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José Fernández Navarro

dblp:209/7810 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2019
0000-0002-4035-5258ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 3 · 2 first-author

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
3 papers
Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › transcriptomics
spatial transcriptomics
1.032019
ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets · Bioinform. 2019
ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets · Bioinform. 2018
ST Pipeline: an automated pipeline for spatial mapping of unique transcripts · Bioinform. 2017
Bioinformatics and computational biology › transcriptomics
spatial gene expression
0.112018
ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets · Bioinform. 2018
Bioinformatics and computational biology
transcriptomics
0.112018
ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets · Bioinform. 2018
Bioinformatics and computational biology › transcriptomics
RNA-seq analysis
0.112017
ST Pipeline: an automated pipeline for spatial mapping of unique transcripts · Bioinform. 2017
Bioinformatics and computational biology › transcriptomics › RNA-seq analysis
RNA-seq data processing
0.112017
ST Pipeline: an automated pipeline for spatial mapping of unique transcripts · Bioinform. 2017

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

interactive visualization · 0.4image processing · 0.3automated pipeline · 0.3
YearPublicationVenuePosition
2019 ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets
abstract
MOTIVATION: Spatial Transcriptomics (ST) is a technique that combines high-resolution imaging with spatially resolved transcriptome-wide sequencing. This novel type of data opens up many possibilities for analysis and visualization, most of which are either not available with standard tools or too complex for normal users. RESULTS: Here, we present a tool, ST Viewer, which allows real-time interaction, analysis and visualization of Spatial Transcriptomics datasets through a seamless and smooth user interface. AVAILABILITY AND IMPLEMENTATION: The ST Viewer is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_viewer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
José Fernández Navarro, Joakim Lundeberg, Patrik L. Ståhl
Bioinform.1
2018 ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets
abstract
Motiviation: Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps. Results: Here we present ST Spot Detector, a web tool that automates and facilitates this alignment through a user friendly interface. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.
Kim Wong, José Fernández Navarro, Ludvig Bergenstråhle, Patrik L. Ståhl, Joakim Lundeberg
Bioinform.2
2017 ST Pipeline: an automated pipeline for spatial mapping of unique transcripts
abstract
MOTIVATION: In recent years we have witnessed an increase in novel RNA-seq based techniques for transcriptomics analysis. Spatial transcriptomics is a novel RNA-seq based technique that allows spatial mapping of transcripts in tissue sections. The spatial resolution adds an extra level of complexity, which requires the development of new tools and algorithms for efficient and accurate data processing. RESULTS: Here we present a pipeline to automatically and efficiently process RNA-seq data obtained from spatial transcriptomics experiments to generate datasets for downstream analysis. AVAILABILITY AND IMPLEMENTATION: The ST Pipeline is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_pipeline. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
José Fernández Navarro, Joel Sjöstrand, Fredrik Salmén, Joakim Lundeberg, Patrik L. Ståhl
Bioinform.1