General Information
| Description | Paired allelic log-intensity-ratio-based normalization method for SNP-CGH arrays |
| Homepage | http://publichealth.lsuhsc.edu/PAIR.html |
Publications
PAIR: paired allelic log-intensity-ratio-based normalization method for SNP-CGH arrays
Motivation:
Normalization is critical in DNA copy number analysis. We propose a new method to correctly identify two-copy probes from the genome to obtain representative references for normalization in single nucleotide polymorphism arrays. The method is based on a two-state Hidden Markov Model. Unlike most currently available methods in the literature, the proposed method does not need to assume that the percentage of two-copy state probes is dominant in the genome, as long as there do exist two-copy probes.
Results:
The real data analysis and simulation study show that the proposed algorithm is successful in that (i) it performs as well as the current methods (e.g. CGHnormaliter and popLowess) for samples with dominant two-copy states and outperforms these methods for samples with less dominant two-copy states; (ii) it can identify the copy-neutral loss of heterozygosity; and (iii) it is efficient in terms of the computational time used.
Availability:
R scripts are available at http://publichealth.lsuhsc.edu/PAIR.html.
Contact:
zfang@lsuhsc.edu
Supplementary information:
Supplementary data are available at Bioinformatics online.
PAIR: the predicted Arabidopsis interactome resource
The predicted Arabidopsis interactome resource (PAIR, http://www.cls.zju.edu.cn/pair/ ), comprised of 5990 experimentally reported molecular interactions in Arabidopsis thaliana together with 145 494 predicted interactions, is currently the most comprehensive data set of the Arabidopsis interactome with high reliability. PAIR predicts interactions by a fine-tuned support vector machine model that integrates indirect evidences for interaction, such as gene co-expressions, domain interactions, shared GO annotations, co-localizations, phylogenetic profile similarities and homologous interactions in other organisms (interologs). These predictions were expected to cover 24% of the entire Arabidopsis interactome, and their reliability was estimated to be 44%. Two independent example data sets were used to rigorously validate the prediction accuracy. PAIR features a user-friendly query interface, providing rich annotation on the relationships between two proteins. A graphical interaction network browser has also been integrated into the PAIR web interface to facilitate mining of specific pathways.
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