SearchGUI: a highly adaptable common interface for proteomics search and de novo engines

Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the originating proteins. We here present the latest developments in SearchGUI, a common open-source interface for the most frequently used freely available proteomics search and de novo engines that has evolved into a central component in numerous bioinformatics workflows.acceptedVersio

Automated splitting into batches for observational biomedical studies with sequential processing

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.publishedVersio

MassSorter: a tool for administrating and analyzing data from mass spectrometry experiments on proteins with known amino acid sequences

BACKGROUND: Proteomics is the study of the proteome, and is critical to the understanding of cellular processes. Two central and related tasks of proteomics are protein identification and protein characterization. Many small laboratories are interested in the characterization of a small number of proteins, e.g., how posttranslational modifications change under different conditions. RESULTS: We have developed a software tool called MassSorter for administrating and analyzing data from peptide mass fingerprinting experiments on proteins with known amino acid sequences. It is meant for small scale mass spectrometry laboratories that are interested in posttranslational modifications of known proteins. Several experiments can be compared simultaneously, and the matched and unmatched peak values are clearly indicated. The hits can be sorted according to m/z values (default) or according to the sequence of the protein. Filters defined by the user can mark autolytic protease peaks and other contaminating peaks (keratins, proteins co-migrating with the protein of interest, etc.). Unmatched peaks can be further analyzed for unexpected modifications by searches against a local version of the UniMod database. They can also be analyzed for unexpected cleavages, a highly useful feature for proteins that undergo maturation by proteolytic cleavage, creating new N- or C-terminals. Additional tools exist for visualization of the results, like sequence coverage, accuracy plots, different types of statistics, 3D models, etc. The program and a tutorial are freely available for academic users at . CONCLUSION: MassSorter has a number of useful features that can promote the analysis and administration of MS-data

Blind search for post-translational modifications and amino acid substitutions using peptide mass fingerprints from two proteases

<p>Abstract</p> <p>Background</p> <p>Mass spectrometric analysis of peptides is an essential part of protein identification and characterization, the latter meaning the identification of modifications and amino acid substitutions. There are two main approaches for characterization: (i) using a predefined set of possible modifications and substitutions or (ii) performing a blind search. The first option is straightforward, but can not detect modifications or substitutions outside the predefined set. A blind search does not have this limitation, and therefore has the potential of detecting both known and unknown modifications and substitutions. Combining the peptide mass fingerprints from two proteases result in overlapping sequence coverage of the protein, thereby offering alternative views of the protein and a novel way of indicating post-translational modifications and amino acid substitutions.</p> <p>Results</p> <p>We have developed an algorithm and a software tool, MassShiftFinder, that performs a blind search using peptide mass fingerprints from two proteases with different cleavage specificities. The algorithm is based on equal mass shifts for overlapping peptides from the two proteases used, and can indicate both post-translational modifications and amino acid substitutions. In most cases it is possible to suggest a restricted area within the overlapping peptides where the mass shift can occur. The program is available at <url>http://www.bioinfo.no/software/massShiftFinder</url>.</p> <p>Conclusion</p> <p>Without any prior assumptions on their presence the described algorithm is able to indicate post-translational modifications or amino acid substitutions in MALDI-TOF experiments on identified proteins, and can thereby direct the involved peptides to subsequent TOF-TOF analysis. The algorithm is designed for detailed and low-throughput characterization of single proteins.</p

Detecting single amino acids and small peptides by combining isobaric tags and peptidomics

Single amino acids and small endogenous peptides play important roles in maintaining a properly functioning organism. These molecules are however currently only routinely identified in targeted approaches. In a small proof-of-concept mass spectrometry experiment, we found that by combining isobaric tags and peptidomics, and by targeting singly charged molecules, we were able to identify a significant amount of single amino acids and small endogenous peptides using a basic mass-based identification approach. While there is still room for improvement, our simple test indicates that a limited amount of extra work when setting up the mass spectrometry experiment could potentially lead to a wealth of additional information.acceptedVersio

Pladipus enables universal distributed computing in proteomics bioinformatics

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open -source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries

Anatomy and evolution of database search engines — a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.acceptedVersio

OLS Dialog: An open-source front end to the Ontology Lookup Service

<p>Abstract</p> <p>Background</p> <p>With the growing amount of biomedical data available in public databases it has become increasingly important to annotate data in a consistent way in order to allow easy access to this rich source of information. Annotating the data using controlled vocabulary terms and ontologies makes it much easier to compare and analyze data from different sources. However, finding the correct controlled vocabulary terms can sometimes be a difficult task for the end user annotating these data.</p> <p>Results</p> <p>In order to facilitate the location of the correct term in the correct controlled vocabulary or ontology, the Ontology Lookup Service was created. However, using the Ontology Lookup Service as a web service is not always feasible, especially for researchers without bioinformatics support. We have therefore created a Java front end to the Ontology Lookup Service, called the OLS Dialog, which can be plugged into any application requiring the annotation of data using controlled vocabulary terms, making it possible to find and use controlled vocabulary terms without requiring any additional knowledge about web services or ontology formats.</p> <p>Conclusions</p> <p>As a user-friendly open source front end to the Ontology Lookup Service, the OLS Dialog makes it straightforward to include controlled vocabulary support in third-party tools, which ultimately makes the data even more valuable to the biomedical community.</p