OpenMS – An open-source software framework for mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.</p> <p>Results</p> <p>We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.</p> <p>Conclusion</p> <p>OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at <url>http://www.openms.de</url>.</p

LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.</p> <p>Results</p> <p>We present <it>LC-MSsim</it>, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, <it>LC-MSsim </it>writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.</p> <p>Conclusion</p> <p><it>LC-MSsim </it>generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that <it>LC-MSsim </it>will be useful to the wider community to perform benchmark studies and comparisons between computational tools.</p

Binary decision diagrams for random boolean fucntions

Binary Decision Diagrams (BDDs) are a data structure for Boolean functions which are also known as branching programs. In ordered binary decision diagrams (OBDDs), the tests have to obey a fixed variable ordering. In free binary decision diagrams (FBDDs), each variable can be tested at most once. The efficiency of new variants of the BDD concept is usually demonstrated with spectacular (worst-case) examples. We pursue another approach and compare the representation sizes of almost all Boolean functions. Whereas I. Wegener proved that for `most&apos; values of n the expected OBDD size of a random Boolean function of n variables is equal to the worst-case size up to terms of lower order, we show that this is not the case for n within intervals of constant length around the values n = 2 h + h. Furthermore, ranges of n exist for which minimal FBDDs are almost always at least a constant factor smaller than minimal OBDDs. Our main theorems have doubly exponentially small probability bounds (in ..

An Algorithm for Feature Finding in LC/MS Raw Data

Abstract. Liquid chromatography coupled with mass spectrometry is an established method in shotgun proteomics. A key step in the data processing pipeline is to transform the raw data acquired by the mass spectrometer into a list of features. In this context, a feature is defined as the twodimensional integration with respect to retention time (RT) and mass-over-charge (m/z) of the eluting signal belonging to a single charge variant of a measurand (e. g., a peptide). Features are described by attributes like average mass-to-charge ratio, centroid retention time, intensity, and quality. We present a new algorithm for feature finding which has been developed as a part of a combined experimental and algorithmic approach to absolutely quantify proteins from complex samples with unprecedented precision. The method was applied to the analysis of myoglobin in human blood serum, which is an important diagnostic marker for myocardial infarction. Our approach was able to determine the absolute amount of myoglobin in a serum sample through a series of standard addition experiments with a relative error of 2.5%. It compares favorably to a manual analysis of the same data set since we could improve the precision and conduct the whole analysis pipeline in a small fraction of the time. We anticipate that our automatic quantitation method will facilitate further absolute or relative quantitation of even more complex peptide samples. The algorithm was implemented in the publicly available software framework OpenMS (www.OpenMS.de

Ordered Binary Decision Diagrams and the Shannon Effect

We investigate the size and structure of ordered binary decision diagrams (OBDDs) for random Boolean functions. It was known that for most values of n, the expected OBDD size of a random Boolean function with n variables is equal to the worst-case size up to terms of lower order. Such a phenomenon is generally called strong Shannon effect. Here we show that the strong Shannon effect is not valid for all n. Instead it undergoes a certain periodic ‘phase transition’: If n lies within intervals of constant width around the values n = 2 h + h, then the strong Shannon effect does not hold, whereas it does hold outside these intervals. Our analysis provides doubly exponential probability bounds and generalises to ordered Kronecker functional decision diagrams (OKFDDs)

On the Evolution of the Worst-Case OBDD Size

We prove matching lower and upper bounds on the worst-case OBDD size of a Boolean function, revealing an interesting oscillating behavior

Séminaire Lotharingien de Combinatoire 54A (2007), Article B54Ak A DIRECT DECOMPOSITION OF 3-CONNECTED PLANAR GRAPHS

ABSTRACT. We present a decomposition strategy for c-nets, i. e., rooted 3-connected planar maps. The decomposition yields an algebraic equation for the number of c-nets with a given number of vertices and a given size of the outer face. The decomposition also leads to a deterministic and polynomial time algorithm to sample c-nets uniformly at random. Using rejection sampling, we can also sample isomorphism types of convex polyhedra, i.e., 3-connected planar graphs, uniformly at random. RÉSUMÉ. Nous proposons une stratégie de décomposition pour les cartes pointées 3connexes (c-réseaux). Cette décomposition permet d’obtenir une équation algébrique pour le nombre de c-réseaux suivant le nombre de sommets et la taille de la face extèrieure. On en déduit un algorithme de complexité en temps polynomiale pour le tirage aléatoire uniforme des c-réseaux. En utilisant une méthode à rejet, nous obtenons aussi un algorithme de tirage aléatoire uniforme pour les graphes planaires 3-connexes. 1

Size and Structure of Random Ordered Binary Decision Diagrams (Extended Abstract)

We investigate the size and structure of ordered binary decision diagrams (OBDDs) for random Boolean functions. Wegener [Weg94] proved that for &quot;most&quot; values of n , the expected OBDD-size of a random Boolean function with n variables equals the worst-case size up to terms of lower order. Our main result is that this phenomenon, also known as strong Shannon effect, shows a threshold behaviour: The strong Shannon effect does not hold within intervals of constant width around the values n = 2 h +h , but it does hold outside these intervals. Also, the oscillation of the expected and the worst-case size is described. Methodical innovations of our approach are a functional equation to locate &quot;critical levels&quot; in OBDDs and the use of Azuma&apos;s martingale inequality and Chvatal&apos;s large deviation inequality for the hyperg..