Cross-platform ultradeep transcriptomic profiling of human reference RNA samples by RNA-Seq

Pepper (Capsicum annuum L.) fruits are one of the most consumed vegetables worldwide. This produce has a great agro-economical relevance, since it is extensively cultivated. These fruits are characterized by their high content of vitamins C and A [1]. Capsicum annuum has many varieties, whose fruits differ in size, shape, color, and pungency, this last characteristic being due to the presence, in different degrees, of capsaicinoids and alkaloids, which are exclusive to the genus Capsicum [2]. The present study focuses on the transcriptomic profiling of an autochthonous Spanish variety called “Padrón” (mild hot) [3]. Pepper “Padrón” plants were grown in farms under the local conditions (42°44′05″ N 8°37′42″ W), and fruits at both green and red ripe ripening stages were collected. The transcriptome profiling was carried out in both types of fruits by RNA sequencing (RNA-seq) using the NextSeq550 system (Illumina®) [4,5]. RNA-seq analysis revealed that the expression of more than half of the 17,499 identified transcripts was modulated during ripening. Comparing to green fruits, 5626 and 5241 genes were up- and down-regulated, respectively, in red fruits. These differentially expressed genes (DEGs) have been analyzed to determine the functional categories that orchestrate the ripening process at the genetic level of this non-climacteric fruit.

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Transcriptomic Profiling of Adipose Derived Stem Cells Undergoing Osteogenesis by RNA-Seq

Scientific Reports ◽

10.1038/s41598-019-48089-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 8

Author(s):

Shahensha Shaik ◽

Elizabeth C. Martin ◽

Daniel J. Hayes ◽

Jeffrey M. Gimble ◽

Ram V. Devireddy

Keyword(s):

Stem Cells ◽

Adipose Derived Stem Cells ◽

Rna Seq ◽

Transcriptomic Profiling

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Longitudinal peripheral tissue RNA‐Seq transcriptomic profiling, hyperalgesia, and wound healing in the rat plantar surgical incision model

The FASEB Journal ◽

10.1096/fj.202100347r ◽

2021 ◽

Vol 35 (10) ◽

Author(s):

Taichi Goto ◽

Matthew R. Sapio ◽

Dragan Maric ◽

Jeffrey M. Robinson ◽

Anthony F. Domenichiello ◽

...

Keyword(s):

Wound Healing ◽

Peripheral Tissue ◽

Rna Seq ◽

Transcriptomic Profiling ◽

Surgical Incision

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Cross-platform normalization of microarray and RNA-seq data for machine learning applications

10.7287/peerj.preprints.1460 ◽

2015 ◽

Author(s):

Jeffrey A Thompson ◽

Jie Tan ◽

Casey S Greene

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Machine Learning Algorithms ◽

Quantile Normalization ◽

Learning Approaches ◽

Rna Seq ◽

Distribution Matching ◽

Machine Learning Applications ◽

Cross Platform ◽

R Programming

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization and a simple log2 transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.

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Cross-platform normalization of microarray and RNA-seq data for machine learning applications

PeerJ ◽

10.7717/peerj.1621 ◽

2016 ◽

Vol 4 ◽

pp. e1621 ◽

Cited By ~ 42

Author(s):

Jeffrey A. Thompson ◽

Jie Tan ◽

Casey S. Greene

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Machine Learning Algorithms ◽

Quantile Normalization ◽

Learning Approaches ◽

Rna Seq ◽

Distribution Matching ◽

Machine Learning Applications ◽

Cross Platform ◽

R Programming

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simplelog2transformation, on both simulated and biological datasets of gene expression. Our evaluation included both supervised and unsupervised machine learning approaches. We found that TDM exhibited consistently strong performance across settings and that quantile normalization also performed well in many circumstances. We also provide a TDM package for the R programming language.

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Reproducible Bioconductor workflows using browser-based interactive notebooks and containers

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx120 ◽

2017 ◽

Vol 25 (1) ◽

pp. 4-12 ◽

Cited By ~ 15

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.

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RNA-Seq-Derived Whole Genome Transcriptomic Profiling Following Challenge to Mt. Cedar in a Pollen Challenge Chamber Uncover Novel Insights Into Allergic Rhinoconjunctivitis Pathogenesis

Journal of Allergy and Clinical Immunology ◽

10.1016/j.jaci.2012.12.825 ◽

2013 ◽

Vol 131 (2) ◽

pp. AB40

Author(s):

Nathan Harper ◽

John Castiblanco ◽

Cynthia Rather ◽

Charles Andrews ◽

Daniel Ramirez ◽

...

Keyword(s):

Allergic Rhinoconjunctivitis ◽

Whole Genome ◽

Rna Seq ◽

Transcriptomic Profiling

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Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq

Scientific Data ◽

10.1038/sdata.2014.21 ◽

2014 ◽

Vol 1 (1) ◽

Cited By ~ 18

Author(s):

Binsheng Gong ◽

Charles Wang ◽

Zhenqiang Su ◽

Huixiao Hong ◽

Jean Thierry-Mieg ◽

...

Keyword(s):

Rat Liver ◽

Study Design ◽

Rna Seq ◽

Transcriptomic Profiling ◽

Comprehensive Study

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RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis

BMC Bioinformatics ◽

10.1186/s12859-021-04211-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Alessandro La Ferlita ◽

Salvatore Alaimo ◽

Sebastiano Di Bella ◽

Emanuele Martorana ◽

Georgios I. Laliotis ◽

...

Keyword(s):

Data Analysis ◽

Transcriptome Profiling ◽

Biological Species ◽

Third Party ◽

Rna Seq ◽

Rna Molecules ◽

Non Coding Rna ◽

Cross Platform ◽

Cloud Environments ◽

User Friendly

Abstract Background RNA-Seq is a well-established technology extensively used for transcriptome profiling, allowing the analysis of coding and non-coding RNA molecules. However, this technology produces a vast amount of data requiring sophisticated computational approaches for their analysis than other traditional technologies such as Real-Time PCR or microarrays, strongly discouraging non-expert users. For this reason, dozens of pipelines have been deployed for the analysis of RNA-Seq data. Although interesting, these present several limitations and their usage require a technical background, which may be uncommon in small research laboratories. Therefore, the application of these technologies in such contexts is still limited and causes a clear bottleneck in knowledge advancement. Results Motivated by these considerations, we have developed RNAdetector, a new free cross-platform and user-friendly RNA-Seq data analysis software that can be used locally or in cloud environments through an easy-to-use Graphical User Interface allowing the analysis of coding and non-coding RNAs from RNA-Seq datasets of any sequenced biological species. Conclusions RNAdetector is a new software that fills an essential gap between the needs of biomedical and research labs to process RNA-Seq data and their common lack of technical background in performing such analysis, which usually relies on outsourcing such steps to third party bioinformatics facilities or using expensive commercial software.

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