Thursday, March 9, 2023

Re: The Festival of Genomics and Biodata, October 2023, Boston

Title & Bullets for Festival of Genomics Talk

Personal Functional Genomics

* EN-TEx includes >1500 datasets (>25 tissues X 10 assays) mapped to 4
personal genomes
* Comprehensive catalog of allele-specific activity, decorating
regulatory elements
* Model for transferring known eQTLs to difficult-to-profile tissues
(eg skin=>heart)
* Transformer model for predicting allelic activity based on local
sequence context


Saturday, January 28, 2023

Invited Session for JSM 2023

Title: Single Cell Data Analysis: Computational methods for
characterizing cell types

I will describe two techniques for the analysis of single-cell
sequencing data. (1) Forest Fire Clustering. This is an efficient and
interpretable method for cell-type discovery from single-cell data. It
makes minimal prior assumptions and, different from current
approaches, calculates a non-parametric posterior probability that
each cell is assigned a cell-type label. These posterior distributions
allow for the evaluation of a label confidence for each cell and
enable the computation of "label entropies," highlighting transitions
along developmental trajectories. (2) SCAN-ATAC-Sim. It is difficult
to benchmark the performance of various scATAC-seq analysis techniques
(such as clustering and deconvolution) without having a priori a known
set of gold-standard cell types. To simulate scATAC-seq experiments
with known cell-type labels, we introduce an efficient and scalable
scATAC-seq simulation method that down-samples bulk ATAC-seq data
(e.g., from representative cell lines or tissues). Our protocol uses a
consistent but tunable signal-to-noise ratio across cell types in a
scATAC-seq simulation.

SCAN-ATAC-Sim: a scalable and efficient method for simulating
single-cell ATAC-seq data from bulk-tissue experiments.
Z Chen, J Zhang, J Liu, Z Zhang, J Zhu, D Lee, M Xu, M Gerstein
(2021). Bioinformatics 37: 1756-8.

Forest Fire Clustering for single-cell sequencing combines iterative
label propagation with parallelized Monte Carlo simulations.
Z Chen, J Goldwasser, P Tuckman, J Liu, J Zhang, M Gerstein (2022).
Nat Commun 13: 3538.

Friday, July 8, 2022

A new reply has been shared with you on 7/9/2022 11:43:18 a.m.

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Sunday, June 19, 2022

visiting McGill on 7/25

Topics in Neurogenomics & Genomic Privacy

My talk will focus on neurogenomics and how this has inspired my group
to become interested in privacy and security. I'll first talk about
work in the PsychENCODE consortium and how we have developed tools,
particularly clustering algorithms, related to this area. Then, I'll
talk about how important it is to keep large-scale cohort data,
particularly for mental illness, both private and secure. I'll
highlight some techniques that we have developed for security,
particularly in relation to putting a genome on a blockchain.

Friday, May 20, 2022

A new document has been shared with you on 5/20/2022 3:29:37 p.m. - LPS



Scanned document.pdf

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Rev: 353836484639
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Wednesday, March 16, 2022

Fwd: AACR Annual Meeting 2022-session information needed

Session Title:

Functional Role of Long Tail Coding and Non-coding Mutations in Cancer

Session Abstract:

This session will focus on the role of non-coding mutations in cancer.
As most know, the overwhelming part of the genome is noncoding, and
there are many somatic mutations in cancer in this region. It is still
debated the degree to which many of these mutations are under positive
selection and whether these mutations could represent either strong or
weak drivers and affect cancer progression. This session will tackle
these issues.


Friday, February 25, 2022

Fwd: Mechanick Lecture at UMD

Neurogenomics and 2 topics inspired by this, Quantum Computing & Privacy


My seminar will discuss various data-science issues related to
neurogenomics. First, I will focus on classic disorders of the brain,
which affect nearly a fifth of the world's population. Robust
phenotype-genotype associations have been established for several
psychiatric diseases (e.g., schizophrenia and bipolar disorder). However,
understanding their molecular causes is still a challenge. To address
this, the PsychENCODE consortium generated thousands of transcriptome
(bulk and single-cell) datasets from nearly 2000 individuals. Using these
data, we have developed interpretable machine learning approaches for
deciphering functional genomic elements and linkages in the brain and
psychiatric disorders. Specifically, we developed a deep-learning
model embedding the physical regulatory network to predict phenotype
from genotype. Our model uses a conditional Deep Boltzmann Machine
architecture and introduces lateral connectivity at the visible layer
to embed the biological structure learned from the regulatory network
and QTL linkages. Our model improves disease prediction (6X compared
to additive polygenic risk scores), highlights key genes for
disorders, and imputes missing transcriptome information from genotype
data alone. Next, I will look at the "data exhaust" from this activity
- that is, how one can find other things from the genomic analyses
than what is necessarily intended. I will focus on genomic privacy,
which is a main stumbling block in large-scale
genomic studies. In particular, I will look at how the quantifications
of expression levels can reveal something about the subjects studied
and how one can take steps to sanitize the data and protect patient
anonymity. Lastly, I will look at how the kinds of interpretable
deep-learning models we have developed can be extended to incorporate
Quantum Machine Learning approaches. We have developed Quantum
Boltzmann machines and Quantum Neural Network analogues of the
architectures from the first part of the talk, which we test using
simulations and implementations on quantum hardware. We show hints
that such models may outperform matched classical models on certain
genomics tasks, and show how parameters unique to the quantum versions
of the models can be interpreted in the context of gene regulation