INtegration of VAriant Reads pipeline
This repository contains code for detection and quantification of ctDNA from plasma samples where a list of patient-specific mutations is already known using the INVAR algorithm.
INVAR uses error-suppressed sequencing data, and weights and aggregates signal across up to thousands of patient-specific loci. INVAR is optimised to run in parallel on a Slurm cluster.
Authors: J. C. M. Wan* & K. Heider* et al.
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Version 0.7.1 Released (13th March 2019)
We have updated the pipeline in response to reviewers' feedback, as follows:
- Removed additional Forward/Reverse bias filter
- Modified size characterisation step so that it is performed in a leave-one-out manner, i.e. excluding the sample being tested
- This is the version that was used in the paper, and can be found here.
Version 0.7.0 Released (13th November 2018)
We have made a number of improvements to the pipeline to enhance robustness, and enable novel applications to limited input samples. The updates are as follows:
- All healthy individuals' samples are withheld until the end of the pipeline, where a FPR calculation is performed.
- We modified the calculation of IMAF so that the value is not influenced by fragment sizes. Likelihood ratios, which are used for assessing the significance of ctDNA detection, are still influenced by both family size and tumour allele fractions.
- The INVAR pipeline can now handle blood spot data (config2.R now has the variable is.blood_spot, please set to TRUE if using). When running on blood spot data, no outlier-suppression is needed.
Version 0.6.0 Released (2nd August 2018)
The first version of the INVAR pipeline is available. The source code is made available here for reviewer access in the first instance.
- Made a basic Wiki for the repository
- Added example files
Copyright © 2020, The Chancellor, Masters and Scholars of the University of Cambridge, Jonathan C. M. Wan, Katrin Heider, Eyal Fisher, James Morris and Dineika Chandrananda, all rights reserved.
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