Apr 2022
Speaker1: Sakhaa Al-Saedi
Title: Systematic Genetic Analysis of Host Genetic Risk Variants of COVID-19 Severity
Abstract:
Coronavirus
2019 is a pandemic that has caused over 440 million infections and 5
million deaths. Its mortality rate fluctuates worldwide, primarily due to the different severity levels of infection
among individuals. This highlights a factor of genetic variation in
individual immune responses against the virus. Since host genetic
variants play an essential role in various immune responses, we conduct a
systematic genetic analysis of risk variants related to increasing the
severity of COVID-19. We investigate the influence of the molecular
functions of such variants on the development of COVID-19 severity and
highlight their effects on human organ systems. The molecular networks
of COVID-19 risk variants among host human genes are constructed to
explore their genetic characteristics and shared biological
disease-phenotype pathways. Collectively, this systematic analysis maps
the association of COVID-19 with common comorbidities in specific
populations. Also, it leads to a better understanding of its genetic
basis and identifies the host genes to be targeted to tackle the
COVID-19 pandemic and reduce its death toll. We aim to further our
research in discovering potential therapeutic targets to alleviate the
severity of the disease.
Bio:
Sakhaa
Al-Saedi is a Ph.D. student and the founder of the startup Medvation,
inventing educational kits that teach children concepts of robotics and
machine learning through fun and engaging methods. She completed her
bachelor's degree in Computer Science in 2017 from Taibah University.
Before starting her Master's degree at KAUST in 2018, she worked as a
product developer at the prototyping lab of the Namma Al-Munawara
company in Madinah. She completed her master's degree in Computer
Science at KAUST in 2020. There, she worked on human genome sequencing
to evaluate the impact of Saudi-specific allele frequencies on variant
calling. Sakhaa's research interests include applying deep learning
algorithms in the development of genetic variant calling workflows for
analyzing human genome sequencing data, developing a platform for
integrating multi-omics data, as well as generating AI art from
biomedical and genetic data. She is currently working in the Comparative
Genomics and Genetics Lab (CGG) and Structural and Functional Bioinformatics Group (SFB)
of Professor Takashi Gojobori and Professor Xin Gao, developing an
automated genetic-based medical diagnostic system for treatment of
infectious diseases using causal deep learning.
Speaker2: Azza Althagafi
Title: DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning
Abstract:
Structural
genomic variants account for much of human variability and are involved
in several diseases. Structural variants are complex and may affect
coding regions of multiple genes, or affect the functions of genomic
regions in different ways from single nucleotide variants. Interpreting
the phenotypic consequences of structural variants relies on information
about gene functions, haploinsufficiency or triplosensitivity, and
other genomic features. Phenotype-based methods for identifying variants
that are involved in genetic diseases combine molecular features with
prior knowledge about the phenotypic consequences of altering gene
functions. While phenotype-based methods have been applied successfully
to single nucleotide variants as well as short insertions and deletions,
the complexity of structural variants makes it more challenging to link
them to phenotypes. Furthermore, structural variants can affect a large
number of coding regions, and phenotype information may not be
available for all of them. We developed DeepSVP, a computational method
to prioritize structural variants involved in genetic diseases by
combining genomic and gene functions information. We incorporate
phenotypes linked to genes, functions of gene products, gene expression
in individual cell types, and anatomical sites of expression, and
systematically relate them to their phenotypic consequences through
ontologies and machine learning. DeepSVP significantly improves the
success rate of finding causative variants in several benchmarks and can
identify novel pathogenic structural variants in consanguineous
families.
Bio:
Azza Althagafi is a
PhD candidate in Computer Science, Computational Bioscience Research
Center, Bio-Ontology Research Group (BORG) under the supervision of
Professor Robert Hoehndorf. She is a lecturer in Computer Science at
Taif University (TU), Taif, Saudi Arabia. Azza received her Bachelor's
degree in Computer Science from Umm Al Qura University (UQU), Makkah,
Saudi Arabia, and her Master's degree in Computer Science from KAUST.
Azza's research interest lies at the intersection between computer
science and biology. She is interested in developing machine learning
methodologies related to deep learning, building computational models,
and designing algorithms to tackle key open problems that help in the
understanding and interpretation of the human genomes and the genetic
variants underlying the diseases.