What is he best known for?
The fields of study he is best known for:
His scientific interests lie mostly in Genetics, Genome, Gene, Computational biology and Genomics.
Human genome, Transposable element, DNA sequencing, Copy-number variation and Small RNA are among the areas of Genetics where W. Richard McCombie concentrates his study.
Much of his study explores Genome relationship to Oryza sativa.
His Computational biology research is multidisciplinary, incorporating elements of Gene density, Personal genomics and Hybrid genome assembly, Contig.
His research in Gene density intersects with topics in Contig Mapping, DNA sequencing theory, Cancer Genome Project, Gene Annotation and Cancer genome sequencing.
The various areas that he examines in his Genomics study include Solanum, Solanum tuberosum, Botany and Shotgun sequencing.
His most cited work include:
- Initial sequencing and analysis of the human genome. (18816 citations)
- Initial sequencing and comparative analysis of the mouse genome. (5789 citations)
- The B73 Maize Genome: Complexity, Diversity, and Dynamics (3021 citations)
What are the main themes of his work throughout his whole career to date?
W. Richard McCombie mainly investigates Genetics, Genome, Gene, Computational biology and DNA sequencing.
Human genome, Sequence assembly, Shotgun sequencing, Hybrid genome assembly and Methylation are the core of his Genetics study.
His studies in Genomics, Reference genome, Whole genome sequencing, Transposable element and Synteny are all subfields of Genome research.
His study looks at the relationship between Gene and fields such as Cancer, as well as how they intersect with chemical problems.
His research integrates issues of Exome sequencing, Sequence analysis, DNA, Illumina dye sequencing and Nanopore sequencing in his study of Computational biology.
W. Richard McCombie has researched DNA sequencing in several fields, including Personal genomics and Sequence.
He most often published in these fields:
- Genetics (64.29%)
- Genome (42.86%)
- Gene (34.42%)
What were the highlights of his more recent work (between 2017-2021)?
- Gene (34.42%)
- Computational biology (31.17%)
- Genome (42.86%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Gene, Computational biology, Genome, Genetics and Bipolar disorder.
His Computational biology research integrates issues from Transcriptome, Transcription factor and DNA.
His study in Genome is interdisciplinary in nature, drawing from both Cancer, Breast cancer and DNA sequencing.
His Cancer research is multidisciplinary, incorporating elements of Nanopore sequencing and Genomics.
He is interested in DISC1, which is a field of Genetics.
His work on Personal genomics is typically connected to Probabilistic logic and Computational analysis as part of general Human genome study, connecting several disciplines of science.
Between 2017 and 2021, his most popular works were:
- Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants (69 citations)
- Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato. (63 citations)
- Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line (60 citations)
In his most recent research, the most cited papers focused on:
W. Richard McCombie focuses on Genome, Computational biology, Gene, RNA and Genetics.
His Genome research includes elements of Quantitative trait locus, Gene dosage and Sequence analysis.
His study in Computational biology is interdisciplinary in nature, drawing from both Transcriptome, Haplotype, Data sequences and Massive parallel sequencing, DNA sequencing.
His Transcriptome research incorporates themes from Whole genome sequencing, Breast cancer, DNA and Sequence.
His work carried out in the field of RNA brings together such families of science as RNA-Seq, Alternative splicing and Plant genetics.
W. Richard McCombie applies his multidisciplinary studies on Genetics and Bipolar disorder in his research.
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.
