinson

Tan

2016

International Journal of Genomics

Self Cite

View full text Add to dashboard Cite

Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome and is becoming a standard approach in studying genetic variants in diseases. Understanding the exomes of individuals at single base resolution allows the identification of actionable mutations for disease treatment and management. WES technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. Novel computational tools and methods have been developed to analyze and interpret WES data. Here, we review some of the current tools that are being used to analyze WES data. These tools range from the alignment of raw sequencing reads all the way to linking variants to actionable therapeutics. Strengths and weaknesses of each tool are discussed for the purpose of helping researchers make more informative decisions on selecting the best tools to analyze their WES data.

show abstract

“…In this study, we used the FDA-approved drugs and experimental compounds obtained from DSigDB. 7 DSigDB currently holds 17,389 unique compounds and 19,531 drug target genes, and is freely available at …”

Section: Methodsmentioning

confidence: 99%

“…There are many databases and repositories of drugs and target relationships, such as PubChem, 3 ChemBank, 4 DrugBank, 5 BindingDB, 6 and DSigDB. 7 The U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) represents one of the most popular AEDB of FDA-approved drugs. FAERS is designed to support the FDA's postmarketing safety surveillance program for drugs and therapeutic products.…”

Section: Introductionmentioning

confidence: 99%

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Federer

Yoo

Tan

2016

ASSAY and Drug Development Technologies

Self Cite

View full text Add to dashboard Cite

Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from (), a database of clinical studies around the world. By extracting drug and AE information from and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

show abstract

DSigDB: drug signatures database for gene set analysis

Abstract: aikchoon.tan@ucdenver.edu.

Cited by 325 publications

References 10 publications

CYCLOPS reveals human transcriptional rhythms in health and disease

CYCLOPS reveals human transcriptional rhythms in health and disease

A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

Contact Info

Product

Resources

About