MOTIVATION: Insertional mutagenesis allows for the creation of loss-of-function mutations on a genome-wide scale. In theory, every gene can be 'knocked out' via the insertion of an additional DNA sequence. Resources of sequence-indexed mutants of plant and animal model organisms are instrumental for functional genomics studies. Such repositories significantly speed up the acquisition of interesting genotypes and allow for the validation of hypotheses regarding phenotypic consequences in reverse genetics. To create such resources, comprehensive sequencing of flanking sequence tags using protocols such as Mutant-seq requires various downstream computational tasks, and these need to be performed in an efficient and reproducible manner. RESULTS: Here, we present MuWU, an automated Mutant-seq workflow utility initially created for the identification of Mutator insertion sites of the BonnMu resource, representing a reverse genetics mutant collection for functional genetics in maize (Zea mays). MuWU functions as a fast, one-stop downstream processing pipeline of Mutant-seq reads. It takes care of all complex bioinformatic tasks, such as identifying tagged genes and differentiating between germinal and somatic mutations/insertions. Furthermore, MuWU automatically assigns insertions to the corresponding mutated seed stocks. We discuss the implementation and how parameters can easily be adapted to use MuWU for other species/transposable elements. AVAILABILITY AND IMPLEMENTATION: MuWU is a Snakemake-based workflow and freely available at https://github.com/tgstoecker/MuWU. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The analysis of spotted cDNA microarrays involves scanning of color signals from fluorescent dyes. A common problem is that a given scanning intensity is not usually optimal for all spotted cDNAs. Specifically, some spots may be at the saturation limit, resulting in poor separation of signals from different tissues or conditions. The problem may be addressed by multiple scans with varying scanning intensities. Multiple scanning intensities raise the question of how to combine different signals from the same spot, particularly when measurement error is not negligible. RESULTS: This paper suggests a non-linear latent regression model for this purpose. It corrects for biases caused by the saturation limit and efficiently combines data from multiple scans. Combining multiple scans also allows reduction of technical error particularly for cDNA spots with low signal. The procedure is exemplified using cDNA expression data from maize. AVAILABILITY: All methods were implemented using standard procedures available in the SAS/STAT module of the SAS System. Programming statements are available from the first author upon request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The supplementary data are available at Bioinformatics online.