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Adrian Baez-Ortega

dblp:167/1728 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2019
0000-0002-9201-4420ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 2 · 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
1 paper
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
High-performance computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics › microbial genomics
bacterial genome analysis
0.212015
IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data · Bioinform. 2015
Bioinformatics and computational biology
comparative genomics
0.112015
IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data · Bioinform. 2015
High-performance computing
scientific computing systems
0.112015
IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data · Bioinform. 2015

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

sequence assembly · 0.4genome annotation · 0.4bacterial classification · 0.4
YearPublicationVenuePosition
2019 Computational approaches for discovery of mutational signatures in cancer
abstract
The accumulation of somatic mutations in a genome is the result of the activity of one or more mutagenic processes, each of which leaves its own imprint. The study of these DNA fingerprints, termed mutational signatures, holds important potential for furthering our understanding of the causes and evolution of cancer, and can provide insights of relevance for cancer prevention and treatment. In this review, we focus our attention on the mathematical models and computational techniques that have driven recent advances in the field.
Adrian Baez-Ortega, Kevin Gori
Briefings Bioinform.1
2015 IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data
abstract
UNLABELLED: We introduce IonGAP, a publicly available Web platform designed for the analysis of whole bacterial genomes using Ion Torrent sequence data. Besides assembly, it integrates a variety of comparative genomics, annotation and bacterial classification routines, based on the widely used FASTQ, BAM and SRA file formats. Benchmarking with different datasets evidenced that IonGAP is a fast, powerful and simple-to-use bioinformatics tool. By releasing this platform, we aim to translate low-cost bacterial genome analysis for microbiological prevention and control in healthcare, agroalimentary and pharmaceutical industry applications. AVAILABILITY AND IMPLEMENTATION: IonGAP is hosted by the ITER's Teide-HPC supercomputer and is freely available on the Web for non-commercial use at http://iongap.hpc.iter.es. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Adrian Baez-Ortega, Fabian Lorenzo-Diaz, Mariano Hernandez, Carlos Ignacio Gonzalez-Vila, José Luis Roda García, Marcos Colebrook
Bioinform.1