Abstract
The cancer genome is abnormal genome, and the ability tomonitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA).With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.
| Original language | English (US) |
|---|---|
| Article number | e81 |
| Journal | Nucleic Acids Research |
| Volume | 44 |
| Issue number | 9 |
| DOIs | |
| State | Published - May 19 2016 |
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ASJC Scopus subject areas
- Genetics
Cite this
Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation. / Hibsh, Dror; Buetow, Kenneth; Yaari, Gur; Efroni, Sol.
In: Nucleic Acids Research, Vol. 44, No. 9, e81, 19.05.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation
AU - Hibsh, Dror
AU - Buetow, Kenneth
AU - Yaari, Gur
AU - Efroni, Sol
PY - 2016/5/19
Y1 - 2016/5/19
N2 - The cancer genome is abnormal genome, and the ability tomonitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA).With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.
AB - The cancer genome is abnormal genome, and the ability tomonitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA).With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.
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U2 - 10.1093/nar/gkw031
DO - 10.1093/nar/gkw031
M3 - Article
VL - 44
JO - Nucleic Acids Research
JF - Nucleic Acids Research
SN - 0305-1048
IS - 9
M1 - e81
ER -