RECOMB-seq 2023: About conference, Proceedings, Program, Posters, Call for papers, Committees
Shilpa Garg
Novo Nordisk Foundation Center for Biosustainability, Genomics Sustainable Solutions, Denmark
Chromosome-scale haplotype-resolved genomics: methods and applications
Abstract
Reconstructing the complete phased sequences in human and non-human species is important in medical, biosustainability, and comparative genetics, for understanding the genetic basis of complex traits. The unprecedented advancements in sequencing technologies have opened up new avenues to reconstruct these phased sequences that would enable a deeper understanding of molecular, cellular, and developmental processes underlying complex diseases and bio-based chemical production. Despite these interesting sequencing innovations, the reference genomes of humans and microbes like fungi are unphased, and thus annotating novel expression and methylation results are incomplete and inaccurate, which affects the interpretation of molecular genetics and epigenetics of diseases and bio-based chemical production. There is a pressing need for streamlined, production-level, easy-to-use computational approaches that can reconstruct high-quality chromosome-scale phased sequences, and that can be applied to human genomes and microbes at scale.
In this talk, first, I will present an efficient combinatorial phasing model that leverages new long-range Strand-specific technology and long reads to generate chromosome-scale phasing. Second, I present an efficient algorithm to perform accurate haplotype-resolved assembly of human individuals. This method takes advantage of new long accurate data types (PacBio HiFi) and long-range Hi-C data. We for the first time can generate accurate chromosome-scale phased assemblies with base-level-accuracy of Q50 and continuity of 25Mb within 24 hours per sample, therefore, setting up a milestone in the genomic community. Third, I will present the generalized graph-based method for phased assembly of cancer genomics that produced the first precise somatic and germline structural variant landscape required for better drug therapeutics.
In summary, my works efficiently and robustly combine data from a variety of sequencing technologies to produce high-quality phased assemblies. These computational methods will enable high-quality precision medicine and facilitate new and unbiased studies of human (and non-human) haplotype variation which are currently goals of the Human Genome Reference Project, the European Reference genome atlas, and the Global Alliance of biofoundries.
About the speaker
Shilpa is a Senior Researcher/Associate Professor at the Technical University of Denmark. She was an NIH K99/R00 PI at Harvard Medical School and finished her Ph.D. in Computer Science at Max Planck Institute for Informatics. She is passionate to apply the power of computers for biological applications in human health and bio-based chemical production. She has published in prestigious journals like Bioinformatics, Nature Communications, and Nature Biotechnology.
Gioele La Manno
Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
RNA Velocity from dynamical exploration to inference
Abstract
RNA velocity is a computational approach that estimates the temporal component of gene expression from snapshot single-cell RNA sequencing data. By considering intronic and exonic mappings and modeling RNA metabolism, it approximates the future state of individual cells, becoming a key tool for studying embryogenesis.
In this talk, I will trace the evolution of RNA velocity methods and highlight their advancements over the years. I will emphasize the transition from simple gene-wise models to joint, Bayesian, and generative models, discussing both the improvements and remaining limitations.
Lastly, I will present our lab's latest work on VeloCycleVI, a tailored RNA velocity framework designed to study cell cycle-driven gene expression changes and their speed. The robustness and inference capabilities of this model showcase the potential for manifold-consistent modeling approaches in this rapidly evolving field.
"About the speaker
Prof. La Manno is a computational and developmental biologist heading the Neurodevelopmental Systems Biology Laboratory at the Swiss Federal Institute of Technology Lausanne (EPFL). He has made significant early contributions in the field single-cell RNA sequencing, particularly in mapping the heterogeneity of the developing mammalian nervous system. Prof. La Manno is renowned for inventing the RNA Velocity analysis framework and developing spatial transcriptomics tomographical methods. His lab has also been at the forefront of research into lipid heterogeneity at the single-cell level. Throughout his career, he has earned numerous recognitions, such as the appointment to ELISIR Scholar, the EMPIRIS Award, the SIB Innovation Award, and the Dimitris N. Chorafas Prize.