Bio-Informatics seminars

The presentation will provide an overview of the expanding role of genetic factors in epilepsy. It will delve into the fundamentals of this field and elucidate how digital tools and resources can aid in the re-evaluation of genetic test results. In the initial segment of the presentation, Dr. Lal will examine the advancements made over the past two decades regarding the genetic architecture of various epilepsy types. Additionally, he will present research studies in which he has actively participated, offering concrete examples. Subsequently, during the second part of the talk, Dr. Lal will share the ongoing research projects that focus on epilepsy genetics, bioinformatics, and health record data science.

Genome-wide association studies (GWAS) have identified multiple disease-associated genetic variations across different psychiatric disorders raising the question of how these genetic variants relate to the corresponding pharmacological treatments. In this talk, I will outline our work investigating whether functional information from a range of open bioinformatics datasets such as protein interaction network (PPI), brain eQTL, and gene expression pattern across the brain can uncover the relationship between GWAS-identified genetic variation and the genes targeted by current drugs for psychiatric disorders. Focusing on four psychiatric disorders---ADHD, bipolar disorder, schizophrenia, and major depressive disorder---we assess relationships between the gene targets of drug treatments and GWAS hits and show that while incorporating information derived from functional bioinformatics data, such as the PPI network and spatial gene expression, can reveal links for bipolar disorder, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeds null expectations. This relatively low degree of correspondence across modalities suggests that the genetic mechanisms driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms used for targeting symptom manifestations through pharmacological treatments and that novel approaches for understanding and treating psychiatric disorders may be required.

The Addgene AAV Data Hub was launched to help scientists share data and protocols obtained from AAV experiments. Our longterm goal is to provide scientists with a resource to help guide AAV selection and use by providing data from individual labs on AAV performance.

The precise spatial localization of molecular signals within tissues richly informs the mechanisms of tissue formation and function. Here, we’ll introduce Slide-seq, a technology which enables transcriptome-wide measurements with near-single cell spatial resolution. We’ll describe recent experimental and computational advances to enable Slide-seq in biological contexts in biological contexts where high detection sensitivity is important. More broadly, we’ll discuss the promise and challenges of spatial transcriptomics for tissue genomics. Lastly, we’ll touch upon novel molecular recording technologies, which allows recording of the absolute time dynamics of gene expression in live systems into DNA sequences.

I will introduce a web portal for the retinal neuroscience community to explore the catalog of mouse retinal ganglion cell (RGC) types, including data on light responses, correspondences with morphological types in EyeWire, and gene expression data from single-cell transcriptomics. Our current classification includes 43 types, accounting for 90% of the cells in EyeWire. Many of these cell types have new stories to tell, and I will cover two of them that represent opposite ends of the spectrum of levels of analysis in my lab. First, I will introduce the “Bursty Suppressed-by-Contrast” RGC and show how its intrinsic properties rather than its synaptic inputs differentiate its function from that of a different well-known RGC type. Second, I will present the histogram of cell types that project to the Olivary Pretectal Nucleus, focusing on the recently discovered M6 ipRGC.