We are looking for an enthusiastic and ambitious student to develop and apply sophisticated data-mining methods and computational models at the interface of biology, mathematics and computer science. The sequencing of genomes has opened unparalleled opportunities to compare multiple genomes and identify DNA sequences that modulate ageing in humans or determine species differences in ageing and longevity.
There is also an urgent need to understand how genes associated with ageing collectively regulate the ageing process. We are analysing gene expression data and developing gene networks to deepen our knowledge of how genes interact with each other and with the environment to gain new insights into the genetics of ageing and identify new candidate genes for experimental validation.
Computational approaches to identify functional genetic variants in cancer genomes
The exact direction of this project, however, will be adapted to fit the research interests of the student. Though this project is primarily computational, our group also has wet lab facilities and thus it is possible to experimentally validate any computational predictions emerging from this project. Training associated with this project:.
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This project will provide a rich and diverse training in contemporary bioinformatics techniques, genetics and biogerontology. The student will also obtain training in modern methods in genomics, including in the generation and analysis of high-throughput data from next-generation sequencing platforms. In addition to the generic skills training that is provided through the Institute and University PhD programme, the student will be supported by an excellent infrastructure and will work closely with experts on the biology and genetics of ageing, bioinformatics and genomics.
This diverse and stimulating environment will allow a creative and talented student to develop key skills and the project is flexible enough to allow the student to develop his or her own research interests. Correspondence 19 September Comments and Opinion 16 September Open Access.
Conventional experiments for generating proteins with improved properties by directed evolution are iterative, lengthy and costly. Now, a label-free assay has been developed for ultrahigh-throughput microfluidic screening that can dramatically accelerate the discovery of superior biocatalysts from a single round of genetic randomization.
Outdated knowledge of arterial perfusion territories still guides clinical decisions and management in acute and chronic stroke. In a new study, application of machine learning techniques provides more detail than ever before, laying the foundation for improved stroke management and new research.
Research Highlights 11 September In mice, CREB— Zfp interactions in the prefrontal cortex drive a transcriptional network that increases resilience to chronic social defeat stress. Advanced search. Skip to main content. Search My Account Login. Atom RSS Feed Computational biology and bioinformatics Definition Computational biology and bioinformatics is an interdisciplinary field that develops and applies computational methods to analyse large collections of biological data, such as genetic sequences, cell populations or protein samples, to make new predictions or discover new biology.
Vernooij Nature Reviews Neurology , John A. Related Subjects Biochemical reaction networks Cellular signalling networks Classification and taxonomy Communication and replication Computational models Computational neuroscience Computational platforms and environments Data acquisition Data integration Data mining Data processing Data publication and archiving Databases Functional clustering Gene ontology Gene regulatory networks Genome informatics Hardware and infrastructure High-throughput screening Image processing Literature mining Machine learning Microarrays Network topology Phylogeny Power law Predictive medicine Probabilistic data networks Programming language Protein analysis Protein design Protein folding Protein function predictions Protein structure predictions Proteome informatics Quality control Scale invariance Sequence annotation Software Standards Statistical methods Virtual drug screening.
Show more. Our group uses a combination of statistical and experimental approaches to map mutations that affect gene regulation in humans. The Genome Reference Informatics Team analyses genome assemblies to reveal and correct quality issues and to identify and add variation. It forms the Sanger division of the Genome Reference Consortium. The Hemberg group is interested in developing quantitative models of gene expression.
Our approach is theoretical and we strive to develop novel mathematical models as well as computational tools that can be used by other researchers. The activities of the Vertebrate genome analysis team revolved around generating and presenting core vertebrate genome annotation, particularly in the form of reference genesets, and maintaining the reference genome sequences of human, mouse and zebrafish.
We are interested in all aspects of gene regulation by non-coding RNA. High-throughput sequencing has opened up a new chapter in the study of molecular evolution and genetics, allowing us to study in detail how genetic composition of populations change as they respond to external pressures such as drug therapies. Our group contributes to this effort by developing scalable methods for biomedical applications of data. We further use these data to address basic biological research questions such as how drug resistance arises.
We measure, model, and modulate cell state. We develop probabilistic models as well as software tools to accurately analyse the readouts. The Trynka group combines experimental and computational approaches to study how genetics control the immune system and predispose individuals to autoimmune diseases.
Computational Biology & Medicine
This group consists of manual annotators and software developers. The HAVANA team provides the manual annotation of human, mouse, zebrafish and other vertebrate genomes that appear in the Vega browser. Our software is written and developed by the Annosoft team.
Approach Computational methods and resources for studying genetic variation: Since its inception, the Sanger Institute has been a leader in the development of software, methods and resources for the analysis of large-scale DNA sequence data. Computational analysis of genome regulation: The central goals in genomics are to understand how genome functions are affected by genetic variation. Genome Reference Consortium The GRC aims to ensure that the human, mouse and zebrafish reference assemblies are biologically relevant by closing gaps, fixing errors and representing complex variation.
Genome Reference Informatics As the impact of the human reference genome assembly on biomedical research has shown, the availability of a high quality reference genome assembly is essential for the understanding of a species' biology.