What is he best known for?
The fields of study he is best known for:
Nicholas Katsanis mainly investigates Genetics, Cilium, Bardet–Biedl syndrome, Cell biology and Allele.
His work on Genetics deals in particular with Ciliopathy, Ciliopathies, Locus, Gene and Genetic variation.
The concepts of his Ciliopathy study are interwoven with issues in Mutation and Nephronophthisis.
His Cilium study combines topics from a wide range of disciplines, such as TMEM67, Ciliary transition zone, Endocrinology and Internal medicine.
His Cell biology research is multidisciplinary, relying on both Centrosome, Basal body and Zebrafish.
The various areas that Nicholas Katsanis examines in his Allele study include Phenotype, Receptor, Genome-wide association study and Genotype.
His most cited work include:
- A syndrome of altered cardiovascular, craniofacial, neurocognitive and skeletal development caused by mutations in TGFBR1 or TGFBR2 (1278 citations)
- The Ciliopathies: An Emerging Class of Human Genetic Disorders (938 citations)
- Comparative Genomics Identifies a Flagellar and Basal Body Proteome that Includes the BBS5 Human Disease Gene (665 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Genetics, Phenotype, Zebrafish, Cilium and Cell biology.
His studies in Allele, Gene, Bardet–Biedl syndrome, Locus and Missense mutation are all subfields of Genetics research.
His study in Bardet–Biedl syndrome is interdisciplinary in nature, drawing from both Polydactyly and Genetic heterogeneity.
Nicholas Katsanis has researched Phenotype in several fields, including Neuroscience and Microcephaly.
His study focuses on the intersection of Zebrafish and fields such as Mutation with connections in the field of Molecular biology.
Nicholas Katsanis interconnects Ciliopathies, Basal body, Ciliopathy and Intraflagellar transport in the investigation of issues within Cilium.
He most often published in these fields:
- Genetics (79.19%)
- Phenotype (25.28%)
- Zebrafish (27.96%)
What were the highlights of his more recent work (between 2018-2021)?
- Genetics (79.19%)
- Zebrafish (27.96%)
- Phenotype (25.28%)
In recent papers he was focusing on the following fields of study:
Nicholas Katsanis focuses on Genetics, Zebrafish, Phenotype, Exome sequencing and Gene.
His work in Haploinsufficiency, Ciliopathy, Copy-number variation, Gene duplication and Nonsynonymous substitution are all subfields of Genetics research.
Nicholas Katsanis has included themes like Polydactyly, Cilium, KIF3A, Retinitis pigmentosa and Allele in his Ciliopathy study.
His Cilium study improves the overall literature in Cell biology.
His Zebrafish research includes themes of Point mutation and Craniofacial.
His Gene course of study focuses on Computational biology and Expressivity and Pleiotropy.
Between 2018 and 2021, his most popular works were:
- Genetic variation across the human olfactory receptor repertoire alters odor perception (32 citations)
- SSBP1 mutations cause mtDNA depletion underlying a complex optic atrophy disorder. (23 citations)
- SSBP1 mutations cause mtDNA depletion underlying a complex optic atrophy disorder. (23 citations)
In his most recent research, the most cited papers focused on:
His primary areas of study are Genetics, Zebrafish, Phenotype, Exome sequencing and Mutation.
Genetics is represented through his KIF3A, Cilium, KIFAP3, Kinesin and Intraflagellar transport research.
Nicholas Katsanis combines subjects such as Exome, Cas9, Microcephaly, Genome editing and Craniofacial with his study of Zebrafish.
His work deals with themes such as Gene duplication, Transcriptome and Copy-number variation, which intersect with Phenotype.
His Exome sequencing research is multidisciplinary, incorporating perspectives in CRISPR, Guide RNA, Point mutation, Ciliopathy and Retinitis pigmentosa.
His Mutation study integrates concerns from other disciplines, such as Hereditary Optic Atrophy, Atrophy and Mitochondrial DNA, Mitochondrial myopathy, Mitochondrial DNA replication.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
