|в Group of macromolecular interactions (MIPT) (посмотреть профиль)|
|Город||Moscow, Russian Federation|
PhD или аспирант
Курсовая или диплом
Group of macromolecular interactions
MIPT Genomic Center and Genome Engineering Lab are starting an international collaboration focussed on macromolecular interactions. The major goal of this pursuit is to establish MIPT expertise in interactomics, both experimental and bioinformatic, by participating in projects with leading world experts in macromolecule analysis - see also: http://www.macromolecule.fun
More details and application form - follow the link
We are looking for students
This call is an open invitation for MIPT students with advanced skills in bioinformatics and data science to join the group. We will select the two most qualified candidates for BSc and/or MSc programmes. The selected students will work under the guidance of a Team Leader within the MIPT Genome Engineering Lab and will participate actively in ongoing projects with highly successful research groups at international centers of excellence: e.g. ERIBA, The Rockefeller University, NYU Langone Health, MIT, and Arizona State University. The project duties will include data processing and analysis, application of network theory, big data “scraping” and unification, and multidimensional visualisations - applied across a wide range of biological topics. Student members of the collaborative team will be expected to maintain constant interactions with the international partners, be willing to visit partner labs for periods of up to three months, with the objective of publishing not less than one project per year in respected scholarly journals.
Сandidates we are looking for:
We have a history of working with MIPT students over the last 5 years; these students have been active in ongoing projects [2, 3] (additional manuscript in preparation). Most projects focus on data processing and analysis as it applies to charting the behaviors of macromolecules. In addition, elements of network analysis were employed combine newly generated experimental data with publicly available data retrieved from databases. We will in this tradition, while also growing to keep pace with rapidly advancing experimental technologies. We expect new candidates to meet the following requirements:
- Strong knowledge of cell biology
- Understanding of protein and peptide mass-spectrometry
- Strong skills in data analysis and statistics
- Skills in R and Python programming
- Written and oral English
This year’s projects will be based on new multidimensional datasets and studies of protein complex dynamics acquired for variety of targets (macromolecular complexes), carried out in collaboration with NYU Langone Health, The Rockefeller University, MIT, and others, and will certainly lead to joint publications in top tier journals.
We expect students to go in depth in several areas of data analysis:
- Raw data signal processing in mass spectrometry
- Data analysis and integration for multidimensional (high content) experimental frameworks, including statistical testing
- Network algorithms and merging networks from different sources
Software development will be done with the additional supervision of Dr. Dmitry Alexeev (former asst. prof. at MIPT [google scholar]) and will be based on weekly scrum sessions including the participation of the international team of Dr. John LaCava (The Rockefeller University and European Research Institute for the Biology of Ageing).
An objective for this joint research is the transfer of a ‘macromolecule interaction charting platform technology’ to the lab of Dr. Pavel Volchkov at MIPT, and further applying this framework to objects studied at MIPT to decipher the structure/function connectivity exceeding the level of details achieved in our earlier published works [e.g. refs 1-4].
1 - Hakhverdyan et al. (2015). Rapid, optimized interactomic screening. Nature methods, 12(6), 553-60.
2 - Taylor et al. (2018). Dissection of affinity captured LINE-1 macromolecular complexes. Elife, 7, e30094.
3 - Ardeljan et al. (2019). LINE-1 ORF2p Expression is Nearly Imperceptible in Human Cancers. BioRxiv, doi: https://doi.org/10.1101/744425 - (in review at Mobile DNA)
4 - Taylor et al. (2013). Affinity proteomics reveals human host factors implicated in discrete stages of LINE-1 retrotransposition. Cell, 155(5), 1034-48.
Part time work in the lab in MIPT, with internship possibilities in Netherlands and New-York. Monetary compensation is TBD.