AI seminar: January 26 2022
Dear all, Our next AI seminar on *"Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes" *by professor Liang Huang is scheduled to be on January 26th (Tomorrow), 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT... *Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes* Liang Huang Associate Professor Computer Science Oregon State University *Abstract:* The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, a linear-time algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics. This work has been published at Proceedings of National Academy of Sciences, marking the first PNAS paper ever from OSU EECS. *Speaker Bio:* Liang Huang (PhD, UPenn, 2008) is an Associate Professor of Computer Science at Oregon State University and a Distinguished Scientist at Baidu Research USA. He is a leading computational linguist, and was recognized at ACL 2008 (Best Paper Award) and ACL 2019 (Keynote Speech), but in recent years he has been more interested in applying his algorithmic expertise to biology problems such as RNA folding and RNA design. Since the outbreak of COVID-19, he has shifted his attention to the fight against the virus, which resulted in high-impact work such as efficient algorithms for stable mRNA vaccine design (under review at Science, being used by 30+ companies), and for homologous folding of RNA genomes (PNAS, 2021). *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.
AI Seminar starting in a few minutes. See you there. Prasad From: Mangannavar, Rajesh Devaraddi <mangannr@oregonstate.edu> Sent: Tuesday, January 25, 2022 2:10 PM To: eecs-grads@engr.orst.edu; eecs-faculty@engr.orst.edu; ai@engr.orst.edu; ai-seminar@engr.orst.edu Cc: Tadepalli, Prasad <tadepall@engr.orst.edu> Subject: AI seminar: January 26 2022 Dear all, Our next AI seminar on "Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes" by professor Liang Huang is scheduled to be on January 26th (Tomorrow), 1-2 PM PST. It will be followed by a 30 minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT... Linear-Time Algorithm to Find the Achilles' Heels of SARS-CoV-2 Genomes Liang Huang Associate Professor Computer Science Oregon State University Abstract: The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, a linear-time algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics. This work has been published at Proceedings of National Academy of Sciences, marking the first PNAS paper ever from OSU EECS. Speaker Bio: Liang Huang (PhD, UPenn, 2008) is an Associate Professor of Computer Science at Oregon State University and a Distinguished Scientist at Baidu Research USA. He is a leading computational linguist, and was recognized at ACL 2008 (Best Paper Award) and ACL 2019 (Keynote Speech), but in recent years he has been more interested in applying his algorithmic expertise to biology problems such as RNA folding and RNA design. Since the outbreak of COVID-19, he has shifted his attention to the fight against the virus, which resulted in high-impact work such as efficient algorithms for stable mRNA vaccine design (under review at Science, being used by 30+ companies), and for homologous folding of RNA genomes (PNAS, 2021). Please watch this space for future AI Seminars : https://eecs.oregonstate.edu/ai-events<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Feecs.oregonstate.edu%2Fai-events&data=04%7C01%7Cprasad.tadepalli%40oregonstate.edu%7Ca99dc2d356b74d3820c808d9e04f7159%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C637787454078134446%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=zQJoEE3rvHJv9G4lCnF1nZ6tnVxpCrVY1A2I5pBW8o0%3D&reserved=0> Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.
participants (2)
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Mangannavar, Rajesh Devaraddi -
Tadepalli, Prasad