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AISAIC: a software suite for accurate identification of significant aberrations in cancers
Bai Zhang1, Xuchu Hou, Xiguo Yuan
1Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, School of Computer Science and Technology, Xidian University, Xi'an 710126, China, Department of Oncology and Department of Gynecology/Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20007, USA Department of Oncology and Department of Physiology and Biophysics, Georgetown University, Washington, DC 20057, USA and Department of Electrical Engineering and Computer Science, University of Michigan, An Arbor, MI 48109, USA.
Unlabelled:
Accurate identification of significant aberrations in cancers (AISAIC) is a systematic effort to discover potential cancer-driving genes such as oncogenes and tumor suppressors. Two major confounding factors against this goal are the normal cell contamination and random background aberrations in tumor samples. We describe a Java AISAIC package that provides comprehensive analytic functions and graphic user interface for integrating two statistically principled in silico approaches to address the aforementioned challenges in DNA copy number analyses. In addition, the package provides a command-line interface for users with scripting and programming needs to incorporate or extend AISAIC to their customized analysis pipelines. This open-source multiplatform software offers several attractive features: (i) it implements a user friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects deletion types directly from copy number signals using a Bayes hypothesis test; (iii) it estimates the fraction of normal contamination for each sample; (iv) it produces unbiased null distribution of random background alterations by iterative aberration-exclusive permutations; and (v) it identifies significant consensus regions and the percentage of homozygous/hemizygous deletions across multiple samples. AISAIC also provides users with a parallel computing option to leverage ubiquitous multicore machines.
Availability And Implementation:
AISAIC is available as a Java application, with a user's guide and source code, at https://code.google.com/p/aisaic/.
