1 - 28 of 28 results

GISTIC / Genomic Identification of Significant Targets in Cancer

Recognizes likely driver somatic copy-number alterations (SCNAs). GISTIC evaluates the frequency and amplitude of observed events. It can be adapted to other copy-number analysis workflows. This tool can integrate other software estimating and segmenting copy-number values from sequencing coverage data. It has been tested on multiple cancer types, including glioblastoma, lung adenocarcinoma, melanoma or colorectal carcinoma.

WIFA / Wavelet-based Identification of Focal genomic Aberrations

Allows detection of broad and focal aberrations in single nucleotide polymorphism SNP array data sets. WIFA can (i) distinguish signals from noise among probes having high aberrations, (ii) detect focal aberrations as well as the amount of aberrations, and (iii) consider the consistency of aberrations in multiple samples. The software was used to detect cancer related genes in both glioblastoma multiforme (GBM) and lung data sets. It has the potential to be broadly applied to detect various kinds of focal aberrations.

ASP / Autocorrelation Scanning Profile

A mathematical tool to find repeating patterns in time series data to and to assess the dependence of measurement error between neighboring probes. ASP has the following appealing properties: (i) the autocorrelation within each segment of a constant mean is expected to be 0; (ii) at the junction of an abrupt change-point, however, the autocorrelation rises significantly above 0. We observe different patterns when the ASP method is applied to published datasets. Our results show that ASP can be used to check for CNA data quality and refine the analysis.


A software tool for fast and accurate CNA detection using signal intensity data from SNP genotyping arrays. PennCNV-tumor is an HMM-based method that is loosely based on the model used in PennCNV, an algorithm developed specifically for germline CNV detection. When compared against other popular methods, our approach performs comparably in terms of the detection and estimation of CNAs but shows marked improvements in the estimation of stromal contamination and runtime efficiency.

PICNIC / Predicting Integral Copy Numbers In Cancer

An algorithm designed to identify copy number segments and genotypes in cancer using a SNP6 'cel' file as input. Although using generalized copy number software upon cancer data can lead to systematic errors which are most apparent with anueploid genomes, techniques bespoke to tumor data, such as PICNIC, provide an effective means by which these biases can be overcome and provide accurate information regarding allelic integer copy number and genotype information in cancer.

FACADE / Fast Algorithm for Calling After Detection of Edges

A rapid segmentation and calling algorithm that performs competitively with other popular algorithms, while demonstrating rapid execution times which can be orders of magnitude faster than established algorithms. This is accomplished by utilizing edge detection in combination with non-parametric statistics. Additionally, FACADE requires no specialized knowledge from the user, or complex software environments. FACADE is designed to handle the next generation high-resolution copy number platforms due to the linear scalability of the algorithm. FACADE fills the need, in both research and clinical settings, for rapid accurate segmentation demanded by high-resolution array platforms, large data sets and other situations where long execution times are not tolerable.