A curated transcriptome dataset collection to investigate the development and differentiation of the human placenta and its associated pathologies

Compendia of large-scale datasets made available in public repositories provide a precious opportunity to discover new biomedical phenomena and to fill gaps in our current knowledge. In order to foster novel insights it is necessary to ensure that these data are made readily accessible to research investigators in an interpretable format. Here we make a curated, public, collection of transcriptome datasets relevant to human placenta biology available for further analysis and interpretation via an interactive data browsing interface. We identified and retrieved a total of 24 datasets encompassing 759 transcriptome profiles associated with the development of the human placenta and associated pathologies from the NCBI Gene Expression Omnibus (GEO) and present them in a custom web-based application designed for interactive query and visualization of integrated large-scale datasets (http://placentalendocrinology.gxbsidra.org/dm3/landing.gsp). We also performed quality control checks using relevant biological markers. Multiple sample groupings and rank lists were subsequently created to facilitate data query and interpretation. Via this interface, users can create web-links to customized graphical views which may be inserted into manuscripts for further dissemination, or e-mailed to collaborators for discussion. The tool also enables users to browse a single gene across different projects, providing a mechanism for developing new perspectives on the role of a molecule of interest across multiple biological states. The dataset collection we created here is available at: http://placentalendocrinology.gxbsidra.org/dm3.

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

F1000Research ◽

10.12688/f1000research.8204.1 ◽

2016 ◽

Vol 5 ◽

pp. 327 ◽

Cited By ~ 4

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.

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

F1000Research ◽

10.12688/f1000research.8182.2 ◽

2016 ◽

Vol 5 ◽

pp. 291 ◽

Cited By ~ 11

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.

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GEOMetaCuration: A web-based application for accurate manual curation of Gene Expression Omnibus metadata

10.1101/257444 ◽

2018 ◽

Author(s):

Zhao Li ◽

Jin Li ◽

Peng Yu

Keyword(s):

Gene Expression ◽

Large Scale ◽

Gene Expression Omnibus ◽

Biological Data ◽

Use Case ◽

Web Based ◽

Link Type ◽

Manual Curation ◽

Development Framework ◽

Biological Discovery

AbstractMetadata curation has become increasingly important for biological discovery and biomedical research because a large amount of heterogeneous biological data is currently freely available. To facilitate efficient metadata curation, we developed an easy-to-use web-based curation application, GEOMetaCuration, for curating the metadata of Gene Expression Omnibus datasets. It can eliminate mechanical operations that consume precious curation time and can help coordinate curation efforts among multiple curators. It improves the curation process by introducing various features that are critical to metadata curation, such as a back-end curation management system and a curator-friendly front-end. The application is based on a commonly used web development framework of Python/Django and is open-sourced under the GNU General Public License V3. GEOMetaCuration is expected to benefit the biocuration community and to contribute to computational generation of biological insights using large-scale biological data. An example use case can be found at the demo website: http://geometacuration.yubiolab.org. Source code URL: https://bitbucket.com/yubiolab/GEOMetaCuration

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Ribo-Seq and RNA-Seq of TMA46 (DFRP1) and GIR2 (DFRP2) knockout yeast strains

F1000Research ◽

10.12688/f1000research.74727.1 ◽

2021 ◽

Vol 10 ◽

pp. 1162

Author(s):

Artyom A. Egorov ◽

Desislava S. Makeeva ◽

Nadezhda E. Makarova ◽

Dmitri A. Bykov ◽

Yanislav S. Hrytseniuk ◽

...

Keyword(s):

Mrna Translation ◽

Gene Expression Omnibus ◽

Ribosome Profiling ◽

Rna Seq ◽

General Stress Response ◽

Ncbi Gene Expression Omnibus ◽

Ribosome Stalling ◽

Link Type ◽

Gene Level

In eukaryotes, stalled and collided ribosomes are recognized by several conserved multicomponent systems, which either block protein synthesis in situ and resolve the collision locally, or trigger a general stress response. Yeast ribosome-binding GTPases RBG1 (DRG1 in mammals) and RBG2 (DRG2) form two distinct heterodimers with TMA46 (DFRP1) and GIR2 (DFRP2), respectively, both involved in mRNA translation. Accumulated evidence suggests that the dimers play partially redundant roles in elongation processivity and resolution of ribosome stalling and collision events, as well as in the regulation of GCN1-mediated signaling involved in ribosome-associated quality control (RQC). They also genetically interact with SLH1 (ASCC3) helicase, a key component of RQC trigger (RQT) complex disassembling collided ribosomes. Here, we present RNA-Seq and ribosome profiling (Ribo-Seq) data from S. cerevisiae strains with individual deletions of the TMA46 and GIR2 genes. Raw RNA-Seq and Ribo-Seq data as well as gene-level read counts are available in NCBI Gene Expression Omnibus (GEO) repository under GEO accession GSE185458 and GSE185286.

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A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery

F1000Research ◽

10.12688/f1000research.8182.1 ◽

2016 ◽

Vol 5 ◽

pp. 291 ◽

Cited By ~ 1

Author(s):

Darawan Rinchai ◽

Sabri Boughorbel ◽

Scott Presnell ◽

Charlie Quinn ◽

Damien Chaussabel

Keyword(s):

Knowledge Discovery ◽

Web Application ◽

Large Scale ◽

Contextual Information ◽

Human Monocyte ◽

Biomedical Knowledge ◽

Relevant Group ◽

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 at http://monocyte.gxbsidra.org/dm3/landing.gsp.

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Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

Current Pharmaceutical Design ◽

10.2174/1381612826666200311130133 ◽

2020 ◽

Vol 26 (29) ◽

pp. 3619-3630

Author(s):

Saumya Choudhary ◽

Dibyabhaba Pradhan ◽

Noor S. Khan ◽

Harpreet Singh ◽

George Thomas ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Gene ◽

Therapeutic Targets ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Keratinocyte Differentiation ◽

Hub Genes ◽

Lesional Skin ◽

Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

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Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds

Scientific Data ◽

10.1038/s41597-021-01008-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Deborah O. Dele-Oni ◽

Karen E. Christianson ◽

Shawn B. Egri ◽

Alvaro Sebastian Vaca Jacome ◽

Katherine C. DeRuff ◽

...

Keyword(s):

Large Scale ◽

Cell Model ◽

Cellular Responses ◽

Proteomic Profiling ◽

Reduced Representation ◽

Link Type ◽

Original Dataset ◽

Quality Control Metrics ◽

Biological Insight ◽

Chromatin Profiling

AbstractWhile gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of “connectivity” to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics.

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A damage-specific DNA binding protein. Large scale purification from human placenta and characterization.

Journal of Biological Chemistry ◽

10.1016/s0021-9258(20)65154-7 ◽

1982 ◽

Vol 257 (11) ◽

pp. 6394-6401

Author(s):

R S Feldberg ◽

J L Lucas ◽

A Dannenberg

Keyword(s):

Dna Binding ◽

Human Placenta ◽

Large Scale ◽

Binding Protein ◽

Dna Binding Protein

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Systematic Detection of Large-Scale Multi-Gene Horizontal Transfer in Prokaryotes

Molecular Biology and Evolution ◽

10.1093/molbev/msab043 ◽

2021 ◽

Author(s):

Lina Kloub ◽

Sean Gosselin ◽

Matthew Fullmer ◽

Joerg Graf ◽

J Peter Gogarten ◽

...

Keyword(s):

Gene Transfer ◽

Large Scale ◽

Single Gene ◽

Gene Families ◽

Microbial Evolution ◽

Phylogenetic Distance ◽

Secretion Systems ◽

Type Iii Secretion Systems ◽

A Genome ◽

Conserved Gene

Abstract Horizontal gene transfer (HGT) is central to prokaryotic evolution. However, little is known about the “scale” of individual HGT events. In this work, we introduce the first computational framework to help answer the following fundamental question: How often does more than one gene get horizontally transferred in a single HGT event? Our method, called HoMer, uses phylogenetic reconciliation to infer single-gene HGT events across a given set of species/strains, employs several techniques to account for inference error and uncertainty, combines that information with gene order information from extant genomes, and uses statistical analysis to identify candidate horizontal multi-gene transfers (HMGTs) in both extant and ancestral species/strains. HoMer is highly scalable and can be easily used to infer HMGTs across hundreds of genomes. We apply HoMer to a genome-scale dataset of over 22000 gene families from 103 Aeromonas genomes and identify a large number of plausible HMGTs of various scales at both small and large phylogenetic distances. Analysis of these HMGTs reveals interesting relationships between gene function, phylogenetic distance, and frequency of multi-gene transfer. Among other insights, we find that (i) the observed relative frequency of HMGT increases as divergence between genomes increases, (ii) HMGTs often have conserved gene functions, and (iii) rare genes are frequently acquired through HMGT. We also analyze in detail HMGTs involving the zonula occludens toxin and type III secretion systems. By enabling the systematic inference of HMGTs on a large scale, HoMer will facilitate a more accurate and more complete understanding of HGT and microbial evolution.

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