Title |
Copy number analysis by low coverage whole genome sequencing using ultra low-input DNA from formalin-fixed paraffin embedded tumor tissue
|
---|---|
Published in |
Genome Medicine, November 2016
|
DOI | 10.1186/s13073-016-0375-z |
Pubmed ID | |
Authors |
Tanjina Kader, David L. Goode, Stephen Q. Wong, Jacquie Connaughton, Simone M. Rowley, Lisa Devereux, David Byrne, Stephen B. Fox, Gisela Mir Arnau, Richard W. Tothill, Ian G. Campbell, Kylie L. Gorringe |
Abstract |
Unlocking clinically translatable genomic information, including copy number alterations (CNA), from formalin-fixed paraffin-embedded (FFPE) tissue is challenging due to low yields and degraded DNA. We describe a robust, cost-effective low-coverage whole genome sequencing (LC WGS) method for CNA detection using 5 ng of FFPE-derived DNA. CN profiles using 100 ng or 5 ng input DNA were highly concordant and comparable with molecular inversion probe (MIP) array profiles. LC WGS improved CN profiles of samples that performed poorly using MIP arrays. Our technique enables identification of driver and prognostic CNAs in archival patient samples previously deemed unsuitable for genomic analysis due to DNA limitations. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 44% |
Montenegro | 1 | 11% |
Sweden | 1 | 11% |
India | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 7 | 78% |
Members of the public | 1 | 11% |
Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 2% |
China | 1 | <1% |
Unknown | 110 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 27 | 24% |
Student > Ph. D. Student | 18 | 16% |
Student > Master | 15 | 13% |
Student > Bachelor | 8 | 7% |
Student > Doctoral Student | 7 | 6% |
Other | 14 | 12% |
Unknown | 24 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 26 | 23% |
Agricultural and Biological Sciences | 20 | 18% |
Medicine and Dentistry | 19 | 17% |
Engineering | 4 | 4% |
Computer Science | 3 | 3% |
Other | 11 | 10% |
Unknown | 30 | 27% |