Posts classified under: Workshops

BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260
11/10/22 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Liana Lareau Assistant Professor, Department of Bioengineering University of California, Berkeley

TITLE:

Revealing patterns of alternative splicing in single cells

ABSTRACT:

Alternative splicing shapes the output of the genome and contributes to each cell’s unique identity, but single-cell RNA sequencing has struggled to capture its impact. We have shown that low recovery of mRNAs from single cells can lead to misleading conclusions about alternative splicing and its regulation. To address this, we have developed a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them, enabling scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.

SUGGESTED READINGS:

CF Buen Abad Najar, N Yosef, LF Lareau. Coverage-dependent bias creates the appearance of binary splicing in single cells. eLife, 2020. https://elifesciences.org/articles/54603

CF Buen Abad Najar, P Burra, N Yosef, LF Lareau. Identifying cell state–associated alternative splicing events and their coregulation. Genome Research, 2022 https://genome.cshlp.org/content/32/7/1385.short

Zoom link: https://stanford.zoom.us/j/92874055477pwd=aThzNmpmNEQ1L2FjV0E5ZXF5SDR 1UT09&from=addon
Password: 705300

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BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260
11/17/22 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Lorin Crawford
Principal Researcher, Microsoft Research New England; Associate Professor of Biostatistics, Brown University http://lorincrawford.com/

TITLE:

Machine Learning for Human Genetics: A Multi-Scale View on Complex Traits and Disease

ABSTRACT:

A common goal in genome-wide association (GWA) studies is to characterize the relationship between genotypic and phenotypic variation. Linear models are widely used tools in GWA analyses, in part, because they provide significance measures which detail how individual single nucleotide polymorphisms (SNPs) are statistically associated with a trait or disease of interest. However, traditional linear regression largely ignores non-additive genetic variation, and the univariate SNP-level mapping approach has been shown to be underpowered and challenging to interpret for certain trait architectures. While machine learning (ML) methods such as neural networks are well known to account for complex data structures, these same algorithms have also been criticized as “black box” since they do not naturally carry out statistical hypothesis testing like classic linear models. This limitation has prevented ML approaches from being used for association mapping tasks in GWA applications. In this talk, we present flexible and scalable classes of Bayesian feedforward models which provide interpretable probabilistic summaries such as posterior inclusion probabilities and credible sets which allows researchers to simultaneously perform (i) fine- mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. While analyzing real data assayed in diverse self-identified human ancestries from the UK Biobank, the Biobank Japan, and the PAGE consortium we demonstrate that interpretable ML has the power to increase the return on investment in multi-ancestry biobanks. Furthermore, we highlight that by prioritizing biological mechanism we can identify associations that are robust across ancestries—suggesting that ML can play a key role in making personalized medicine a reality for all.

SUGGESTED READINGS:

A.R. Martin, M. Kanai, Y. Kamatani, Y. Okada, B.M. Neale, and M.J. Daly (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics. 51: 584–591.

S.P. Smith, S. Shahamatdar, W. Cheng, S. Zhang, J. Paik, M. Graff, C. Haiman, T.C. Matise, K.E. North, U. Peters, E. Kenny, C. Gignoux, G. Wojcik, L. Crawford, and S. Ramachandran (2022). Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries. American Journal of Human Genetics. 109: 871-884.

P. Demetci, W. Cheng, G. Darnell, X. Zhou, S. Ramachandran, and L. Crawford (2021). Multi-scale inference of genetic architecture using biologically annotated neural networks. PLOS Genetics. 17(8): e1009754.

Zoom link: https://stanford.zoom.us/j/92874055477pwd=aThzNmpmNEQ1L2FjV0E5ZXF5SDR1UT09 &from=addon
Password: 705300

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BIODS 260
12/1/22
1:30 pm-2:30 pm
MSOB x303
Marina Sirota, PhD Associate Professor
Associate Director of Advocacy and Outreach Bakar Computational Health Sciences Institute (BCHSI) University of California, San Francisco

TITLE:

Leveraging Molecular and Clinical Data to Improve Women’s Health in the Era of Precision Medicine

ABSTRACT:

Each year, 15 million babies (representing 10% of the world’s births) are born preterm, defined as before the 37th week of gestation. Survival for most children born preterm has improved considerably, but surviving children remain at increased risk for a variety of serious complications, many of which contribute to lifelong challenges for individuals and their families, as well as to burdensome economic costs to society. The exact mechanism of spontaneous preterm birth is unknown, though a variety of social, environmental, and maternal factors have been implicated in its cause. We are in particular interested in applying computational integrative methods to investigate the role of the immune system in pregnancy (Cell Press Sneak Peak 2021) and elucidating genetic (Sci Rep 2018), transcriptomic (Front Immunol 2018), microbiome (Front Microbio 2020), environmental (Environ Health 2018), and clinical determinants of preterm birth. Moreover, through the March of Dimes (MOD) Database for Preterm Birth Research, we are leading efforts to organize scientific data and research across all MOD- funded Prematurity Research Centers with the goal of enhancing research collaboration and coordination to accelerate the overall pace of discovery in this field (Sci Data 2018). This work is funded by the National Library of Medicine at NIH, March of Dimes and The Burroughs Wellcome Fund.

SUGGESTED READING:

https://www.nature.com/articles/s41598-021-91625-1

https://doi.org/10.1186/s12916-022-02522-x

WEBSITE: http://sirotalab.ucsf.edu

Zoom link: https://stanford.zoom.us/j/92874055477pwd=aThzNmpmNEQ1L2Fj V0E5ZXF5SDR1UT09&from=addon
Password: 705300

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BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260
12/08/22 1:30PM-2:50PM
MSOB X303

Lior Pachter Fireside chat

Moderated by Barbara Engelhardt

Lior Pachter was born in Ramat Gan, Israel, and grew up in Pretoria, South Africa where he attended Pretoria Boys High School. After receiving a B.S. in Mathematics from Caltech in 1994, He left for MIT where he was awarded a PhD in applied mathematics in 1999. He then moved to the University of California at Berkeley where he was a postdoctoral researcher (1999-2001), assistant professor (2001-2005), associate professor (2005-2009), and until 2018 the Raymond and Beverly Sackler professor of computational biology and professor of mathematics and molecular and cellular biology with a joint appointment in computer science. Since January 2017 he has been the Bren professor of computational biology at Caltech.

His research interests span the mathematical and biological sciences, and he has authored over 100 research articles in the areas of algorithms, combinatorics, comparative genomics, algebraic statistics, molecular biology and evolution. He has taught a wide range of courses in mathematics, computational biology and genomics. He is a Fellow of the International Society of Computational Biology and has been awarded a National Science Foundation CAREER award, a Sloan Research Fellowship, the Miller Professorship, and a Federal Laboratory Consortium award for the successful technology transfer of widely used sequence alignment software developed in his group