Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Regina H. Reynolds

dblp:303/1613 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
2since 2021 · last 2022
0000-0001-6470-7919ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › transcriptomics
isoform expression visualization
0.612022
ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2 · Bioinform. 2022
Bioinformatics and computational biology
transcriptomics
0.612022
ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2 · Bioinform. 2022
Bioinformatics and computational biology › network bioinformatics › biological network analysis
gene co-expression network analysis
0.512021
Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights · Bioinform. 2021
Bioinformatics and computational biology › genomics
genome visualization
0.212022
ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2 · Bioinform. 2022

Methods — techniques the papers use, named apart from their topics

ggplot2 · 0.6
YearPublicationVenuePosition
2022 ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2
abstract
MOTIVATION: The advent of long-read sequencing technologies has increased demand for the visualization and interpretation of transcripts. However, tools that perform such visualizations remain inflexible and lack the ability to easily identify differences between transcript structures. Here, we introduce ggtranscript, an R package that provides a fast and flexible method to visualize and compare transcripts. As a ggplot2 extension, ggtranscript inherits the functionality and familiarity of ggplot2 making it easy to use. AVAILABILITY AND IMPLEMENTATION: ggtranscript is an R package available at https://github.com/dzhang32/ggtranscript (DOI: https://doi.org/10.5281/zenodo.6374061) via an open-source MIT licence. Further documentation is available at https://dzhang32.github.io/ggtranscript/.
Emil K. Gustavsson, David Zhang 0004, Regina H. Reynolds, Sonia García-Ruíz, Mina Ryten
Bioinform.3
2021 Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights
abstract
MOTIVATION: Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behavior for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. RESULTS: We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. AVAILABILITYAND IMPLEMENTATION: The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open-source software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Juan A. Sánchez, Ana Luisa Gil-Martínez, Alejandro Cisterna, Sonia García-Ruíz, Alicia Gómez-Pascual, Regina H. Reynolds, Mike A. Nalls, John Hardy, Mina Ryten, Juan A. Botía Blaya
Bioinform.6