Luciano Fernandez-Ricaud

ORCID: 0000-0003-4799-4084
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About
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Research Areas
  • Bioinformatics and Genomic Networks
  • Fungal and yeast genetics research
  • Advanced Proteomics Techniques and Applications
  • Gene expression and cancer classification
  • Microbial Metabolic Engineering and Bioproduction
  • Metabolomics and Mass Spectrometry Studies
  • Genomics and Phylogenetic Studies
  • CRISPR and Genetic Engineering
  • Innovation and Socioeconomic Development
  • Cell Image Analysis Techniques
  • Genetic Mapping and Diversity in Plants and Animals
  • Gut microbiota and health

University of Gothenburg
2004-2016

The capacity to map traits over large cohorts of individuals-phenomics-lags far behind the explosive development in genomics. For microbes, estimation growth is key phenotype because its link fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved phenotypes at unprecedented scale. Advancements were achieved through introduction transmissive scanning hardware software technology, frequent acquisition exact colony population size...

10.1534/g3.116.032342 article EN cc-by G3 Genes Genomes Genetics 2016-08-01

Abstract Background Phenomics is a field in functional genomics that records variation organismal phenotypes the genetic, epigenetic or environmental context at massive scale. For microbes, key phenotype growth population size because it contains information directly linked to fitness. Due technical innovations and extensive automation our capacity record complex dynamic microbial data rapidly outpacing dissect visualize this extract fitness components contains, hampering progress all fields...

10.1186/s12859-016-1134-2 article EN cc-by BMC Bioinformatics 2016-06-23

The rapid recent evolution of the field phenomics—the genome-wide study gene dispensability by quantitative analysis phenotypes—has resulted in an increasing demand for new data and visualization tools. Following introduction a novel approach precise, quantification Saccharomyces cerevisiae we here announce public resource mining, filtering visualizing phenotypic data—the PROPHECY database. is designed to allow easy flexible access physiologically relevant growth behaviour mutant strains...

10.1093/nar/gki126 article EN Nucleic Acids Research 2004-12-17

Connecting genotype to phenotype is fundamental in biomedical research and our understanding of disease. Phenomics--the large-scale quantitative phenotypic analysis genotypes on a genome-wide scale--connects automated data generation with the development novel tools for integration, mining visualization. Our yeast phenomics database PROPHECY available at http://prophecy.lundberg.gu.se. Via phenotyping 984 heterozygous diploids all essential genes analysed presented have been extended now...

10.1093/nar/gkl1029 article EN cc-by-nc Nucleic Acids Research 2006-12-06

Genome editing using versions of the bacterial CRISPR/Cas9 system can be used to probe function selected genes in any organism. Green Listed is a web-based tool that rapidly designs custom CRISPR screens targeting sets defined by user. It could thus design for example all differentially expressed during specific stimuli or related pathway function, as well generate targeted secondary following large-scale screen.The software, including demo explanatory texts and videos, available through...

10.1093/bioinformatics/btw739 article EN Bioinformatics 2016-11-19

ABSTRACT The capacity to map traits over large cohorts of individuals – phenomics lags far behind the explosive development in genomics. For microbes estimation growth is key phenotype. We introduce an automated microbial framework that delivers accurate and highly resolved phenotypes at unprecedented scale. Advancements were achieved through introduction transmissive scanning hardware software technology, frequent acquisition precise colony population size measurements, extraction rates...

10.1101/031443 preprint EN bioRxiv (Cold Spring Harbor Laboratory) 2015-11-12

Clumping of gene properties like expression or mutant phenotypes along chromosomes is commonly detected using completely random null-models where their location equally likely across the chromosomes. Interpretation statistical tests based on these assumptions may be misleading if dependencies exist that are unequal between in different chromosomal parts. One such regional dependency telomeric effect, observed several studies Saccharomyces cerevisiae, under which e.g. essential genes less to...

10.2202/1544-6115.1428 article EN Statistical Applications in Genetics and Molecular Biology 2009-01-26
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