scholarly journals A curated transcriptome dataset collection to investigate the functional programming of human hematopoietic cells in early life

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 414 ◽  
Author(s):  
Mahbuba Rahman ◽  
Sabri Boughorbel ◽  
Scott Presnell ◽  
Charlie Quinn ◽  
Chiara Cugno ◽  
...  
Keyword(s):  
Gene Expression ◽  
Early Life ◽  
Functional Programming ◽  
Web Application ◽  
Large Scale ◽  
Single Gene ◽  
Hematopoietic Cells ◽  
Biomedical Knowledge ◽  
Specific Expression ◽  
Age Related

Compendia of large-scale datasets made available in public repositories provide an opportunity to identify and fill gaps in biomedical knowledge. But first, these data need to be made readily accessible to research investigators for interpretation. Here we make available a collection of transcriptome datasets to investigate the functional programming of human hematopoietic cells in early life. Thirty two datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom web application called the Gene Expression Browser (GXB), which was designed for interactive query and visualization of integrated large-scale data. Quality control checks were performed. Multiple sample groupings and gene rank lists were created allowing users to reveal age-related differences in transcriptome profiles, changes in the gene expression of neonatal hematopoietic cells to a variety of immune stimulators and modulators, as well as during cell differentiation. Available demographic, clinical, and cell phenotypic information can be overlaid with the gene expression data and used to sort samples. Web links to customized graphical views can be generated and subsequently inserted in manuscripts to report novel findings. GXB also enables browsing of a single gene across projects, thereby providing new perspectives on age- and developmental stage-specific expression of a given gene across the human hematopoietic system. This dataset collection is available at: http://developmentalimmunology.gxbsidra.org/dm3/geneBrowser/list.

scholarly journals A curated transcriptome dataset collection to investigate the immunobiology of HIV infection

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 327 ◽  
Author(s):  
Jana Blazkova ◽  
Sabri Boughorbel ◽  
Scott Presnell ◽  
Charlie Quinn ◽  
Damien Chaussabel
Keyword(s):  
Gene Expression ◽  
Hiv Infection ◽  
Web Application ◽  
Large Scale ◽  
Single Gene ◽  
Gene Expression Omnibus ◽  
Biomedical Knowledge ◽  
Ncbi Gene Expression Omnibus ◽  
Interactive Query ◽  
Public Repositories

Compendia of large-scale datasets available in public repositories provide an opportunity to identify and fill current gaps in biomedical knowledge. But first, these data need to be readily accessible to research investigators for interpretation. Here, we make available a collection of transcriptome datasets relevant to HIV infection. A total of 2717 unique transcriptional profiles distributed among 34 datasets were identified, retrieved from the NCBI Gene Expression Omnibus (GEO), and loaded in a custom web application, the Gene Expression Browser (GXB), designed for interactive query and visualization of integrated large-scale data. Multiple sample groupings and rank lists were created to facilitate dataset query and interpretation via this interface. Web links to customized graphical views can be generated by users and subsequently inserted in manuscripts reporting novel findings, such as discovery notes. The tool also enables browsing of a single gene across projects, which can provide new perspectives on the role of a given molecule across biological systems. This curated dataset collection is available at:http://hiv.gxbsidra.org/dm3/geneBrowser/list.

scholarly journals ­A curated transcriptomic dataset collection relevant to embryonic development associated with in vitro fertilization in healthy individuals and patients with polycystic ovary syndrome

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 181 ◽  
Author(s):  
Rafah Mackeh ◽  
Sabri Boughorbel ◽  
Damien Chaussabel ◽  
Tomoshige Kino
Keyword(s):  
Gene Expression ◽  
Polycystic Ovary Syndrome ◽  
Web Application ◽  
Large Scale ◽  
Polycystic Ovary ◽  
Expression Profiles ◽  
Single Gene ◽  
Ovary Syndrome ◽  
Vitro Fertilization

The collection of large-scale datasets available in public repositories is rapidly growing and providing opportunities to identify and fill gaps in different fields of biomedical research. However, users of these datasets should be able to selectively browse datasets related to their field of interest. Here we made available a collection of transcriptome datasets related to human follicular cells from normal individuals or patients with polycystic ovary syndrome, in the process of their development, during in vitro fertilization. After RNA-seq dataset exclusion and careful selection based on study description and sample information, 12 datasets, encompassing a total of 85 unique transcriptome profiles, were identified in NCBI Gene Expression Omnibus and uploaded to the Gene Expression Browser (GXB), a web application specifically designed for interactive query and visualization of integrated large-scale data. Once annotated in GXB, multiple sample grouping has been made in order to create rank lists to allow easy data interpretation and comparison. The GXB tool also allows the users to browse a single gene across multiple projects to evaluate its expression profiles in multiple biological systems/conditions in a web-based customized graphical views. The curated dataset is accessible at the following link: http://ivf.gxbsidra.org/dm3/landing.gsp.

scholarly journals The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

2021 ◽  
Author(s):  
Ekaterina Bourova-Flin ◽  
Samira Derakhshan ◽  
Afsaneh Goudarzi ◽  
Tao Wang ◽  
Anne-Laure Vitte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Gene Expression Signature ◽  
Predictive Biomarkers ◽  
Specific Gene ◽  
Specific Expression ◽  
Intrinsic Nature ◽  
Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

scholarly journals NETWORKS FROM GENE EXPRESSION TIME SERIES: CHARACTERIZATION OF CORRELATION PATTERNS

2007 ◽  
Vol 17 (07) ◽  
pp. 2477-2483 ◽  
Author(s):  
D. REMONDINI ◽  
N. NERETTI ◽  
C. FRANCESCHI ◽  
P. TIERI ◽  
J. M. SEDIVY ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Single Gene ◽  
Biological Information ◽  
Reconstruction Method ◽  
Gene Expression Time Series ◽  
Expression Time

We address the problem of finding large-scale functional and structural relationships between genes, given a time series of gene expression data, namely mRNA concentration values measured from genetically engineered rat fibroblasts cell lines responding to conditional cMyc proto-oncogene activation. We show how it is possible to retrieve suitable information about molecular mechanisms governing the cell response to conditional perturbations. This task is complex because typical high-throughput genomics experiments are performed with high number of probesets (103–104 genes) and a limited number of observations (< 102 time points). In this paper, we develop a deepest analysis of our previous work [Remondini et al., 2005] in which we characterized some of the main features of a gene-gene interaction network reconstructed from temporal correlation of gene expression time series. One first advancement is based on the comparison of the reconstructed network with networks obtained from randomly generated data, in order to characterize which features retrieve real biological information, and which are instead due to the characteristics of the network reconstruction method. The second and perhaps more relevant advancement is the characterization of the global change in co-expression pattern following cMyc activation as compared to a basal unperturbed state. We propose an analogy with a physical system in a critical state close to a phase transition (e.g. Potts ferromagnet), since the cell responds to the stimulus with high susceptibility, such that a single gene activation propagates to almost the entire genome. Our result is relative to temporal properties of gene network dynamics, and there are experimental evidence that this can be related to spatial properties regarding the global organization of chromatine structure [Knoepfler et al., 2006].

scholarly journals Modulation of Aromatase by Phytoestrogens

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Edwin D. Lephart
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cellular Proliferation ◽  
Food Sources ◽  
Aromatase Activity ◽  
Human Tissues ◽  
Research Attention ◽  
Chemical Structures ◽  
Age Related ◽  
Cyp19 Gene

The aromatase enzyme catalyzes the conversion of androgens to estrogens in many human tissues. Estrogens are known to stimulate cellular proliferation associated with certain cancers and protect against adverse symptoms during the peri- and postmenopausal intervals. Phytoestrogens are a group of plant derived naturally occurring compounds that have chemical structures similar to estrogen. Since phytoestrogens are known to be constituents of animal/human food sources, these compounds have received increased research attention. Phytoestrogens may contribute to decreased cancer risk by the inhibition of aromatase enzyme activity and CYP19 gene expression in human tissues. This review covers (a) the aromatase enzyme (historical descriptions on function, activity, and gene characteristics), (b) phytoestrogens in their classifications and applications to human health, and (c) a chronological coverage of aromatase activity modulated by phytoestrogens from the early 1980s to 2015. In general, phytoestrogens act as aromatase inhibitors by (a) decreasing aromatase gene expression, (b) inhibiting the aromatase enzyme itself, or (c) in some cases acting at both levels of regulation. The findings presented herein are consistent with estrogen’s impact on health and phytoestrogen’s potential as anticancer treatments, but well-controlled, large-scale studies are warranted to determine the effectiveness of phytoestrogens on breast cancer and age-related diseases.

scholarly journals A curated compendium of monocyte transcriptome datasets of relevance to human monocyte immunobiology research

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 291 ◽  
Author(s):  
Darawan Rinchai ◽  
Sabri Boughorbel ◽  
Scott Presnell ◽  
Charlie Quinn ◽  
Damien Chaussabel
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Large Scale ◽  
Contextual Information ◽  
Human Monocyte ◽  
Relevant Group ◽  
Interactive Data ◽  
Public Repositories ◽  
Data Browsing ◽  
Public Datasets

Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online athttp://monocyte.gxbsidra.org/dm3/landing.gsp.

scholarly journals Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

2020 ◽  
Author(s):  
Kristen R. Maynard ◽  
Leonardo Collado-Torres ◽  
Lukas M. Weber ◽  
Cedric Uytingco ◽  
Brianna K. Barry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Web Application ◽  
Dorsolateral Prefrontal Cortex ◽  
Large Scale ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Sequencing Data ◽  
Dorsolateral Prefrontal ◽  
Transcriptomics Data

AbstractWe used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD).

scholarly journals Human iPSC-derived retinal pigment epithelium: a model system for identifying and functionally characterizing causal variants at AMD risk loci

2018 ◽  
Author(s):  
Erin N. Smith ◽  
Agnieszka D’Antonio-Chronowska ◽  
William W. Greenwald ◽  
Victor Borja ◽  
Lana R. Aguiar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Retinal Pigment Epithelium ◽  
Pigment Epithelium ◽  
Retinal Pigment ◽  
Induced Pluripotent Stem Cell ◽  
Age Related Macular Degeneration ◽  
Molecular Characteristics ◽  
Specific Expression ◽  
Age Related ◽  
Causal Variants

SummaryWe evaluate whether human induced pluripotent stem cell-derived retinal pigment epithelium (iPSC-RPE) cells can be used to prioritize and functionally characterize causal variants at age-related macular degeneration (AMD) risk loci. We generated iPSC-RPE from six subjects and show that they have morphological and molecular characteristics similar to native RPE. We generated RNA-seq, ATAC-seq, and H3K27ac ChIP-seq data and observe high similarity in gene expression and enriched transcription factor motif profiles between iPSC-RPE and human fetal-RPE. We performed fine-mapping of AMD risk loci by integrating molecular data from the iPSC-RPE, adult retina, and adult RPE, which identified rs943080 as the probable causal variant at VEGFA. We show that rs943080 is associated with altered chromatin accessibility of a distal ATAC-seq peak, decreased overall gene expression of VEGFA, and allele specific expression of a non-coding transcript. These results provide insight into the mechanism underlying the association of the VEGFA locus with AMD.

scholarly journals Aging Atlas: a multi-omics database for aging biology

2020 ◽  
Vol 49 (D1) ◽  
pp. D825-D830 ◽  
Author(s):  
◽  
Guang-Hui Liu ◽  
Yiming Bao ◽  
Jing Qu ◽  
Weiqi Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
High Throughput ◽  
Large Scale ◽  
Wide Spectrum ◽  
Molecular Profile ◽  
Aging Research ◽  
Related Data ◽  
Age Related ◽  
Wide Range

Abstract Organismal aging is driven by interconnected molecular changes encompassing internal and extracellular factors. Combinational analysis of high-throughput ‘multi-omics’ datasets (gathering information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and pharmacogenomics), at either populational or single-cell levels, can provide a multi-dimensional, integrated profile of the heterogeneous aging process with unprecedented throughput and detail. These new strategies allow for the exploration of the molecular profile and regulatory status of gene expression during aging, and in turn, facilitate the development of new aging interventions. With a continually growing volume of valuable aging-related data, it is necessary to establish an open and integrated database to support a wide spectrum of aging research. The Aging Atlas database aims to provide a wide range of life science researchers with valuable resources that allow access to a large-scale of gene expression and regulation datasets created by various high-throughput omics technologies. The current implementation includes five modules: transcriptomics (RNA-seq), single-cell transcriptomics (scRNA-seq), epigenomics (ChIP-seq), proteomics (protein–protein interaction), and pharmacogenomics (geroprotective compounds). Aging Atlas provides user-friendly functionalities to explore age-related changes in gene expression, as well as raw data download services. Aging Atlas is freely available at https://bigd.big.ac.cn/aging/index.

scholarly journals Young-Onset Colon Cancer and Recurrence Risk by Gene Expression

2020 ◽  
Vol 112 (11) ◽  
pp. 1170-1173
Author(s):  
George J Chang ◽  
Y Nancy Y You ◽  
Christy A Russell ◽  
Marni B Tierno ◽  
Michelle Turner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Single Gene ◽  
Recurrence Score ◽  
Low Risk ◽  
Stage Ii ◽  
Young Onset ◽  
Age Related ◽  
Incidence And Mortality ◽  
Gene Assay

Abstract The incidence and mortality from colorectal cancer in younger adults (younger than 55 years) is increasing. We reviewed the complete database of a gene-expression test, Oncotype DX Colon Recurrence Score test, to determine age-related differences in recurrence score (RS) and single-gene results (7 cancer-related of the 12-gene assay). We included 20 478 stage II and III A and B colon cancer patients submitted to Genomic Health. RS results were grouped by low-, intermediate-, and high-risk groups. Single-gene scores were described using median and interquartile range. Of all patients 72.5% and 72.6% of those younger than 40 years had low-risk RS. Comparing older with younger patients, RS or single-gene expression did not differ by age group or stage. Young-onset colon cancer does not differ by expression of the RS component genes. Most patients with stage II and III colon cancer have low-risk disease as measured by the 12-gene assay, regardless of age.

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