• Eric Merzetti

BioNet Alberta is a unique approach to capacity building in Bioinformatics and Computational Biology (B/CB) across the province of Alberta. This network-based approach focuses on building community engagement, encouraging collaboration between local researchers and providing administrative support toward any activity that would further propel Bioinformatic activities. One of the key deliverables of BioNet is a pan-Alberta Bioinformatics service platform, currently under development, capable of analyzing large amounts of bulk experimental data. Located on campus at the University of Lethbridge and in collaboration with the Southern Alberta Genome Sciences Centre, this service platform provides a local option for B/CB analysis, with a special focus on combining both Illumina and Nanopore sequencing data.

• R. Krishna Murthy Karuturi, The Jackson Laboratory, United States

Bioinformatics core staff play a key role in the success of bioinformatics cores. Their role is a complex interplay of being researchers, collaborators, mentors, managers of bioinformatics infrastructure and service personnel. They are expected to be at the forefront of the technological advancement while meeting demands of highly varying nature. Such a complex environment and complex role requires mentoring of the bioinformatics staff on various aspects of their function. In this talk, I will present 10 simple rules for mentoring and managing bioinformatics core staff.

• Madelaine Gogol

There have been many recent developments in the area of spatial transcriptomics. Here we will discuss our experiences with our initial projects on the 10x Visium and slide-seq platforms on slices of developing chicken embryo trunk. We also have corresponding scRNA-seq data for comparison and integration. This talk will be geared towards our fellow bioinformaticians in hopes of giving them more information about this type of data and some of the potential analysis that can be used, as well as potential pitfalls.

• Simone Marini, University of Florida, United States

Single-cell RNA sequencing (scRNAseq) is a novel technology revolutionizing our ability to study tissues. By measuring transcriptomes at the single-cell level, scRNAseq enables identification of cellular heterogeneity within a tissue sample in far greater detail than conventional (bulk) RNA sequencing, where the diversity of the genetic signal is averaged over the whole sample. However, this additional granularity brings an additional data complexity layer, which in turn makes data interpretation more difficult. In this talk we will discuss the main aspects and problems of scRNAseq data analysis, as well as numerous state-of-the-art algorithms and techniques elaborated to extract information from it, in the context of multiple samples and multiple conditions.

• Multiple

We will break into small groups for discussion of topics TBA.

• Multiple

We will break into small groups for discussion of topics TBA.

• Multiple

Groups report their findings back to the main group.