Seminars 2022

Date: November 28^th, 2022.
Speaker:  István Szilágyi, MTA-SZTE Lendület Biocolloids Research Group, Department of Physical Chemistry and Materials Science, University of Szeged .
Topic: "Light scattering techniques – versatile tools to study nanoparticle dispersions".
Abstract: Due to the recent landmark developments in nanoscience, i.e., scientific research concerned with materials in the size range of 1-100 nm, there is an increasing demand for experimental techniques, which are suitable to characterize key physicochemical properties of nanoparticle systems. In this way, scattering techniques based on the interaction of electromagnetic waves with matter became popular tools to investigate size, charge, and shape of nanoparticles once they are dispersed in liquids. During this seminar, static (SLS), dynamic (DLS) and electrophoretic (ELS) light scattering methods will be introduced. The angular dependence of the intensity of the scattered light recorded in an SLS experiment allows the determination of the size, mass, and nature interparticle forces. The DLS method utilizes the thermal motion of particles to measure hydrodynamic size through correlation of the Brownian motion with the fluctuation of the scattered intensity. Movements of charged nanoparticles in an electric field result in frequency shift in the electromagnetic wave due to the Doppler effect and hence, this is utilized to calculate the velocity and electrophoretic mobility of such particles by ELS. Summarily, these techniques provide great opportunities to comprehensively investigate nanoparticle systems. Beside discussing the principles and theoretical background of these methods, practical examples will be also given during the lecture.

Date: October 19^th, 2022.
Speaker: Yang Lu, Postdoctoral Fellow, Department of Genome Sciences, University of Washington.
Topic: "DIAmeter: matching peptides to data-independent acquisition mass spectrometry data".
Abstract: Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories. We propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool.

Date: October 17^th, 2022.
Speaker: Ksenia Cheloshkina,  research assistant at International Laboratory of Bioinformatics, HSE, Moscow.
Topic: "Machine learning based approaches for analysis of cancer genome breakpoints".
Abstract: DNA breakpoints are common in cancer genomes. It has previously been shown that cancer breakpoints are difficult to predict so it is of interest to study the relationships between various genome features and breakpoints hotspots - regions of genome with high breakpoints density. In the research we answer this and some related questions with the help of machine learning methods.

Date: October 13^th, 2022.
Speaker: William Stafford Noble, Professor, Department of Genome Sciences Department of Computer Science and Engineering University of Washington.
Topic: "Deep learning applications in proteomics mass spectrometry".
Abstract: Tandem mass spectrometry analysis of biological samples yields large, complex data that is ripe for analysis using machine learning techniques. In this talk, I will describe two recent projects in which we used mass spectrometry data to train deep neural network models. The first project involves training a Siamese network to project peptide mass spectra into a learned latent space in such a way that spectra generated by the same peptide are close together and vice versa. We used the trained model, called GLEAMS, to perform large-scale spectrum clustering, and we used the resulting clusters to explore the dark proteome of repeatedly observed yet consistently unidentified mass spectra. The second project involves training a transformer model to translate a mass spectrum, represented as an ordered series of peaks, into an amino acid sequence. The resulting de novo peptide sequencing tool, called Casanovo, substantially outperforms existing methods for this important problem.

Date: October 12^th, 2022.
Speaker: Vadim Demichev,Group leader, Institute of Biochemistry, Charite University Medicine Berlin.
Topic: "QA on Data Independent Acquisition for tandem mass spectrometry".
Abstract: During this seminar we will discuss the main differences between the Data Independent Acquisition (DIA) and Data Dependent Acquisition (DDA), we will discuss the advantages and disadvantages of both approaches, mainly focusing on their computational aspects. 

Date: September 1^st, 2022.
Speaker: Anna Tkachev, Junior Research Scientist at Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skoltech.
Topic: "LC-MS-based lipidomics: data analysis challenges and applications to psychiatric disorder research".
Abstract: Technological advances have been indispensable to progress in biological sciences. Prior to the developments in mass spectrometry methods, detailed molecular compositions could not be quantified. In particular, these developments have pushed lipids – essential building blocks of all living cells and key players in energy metabolism – out of the obscurity they have been placed in previously. Untargeted lipidomics analysis, in particular, has the potential of broadening our understanding of lipid function in health and disease, since it is aimed at quantifying these compounds in as much of a broad and unbiased manner as practicably achievable. Studying complex phenotypes, including mental illnesses, from such novel perspectives is especially relevant. In this talk, I will present results from several studies related to altered lipid profiles in psychiatric disorders. Challenges related to untargeted lipidomics data analysis will be discussed, as well: while untargeted lipidomics analysis is potentially extremely informative, computational methods and data processing standards are lagging behind.

Date: April 29th, 2022
Speaker: Nikita Moshkov,  University of Szeged
Topic: "Application of deep learning algorithms to single-cell segmentation and phenotypic profiling"