Assembly, Annotation, and Comparative Analysis of Apomictic Boechera Species GenomePrincipal investigator of the project Vladimir Brukhin
Apomixis is asexual way of plant reproduction through seeds, which could be found in more than 400 plant species representing almost 40 families. It is believed that apomixis evolved independently in several taxa from sexual ancestors. Apomixis could be considered as a developmental variation of sexual reproduction in which some steps are lost, reduced, deregulated or desynchronized (Fig.1). The main features of gametophytic apomixis are:
- Avoidance of meiosis (apomeiosis);
- Embryo formation via parthenogenesis;
- Functional endosperm develops either autonomously or pseudogamously (central cell fertilized by sperm cell).
Thus, apomictic and sexual reproduction are closely related and share many regulatory components. Molecular and genetic basis underlying apomixis and amphymixis (sexual reproduction) regulation still remains poorly understood. If apomixis is engineered in crop plants that will revolutionize agriculture as heterosis can permanently be fixed in many consecutive plant generations. So, a better understanding of the molecular basis of apomixis is important. The potential of apomixis as a next generation technology for plant breeding attracts huge interest of scientists to elucidate the molecular and genetic mechanisms of its regulation.Please read on!
Follicular Lymphoma (FL) is a slow-growing cancer of the lymphatic system, which affects one in every three thousand individuals. While existing frontline therapies are effective at suppressing FL, it remains an incurable disease with a median survival of 8-10 years. A fifth of the patients develop resistance to the standard treatment within the first two years. Understanding the mechanisms facilitating resistance and identifying early molecular prognostic indicators remains a high priority.
FL tumors have a complex organization, and in addition to the transformed B cells contain a variety of other, non-malignant cell types. It has been demonstrated that the composition of such tumor microenvironment shows significant prognostic association, however the mechanisms and cellular interactions contributing to such association remain unclear. Some portions of the tumor microenvironment may reflect antitumor inflammatory response, while others can facilitate tumor growth. Investigating tumor-microenvironment interactions has been challenging because within the broad cell types that could be readily identified using existing experimental techniques there exist multiple subsets of cells, such as T cells or macrophage subtypes, that can often exert opposing effect.Please read on!
GARFIELD – Genome Browser
The Genome Browser used in Dobzhansky Center allows not only represent genetic data in a convenient way, but also to accumulate all the genetic data about selected species. Currently the browser based on UCSC Genome browser platform and contain all genetic information about Felis Catus (domestic cat). The browser is available at URL: http://garfield.dobzhanskycenter.org and will be extended to other species in the project.Please read on!
Genome Russia Project
Genes are the basic “instruction book” for the cells that make up our bodies, and are made out of DNA. The DNA of a person is more than 99% the same as the DNA of any other unrelated person. But no two people have exactly the same DNA except identical twins. Differences in DNA are called genetic variations. They explain some of the physical differences among people, and partly explain why some people get diseases like cancer, diabetes, asthma, and depression, while others do not. Such diseases may also be affected by factors like diet, exercise, smoking, and pollution in the environment, which makes it hard to figure out which genes affect the diseases.
The objectives of the project “Genome Russia” are to develop an open access web-based database containing anonymous information on the whole-genome sequences of at least 3,000 men and women originating from the different regions of Russia, whose ancestors are indigenous to the region for several generations, as well as the description the genome variations in these groups, the detection of the features that affect the spread of diseases and the creation of a database of medically-relevant genomic variants characteristic to the Russian population, which would be the basis for developing the principles of the future personalized medicine.Please read on!
Genome-based Mycobacterium Tuberculosis Variation Database
Comparative genomics of Mycobacterium tuberculosis clinical strains spread in Russia – is one of the research projects conducted at Theodosius Dobzhansky Center for Genome Bioinformatics. We are studying genomic signatures associated with M. tuberculosis clinical and microbiological features.
A Database of M. tuberculosis Genome Variations (GMTV) developed by our team, integrates clinical, epidemiological and microbiological information with genome variations based on whole genome sequencing data. This research is conducted in collaboration with St. Petersburg Research institute of Phthisiopulmonology and St. Petersburg Pasteur Institute, and supported by St. Petersburg State University and Russian Foundation for Basic Research (RFBR) grants.Please read on!
GWATCH - web-based genome browser for genome-wide association studies
GWATCH is a web-based genome browser designed to automate analysis, visualization and release of data from genome-wide association studies (GWAS) and whole genome sequence association studies of genetic epidemiological cohorts. For any association study, GWATCH allows cataloging and viewing of significant statistical results of association tests (p-values, odds ratios, hazard ratios and others) for single or multiple variants (SNP, indels, CNV), for single or multiple tests.
GWAS data are collected and subjected to quality control (call rates by individual and by SNP etc.) by the researchers. Statistical association tests are designed to help detect genetic differences among study groups with alternative phenotypes or disease outcomes. Each SNP-test combination includes information on patient counts by category, p-values and a Quantitative Association Statistic (QAS, a general term for statistics explaining direction and strength of associations: odds ratio, hazard ratio, relative hazard and so on, depending on the particular statistical test). An unabridged data file listing association test, categorical patient counts, p-values and QAS for each SNP-test combination comprises the initial input to GWATCH.Please read on!