scholarly journals MECP2pedia: A Comprehensive Transcriptome Portal for MECP2 Disease Research

2021 ◽  
Author(s):  
Ying-Wooi Wan ◽  
Alexander Jonathan Trostle ◽  
Lucian Li ◽  
Seon-Youn Kim ◽  
Jiasheng Wan ◽  
...  
Keyword(s):  
Big Data ◽  
Neurological Disease ◽  
Transcriptome Data ◽  
Valuable Resource ◽  
Signal To Noise ◽  
Individual Study ◽  
Transcriptomic Data ◽  
Disease Research ◽  
Core Set ◽  
Transcriptomic Studies

Mutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2s molecular role. Focusing on individual transcriptomic studies yields inconsistent differentially expressed genes. We have aggregated and homogeneously processed modern public MeCP2 transcriptome data, which we present in a web portal. With this big data, we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found distinct consistently up and downregulated subsets within these genes. We observe enrichment for this mouse core in other species MeCP2 models and see overlap between this core and ASD models. Analysis of signal to noise finds that many studies lack enough biological replicates. By integrating and examining transcriptomic data at scale, we have generated a valuable resource and insight on MeCP2 function.

scholarly journals Blood transcriptome analysis of patients with uncomplicated bacterial infection and sepsis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Velma Herwanto ◽  
Benjamin Tang ◽  
Ya Wang ◽  
Maryam Shojaei ◽  
Marek Nalos ◽  
...  
Keyword(s):  
Bacterial Infection ◽  
Healthy Controls ◽  
Beta Globin ◽  
Transcriptome Data ◽  
Valuable Resource ◽  
Globin Mrna ◽  
Blood Samples ◽  
Data Description ◽  
Pathway Activation ◽  
Blood Transcriptome

Abstract Objectives Hospitalized patients who presented within the last 24 h with a bacterial infection were recruited. Participants were assigned into sepsis and uncomplicated infection groups. In addition, healthy volunteers were recruited as controls. RNA was prepared from whole blood, depleted from beta-globin mRNA and sequenced. This dataset represents a highly valuable resource to better understand the biology of sepsis and to identify biomarkers for severe sepsis in humans. Data description The data presented here consists of raw and processed transcriptome data obtained by next generation RNA sequencing from 105 peripheral blood samples from patients with uncomplicated infections, patients who developed sepsis, septic shock patients, and healthy controls. It is provided as raw sequenced reads and as normalized log2 transformed relative expression levels. This data will allow performing detailed analyses of gene expression changes between uncomplicated infections and sepsis patients, such as identification of differentially expressed genes, co-regulated modules as well as pathway activation studies.

Meta‐analysis of transcriptomic studies of cytokinin‐treated rice roots defines a core set of cytokinin‐response genes

2021 ◽  
Author(s):  
Joanna K. Polko ◽  
Kevin C. Potter ◽  
Christian A. Burr ◽  
G. Eric Schaller ◽  
Joseph J. Kieber
Keyword(s):  
Meta Analysis ◽  
Rice Roots ◽  
Core Set ◽  
Cytokinin Response ◽  
Transcriptomic Studies

scholarly journals Creation of an Open-Access, Mutation-Defined Fibroblast Resource for Neurological Disease Research

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
pp. e43099 ◽  
Author(s):  
Selina Wray ◽  
Matthew Self ◽  
Patrick A. Lewis ◽  
Jan-Willem Taanman ◽  
Natalie S. Ryan ◽  
...  
Keyword(s):  
Open Access ◽  
Neurological Disease ◽  
Disease Research

scholarly journals Draft Genome of Cochliopodium minus (Amoebozoa): Insights into Its Complex Sexual Behavior, Across Domain Gene Acquisitions and Metazoan Type Signaling

2022 ◽  
Author(s):  
Yonas I. Tekle ◽  
Fang Wang ◽  
Hanh Tran ◽  
T. Danielle Hayes ◽  
Joseph F. Ryan
Keyword(s):  
Sexual Behavior ◽  
Tyrosine Kinases ◽  
Draft Genome ◽  
Model Organisms ◽  
Transcriptome Data ◽  
Ideal Model ◽  
Transcriptomic Data ◽  
Draft Genome Assembly ◽  
Genomic Analyses ◽  
Key Genes

Abstract To date, genomic analyses in amoebozoans have been mostly limited to model organisms or medically important lineages. Consequently, the vast diversity of Amoebozoa genomes remain unexplored. A draft genome of Cochliopodium minus, an amoeba characterized by extensive cellular and nuclear fusions, is presented. C. minus has been a subject of recent investigation for its unusual sexual behavior. Cochliopodium’s sexual activity occurs during vegetative stage making it an ideal model for studying sexual development, which is sorely lacking in the group. Here we generate a C. minus draft genome assembly. From this genome, we detect a substantial number of lateral gene transfer (LGT) instances from bacteria (15%), archaea (0.9%) and viruses (0.7%) the majority of which are detected in our transcriptome data. We identify the complete meiosis toolkit genes in the C. minus genome, as well as the absence of several key genes involved in plasmogamy and karyogamy. Comparative genomics of amoebozoans reveals variation in sexual mechanism exist in the group. Similar to complex eukaryotes, C. minus (some amoebae) possesses Tyrosine kinases and duplicate copies of SPO11. We report a first example of alternative splicing in a key meiosis gene and draw important insights on molecular mechanism of sex in C. minus using genomic and transcriptomic data.

Ethical considerations for monetizable big data in healthcare (Preprint)

2020 ◽  
Author(s):  
Eli M. Cahan ◽  
Tina Hernandez-Boussard ◽  
Sonoo Thadaney-Israni
Keyword(s):  
Cervical Cancer ◽  
Big Data ◽  
Personalized Medicine ◽  
Hela Cells ◽  
Ethical Considerations ◽  
Equitable Distribution ◽  
Demographic Groups ◽  
Transcriptomic Data ◽  
Scientific Advancement ◽  
Long Run

UNSTRUCTURED Since Henrietta Lacks’ death from cervical cancer, so-called HeLa cells have become a ubiquitous substrate for scientific advancement. Such scientific advancement has bred innovation, and innovation, profit. Yet, these innovations have been unevenly distributed across demographic groups. To ensure the ethical conduct of research and the equitable distribution of its benefits, biospecimens, like HeLa’s tissue, were protected under consent conventions of the Belmont Report. Likewise, during the genomic era, the “biospecimen" concept was forced to evolve again. A third era—that of informatics—has the potential to empower truly “personalized” medicine. Nonetheless, issues related to unequal focus of research efforts and unequal provision of innovation already exist. Additionally, with increasing ease of re-identification, the anonymity of individualized data is at risk. Redesign of consent protocols and redefinition of the biospecimen concept may be required once more to protect the donors of de-identified transcriptomic data—and their families, in the long-run.

scholarly journals Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data

2022 ◽  
Vol 12 ◽  
Author(s):  
Qingxia Yang ◽  
Yaguo Gong
Keyword(s):  
Feature Selection ◽  
Thyroid Nodules ◽  
Feature Selection Method ◽  
Selection Method ◽  
Classification Model ◽  
Molecular Testing ◽  
Hub Genes ◽  
Transcriptomic Data ◽  
Key Genes ◽  
Transcriptomic Studies

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5–15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly. However, gene signatures of thyroid nodules identified in a plenty of transcriptomic studies are highly inconsistent and extremely difficult to be applied in clinical application. Therefore, it is highly necessary to identify consistent signatures to discriminate benign or malignant thyroid nodules. In this study, five independent transcriptomic studies were combined to discover the gene signature between benign and malignant thyroid nodules. This combined dataset comprises 150 malignant and 93 benign thyroid samples. Then, there were 279 differentially expressed genes (DEGs) discovered by the feature selection method (Student’s t test and fold change). And the weighted gene co-expression network analysis (WGCNA) was performed to identify the modules of highly co-expressed genes, and 454 genes in the gray module were discovered as the hub genes. The intersection between DEGs by the feature selection method and hub genes in the WGCNA model was identified as the key genes for thyroid nodules. Finally, four key genes (ST3GAL5, NRCAM, MT1F, and PROS1) participated in the pathogenesis of malignant thyroid nodules were validated using an independent dataset. Moreover, a high-performance classification model for discriminating thyroid nodules was constructed using these key genes. All in all, this study might provide a new insight into the key differentiation of benign and malignant thyroid nodules.

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