MOTIVATION: Leucine-rich nuclear export signals (NESs) are short amino acid motifs that mediate binding of cargo proteins to the nuclear export receptor CRM1, and thus contribute to regulate the localization and function of many cellular proteins. Computational prediction of NES motifs is of great interest, but remains a significant challenge. RESULTS: We have developed a novel approach for amino acid motif searching that can be used for NES prediction. This approach, termed Wregex (weighted regular expression), combines regular expressions with a position-specific scoring matrix (PSSM), and has been implemented in a web-based, freely available, software tool. By making use of a PSSM, Wregex provides a score to prioritize candidates for experimental testing. Key features of Wregex include its flexibility, which makes it useful for searching other types of protein motifs, and its fast execution time, which makes it suitable for large-scale analysis. In comparative tests with previously available prediction tools, Wregex is shown to offer a good rate of true-positive motifs, while keeping a smaller number of potential candidates.

BACKGROUND: Protein inference from peptide identifications in shotgun proteomics must deal with ambiguities that arise due to the presence of peptides shared between different proteins, which is common in higher eukaryotes. Recently data independent acquisition (DIA) approaches have emerged as an alternative to the traditional data dependent acquisition (DDA) in shotgun proteomics experiments. MSE is the term used to name one of the DIA approaches used in QTOF instruments. MSE data require specialized software to process acquired spectra and to perform peptide and protein identifications. However the software available at the moment does not group the identified proteins in a transparent way by taking into account peptide evidence categories. Furthermore the inspection, comparison and report of the obtained results require tedious manual intervention. Here we report a software tool to address these limitations for MSE data. RESULTS: In this paper we present PAnalyzer, a software tool focused on the protein inference process of shotgun proteomics. Our approach considers all the identified proteins and groups them when necessary indicating their confidence using different evidence categories. PAnalyzer can read protein identification files in the XML output format of the ProteinLynx Global Server (PLGS) software provided by Waters Corporation for their MSE data, and also in the mzIdentML format recently standardized by HUPO-PSI. Multiple files can also be read simultaneously and are considered as technical replicates. Results are saved to CSV, HTML and mzIdentML (in the case of a single mzIdentML input file) files. An MSE analysis of a real sample is presented to compare the results of PAnalyzer and ProteinLynx Global Server. CONCLUSIONS: We present a software tool to deal with the ambiguities that arise in the protein inference process. Key contributions are support for MSE data analysis by ProteinLynx Global Server and technical replicates integration. PAnalyzer is an easy to use multiplatform and free software tool.

This paper presents attenuation values due to the influence of orography in the MW band, based on an analysis of data recorded in a measurement campaign. The decisive influence of the distance of the obstacle from the receiver is demonstrated, and this parameter has been considered in the analysis. Values for the range of the influence are indicated for different values of the height of a single obstacle. These attenuation values are greater than the ones assumed in classical methods, and coincide with measurement campaigns developed by DRM (digital radio mondiale) consortium members. Accuracy in calculating the attenuation due to the irregular terrain is necessary in planning the coverage of digital services, where it is necessary to provide adequate field strength in a high percentage of locations.