Optimization of tau identification in ATLAS experiment using multivariate tools

In elementary particle physics the efficient analysis of huge amount of collected data require the use of sophisticated selection and analysis algorithms. We have implemented a Support Vector Machine (SVM) integrated with the CERN TMVA/ROOT package. SVM approach to signal and background separation is based on building a separating hyperplane defined by the support vectors. The margin between them and the hyperplane is maximized. The extensions to a non-linear separation is performed by mapping the input vectors into a high dimensional space, in which data can be linearly separated. The use of kernel functions allows to perform computations in a high dimension feature space without explicitly knowing a mapping function. Our SVM implementation is based on Platt's Sequential Minimal Optimization (SMO) algorithm and includes various kernel functions like a linear function, polynomial and Gaussian. The identification of hadronic decays of tau leptons in the ATLAS experiment using a tau1P3P package is performed using, beside the baseline cut analysis, also multivariate analysis tools: neural network, PDE_RS and our implementation of the SVM algorithm. The use and the comparison of the three algorithms is presented

AS2TS system for protein structure modeling and analysis

We present a set of programs and a website designed to facilitate protein structure comparison and protein structure modeling efforts. Our protein structure analysis and comparison services use the LGA (local-global alignment) program to search for regions of local similarity and to evaluate the level of structural similarity between compared protein structures. To facilitate the homology-based protein structure modeling process, our AL2TS service translates given sequence–structure alignment data into the standard Protein Data Bank (PDB) atom records (coordinates). For a given sequence of amino acids, the AS2TS (amino acid sequence to tertiary structure) system calculates (e.g. using PSI-BLAST PDB analysis) a list of the closest proteins from the PDB, and then a set of draft 3D models is automatically created. Web services are available at

Older-Patient-Specific Cancer Trials: A Pooled Analysis of 2,277 Patients (A151715).

BACKGROUND: Less than 3% of older patients with cancer are enrolled in clinical trials. To reverse this underrepresentation, we compared older patients enrolled with older-patient-specific trials, defined as those designed for older patients with cancer, with those enrolled in age-unspecified trials. MATERIALS AND METHODS: We focused on individual patient data from those ≥65 years (younger patients excluded) and included all Alliance phase III adjuvant breast cancer trials from 1985-2012. RESULTS: Among 2,277 patients, 1,014 had been enrolled to older-patient-specific and 1,263 to age-unspecified trials. The median age (range) in the older-patient-specific trials was 72 (65-89) years compared with 68 (65-84) years in the cohort of older patients in age-unspecified trials; CONCLUSION: Older-patient-specific trials appear to address this underrepresentation of older patients with ostensibly comparable outcomes

MannDB – A microbial database of automated protein sequence analyses and evidence integration for protein characterization

BACKGROUND: MannDB was created to meet a need for rapid, comprehensive automated protein sequence analyses to support selection of proteins suitable as targets for driving the development of reagents for pathogen or protein toxin detection. Because a large number of open-source tools were needed, it was necessary to produce a software system to scale the computations for whole-proteome analysis. Thus, we built a fully automated system for executing software tools and for storage, integration, and display of automated protein sequence analysis and annotation data. DESCRIPTION: MannDB is a relational database that organizes data resulting from fully automated, high-throughput protein-sequence analyses using open-source tools. Types of analyses provided include predictions of cleavage, chemical properties, classification, features, functional assignment, post-translational modifications, motifs, antigenicity, and secondary structure. Proteomes (lists of hypothetical and known proteins) are downloaded and parsed from Genbank and then inserted into MannDB, and annotations from SwissProt are downloaded when identifiers are found in the Genbank entry or when identical sequences are identified. Currently 36 open-source tools are run against MannDB protein sequences either on local systems or by means of batch submission to external servers. In addition, BLAST against protein entries in MvirDB, our database of microbial virulence factors, is performed. A web client browser enables viewing of computational results and downloaded annotations, and a query tool enables structured and free-text search capabilities. When available, links to external databases, including MvirDB, are provided. MannDB contains whole-proteome analyses for at least one representative organism from each category of biological threat organism listed by APHIS, CDC, HHS, NIAID, USDA, USFDA, and WHO. CONCLUSION: MannDB comprises a large number of genomes and comprehensive protein sequence analyses representing organisms listed as high-priority agents on the websites of several governmental organizations concerned with bio-terrorism. MannDB provides the user with a BLAST interface for comparison of native and non-native sequences and a query tool for conveniently selecting proteins of interest. In addition, the user has access to a web-based browser that compiles comprehensive and extensive reports. Access to MannDB is freely available at

Towards Reliable Automatic Protein Structure Alignment

A variety of methods have been proposed for structure similarity calculation, which are called structure alignment or superposition. One major shortcoming in current structure alignment algorithms is in their inherent design, which is based on local structure similarity. In this work, we propose a method to incorporate global information in obtaining optimal alignments and superpositions. Our method, when applied to optimizing the TM-score and the GDT score, produces significantly better results than current state-of-the-art protein structure alignment tools. Specifically, if the highest TM-score found by TMalign is lower than (0.6) and the highest TM-score found by one of the tested methods is higher than (0.5), there is a probability of (42%) that TMalign failed to find TM-scores higher than (0.5), while the same probability is reduced to (2%) if our method is used. This could significantly improve the accuracy of fold detection if the cutoff TM-score of (0.5) is used. In addition, existing structure alignment algorithms focus on structure similarity alone and simply ignore other important similarities, such as sequence similarity. Our approach has the capacity to incorporate multiple similarities into the scoring function. Results show that sequence similarity aids in finding high quality protein structure alignments that are more consistent with eye-examined alignments in HOMSTRAD. Even when structure similarity itself fails to find alignments with any consistency with eye-examined alignments, our method remains capable of finding alignments highly similar to, or even identical to, eye-examined alignments.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

GIS: a comprehensive source for protein structure similarities

A web service for analysis of protein structures that are sequentially or non-sequentially similar was generated. Recently, the non-sequential structure alignment algorithm GANGSTA+ was introduced. GANGSTA+ can detect non-sequential structural analogs for proteins stated to possess novel folds. Since GANGSTA+ ignores the polypeptide chain connectivity of secondary structure elements (i.e. α-helices and β-strands), it is able to detect structural similarities also between proteins whose sequences were reshuffled during evolution. GANGSTA+ was applied in an all-against-all comparison on the ASTRAL40 database (SCOP version 1.75), which consists of >10 000 protein domains yielding about 55 × 106 possible protein structure alignments. Here, we provide the resulting protein structure alignments as a public web-based service, named GANGSTA+ Internet Services (GIS). We also allow to browse the ASTRAL40 database of protein structures with GANGSTA+ relative to an externally given protein structure using different constraints to select specific results. GIS allows us to analyze protein structure families according to the SCOP classification scheme. Additionally, users can upload their own protein structures for pairwise protein structure comparison, alignment against all protein structures of the ASTRAL40 database (SCOP version 1.75) or symmetry analysis. GIS is publicly available at http://agknapp.chemie.fu-berlin.de/gplus