Clinical Omics – Multimedia

This listicle explores data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches in proteomic analysis, including their characteristics, advantages and disadvantages.

Download this app note to discover a DIA workflow that can identify and quantify 5200 protein groups in single-shot analysis, delivers reproducible identification and quantification information and is suited for high-throughput proteomic profiling of hundreds of samples per day.

Traditionally data acquisition has been the bottleneck for large scale proteomics. This has also remained one of the limitations in leveraging mass spectrometry within the clinic.

Mass spectrometers are integral to clinical labs owing to their versatility, specificity, high throughput and ability to detect and quantify unknown components and highly complex samples. Discover several clinical applications of MS in this listicle.

In a COVID-19 special of Teach Me in 10, we're joined by Dr Clare Paterson, senior clinical R&D scientist at SomaLogic, who is currently working on using "omics" approaches to understand the biology of SARS-CoV-2.

Download this list to discover more about the latest research in clinical Omics, including how these fields are revolutionizing diagnostics, disease prediction and treatment strategies.

2019 has been an exciting year for the proteomics community at Technology Networks. As the year draws to a close, we're reflecting on some of the key insights we have gained.

Mass spectrometry is a powerful analytical tool with a myriad of applications spanning biology, chemistry, clinical research, pharma and beyond. Take a closer look at what this versatile technique has to offer in our latest eBook.

Our objective is to validate diagnostic services using Oxford Nanopore’s Minion in the first instance and to evaluate the cost and performance compared to existing sequencing technology in areas such as tumour DNA sequencing (and circulating tumour DNA), virology, microbiology, genetics and HLA-typing.

This lecture will use our experiences at The University of Chicago Medicine to highlight some of the common analytical, technical, and regulatory challenges that bioinformatics groups face when helping to set up oncology genomics clinical diagnostics practices.