PierianDX, ArcherDX Partner Up
News Jul 20, 2016
ArcherDX and PierianDx announced a co-marketing and licensing agreements that will provide enhanced NGS testing capabilities to clinical labs. Under the terms of the agreement, PierianDx will integrate Archer® targeted NGS testing pipelines into the PierianDx Clinical Genomicist Workspace™ (CGW).
Archer’s FusionPlex® and VariantPlex™ target enrichment assays, coupled with its bioinformatics software, Archer Analysis, report known and novel gene fusions, CNVs, point mutations and relative expression levels from NGS libraries.
“Integrating Archer Analysis and the corresponding target enrichment assays into CGW enables PierianDx’s partner laboratories to offer the most comprehensive, clinically relevant results to oncologists,” said PierianDx CEO Ted Briscoe. “Archer’s FusionPlex and VariantPlex assays provide more granular details about CNVs, fusions and point mutations in their patient samples, providing enhanced NGS testing capability to clinical labs seeking a complete solution for their personalized medicine programs.”
“NGS data contains only the primary sequencing information for downstream analysis, and ultimately, reporting of clinically relevant variants in a scalable manner is the desired end result,” said Todd Pollard, Vice President of Global Sales and Support at ArcherDX. “Partnering with PierianDx enables laboratories who use the Archer assays to seamlessly process clinical cases from accessioning to final report using PierianDx’s comprehensive workflow, curated knowledgebase, and partner-sharing network.”
Archaeology researchers are benefitting from the University’s first high performance computing (HPC) system. Revolutionising the capacity for data collation, the HPC cluster enables the archaeological team to effectively preserve endangered or destroyed heritage across the world, the Temple of Bel in Palmyra, Kathmandu and Notre Dame.
North Carolina State University researchers have developed a new framework for building deep neural networks via grammar-guided network generators. In experimental testing, the new networks - called AOGNets - have outperformed existing state-of-the-art frameworks, including the widely-used ResNet and DenseNet systems, in visual recognition tasks.