scholarly journals All of gene expression (AOE): an integrated index for public gene expression databases

2019 ◽  
Author(s):  
Hidemasa Bono
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Sequence Data ◽  
Application Programming Interface ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Rna Seq ◽  
Functional Interpretation ◽  
Data Repositories ◽  
Genomic Expression

AbstractGene expression data have been archived as microarray and RNA-seq datasets in two public databases, Gene Expression Omnibus (GEO) and ArrayExpress (AE). In 2018, the DNA DataBank of Japan started a similar repository called the Genomic Expression Archive (GEA). These databases are useful resources for the functional interpretation of genes, but have been separately maintained and may lack RNA-seq data, while the original sequence data are available in the Sequence Read Archive (SRA).We constructed an index for those gene expression data repositories, called All Of gene Expression (AOE), to integrate publicly available gene expression data. The web interface of AOE can graphically query data in addition to the application programming interface. By collecting gene expression data from RNA-seq in the SRA, AOE also includes data not included in GEO and AE.AOE is accessible as a search tool from the GEA website and is freely available at https://aoe.dbcls.jp/.

scholarly journals Massive Mining of Publicly Available RNA-seq Data from Human and Mouse

2017 ◽  
Author(s):  
Alexander Lachmann ◽  
Denis Torre ◽  
Alexandra B. Keenan ◽  
Kathleen M. Jagodnik ◽  
Hyojin J. Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Major Gene ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Rna Seq ◽  
Data Repositories ◽  
Transcript Quantification ◽  
Web Resource ◽  
Human And Mouse

RNA-sequencing (RNA-seq) is currently the leading technology for genome-wide transcript quantification. While the volume of RNA-seq data is rapidly increasing, the currently publicly available RNA-seq data is provided mostly in raw form, with small portions processed non- uniformly. This is mainly because the computational demand, particularly for the alignment step, is a significant barrier for global and integrative retrospective analyses. To address this challenge, we developed all RNA-seq and ChIP-seq sample and signature search (ARCHS4), a web resource that makes the majority of previously published RNA-seq data from human and mouse freely available at the gene count level. Such uniformly processed data enables easy integration for downstream analyses. For developing the ARCHS4 resource, all available FASTQ files from RNA-seq experiments were retrieved from the Gene Expression Omnibus (GEO) and aligned using a cloud-based infrastructure. In total 137,792 samples are accessible through ARCHS4 with 72,363 mouse and 65,429 human samples. Through efficient use of cloud resources and dockerized deployment of the sequencing pipeline, the alignment cost per sample is reduced to less than one cent. ARCHS4 is updated automatically by adding newly published samples to the database as they become available. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene landing pages that provide average expression across cell lines and tissues, top co-expressed genes, and predicted biological functions and protein-protein interactions for each gene based on prior knowledge combined with co-expression. Benchmarking the quality of these predictions, co-expression correlation data created from ARCHS4 outperforms co-expression data created from other major gene expression data repositories such as GTEx and CCLE.ARCHS4 is freely accessible at: http://amp.pharm.mssm.edu/archs4

scholarly journals IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

2019 ◽  
Vol 15 (2) ◽  
pp. e1006792 ◽  
Author(s):  
Brandon Monier ◽  
Adam McDermaid ◽  
Cankun Wang ◽  
Jing Zhao ◽  
Allison Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Data Analysis ◽  
Interpretation System

Abstract P3-04-10: Comparison between RNA-Seq and Affymetrix gene expression data

2012 ◽  
Author(s):  
D Fumagalli ◽  
B Haibe-Kains ◽  
S Michiels ◽  
DN Brown ◽  
D Gacquer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Rna Seq ◽  
Affymetrix Gene Expression

scholarly journals recount-brain: a curated repository of human brain RNA-seq datasets metadata

2019 ◽  
Author(s):  
Ashkaun Razmara ◽  
Shannon E. Ellis ◽  
Dustin J. Sokolowski ◽  
Sean Davis ◽  
Michael D. Wilson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Gene Expression Data ◽  
Differential Expression Analysis ◽  
Controlled Vocabulary ◽  
The Cancer Genome Atlas ◽  
Tissue Type ◽  
Expression Data ◽  
Rna Seq ◽  
Human Brain Tissue

AbstractThe usability of publicly-available gene expression data is often limited by the availability of high-quality, standardized biological phenotype and experimental condition information (“metadata”). We released the recount2 project, which involved re-processing ∼70,000 samples in the Sequencing Read Archive (SRA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) projects. While samples from the latter two projects are well-characterized with extensive metadata, the ∼50,000 RNA-seq samples from SRA in recount2 are inconsistently annotated with metadata. Tissue type, sex, and library type can be estimated from the RNA sequencing (RNA-seq) data itself. However, more detailed and harder to predict metadata, like age and diagnosis, must ideally be provided by labs that deposit the data.To facilitate more analyses within human brain tissue data, we have complemented phenotype predictions by manually constructing a uniformly-curated database of public RNA-seq samples present in SRA and recount2. We describe the reproducible curation process for constructing recount-brain that involves systematic review of the primary manuscript, which can serve as a guide to annotate other studies and tissues. We further expanded recount-brain by merging it with GTEx and TCGA brain samples as well as linking to controlled vocabulary terms for tissue, Brodmann area and disease. Furthermore, we illustrate how to integrate the sample metadata in recount-brain with the gene expression data in recount2 to perform differential expression analysis. We then provide three analysis examples involving modeling postmortem interval, glioblastoma, and meta-analyses across GTEx and TCGA. Overall, recount-brain facilitates expression analyses and improves their reproducibility as individual researchers do not have to manually curate the sample metadata. recount-brain is available via the add_metadata() function from the recount Bioconductor package at bioconductor.org/packages/recount.

scholarly journals Explainable autoencoder-based representation learning for gene expression data

2021 ◽  
Author(s):  
Yang Yu ◽  
Pathum Kossinna ◽  
Wenyuan Liao ◽  
Qingrun Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Hidden Variables ◽  
Representation Learning ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Data Analysis ◽  
Cancer Genome Atlas ◽  
Modern Machine

Modern machine learning methods have been extensively utilized in gene expression data analysis. In particular, autoencoders (AE) have been employed in processing noisy and heterogenous RNA-Seq data. However, AEs usually lead to "black-box" hidden variables difficult to interpret, hindering downstream experimental validation and clinical translation. To bridge the gap between complicated models and biological interpretations, we developed a tool, XAE4Exp (eXplainable AutoEncoder for Expression data), which integrates AE and SHapley Additive exPlanations (SHAP), a flagship technique in the field of eXplainable AI (XAI). It quantitatively evaluates the contributions of each gene to the hidden structure learned by an AE, substantially improving the expandability of AE outcomes. By applying XAE4Exp to The Cancer Genome Atlas (TCGA) breast cancer gene expression data, we identified genes that are not differentially expressed, and pathways in various cancer-related classes. This tool will enable researchers and practitioners to analyze high-dimensional expression data intuitively, paving the way towards broader uses of deep learning.

scholarly journals Integrative Genomics of the Mammalian Alveolar Macrophage Response to Intracellular Mycobacteria

2020 ◽  
Author(s):  
Thomas J. Hall ◽  
Michael P. Mullen ◽  
Gillian P. McHugo ◽  
Kate E. Killick ◽  
Siobhán C. Ring ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alveolar Macrophage ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Host Response ◽  
Gwas Data ◽  
Data Sets ◽  
Expression Data ◽  
Rna Seq ◽  
Time Point

Abstract BackgroundBovine TB (BTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production, particularly in many developing countries. The key innate immune that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.ResultsThe host gene expression data consisted of bovine RNA-seq data from alveolar macrophages infected with M. bovis at 24 and 48 hours post-infection. These RNA-seq data were analysed using three distinct analysis pipelines and novel response pathways and modules were further refined using cross-comparison and integration of the results. First, a differential expression analysis was carried out to determine the most significantly differentially expressed (DE) genes between conditions at each time point. Second, two networks were constructed at each time point using gene correlation patterns to determine changes in expression across conditions. Functional sub-modules within each correlation network were selected by statistical criteria for modularity. Third, a base gene interaction network of the mammalian host response to mycobacterial infection was generated using the GeneCards database and InnateDB. Differential gene expression data were superimposed on this base network to extract functional modules of interconnected DE genes.ConclusionsBovine GWAS data was obtained from a published BTB susceptibility/resistance study. The results from the three parallel analyses were integrated with this data to determine which of the three approaches identified genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets.

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