scholarly journals Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guan Wang ◽  
Traci Kitaoka ◽  
Ali Crawford ◽  
Qian Mao ◽  
Andrew Hesketh ◽  
...  
Keyword(s):  
Recombinant Human Erythropoietin ◽  
Target Genes ◽  
Rna Seq ◽  
Pathway Enrichment ◽  
Human Erythropoietin ◽  
Tissue Protection ◽  
Wide Range ◽  
Rna Biology ◽  
Cross Platform ◽  
Differential Gene

AbstractRNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (MGI DNBSEQ and Illumina NextSeq 500) and two microarray platforms (GeneChip Human Transcriptome Array 2.0 and Illumina Expression BeadChip) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA DNB-seq generated a multitude of target genes compared to other platforms. Pathway enrichment analyses revealed genes correlate to not only erythropoiesis and oxygen transport but also a wide range of other functions, such as tissue protection and immune regulation. This study provides a knowledge base of genes relevant to EPO biology through cross-platform comparisons and validation.

scholarly journals Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

2021 ◽  
Author(s):  
Guan Wang ◽  
Traci Kitaoka ◽  
Ali Crawford ◽  
Qian Mao ◽  
Andrew Hesketh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Recombinant Human Erythropoietin ◽  
Rna Seq ◽  
Healthy Individuals ◽  
Human Erythropoietin ◽  
Rna Biology ◽  
Platform Comparison ◽  
Cross Platform ◽  
Differential Gene ◽  
Sequencing By Synthesis

Abstract RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (Illumina sequencing by synthesis and MGI DNBSEQ™) and two microarray platforms (Illumina Expression BeadChip and GeneChip™ Human Transcriptome Array 2.0) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA sequencing combined with DNB-seq produced a multitude of genes of biological relevance and significance in response to recombinant human erythropoietin, in contrast to other platforms. Through data triangulation linking genes to functions, genes representing the processes of erythropoiesis as well as non-erythropoietic functions of erythropoietin were unveiled. This study provides a knowledge base of genes characterising the responses to recombinant human erythropoietin through cross-platform comparison and validation.

scholarly journals Reproducible Bioconductor workflows using browser-based interactive notebooks and containers

2017 ◽  
Vol 25 (1) ◽  
pp. 4-12 ◽  
Author(s):  
Reem Almugbel ◽  
Ling-Hong Hung ◽  
Jiaming Hu ◽  
Abeer Almutairy ◽  
Nicole Ortogero ◽  
...  
Keyword(s):  
Interactive Software ◽  
Rna Seq ◽  
Software Environment ◽  
Bioinformatics Analyses ◽  
New Methods ◽  
Wide Range ◽  
Cross Platform ◽  
Differential Gene ◽  
User Friendly ◽  
Bioinformatics Workflows

Abstract Objective Bioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, that allows users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server. Materials and methods We present four different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets, process RNA-seq data and KinomeScan data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need to install any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder. Results BiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods. Conclusion Given the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols, and as ubiquitous.

scholarly journals Reproducible Bioconductor Workflows Using Browser-based Interactive Notebooks and Containers

2017 ◽  
Author(s):  
Reem Almugbel ◽  
Ling-Hong Hung ◽  
Jiaming Hu ◽  
Abeer Almutairy ◽  
Nicole Ortogero ◽  
...  
Keyword(s):  
Interactive Software ◽  
Rna Seq ◽  
Software Environment ◽  
Bioinformatics Analyses ◽  
New Methods ◽  
Wide Range ◽  
Cross Platform ◽  
Differential Gene ◽  
User Friendly ◽  
Bioinformatics Workflows

ABSTRACTObjectiveBioinformatics publications typically include complex software workflows that are difficult to describe in a manuscript. We describe and demonstrate the use of interactive software notebooks to document and distribute bioinformatics research. We provide a user-friendly tool, BiocImageBuilder, to allow users to easily distribute their bioinformatics protocols through interactive notebooks uploaded to either a GitHub repository or a private server.Materials and methodsWe present three different interactive Jupyter notebooks using R and Bioconductor workflows to infer differential gene expression, analyze cross-platform datasets and process RNA-seq data. These interactive notebooks are available on GitHub. The analytical results can be viewed in a browser. Most importantly, the software contents can be executed and modified. This is accomplished using Binder, which runs the notebook inside software containers, thus avoiding the need for installation of any software and ensuring reproducibility. All the notebooks were produced using custom files generated by BiocImageBuilder.ResultsBiocImageBuilder facilitates the publication of workflows with a point-and-click user interface. We demonstrate that interactive notebooks can be used to disseminate a wide range of bioinformatics analyses. The use of software containers to mirror the original software environment ensures reproducibility of results. Parameters and code can be dynamically modified, allowing for robust verification of published results and encouraging rapid adoption of new methods.ConclusionGiven the increasing complexity of bioinformatics workflows, we anticipate that these interactive software notebooks will become as ubiquitous and necessary for documenting software methods as traditional laboratory notebooks have been for documenting bench protocols.

scholarly journals The role of KMT2D and KDM6A in cardiac development: A cross-species analysis in humans, mice, and zebrafish

2020 ◽  
Author(s):  
Rwik Sen ◽  
Ezra Lencer ◽  
Elizabeth A. Geiger ◽  
Kenneth L. Jones ◽  
Tamim H. Shaikh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Development ◽  
Target Genes ◽  
Cardiac Development ◽  
Heart Defects ◽  
Neural Crest Cell ◽  
Kabuki Syndrome ◽  
Rna Seq ◽  
Wide Range

AbstractCongenital Heart Defects (CHDs) are the most common form of birth defects, observed in 4-10/1000 live births. CHDs result in a wide range of structural and functional abnormalities of the heart which significantly affect quality of life and mortality. CHDs are often seen in patients with mutations in epigenetic regulators of gene expression, like the genes implicated in Kabuki syndrome – KMT2D and KDM6A, which play important roles in normal heart development and function. Here, we examined the role of two epigenetic histone modifying enzymes, KMT2D and KDM6A, in the expression of genes associated with early heart and neural crest cell (NCC) development. Using CRISPR/Cas9 mediated mutagenesis of kmt2d, kdm6a and kdm6al in zebrafish, we show cardiac and NCC gene expression is reduced, which correspond to affected cardiac morphology and reduced heart rates. To translate our results to a human pathophysiological context and compare transcriptomic targets of KMT2D and KDM6A across species, we performed RNA sequencing (seq) of lymphoblastoid cells from Kabuki Syndrome patients carrying mutations in KMT2D and KDM6A. We compared the human RNA-seq datasets with RNA-seq datasets obtained from mouse and zebrafish. Our comparative interspecies analysis revealed common targets of KMT2D and KDM6A, which are shared between species, and these target genes are reduced in expression in the zebrafish mutants. Taken together, our results show that KMT2D and KDM6A regulate common and unique genes across humans, mice, and zebrafish for early cardiac and overall development that can contribute to the understanding of epigenetic dysregulation in CHDs.

scholarly journals Design and power analysis for multi-sample single cell genomics experiments

2020 ◽  
Author(s):  
Katharina T. Schmid ◽  
Cristiana Cruceanu ◽  
Anika Böttcher ◽  
Heiko Lickert ◽  
Elisabeth B. Binder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Experimental Design ◽  
Single Cell ◽  
Expression Analysis ◽  
Quantitative Trait ◽  
Power Analysis ◽  
Rna Seq ◽  
Wide Range ◽  
Differential Gene ◽  
Number Of Cells

AbstractBackgroundThe identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons.ResultsHere we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power.ConclusionOur model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

Encoding Gene Expression Using Deep Autoencoders for Expression Inference

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Raju Bhukya
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Reconstruction Error ◽  
Gene Expression Omnibus ◽  
Rna Seq ◽  
Gene Expressions ◽  
Integrated Network ◽  
Geo Dataset ◽  
Cross Platform ◽  
Inference Techniques

Gene expression of an organism contains all the information that characterises its observable traits. Researchers have invested abundant time and money to quantitatively measure the expressions in laboratories. On account of such techniques being too expensive to be widely used, the correlation between expressions of certain genes was exploited to develop statistical solutions. Pioneered by the National Institutes of Health Library of Integrated Network-Based Cellular Signature (NIH LINCS) program, expression inference techniques has many improvements over the years. The Deep Learning for Gene expression (D-GEX) project by University of California, Irvine approached the problem from a machine learning perspective, leading to the development of a multi-layer feedforward neural network to infer target gene expressions from clinically measured landmark expressions. Still, the huge number of genes to be inferred from a limited set of known expressions vexed the researchers. Ignoring possible correlation between target genes, they partitioned the target genes randomly and built separate networks to infer their expressions. This paper proposes that the dimensionality of the target set can be virtually reduced using deep autoencoders. Feedforward networks will be used to predict the coded representation of target expressions. In spite of the reconstruction error of the autoencoder, overall prediction error on the microarray based Gene Expression Omnibus (GEO) dataset was reduced by 6.6%, compared to D-GEX. An improvement of 16.64% was obtained on cross platform normalized data obtained by combining the GEO dataset and an RNA-Seq based 1000G dataset.

scholarly journals A systems genetics approach reveals PbrNSC as a regulator of lignin and cellulose biosynthesis in stone cells of pear fruit

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Runze Wang ◽  
Yongsong Xue ◽  
Jing Fan ◽  
Jia-Long Yao ◽  
Mengfan Qin ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Cell Formation ◽  
Systems Genetics ◽  
Pyrus Pyrifolia ◽  
Rna Seq ◽  
Pear Fruit ◽  
Stone Cell ◽  
Wide Range ◽  
Gene Regulatory

Abstract Background Stone cells in fruits of pear (Pyrus pyrifolia) negatively influence fruit quality because their lignified cell walls impart a coarse and granular texture to the fruit flesh. Results We generate RNA-seq data from the developing fruits of 206 pear cultivars with a wide range of stone cell contents and use a systems genetics approach to integrate co-expression networks and expression quantitative trait loci (eQTLs) to characterize the regulatory mechanisms controlling lignocellulose formation in the stone cells of pear fruits. Our data with a total of 35,897 expressed genes and 974,404 SNPs support the identification of seven stone cell formation modules and the detection of 139,515 eQTLs for 3229 genes in these modules. Focusing on regulatory factors and using a co-expression network comprising 39 structural genes, we identify PbrNSC as a candidate regulator of stone cell formation. We then verify the function of PbrNSC in regulating lignocellulose formation using both pear fruit and Arabidopsis plants and further show that PbrNSC can transcriptionally activate multiple target genes involved in secondary cell wall formation. Conclusions This study generates a large resource for studying stone cell formation and provides insights into gene regulatory networks controlling the formation of stone cell and lignocellulose.

Sign in / Sign up

Export Citation Format

Share Document