This workshop will introduce you to powerful metadata searches for the Sequence Read Archive (SRA) by using interactive metadata queries in the cloud.

This service expands the search tools available for SRA and saves time by finding exactly the data you want more quickly than ever before.

We will discuss metadata searches in AWS and GCP using common database query methods and demonstrate how to use the metadata tables for searching. We'll run through some hands-on exercises:

1. finding sequence data based on k-mer searches for specific taxonomic IDs and
2. filtering runs to find exactly what you want.

We will also provide demonstrations and examples to help you better understand how to build your own searches. We will be using Structured Query Language (SQL) to do these searches but no prior SQL experience is required. By the end of this workshop you will know how to run cloud metadata queries to find SRA data based on parameters that are of interest to you.

Single-cell genomics is rapidly transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is an NIH-funded consortium, which aims to map all of the cell types in the mammalian brain. Researchers within BICCN have sequenced the transcriptomes and epigenomes of >10 million cells from the brains of humans, non-human primates, and mice.

In this interactive workshop, we will introduce participants to tools for accessing, analyzing, and visualizing BICCN data using the Neuroscience Multi-Omic Archive (NeMO Archive) and NeMO Analytics. The NeMO Archive serves as the primary repository for genomics data from the BRAIN Initiative, while NeMO Analytics is a web-based, biologist-friendly visualization and analysis portal for BICCN data.

Participants will learn how to find BICCN data in the NeMO Archive and perform high-throughput data processing in the BICCN Cloud-Computing Environment (powered by Terra, terra.bio); interact with BICCN data at NeMO Analytics and use the gEAR software underlying NeMO Analytics to create publication-ready visualization portals from their own data.

In this workshop, participants will learn about (and have hands-on experience with) new or newly updated resources that can aid in the interpretation of genomic data, including:

1. the Gene Curation Coalition (GenCC) Database;
2. gnomAD: The Genome Aggregation Database;
3. ClinGen’s Dosage Sensitivity Map; and
4. ClinGen’s Community Curation Curation Baseline Annotation.

Launched in December 2020, the GenCC DB is a gene-level knowledgebase for claims on the strength of gene disease relationships made by submitters, much like a “ClinVar for genes.” The GenCC comprises organizations that currently provide online resources (e.g. ClinGen, DECIPHER, GEL PanelApp, OMIM, Orphanet, PanelApp Australia, TGMI’s G2P), as well as diagnostic labs that have committed to sharing their internal curated gene-level knowledge (e.g. Ambry, Illumina, Invitae, Myriad Women’s Health, MGB Laboratory for Molecular Medicine).  With data from over 200,000 individuals, gnomAD contains annotations that aid in variant interpretation, including allele frequency, gene expression data, automated and manually curated loss-of-function annotations, constraint scores, heteroplasmy estimates for mitochondrial variation, and structural annotations.

The ClinGen Dosage Sensitivity Map provides evidence-based assessments of haploinsufficiency and triplosensitivity for genes and genomic regions critical for interpreting copy number variants. Users can search by gene or by genomic coordinates, and the data is downloadable for easy incorporation into genomic pipelines. ClinGen’s Baseline Annotation effort improves data transparency through the use of a web-based annotation tool, Hypothes.is, and has the potential to expedite the evaluation of variant pathogenicity, gene-disease validity and more.

The workshop will include an overview of each resource and then lead participants through interactive demonstrations that will teach participants how to use each resource in the context of genomic analysis and interpretation (e.g. for interpreting gene-disease relationships and variant pathogenicity).

Genome-wide association studies (GWAS) have identified a large number of variants robustly associated with complex traits and diseases. However, pinpointing the gene(s) mediating such associations remains a major challenge as the majority of variants are found in the non-coding regions of the genome. Identifying the right target is key in drug discovery as drugs supported by GWAS evidence are twice as likely to be approved for clinical use.

In this workshop, we will present Open Targets Genetics ^1,2, an open access resource which makes robust connections between GWAS-associated loci and likely causal genes, to enable the identification of new drug targets. 

The portal uses a wide range of resources including FinnGen, GWAS Catalog both curated from literature and full summary statistics (including over 3,000 UK Biobank phenotypes) to locate over 100,000 trait-associated loci. Since our first release in 2018, we have applied systematic fine-mapping across 133,441 published GWAS loci to identify a set of potentially causal variants at each locus. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. More recently, we have implemented a machine learning approach, Locus2Gene², to systematically prioritize causal genes at all trait-associated loci. 

Our analyses are made available through our web portal, for bulk download, and via a GraphQL API, making it one of the most comprehensive tools for users to prioritize genes at associated loci and assess their potential as drug targets.  

Participants will learn to use Open Targets Genetics to establish, visualize and interpret links between genes, variants, and diseases, find shared susceptibility loci between traits, investigate molecular trait-disease colocalization and prioritize drug targets.  

We will start the session with a short presentation about the portal, followed by a live demo of its features and how to access the data. Participants are encouraged to work through an exercise prior to coming to the workshop to familiarize themselves with Open Targets Genetics and the process leading to gene prioritization.