AISAIC: A software suite for accurate identification of significant aberrations in cancers

Bai Zhang, Xuchu Hou, Xiguo Yuan, Ie Ming Shih, Zhen Zhang, Robert Clarke, Roger R. Wang, Yi Fu, Subha Madhavan, Yue Wang, Guoqiang Yu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)431-433
Number of pages3
JournalBioinformatics
Volume30
Issue number3
DOIs
StatePublished - Feb 1 2014
Externally publishedYes

Bibliographical note

Funding Information:
Funding: In Silico Research Centers of Excellence (ISRCE)/NCI (HHSN2612200800001E); National Institutes of Health (12ST1101, CA149147, CA160036, CA164384); and the Natural Science Foundation of China (61201312 and 61070137) (in part).

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