AIC Lab Seminar

The seminar will be held at the Laboratory on AI for Computational Biology, where Research Assistants of the laboratory - Saradwa Khushbu Narottambhai and Sufiyan Muhammad will present their research.

The seminar will take place on June 5 at 14:00.

Title: "Benchmarking the Thermo Fisher Orbitrap Astral Mass Spectrometer"

Abstract:The complexity of human physiology arises from well-orchestrated interactions between trillions of single cells in the body. Mass spectrometry.(MS) based proteomics has emerged as a powerful tool for comprehensive protein analysis, including single-cell applications. The use of mass spectrometer in the analysis.of biological samples has become ubiquitous, marking it as an indispensable tool & device in the realm of proteomics analysis and research. My keen curiosity to unravel the intricacies of the proteome has been a driving force behind the development of innovative technologies that continually expand the horizons of mass spectrometry capabilities. As a result, mass spectrometry has been empowered to confront an ever-expanding array of biological inquiries, catalysing advancements and breakthroughs in our understanding of complex biological systems. However, challenges remain in terms of throughput and proteomic depth, in order to maximize the biological impact of single-cell proteomics by Mass Spectrometry (SCPMS) workflows. This study leverages a novel high resolution, accurate.mass.(HRAM) instrument platform, consisting of both an Orbitrap and an innovative HRAM Asymmetric Track Lossless (Astral) analyzer. The Astral analyzer offers high sensitivity and resolution through lossless ion transfer and a unique flight track design. Ultimately goal is to evaluate.the performance of the Thermo Scientific Orbitrap Astral MS using Data-Independent Acquisition (DIA) and assess proteome depth and quantitative precision for ultra-low input samples. The Orbitrap Astral MS has the potential to revolutionize protein discovery and precision medicine by enabling large-scale studies and faster insights. With its ability to handle high sample volumes and advanced solutions, researchers can uncover new insights and make breakthrough discoveries across a wide range of disciplines, ultimately advancing our understanding of complex diseases and paving the way for precision medicine solutions. As a result, novel proteomics techniques were created that take advantage of each HRAM analyzer advantages. For example, the Orbitrap analyzer can execute full scans with a high dynamic range and resolution, while the Astral analyzer can acquire quick and sensitive HRAM MS/MS scans in synchrony.

Title: Analysis of Semi Correct Annotations for False Discovery Rate Control in Tandem Mass Spectrometry Data

Abstract: With an accentuation on peptide distinguishing proof and misleading disclosure rate (FDR) the executives, this postulation researches the turns of events and utilizations of mass spectrometry (MS) in proteomics. We highlight the significance of MS/MS techniques and MS technology for proteomics by examining their development over time. The capability of computational strategies, like computerized reasoning (simulated intelligence) and AI, to further develop range coordinating and MS/MS information examination is researched. Our methodology includes the utilization of simulated intelligence models, careful exploratory settings, and information handling strategies to improve the accuracy of peptide ID and FDR gauge. That's what the discoveries show, in contrast with the peptide-opposite and peptide-mix draws near, the de Bruijn distraction age technique reliably creates more peptide-range matches (PSMs) across various FDR levels. The de Bruijn method, according to ROC analysis, provides a more accurate depiction of inaccurate target PSM values, particularly at strict FDR levels. A dependable approach to real-time FDR calculation is made possible by incorporating AI models into data processing. This results in significant improvements in recognition accuracy and efficiency. The consequences of these disclosures for proteomics are talked about, with an accentuation on how they could improve the recognizable proof of biomarkers and the determination of sickness. In addition, the study's limitations and potential future research directions are discussed, providing recommendations for MS-based proteomics advancements. By offering an intensive assessment of MS/MS strategies and proposing inventive computational procedures to further develop peptide distinguishing proof and FDR control, this study progresses the discipline.