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José Alquicira-Hernández

dblp:211/5959 · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-9049-7780ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author · 1 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
3 papers
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
single-cell analysis
0.512021
Nebulosa recovers single-cell gene expression signals by kernel density estimation · Bioinform. 2021
Bioinformatics and computational biology › single-cell analysis
single-cell visualization
0.512021
Nebulosa recovers single-cell gene expression signals by kernel density estimation · Bioinform. 2021
Bioinformatics and computational biology › gene regulation
gene regulatory network
0.412020
Programmatic access to bacterial regulatory networks with regutools · Bioinform. 2020
Bioinformatics and computational biology › gene regulation › transcription factor binding site prediction
transcription factor binding site analysis
0.412020
Programmatic access to bacterial regulatory networks with regutools · Bioinform. 2020
Bioinformatics and computational biology › transcriptomics
RNA-seq analysis
0.312017
Rail-RNA: scalable analysis of RNA-seq splicing and coverage · Bioinform. 2017
Bioinformatics and computational biology › sequence analysis › read mapping
spliced alignment
0.312017
Rail-RNA: scalable analysis of RNA-seq splicing and coverage · Bioinform. 2017
Cloud and datacenter computing › cloud applications
cloud-based bioinformatics
0.112017
Rail-RNA: scalable analysis of RNA-seq splicing and coverage · Bioinform. 2017

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

coverage normalization · 0.6cloud-enabled alignment · 0.6kernel density estimation · 0.5r package · 0.4bioconductor · 0.4
YearPublicationVenuePosition
2021 Nebulosa recovers single-cell gene expression signals by kernel density estimation
abstract
SUMMARY: Data sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualized in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression. AVAILABILITY AND IMPLEMENTATION: Nebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
José Alquicira-Hernández, Joseph E. Powell
Bioinform.1
2020 Programmatic access to bacterial regulatory networks with regutools
abstract
SUMMARY: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. AVAILABILITY AND IMPLEMENTATION: regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.
Joselyn Chávez, Carmina Barberena-Jonas, Jesus E. Sotelo-Fonseca, José Alquicira-Hernández, Heladia Salgado, Leonardo Collado-Torres
Bioinform.4
2017 Rail-RNA: scalable analysis of RNA-seq splicing and coverage
abstract
MOTIVATION: RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it requires extra work to obtain analysis products that incorporate data from across samples. RESULTS: We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16 h for US$0.91 per sample. Rail-RNA outputs alignments in SAM/BAM format; but it also outputs (i) base-level coverage bigWigs for each sample; (ii) coverage bigWigs encoding normalized mean and median coverages at each base across samples analyzed; and (iii) exon-exon splice junctions and indels (features) in columnar formats that juxtapose coverages in samples in which a given feature is found. Supplementary outputs are ready for use with downstream packages for reproducible statistical analysis. We use Rail-RNA to identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounding variables. AVAILABILITY AND IMPLEMENTATION: Rail-RNA is open-source software available at http://rail.bio. CONTACTS: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Abhinav Nellore, Leonardo Collado-Torres, Andrew E. Jaffe, José Alquicira-Hernández, Christopher Wilks, Jacob Pritt, James Morton, Jeffrey T. Leek, Ben Langmead
Bioinform.4