This presentation highlights VarSeq’s advanced genomic analysis capabilities, including precise CNV calling for whole exome sequencing, long-read support with allele phasing, and VSPGx for pharmacogenomics, adhering to CPIC and FDA standards. We will discuss benchmark data, case studies, and VarSeq’s “Bring Your Own Cloud” flexibility for deployment on AWS, Azure, Google Cloud, or on-premises, making it a versatile tool for precision medicine.

This talk presents ongoing efforts to extensively characterize the first tumor-normal benchmark genomes from the Genome in a Bottle consortium. A tumor cell line was established from a pancreatic ductal adenocarcinoma sample that was consented for the public release of genomic data. This dataset currently includes WGS measurements from Illumina, ONT, PacBio HiFi, Hi-C, Bionano, Bioskryb single cell, PacBio Onso, Element, and Ultima as well as karyotype data. An open working group of the GIAB consortium is developing somatic variant benchmarks from this data.

Computational analysis of large-scale metagenomics sequencing datasets have proven to be both incredibly valuable for extracting isolate-level taxonomic, and functional insights from complex microbial communities. However, due to an ever-expanding ecosystem of metagenomics-specific methods and file-formats, designing studies which implement seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system: "Core Analysis Metagenomics Pipeline" (CAMP) along with a standardized module and working directory architecture. 

New “hybrid” secondary analysis pipeline combining 30x short-read sequencing and 10x PacBio HiFi sequencing resulting in possibly the most accurate genome analysis available. Benchmarking with a 30x NovaSeq and 10x HiFi coverage, we record only 5,909/1,665 total (SNP/indel) errors relative to the GIAB v4.2.1 HG002 truthset. The pipeline reduces variant calling errors by 74% compared to DeepVariant on 30x NovaSeq data and 26% relative to DV with 30x HiFi data.

TakOmics is a FAIR multi-omics data management and analytics platform designed to meet the intricate demands of omics data workflows. TakOmics automates data transfer processes, centralizes data management, and introduces a data steward role to ensure data integrity and provides enhanced bioinformatics capabilities aimed at streamlining pipeline development and validation. The platform also explores simplifying the validation processes in GxP compliant environments, ensuring high standards of data management. TakOmics represents a significant leap towards integrating advanced analytics and data stewardship within multiomics research, fostering a more efficient, secure, and collaborative scientific environment.

The COVID-19 pandemic necessitated unprecedented scientific collaboration, leveraging relationships among domain experts, mathematical modelers, and scientific computing specialists to inform public health decisions. Among the myriad outcomes from this collaboration is OSPREY, an open science platform for robust epidemic analysis. We will describe the development and use of the OSPREY prototype and work planned to make it a public resource for epidemiologic modeling and analysis.

Harvard Medical School, in collaboration with AWS, has developed a scalable, cloud-powered Cryo-EM infrastructure using SBGrid’s suite of 400+ structural biology tools. This innovative approach enables widespread access to high-performance computing for drug discovery, democratizing advanced data analysis across academic, public sector, and biopharma institutions. By deploying this technology at underfunded research centers, such as Meharry Medical College, this initiative enables breakthroughs in drug discovery and ultimately new treatments for patients.

Modern oncology research generates vast amounts of multi-modal data, but integration remains a challenge. This talk explores how open-source tools like cBioPortal and Open Targets enable large-scale data harmonization to accelerate drug discovery and biomarker research. We’ll share best practices for structuring, visualizing, and analyzing genomics and functional screening data, with real-world applications in pharma and biotech—bridging gaps between target discovery, clinical insights, and data governance.

Advances in Power Computing have been tightly-coupled with applications such as AI, but not so much with Dynamical Modeling for Systems Biology. We want to put to good use the Long Attention Span of open-source development, for an ambitious project meant to grow alongside advances in power computing and in interactomics data.