What is he best known for?
The fields of study he is best known for:
Metagenomics, Genome, Genetics, Genomics and Gene are his primary areas of study.
Gene W. Tyson interconnects Microbial interaction, Anaerobic digestion and Biochemical engineering in the investigation of issues within Metagenomics.
The various areas that he examines in his Genome study include Phylum, Phylogenetics and Computational biology.
When carried out as part of a general Genetics research project, his work on 16S ribosomal RNA, Bacteria, Ribosomal RNA and Nitrospirae is frequently linked to work in Indel, therefore connecting diverse disciplines of study.
His Genomics study incorporates themes from DNA sequencing, Amplicon, Artificial intelligence and Pattern recognition.
His Gene research integrates issues from Evolutionary biology and Proteome.
His most cited work include:
- CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes (2777 citations)
- Community structure and metabolism through reconstruction of microbial genomes from the environment (1806 citations)
- STAMP: statistical analysis of taxonomic and functional profiles (1538 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Metagenomics, Genome, Ecology, Genetics and Computational biology.
His Metagenomics research focuses on Microbiology and how it connects with Biofilm.
Gene W. Tyson has researched Genome in several fields, including Evolutionary biology and Phylogenetics.
His study on Evolutionary biology also encompasses disciplines like
- Phylum that connect with fields like Lineage,
- Archaea, which have a strong connection to Anaerobic oxidation of methane, Methane and Methanogenesis.
In his research on the topic of Ecology, Anaerobic digestion is strongly related with Microbial population biology.
His Computational biology study frequently links to related topics such as Sequence analysis.
He most often published in these fields:
- Metagenomics (30.35%)
- Genome (24.38%)
- Ecology (21.39%)
What were the highlights of his more recent work (between 2018-2021)?
- Methane (13.43%)
- Archaea (14.93%)
- Environmental chemistry (11.94%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Methane, Archaea, Environmental chemistry, Methanogenesis and Microorganism.
His research in the fields of Anaerobic oxidation of methane overlaps with other disciplines such as Mole fraction.
His Archaea research is multidisciplinary, incorporating perspectives in Ecological analysis, Metabolic diversity and Metagenomics.
The concepts of his Metagenomics study are interwoven with issues in Ecology, Replicate and Gut microbiome.
His biological study deals with issues like Evolutionary biology, which deal with fields such as Gene, Phylogenetics, Horizontal gene transfer, Phylogenetic tree and Phylum.
His work on Reference genome as part of general Genome study is frequently linked to Alternative Splice Sites, bridging the gap between disciplines.
Between 2018 and 2021, his most popular works were:
- An evolving view of methane metabolism in the Archaea. (115 citations)
- Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae (48 citations)
- A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). (45 citations)
In his most recent research, the most cited papers focused on:
Gene W. Tyson spends much of his time researching Archaea, Evolutionary biology, Methane, Phylum and Candidatus.
His Archaea research includes themes of Coral reef, Dinoflagellate and Metagenomics.
His studies deal with areas such as Reef, Ecology, Anthozoa, Coral and Holobiont as well as Metagenomics.
His research integrates issues of Phylogenetics, Horizontal gene transfer, Genome and Phylogenetic tree in his study of Evolutionary biology.
His Genome research is multidisciplinary, incorporating elements of Taxon and Type.
His Phylogenetic tree research entails a greater understanding of Gene.
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.
