ichorCNA uses a probabilistic model, implemented as a hidden Markov model (HMM), to simultaneously segment the genome, predict large-scale copy number alterations, and estimate the tumor fraction of a ultra-low-pass whole genome sequencing sample (ULP-WGS).
The methodology and probabilistic model are described in: Adalsteinsson, Ha, Freeman, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. (2017) Nature Communications Nov 6;8(1):1324. doi: 10.1038/s41467-017-00965-y
The analysis workflow consists of 2 tasks:
GC-content bias correction (using HMMcopy) a. Computing read coverage from ULP-WGS b. Data correction and normalization
CNA prediction and estimation of tumor fraction of cfDNA
ichorCNA is developed and maintained by Gavin Ha, Justin Rhoades, and Sam Freeman.
This work was done in collaboration with
Blood Biopsy Group, Group Leader Viktor Adalsteinsson, Broad Institute of MIT and Harvard
Laboratory of Matthew Meyerson, Medical Oncology, Dana-Farber Cancer Institute
Laboratory of J. Christopher Love, Koch Institute for integrative cancer research at MIT
Laboratory of Gad Getz, Cancer Program, Broad Institute
Software License
ichorCNA
Copyright (C) 2017 Broad Institute
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/.
ichorCNA
ichorCNA is a tool for estimating the fraction of tumor in cell-free DNA from ultra-low-pass whole genome sequencing (ULP-WGS, 0.1x coverage).
ichorCNA Wiki Page
For more details on usage/pipelines, outputs, and FAQs, please visit the GitHub Wiki page for ichorCNA
Description
ichorCNA uses a probabilistic model, implemented as a hidden Markov model (HMM), to simultaneously segment the genome, predict large-scale copy number alterations, and estimate the tumor fraction of a ultra-low-pass whole genome sequencing sample (ULP-WGS).
The methodology and probabilistic model are described in:
Adalsteinsson, Ha, Freeman, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. (2017) Nature Communications Nov 6;8(1):1324. doi: 10.1038/s41467-017-00965-y
The analysis workflow consists of 2 tasks:
a. Computing read coverage from ULP-WGS
b. Data correction and normalization
Contacts
If you have any questions or feedback, please contact us at:
Email: ichorcna@broadinstitute.org
Google Group: https://groups.google.com/a/broadinstitute.org/forum/?fromgroups&hl=en#!forum/ichorcna
Acknowledgements
ichorCNA is developed and maintained by Gavin Ha, Justin Rhoades, and Sam Freeman.
This work was done in collaboration with
Software License
ichorCNA Copyright (C) 2017 Broad Institute
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.