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GISTIC specifications

Information


Unique identifier OMICS_02296
Name GISTIC
Alternative name Genomic Identification of Significant Targets in Cancer
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages MATLAB
Computer skills Advanced
Version 2.0.22
Stability Stable
Maintained Yes

Versioning


No version available

Maintainers


  • person_outline Gad Getz
  • person_outline Rameen Beroukhim

Publications for Genomic Identification of Significant Targets in Cancer

GISTIC citations

 (138)
library_books

Genomic profiling of dedifferentiated liposarcoma compared to matched well differentiated liposarcoma reveals higher genomic complexity and a common origin

2018
PMCID: 5880260
PMID: 29610390
DOI: 10.1101/mcs.a002386

[…] ta. For assessing amplifications and deletions, log2 scores > 0.5 were considered gains, whereas log2 scores < −0.5 were considered losses. Focal somatic copy-number alterations were identified using GISTIC2.0 () at the 95% confidence level. See Supplemental Methods for comments on somatic copy-number alterations profiles in FFPE samples. Gene-level copy-number assessment was done using customized […]

library_books

Genomic analysis of liver cancer unveils novel driver genes and distinct prognostic features

2018
Theranostics
PMCID: 5858179
PMID: 29556353
DOI: 10.7150/thno.22010

[…] Aside from point mutations and short insertions/deletions, we used GISTIC2 to analyze DNA copy number alterations (CNAs) based on segmentation data obtained from TCGA to delineate genome-wide focal DNA gain and loss . Significant arm-level alterations include gain of […]

library_books

Genomic analysis of head and neck cancer cases from two high incidence regions

2018
PLoS One
PMCID: 5788352
PMID: 29377909
DOI: 10.1371/journal.pone.0191701

[…] the normalized copy number. Germline copy number alterations were removed using the Database of Genomic Variants []. Identification of significant amplified or deleted regions was performed by using GISTIC 2.0 [] using 99% confidence level and q-value threshold 0.25. Focal amplification or deletion for all the 14 genes sequenced was determined only using the GISTIC copy number value 2 or -2 respe […]

library_books

Efficacy of histology agnostic and molecularly driven HER2 inhibitors for refractory cancers

2018
Oncotarget
PMCID: 5839398
PMID: 29515767
DOI: 10.18632/oncotarget.24188

[…] y 4 × 180K, Agilent technologies, Palo alto, CA []. The copy number alterations detected with CGHa were classified into 5 categories, namely deletion, loss, neutral, gain and amplification, using the GISTIC algorithm []. Amplifications in GISTIC confirmed by a > ×0.7 log2 ratio with a length less than 10 Mb were considered of interest for the current study. […]

library_books

Integrative genomic and transcriptomic analysis of leiomyosarcoma

2018
Nat Commun
PMCID: 5762758
PMID: 29321523
DOI: 10.1038/s41467-017-02602-0

[…] CNA profiles. Segmentation was performed with PSCBS, segmentation files and windows used for CNA estimation were converted to a compound segmentation file and marker files that were used as input for GISTIC2.0, and processing was performed with default parameters. For LMS cell lines, copy numbers were estimated from whole-genome sequencing data using allele-specific copy number estimation from seq […]

library_books

CINdex: A Bioconductor Package for Analysis of Chromosome Instability in DNA Copy Number Data

2017
Cancer Inform
PMCID: 5761903
PMID: 29343938
DOI: 10.1177/1176935117746637

[…] ysis of DNA, Circular Binary Segmentation (CBS), and Hidden Markov Model (HMM). Consensus CNV/CNA detection methods can be categorized into 1-stage and 2-stage approaches. A 2-stage approach, such as Genomic Identification of Significant Targets in Cancer (GISTIC), involves a step of copy number change detection in individual profiles and a subsequent statistical analysis of commonly altered DNA r […]

Citations

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GISTIC institution(s)
Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA; Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA; The Center for Cancer Genome Discovery, Dana Farber Cancer Institute, Boston, MA, USA
GISTIC funding source(s)
Supported by a Genome Characterization Center Grant (U24CA143867) awarded as part of the NCI/NHGRI funded Cancer Genome Atlas (TCGA) project; by Medical Scientist Training Program (MSTP) Award Number T32GM07753 from the National Institute of General Medical Sciences; by NIH K08CA122833, a V Foundation Scholarship, and the Doris Duke Charitable Foundation.

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