Angelman Syndrome and Angelman-like Syndromes Share the Same Calcium-Related Gene Signatures

Angelman-like syndromes are a group of neurodevelopmental disorders that entail clinical presentation similar to Angelman Syndrome (AS). In our previous study, we showed that calcium signaling is disrupted in AS, and we identified calcium-target and calcium-regulating gene signatures that are able to differentiate between AS and their controls in different models. In the herein study, we evaluated these sets of calcium-target and calcium-regulating genes as signatures of AS-like and non-AS-like syndromes. We collected a number of RNA-seq datasets of various AS-like and non-AS-like syndromes and performed Principle Component Analysis (PCA) separately on the two sets of signature genes to visualize the distribution of samples on the PC1–PC2 plane. In addition to the evaluation of calcium signature genes, we performed differential gene expression analyses to identify calcium-related genes dysregulated in each of the studied syndromes. These analyses showed that the calcium-target and calcium-regulating signatures differentiate well between AS-like syndromes and their controls. However, in spite of the fact that many of the non-AS-like syndromes have multiple differentially expressed calcium-related genes, the calcium signatures were not efficient classifiers for non-AS-like neurodevelopmental disorders. These results show that features based on clinical presentation are reflected in signatures derived from bioinformatics analyses and suggest the use of bioinformatics as a tool for classification.

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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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Bioinformatics analyses of retinoblastoma reveal the retinoblastoma progression subtypes

PeerJ ◽

10.7717/peerj.8873 ◽

2020 ◽

Vol 8 ◽

pp. e8873

Author(s):

Manjing Cao ◽

Sha Wang ◽

Jing Zou ◽

Wanpeng Wang

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Gene Expression Omnibus ◽

Consensus Clustering ◽

Related Gene ◽

Bioinformatics Analyses ◽

Gene Signatures ◽

Cell Cycle Related Gene ◽

Diagnostic Capacity ◽

Public Datasets

Introduction Retinoblastoma (RB) is one common pediatric malignant tumor with dismal outcomes. Heterogeneity of RB and subtypes of RB were identified but the association between the subtypes of RB and RB progression have not been fully investigated. Methods Four public datasets were downloaded from Gene expression omnibus and normalization was performed to remove batch effect. Two public datasets were explored to obtain the RB progression gene signatures by differentially expression analysis while another two datasets were iterated for RB subtypes identification using consensus clustering. After the RB progressive subtype gene signatures were identified, we tested the diagnostic capacity of these gene signatures by receiver operation curve. Results Three hundreds and forty six genes that were enriched in cell cycle were identified as the progression signature in RB from two independent datasets. Four subtypes of RB were stratified by consensus clustering. A total of 21 genes from RB progression signature were differentially expressed between RB subtypes. One subtype with low expression cell division genes have less progression of all four subtypes. A panel of five RB subtype genes (CLUL1, CNGB1, ROM1, LRRC39 and RDH12) predict progression of RB. Conclusion Retinoblastoma is a highly heterogeneous tumor and the level of cell cycle related gene expression is associated with RB progression. A subpopulation of RB with high expression of visual perception has less progressive features. LRRC39 is potentially the RB progression subtype biomarker.

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Differential gene expression analyses related to fruit yield of Jatropha curcas L. using RNA-seq

Biotechnology & Biotechnological Equipment ◽

10.1080/13102818.2018.1507757 ◽

2018 ◽

Vol 32 (5) ◽

pp. 1126-1133

Author(s):

Wenkai Hui ◽

Yuantong Yang ◽

Guojiang Wu ◽

Yi Wang ◽

Mohamed Zaky Zayed ◽

...

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Jatropha Curcas ◽

Fruit Yield ◽

Rna Seq ◽

Jatropha Curcas L ◽

Gene Expression Analyses ◽

Differential Gene

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Reproducible Bioconductor Workflows Using Browser-based Interactive Notebooks and Containers

10.1101/144816 ◽

2017 ◽

Cited By ~ 1

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.

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Faculty Opinions recommendation of Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.14267340.15779565 ◽

2012 ◽

Author(s):

Marylyn Ritchie ◽

Stephen Turner

Keyword(s):

Expression Analysis ◽

Transcript Expression ◽

Rna Seq ◽

Differential Gene

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Faculty Opinions recommendation of Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717971189.793469500 ◽

2013 ◽

Author(s):

Stephen Turner

Keyword(s):

Gene Expression ◽

Differential Gene Expression ◽

Rna Seq ◽

Web Tool ◽

Differential Gene

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Best practices on the differential expression analysis of multi-species RNA-seq

Genome Biology ◽

10.1186/s13059-021-02337-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Matthew Chung ◽

Vincent M. Bruno ◽

David A. Rasko ◽

Christina A. Cuomo ◽

José F. Muñoz ◽

...

Keyword(s):

Best Practices ◽

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Single Species ◽

Rna Seq ◽

Species Analysis ◽

Differential Gene ◽

Multiple Species ◽

Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

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Immune-related gene signatures predict the outcome of neoadjuvant chemotherapy

OncoImmunology ◽

10.4161/onci.27884 ◽

2014 ◽

Vol 3 (3) ◽

pp. e27884 ◽

Cited By ~ 50

Author(s):

Gautier Stoll ◽

David Enot ◽

Bernhard Mlecnik ◽

Jérôme Galon ◽

Laurence Zitvogel ◽

...

Keyword(s):

Neoadjuvant Chemotherapy ◽

Related Gene ◽

Gene Signatures ◽

Immune Related Gene

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Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification

F1000Research ◽

10.12688/f1000research.15398.1 ◽

2018 ◽

Vol 7 ◽

pp. 952 ◽

Cited By ~ 26

Author(s):

Michael I. Love ◽

Charlotte Soneson ◽

Rob Patro

Keyword(s):

Software Package ◽

Gene Expression Analysis ◽

Real Data ◽

Transcript Level ◽

Bioinformatic Analysis ◽

Rna Seq ◽

Statistical Framework ◽

Gene Level ◽

Show Evidence ◽

Differential Gene

Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.

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