SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles

ABSTRACT Leukemia results from the expansion of self-renewing hematopoietic cells that are thought to contain mutations that contribute to disease initiation and progression. Studies of the gene expression profiles of human acute myeloid leukemia samples has allowed their classification based on the presence of translocations and French-American-British subtypes, but it is not yet clear whether their molecular signatures reflect the initiating mutations or mutations acquired during progression. To begin to address this question, we examined the expression profiles of normal murine promyelocyte-enriched samples, nontransformed murine promyelocytes expressing human promyelocytic leukemia-retinoic acid receptor alpha (PML-RARα) fusion gene, and primary acute promyelocytic leukemia cells. The expression profile of nontransformed cells expressing PML-RARα was remarkably similar to that of wild-type promyelocytes. In contrast, the expression profiles of fully transformed cells from three acute promyelocytic leukemia model systems were all different, suggesting that the expression signature of acute promyelocytic leukemia cells reflects the genetic changes that contributed to progression. To further evaluate these progression events, we compared two high-penetrance acute promyelocytic leukemia models that both commonly acquire an interstitial deletion of chromosome 2 during progression. The two models exhibited distinct gene expression profiles, suggesting that the dominant molecular signatures of murine acute promyelocytic leukemia can be influenced by several independent progression events.

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ImmuneRegulation: A web-based tool for identifying human immune regulatory elements

10.1101/468124 ◽

2018 ◽

Author(s):

Selim Kalayci ◽

Myvizhi Esai Selvan ◽

Irene Ramos ◽

Chris Cotsapas ◽

Ruth R. Montgomery ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Regulatory Elements ◽

Future Application ◽

Transcriptional Responses ◽

Web Based ◽

Phenotypic Differences ◽

Transcriptome Regulation

ABSTRACTHumans can vary considerably in their healthy immune phenotypes and in their immune responses to various stimuli. We have developed an interactive web-based tool, ImmuneRegulation, to enable discovery of human regulatory elements that drive some of the phenotypic differences observed in gene expression profiles. ImmuneRegulation currently provides the largest centrally integrated resource available in the literature on transcriptome regulation in whole blood and blood cell types, including genotype data from 23,040 individuals, with associated gene expression data from 30,562 experiments, that provide genetic variant-gene expression associations on ∼200 million eQTLs. In addition, it includes 14 million transcription factor (TF) binding region hits extracted from 1945 TF ChIP-seq peaks and the latest GWAS catalog of 67,230 published SNP-trait associations. Users can interactively explore ImmuneRegulation to visualize and discover associations between their gene(s) of interest and their regulators (genetic variants or transcription factors) across multiple cohorts and studies. These regulators can explain cohort or cell type dependent gene expression variations and may be critical in selecting the ideal cohorts or cell types for follow-up studies. Overall, ImmuneRegulation aims to contribute to our understanding of the effects of eQTLs and TFs on heterogeneous transcriptional responses reported in studies on the blood; in the development of molecular signatures of immune response; and facilitate their future application in patient management. ImmuneRegulation is freely available at http://icahn.mssm.edu/immuneregulation.

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PulmonDB: a curated lung disease gene expression database

10.1101/726745 ◽

2019 ◽

Author(s):

Ana B. Villaseñor-Altamirano ◽

Marco Moretto ◽

Alejandra Zayas-Del Moral ◽

Mariel Maldonado ◽

Adrián Munguía-Reyes ◽

...

Keyword(s):

Gene Expression ◽

Disease Gene ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Web Based ◽

Public Repositories ◽

New Hypothesis ◽

Different Sources

ABSTRACTChronic Obstructive Pulmonary Disease (COPD) and Idiopathic Pulmonary Fibrosis (IPF) have contrasting clinical and pathological characteristics, and interesting whole-genome transcriptomic profiles. However, data from public repositories are difficult to reprocess and reanalyze. Here we present PulmonDB, a web-based database (http://pulmondb.liigh.unam.mx/) and R library that facilitates exploration of gene expression profiles for these diseases by integrating transcriptomic data and curated annotation from different sources. We demonstrated the value of this resource by presenting the expression of already well-known genes of COPD and IPF across multiple experiments and the results of two differential expression analyses in which we successfully identified differences and similarities. With this first version of PulmonDB, we create a new hypothesis and compare the two diseases from a transcriptomics perspective.

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ArraySearch: A Web-Based Genomic Search Engine

Comparative and Functional Genomics ◽

10.1155/2012/650842 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Tyler J. Wilson ◽

Steven X. Ge

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Large Body ◽

Gene Expression Profiles ◽

Expression Data ◽

Search Query ◽

Web Based ◽

The Public ◽

Statistical Correlations ◽

Genomic Search

Recent advances in microarray technologies have resulted in a flood of genomics data. This large body of accumulated data could be used as a knowledge base to help researchers interpret new experimental data. ArraySearch finds statistical correlations between newly observed gene expression profiles and the huge source of well-characterized expression signatures deposited in the public domain. A search query of a list of genes will return experiments on which the genes are significantly up- or downregulated collectively. Searches can also be conducted using gene expression signatures from new experiments. This resource will empower biological researchers with a statistical method to explore expression data from their own research by comparing it with expression signatures from a large public archive.

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