What is he best known for?
The fields of study he is best known for:
John V. Moran mainly focuses on Genetics, Long interspersed nuclear element, Genome, Retrotransposon and Human genome.
His Genetics and DNA methylation, Gene density and Personal genomics investigations all form part of his Genetics research activities.
His Long interspersed nuclear element research is multidisciplinary, incorporating perspectives in Somatic cell and Neural stem cell.
His Genome research incorporates themes from Computational biology and DNA, DNA sequencing.
John V. Moran has included themes like Chimpanzee genome project, DNA sequencing theory, Cancer Genome Project, Hybrid genome assembly and Cancer genome sequencing in his Computational biology study.
His studies in Retrotransposon integrate themes in fields like Plasmid, RNA, Mutation, Short Interspersed Nucleotide Elements and Molecular biology.
His most cited work include:
- Initial sequencing and analysis of the human genome. (18816 citations)
- Human L1 Retrotransposon Encodes a Conserved Endonuclease Required for Retrotransposition (898 citations)
- High Frequency Retrotransposition in Cultured Mammalian Cells (820 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Retrotransposon, Genetics, Genome, Human genome and Molecular biology.
His Retrotransposon research incorporates elements of RNA, Reverse transcriptase, Long interspersed nuclear element and Alu element.
As a part of the same scientific study, he usually deals with the Genetics, concentrating on Computational biology and frequently concerns with Whole genome sequencing.
His research integrates issues of Evolutionary biology, Sequence analysis and Genetic variation in his study of Genome.
In his research on the topic of Human genome, Neural stem cell, DNA methylation, Embryonic stem cell, Neuroscience and Germline is strongly related with Somatic cell.
His biological study spans a wide range of topics, including Gene duplication, Mutant and Transfection.
He most often published in these fields:
- Retrotransposon (75.51%)
- Genetics (68.37%)
- Genome (43.88%)
What were the highlights of his more recent work (between 2016-2021)?
- Retrotransposon (75.51%)
- Human genome (39.80%)
- Somatic cell (16.33%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Retrotransposon, Human genome, Somatic cell, Genetics and Gene.
His research on Retrotransposon concerns the broader Genome.
His study in the fields of Reference genome and Mobile genetic elements under the domain of Genome overlaps with other disciplines such as Consensus sequence.
The various areas that John V. Moran examines in his Human genome study include DNA, DNA replication, Endonuclease, Untranslated region and Computational biology.
His Somatic cell research is multidisciplinary, relying on both Germline and Neuroscience, Human brain.
His Disease research extends to Genetics, which is thematically connected.
Between 2016 and 2021, his most popular works were:
- Intersection of diverse neuronal genomes and neuropsychiatric disease: The Brain Somatic Mosaicism Network (149 citations)
- Mobile DNA in Health and Disease. (148 citations)
- Genome-wide de novo L1 Retrotransposition Connects Endonuclease Activity with Replication. (35 citations)
In his most recent research, the most cited papers focused on:
John V. Moran focuses on Human genome, Genetics, Retrotransposon, Genome and Computational biology.
His Human genome research integrates issues from Disease and Somatic cell.
His Disease research is multidisciplinary, incorporating perspectives in Phenotype, Copy-number variation, Germline and Genomics.
John V. Moran interconnects Sequence analysis, Whole genome sequencing and Genetic variation in the investigation of issues within Retrotransposon.
His Genome study deals with the bigger picture of Gene.
He has researched Computational biology in several fields, including Mobile DNA, Transposable element, Jumping Genes and Mobile genetic elements.
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.
