Guidelines and best practices for sequencing and data generation with a focus on ensuring data quality.
In this deliverable, the authors aim to understand how 22 laboratories across 13 European countries carry out clinical protocols in their labs and how they address quality control of their samples in each step of the pipeline. They describe how laboratories undergo the 5 NGS pipeline steps for cancer and germline samples as well as WGS and WES workflows.
Determining variants in the genome involves several bioinformatic procedures, such as eliminating low-quality sequences, aligning sequencing reads to the human reference genome, and establishing confidence in the presence of a variant based on a threshold. Once a variant is identified, it is annotated to predict its effect. The goal of this deliverable is to establish a best practice protocol for data analysis of whole genome sequencing (WGS) for somatic variants. This protocol includes a recommended suite of software tools with settings that ensure results surpass a required quality threshold.
A general conclusion is that all participants have achieved a good level of quality at the sequencing stage, and the metrics measuring it are largely consistent, as there is very little dispersion. Differences in library preparation and sequencing protocols do not appear to significantly impact the expected quality of results. In the deliverable, the authors explain the performance of different participating laboratories for each one of the relevant sequencing metrics, and suggest general best practices to ensure the best quality.
This document aims to bridge the connection between genomic and health data analyses. Achieving this mission mandates a meticulous exploration of existing voids and optimal methodologies within germline and tumor WGS. The central focus lies in the orchestration of a somatic WGS benchmarking initiative, encompassing three distinct challenges: Wet Lab, Full Pipeline, and Dry Lab challenges.
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