Peptigram: A Web-Based Application for Peptidomics Data Visualization.

Tandem mass spectrometry (MS/MS) techniques, developed for protein identification, are increasingly being applied in the field of peptidomics. Using this approach, the set of protein fragments observed in a sample of interest can be determined to gain insights into important biological processes such as signaling and other bioactivities. As the peptidomics era progresses, there is a need for robust and convenient methods to inspect and analyze MS/MS derived data. Here, we present Peptigram, a novel tool dedicated to the visualization and comparison of peptides detected by MS/MS. The principal advantage of Peptigram is that it provides visualizations at both the protein and peptide level, allowing users to simultaneously visualize the peptide distributions of one or more samples of interest, mapped to their parent proteins. In this way rapid comparisons between samples can be made in terms of their peptide coverage and abundance. Moreover, Peptigram integrates and displays key sequence features from external databases and links with peptide analysis tools to offer the user a comprehensive peptide discovery resource. Here, we illustrate the use of Peptigram on a data set of milk hydrolysates. For convenience, Peptigram is implemented as a web application, and is freely available for academic use at http://bioware.ucd.ie/peptigram .

ProViz-a web-based visualization tool to investigate the functional and evolutionary features of protein sequences.

Low-throughput experiments and high-throughput proteomic and genomic analyses have created enormous quantities of data that can be used to explore protein function and evolution. The ability to consolidate these data into an informative and intuitive format is vital to our capacity to comprehend these distinct but complementary sources of information. However, existing tools to visualize protein-related data are restricted by their presentation, sources of information, functionality or accessibility. We introduce ProViz, a powerful browser-based tool to aid biologists in building hypotheses and designing experiments by simplifying the analysis of functional and evolutionary features of proteins. Feature information is retrieved in an automated manner from resources describing protein modular architecture, post-translational modification, structure, sequence variation and experimental characterization of functional regions. These features are mapped to evolutionary information from precomputed multiple sequence alignments. Data are displayed in an interactive and information-rich yet intuitive visualization, accessible through a simple protein search interface. This allows users with limited bioinformatic skills to rapidly access data pertinent to their research. Visualizations can be further customized with user-defined data either manually or using a REST API. ProViz is available at http://proviz.ucd.ie/.

OD-seq: outlier detection in multiple sequence alignments.

BACKGROUND: Multiple sequence alignments (MSA) are widely used in sequence analysis for a variety of tasks. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This paper describes a simple method for automatically detecting outliers and accompanying software called OD-seq. It is based on finding sequences whose average distance to the rest of the sequences in a dataset, is anomalous. RESULTS: The software can take a MSA, distance matrix or set of unaligned sequences as input. Outlier sequences are found by examining the average distance of each sequence to the rest. Anomalous average distances are then found using the interquartile range of the distribution of average distances or by bootstrapping them. The complexity of any analysis of a distance matrix is normally at least O(N(2)) for N sequences. This is prohibitive for large N but is reduced here by using the mBed algorithm from Clustal Omega. This reduces the complexity to O(N log(N)) which makes even very large alignments easy to analyse on a single core. We tested the ability of OD-seq to detect outliers using artificial test cases of sequences from Pfam families, seeded with sequences from other Pfam families. Using a MSA as input, OD-seq is able to detect outliers with very high sensitivity and specificity. CONCLUSION: OD-seq is a practical and simple method to detect outliers in MSAs. It can also detect outliers in sets of unaligned sequences, but with reduced accuracy. For medium sized alignments, of a few thousand sequences, it can detect outliers in a few seconds. Software available as http://www.bioinf.ucd.ie/download/od-seq.tar.gz.

iELM--a web server to explore short linear motif-mediated interactions.

The recent expansion in our knowledge of protein-protein interactions (PPIs) has allowed the annotation and prediction of hundreds of thousands of interactions. However, the function of many of these interactions remains elusive. The interactions of Eukaryotic Linear Motif (iELM) web server provides a resource for predicting the function and positional interface for a subset of interactions mediated by short linear motifs (SLiMs). The iELM prediction algorithm is based on the annotated SLiM classes from the Eukaryotic Linear Motif (ELM) resource and allows users to explore both annotated and user-generated PPI networks for SLiM-mediated interactions. By incorporating the annotated information from the ELM resource, iELM provides functional details of PPIs. This can be used in proteomic analysis, for example, to infer whether an interaction promotes complex formation or degradation. Furthermore, details of the molecular interface of the SLiM-mediated interactions are also predicted. This information is displayed in a fully searchable table, as well as graphically with the modular architecture of the participating proteins extracted from the UniProt and Phospho.ELM resources. A network figure is also presented to aid the interpretation of results. The iELM server supports single protein queries as well as large-scale proteomic submissions and is freely available at http://i.elm.eu.org.

ELM--the database of eukaryotic linear motifs.

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.