ArticleBioinformatics (Oxford, England)2014
AISAIC: a software suite for accurate identification of significant aberrations in cancers.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.Patterns (New York, N.Y.) · 2022Article
- Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data.Frontiers in genetics · 2020Article
- A Density Peak-Based Method to Detect Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2020Article
- Paternal malnutrition programs breast cancer risk and tumor metabolism in offspring.Breast cancer research : BCR · 2018Article
- BACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
unlabelledAccurate 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/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.