Prediction and classification for GPCR sequences based on ligand specific features

Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them are orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 1 subfamilies of GPCRs, a novel method for obtaining class specific features, based on the existence of activating ligand specific patterns, has been developed and utilized for a majority voting classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 1 subfamilies of GPCRs with a high predictive accuracy between 99% and 87% in a three-fold cross validation test. The method also tells us which motifs are significant for class determination which has important design implications. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization

Pseudorapidity Distribution of Charged Particles in PbarP Collisions at root(s)= 630GeV

Using a silicon vertex detector, we measure the charged particle pseudorapidity distribution over the range 1.5 to 5.5 using data collected from PbarP collisions at root s = 630 GeV. With a data sample of 3 million events, we deduce a result with an overall normalization uncertainty of 5%, and typical bin to bin errors of a few percent. We compare our result to the measurement of UA5, and the distribution generated by the Lund Monte Carlo with default settings. This is only the second measurement at this level of precision, and only the second measurement for pseudorapidity greater than 3.Comment: 9 pages, 5 figures, LaTeX format. For ps file see http://hep1.physics.wayne.edu/harr/harr.html Submitted to Physics Letters

On the hierarchical classification of G Protein-Coupled Receptors

Motivation: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. Results: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases

Accumulation of driver and passenger mutations during tumor progression

Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. In the current study, we provide a novel mathematical model that begins to address this challenge. We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion. Using the model, we observe tremendous variation in the rate of tumor development - providing an understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians. Furthermore, the model provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor. Finally, when applied to recent experimental data, the model allows us to calculate, for the first time, the actual selective advantage provided by typical somatic mutations in human tumors in situ. This selective advantage is surprisingly small, 0.005 +- 0.0005, and has major implications for experimental cancer research

Exact solution of a two-type branching process: Clone size distribution in cell division kinetics

We study a two-type branching process which provides excellent description of experimental data on cell dynamics in skin tissue (Clayton et al., 2007). The model involves only a single type of progenitor cell, and does not require support from a self-renewed population of stem cells. The progenitor cells divide and may differentiate into post-mitotic cells. We derive an exact solution of this model in terms of generating functions for the total number of cells, and for the number of cells of different types. We also deduce large time asymptotic behaviors drawing on our exact results, and on an independent diffusion approximation.Comment: 16 page

A Hybrid Likelihood Model for Sequence-Based Disease Association Studies

In the past few years, case-control studies of common diseases have shifted their focus from single genes to whole exomes. New sequencing technologies now routinely detect hundreds of thousands of sequence variants in a single study, many of which are rare or even novel. The limitation of classical single-marker association analysis for rare variants has been a challenge in such studies. A new generation of statistical methods for case-control association studies has been developed to meet this challenge. A common approach to association analysis of rare variants is the burden-style collapsing methods to combine rare variant data within individuals across or within genes. Here, we propose a new hybrid likelihood model that combines a burden test with a test of the position distribution of variants. In extensive simulations and on empirical data from the Dallas Heart Study, the new model demonstrates consistently good power, in particular when applied to a gene set (e.g., multiple candidate genes with shared biological function or pathway), when rare variants cluster in key functional regions of a gene, and when protective variants are present. When applied to data from an ongoing sequencing study of bipolar disorder (191 cases, 107 controls), the model identifies seven gene sets with nominal p-values<0.05, of which one MAPK signaling pathway (KEGG) reaches trend-level significance after correcting for multiple testing. © 2013 Chen et al

Atomic mass dependence of \Xi^- and \overline{\Xi}^+ production in central 250 GeV \pi^- nucleon interactions

We present the first measurement of the atomic mass dependence of central \Xi^- and \overline{\Xi}^+ production. It is measured using a sample of 22,459 \Xi^-'s and \overline{\Xi}^+'s produced in collisions between a 250 GeV \pi^- beam and targets of beryllium, aluminum, copper, and tungsten. The relative cross sections are fit to the two parameter function \sigma_0 A^\alpha, where A is the atomic mass. We measure \alpha = 0.924+-0.020+-0.025, for Feynman-x in the range -0.09 < x_F < 0.15.Comment: 10 pages, revtex, 2 figures, submitted to Phys. Rev.

Construction and Performance of Large-Area Triple-GEM Prototypes for Future Upgrades of the CMS Forward Muon System

At present, part of the forward RPC muon system of the CMS detector at the CERN LHC remains uninstrumented in the high-\eta region. An international collaboration is investigating the possibility of covering the 1.6 < |\eta| < 2.4 region of the muon endcaps with large-area triple-GEM detectors. Given their good spatial resolution, high rate capability, and radiation hardness, these micro-pattern gas detectors are an appealing option for simultaneously enhancing muon tracking and triggering capabilities in a future upgrade of the CMS detector. A general overview of this feasibility study will be presented. The design and construction of small (10\times10 cm2) and full-size trapezoidal (1\times0.5 m2) triple-GEM prototypes will be described. During detector assembly, different techniques for stretching the GEM foils were tested. Results from measurements with x-rays and from test beam campaigns at the CERN SPS will be shown for the small and large prototypes. Preliminary simulation studies on the expected muon reconstruction and trigger performances of this proposed upgraded muon system will be reported.Comment: 7 pages, 25 figures, submitted for publication in conference record of the 2011 IEEE Nuclear Science Symposium, Valencia, Spai

Development and performance of Triple-GEM detectors for the upgrade of the muon system of the CMS experiment

The CMS Collaboration is evaluating GEM detectors for the upgrade of the muon system. This contribution will focus on the R&D performed on chambers design features and will discuss the performance of the upgraded detector

A novel application of Fiber Bragg Grating (FBG) sensors in MPGD

We present a novel application of Fiber Bragg Grating (FBG) sensors in the construction and characterisation of Micro Pattern Gaseous Detector (MPGD), with particular attention to the realisation of the largest triple (Gas electron Multiplier) GEM chambers so far operated, the GE1/1 chambers of the CMS experiment at LHC. The GE1/1 CMS project consists of 144 GEM chambers of about 0.5 m2 active area each, employing three GEM foils per chamber, to be installed in the forward region of the CMS endcap during the long shutdown of LHC in 2108-2019. The large active area of each GE1/1 chamber consists of GEM foils that are mechanically stretched in order to secure their flatness and the consequent uniform performance of the GE1/1 chamber across its whole active surface. So far FBGs have been used in high energy physics mainly as high precision positioning and re-positioning sensors and as low cost, easy to mount, low space consuming temperature sensors. FBGs are also commonly used for very precise strain measurements in material studies. In this work we present a novel use of FBGs as flatness and mechanical tensioning sensors applied to the wide GEM foils of the GE1/1 chambers. A network of FBG sensors have been used to determine the optimal mechanical tension applied and to characterise the mechanical tension that should be applied to the foils. We discuss the results of the test done on a full-sized GE1/1 final prototype, the studies done to fully characterise the GEM material, how this information was used to define a standard assembly procedure and possible future developments.Comment: 4 pages, 4 figures, presented by Luigi Benussi at MPGD 2015 (Trieste, Italy). arXiv admin note: text overlap with arXiv:1512.0848