MOTIVATION: In the past decade, a number of technologies to quantify allele-specific expression (ASE) in a genome-wide manner have become available to researchers. We investigate the application of single-nucleotide polymorphism (SNP) microarrays to this task, exploring data obtained from both cell lines and primary tissue for which both RNA and DNA profiles are available. RESULTS: We analyze data from two experiments that make use of high-density Illumina Infinium II genotyping arrays to measure ASE. We first preprocess each data set, which involves removal of outlier samples, careful normalization and a two-step filtering procedure to remove SNPs that show no evidence of expression in the samples being analyzed and calls that are clear genotyping errors. We then compare three different tests for detecting ASE, one of which has been previously published and two novel approaches. These tests vary at the level at which they operate (per SNP per individual or per SNP) and in the input data they require. Using SNPs from imprinted genes as true positives for ASE, we observe varying sensitivity for the different testing procedures that improves with increasing sample size. Methods that rely on RNA signal alone were found to perform best across a range of metrics. The top ranked SNPs recovered by all methods appear to be reasonable candidates for ASE. AVAILABILITY AND IMPLEMENTATION: Analysis was carried out in R (http://www.R-project.org/) using existing functions.

Abstract Summary: With their many replicates and their random layouts, Illumina BeadArrays provide greater scope fordetecting spatial artefacts than do other microarray technologies. They are also robust to artefact exclusion, yet there is a lack of tools that can perform these tasks for Illumina. We present BASH, a tool for this purpose. BASH adopts the concepts of Harshlight, but implements them in a manner that utilizes the unique characteristics of the Illumina technology. Using bead-level data, spatial artefacts of various kinds can thus be identified and excluded from further analyses. Availability: The beadarray Bioconductor package (version 1.10 onwards), www.bioconductor.org Contact: [email protected] Supplementary information: Additional information and a vignette are included in the beadarray package.

To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.

Predicting the subcellular localisation of proteins is an important part of the elucidation of their functions and interactions.Here, the amino acid sequence motifs that direct proteins to their proper subcellular compartment are surveyed, different methods for localisation prediction are discussed, and some benchmarks for the more commonly used predictors are presented.