What is he best known for?
The fields of study he is best known for:
Daniel Gautheret mostly deals with Genetics, Gene, Computational biology, Polyadenylation and Expressed sequence tag.
His study in Sequence, Gene expression, Nucleic acid secondary structure, RNA and Nucleic acid sequence is carried out as part of his Genetics studies.
His RNA study combines topics from a wide range of disciplines, such as Computation and Minification.
His Gene research focuses on Genome and Podospora anserina.
His study in Computational biology is interdisciplinary in nature, drawing from both Software, CRISPR and Identification.
Daniel Gautheret combines topics linked to Three prime untranslated region with his work on Polyadenylation.
His most cited work include:
- Patterns of Variant Polyadenylation Signal Usage in Human Genes (554 citations)
- RNAMotif, an RNA secondary structure definition and search algorithm (389 citations)
- CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. (311 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Genetics, Computational biology, RNA, Gene and Genome.
His is doing research in Polyadenylation, Non-coding RNA, Untranslated region, Regulation of gene expression and Alternative splicing, both of which are found in Genetics.
His Computational biology study incorporates themes from Annotation, Transcriptome, Sequence analysis and Reference genome.
Daniel Gautheret interconnects RNA-Seq and Intron in the investigation of issues within RNA.
His Gene study frequently draws parallels with other fields, such as Bioinformatics.
His research integrates issues of Structural conformation, Data mining and DNA, DNA sequencing in his study of Genome.
He most often published in these fields:
- Genetics (53.60%)
- Computational biology (40.00%)
- RNA (36.80%)
What were the highlights of his more recent work (between 2016-2021)?
- Computational biology (40.00%)
- RNA (36.80%)
- Genome (24.80%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Computational biology, RNA, Genome, k-mer and Transcriptome.
Daniel Gautheret has researched Computational biology in several fields, including Cancer, Sequence analysis, Set, Reference genome and Transcription.
The RNA study combines topics in areas such as CRISPR, Conserved sequence, Deep sequencing, Messenger RNA and Arginine.
Daniel Gautheret works mostly in the field of Genome, limiting it down to topics relating to DNA and, in certain cases, Point mutation, as a part of the same area of interest.
Gene and Gene expression are the subject areas of his Transcriptome study.
His Gene expression study necessitates a more in-depth grasp of Genetics.
Between 2016 and 2021, his most popular works were:
- CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. (311 citations)
- Native elongating transcript sequencing reveals global anti-correlation between sense and antisense nascent transcription in fission yeast. (28 citations)
- DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition. (19 citations)
In his most recent research, the most cited papers focused on:
Daniel Gautheret mainly focuses on Computational biology, Sequence analysis, RNA, Gene and Regulation of gene expression.
His Computational biology research is multidisciplinary, incorporating elements of CRISPR, MiRBase, microRNA, Multiple sequence alignment and Rfam.
His Sequence analysis research incorporates elements of RNA-Seq, Polyadenylation, Reference genome, RNA splicing and k-mer.
Daniel Gautheret has researched RNA in several fields, including Transcriptome, Arginine, Conserved sequence and Intron.
Many of his research projects under Gene are closely connected to Scoring methods and 1000 Genomes Project with Scoring methods and 1000 Genomes Project, tying the diverse disciplines of science together.
His research in the fields of Regulatory sequence overlaps with other disciplines such as Catabolism.
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