MSDA,a proteomics software suite for in‐depth Mass Spectrometry Data Analysis using grid computing

One of the major bottlenecks in the proteomics field today resides in the computational interpretation of the massive data generated by the latest generation of high‐throughput MS instruments. MS/MS datasets are constantly increasing in size and complexity and it becomes challenging to comprehensively process such huge datasets and afterwards deduce most relevant biological information. The Mass Spectrometry Data Analysis (MSDA, https://msda.unistra.fr ) online software suite provides a series of modules for in‐depth MS/MS data analysis. It includes a custom databases generation toolbox, modules for filtering and extracting high‐quality spectra, for running high‐performance database and de novo searches, and for extracting modified peptides spectra and functional annotations. Additionally, MSDA enables running the most computationally intensive steps, namely database and de novo searches, on a computer grid thus providing a net time gain of up to 99% for data processing.     

Finding Hamiltonian paths in tournaments on clusters

This paper presents a general methodology for the communication-efficient parallelization of graph algorithms using the divide-and-conquer approach and shows that this class of problems can be solved in cluster environments with good communication efficiency. Specifically, the first practical parallel algorithm, based on a general coarse-grained model, for finding Hamiltonian paths in tournaments is presented. On any such parallel machines, this algorithm uses only (3log p+1), where p is the number of processors, communication rounds, which is independent of the tournament size, and can reuse the existing linear-time algorithm in the sequential setting. For theoretical completeness, the algorithm is revised for fine-grained models, where the ratio of computation and communication throughputs is low or the local memory size, , of each individual processor is extremely limited for any , solving the problem with O(log p) communication rounds, while the hidden constant grows with the scalability factor 1/∊. Experiments have been carried out on a Linux cluster of 32 Sun Ultra5 computers and an SGI Origin 2000 with 32 R10000 processors. The algorithm performance on the Linux Cluster reaches 75% of the performance on the SGI Origin 2000 when the tournament size is about one million. Computational resources and technical support are provided by the Center for Computational Research (CCR) at the State University of New York at Buffalo. Chun-Hsi Huang received his Ph.D. degree in Computer Science from the State University of New York at Buffalo in 2001. His is currently an Assistant Professor of Computer Science and Engineering at the University of Connecticut. His interests include High Performance Parallel Computing, Cluster and Grid Computing, Biomedical and Health Informatics, Algorithm Design and Analysis, Experimental Algorithms and Computational Biology. Sanguthevar Rajasekaran received his Ph.D. degree in Computer Science from Harvard University in 1988. Currently he is the UTC Chair Professor of Computer Science and Engineering at the University of Connecticut and the Director of Booth Engineering Center for Advanced Technologies (BECAT). His research interests include Parallel Algorithms, Bioinformatics, Data Mining, Randomized Computing, Computer Simulations, and Combinatorial Optimization. Laurence Tianruo Yang received is Ph.D. degree in Computer Science from the Oxford University. He is currently a professor of Computer Science of the St. Francis Xavier University in Canada. His research interests include high-performance computing, embedded systems, computer archtecture and high-speed networking. Xin He received his Ph.D. degree in Computer Science from the Ohio State University in 1987. He is currently Professor of Computer Science and Engineering at the State University of New York at Buffalo. His research interests include Algorithms, Data Structures, Combinatorics and Computational Geometry.     

Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis

Glucose and xylose are the two most abundant sugars derived from the breakdown of lignocellulosic biomass. While aerobic glucose metabolism is relatively well understood in E. coli, until now there have been only a handful of studies focused on anaerobic glucose metabolism and no ¹³C-flux studies on xylose metabolism. In the absence of experimentally validated flux maps, constraint-based approaches such as MOMA and RELATCH cannot be used to guide new metabolic engineering designs. In this work, we have addressed this critical gap in current understanding by performing comprehensive characterizations of glucose and xylose metabolism under aerobic and anaerobic conditions, using recent state-of-the-art techniques in ¹³C metabolic flux analysis (¹³C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated ¹³C-MFA using the optimal tracers [1,2-¹³C]glucose, [1,6-¹³C]glucose, [1,2-¹³C]xylose and [5-¹³C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U-¹³C]glucose and [U-¹³C]xylose tracers. We demonstrated that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO₂ originates from biomass turnover. Using knockout strains, we also demonstrated that β-oxidation is critical for anaerobic growth on xylose. Quantitative analysis of co-factor balances (NADH/FADH₂, NADPH, and ATP) for different growth conditions provided new insights regarding the interplay of energy and redox metabolism and the impact on E. coli cell physiology.     

The spatial resolutions of the apposition compound eye and its neuro-sensory feature detectors: observation versus theory

For 100 years three ideas dominated efforts to understand the apposition compound eye. In Müller's theory, the eye viewed the panorama through an array of little windows without overlaps and without gaps, with no details within windows. Spatial resolution then depended on the interommatidial angle (Deltaphi) and the number of ommatidia. In the second proposal, the insect detected the temporal modulation of the light, which was limited by the aperture of the lens and the wavelength, assuming good focus. Modulation is the change of intensity in the receptor, usually caused by motion of a spatial contrast in the stimulus. Thirdly, motion was detected from the successive temporal modulations at adjacent visual axes. Recently, two more principles arose. The light-sensitive elements, called rhabdomeres, project through the nodal point of the lens to the outside world, and the resolution was limited by their grain size, like the pixels in a digital camera. Finally, detection of contrast and colour was limited by the signal/noise ratio (SNR) which was improved by brighter light and more visual pigment. These five physical principles provide satisfying explanations of eye function but they all originated from theory. Actual measurements of resolution depend on the operation of the test. The visual system of the honeybee recognizes a limited variety of simple cues, but there is no evidence that the pattern of ommatidial stimulation is re-assembled, or even seen. The known cues are: the temporal modulation of groups of receptors, the direction and angular velocity of motion, some measure of the spatial disruption of the pattern or the length of edge (related to spatial frequency and contrast), colour, the intensity, the position of the centre and the size of large well-separated areas of black or colour, the angle of orientation of a bar or grating, radial or tangential edges, and bilateral symmetry. Neurons connected to more than two adjacent ommatidia collaborate in the detection of cues, and the resolution depends on the neuro-sensory feature detectors at work at the time. Although some behavioural and electrophysiological measurements give a spatial resolution similar to the interommatidial angle, different spatial properties of neuro-sensory detectors predominate at different light intensities and with a diurnal rhythm. During the long history of this topic, the belief that the resolution ought to be Deltaphi has frequently been overturned by experimental measurement.     

Messaging on Gigabit Ethernet: Some Experiments with GAMMA and Other Systems

The Genoa Active Message MAchine (GAMMA) is a lightweight communication system based on the Active Ports paradigm, originally designed for efficient implementation over low-cost Fast Ethernet interconnects. In this paper we report about the recently completed porting of GAMMA to the Packet Engines GNIC-II and the Netgear GA620 Gigabit Ethernet adapters, and provide a comparison among GAMMA, MPI/GAMMA, TCP/IP, and MPICH, on such commodity interconnects, using different performance metrics. With a combination of low end-to-end latency (9.5 s with GNIC-II, 32 s with GA620) and high transmission throughput (almost 97 MByte/s with GNIC-II and 125 MByte/s with GA620, the latter obtained without changing the firmware of the adapter), GAMMA demonstrates the potential for Gigabit Ethernet lightweight protocols to yield messaging performance comparable to the best Myrinet-based messaging systems. This result is of interest, given the envisaged drop in cost of Gigabit Ethernet due to the transition from fiber optic to UTP cabling and ever increasing mass market production of such standard interconnect. We also reports about a technique for message fragmentation that is commonly exploited to increase the throughput with short message. When a different, though more widely used, performance metrics is considered, such a technique results into a performance loss rather than improvement.     

Dynamic Geographic Personal Data – New Opportunity and Challenge Introduced by the Location-Aware Mobile Networks

A new generation of mobile terminals, communicators and smartphones, designed for general public, are emerging from the mobile phone revolution. Their distinctive features include enhanced data capabilities and the ability to locate the user. These features make these new types of applications, location-based and location-aware services, suitable for mobile users. In addition to location-based information access they also provide excellent means for data collection about the mobile users themselves, yielding valuable dynamic and geographic personal data. This type of data has never been available to such an extent before. Availability of such data would result in new opportunities for potential users: in commerce, in the public sector and even for private use. It is also a great challenge to the information systems: in data collection, management and analysis. Privacy protection is maybe the most serious problem to be found in this context. The objective of this paper is to present the opportunities and challenges provided by dynamic geographic personal data in the context of emerging location-aware mobile networks. Here a new service is outlined in parallel with existing and planned location-based services. The new service, designed for collecting, managing and releasing dynamic geographic population data, is called Dynamic Geographic Data Service (DGDS).     

Identification of TEX101-associated Proteins Through Proteomic Measurement of Human Spermatozoa Homozygous for the Missense Variant rs35033974,

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Highlights

• •Missense variant rs35033974 resulted in significantly reduced levels of human TEX101 protein in seminal plasma and spermatozoa.
• •Differential proteomics revealed TEX101-associated testis-specific proteins, including LY6K, which were down-regulated in rs35033974^hh spermatozoa.
• •Deep proteome of human spermatozoa, including some “missing” proteins, was identified.

     

Multiple evolutionary mechanisms drive papillomavirus diversification

The circular, double-stranded 8-kb DNA genome of papillomaviruses (PVes) consists mainly of 4 large genes, E1, E2, L2, and L1. Approximately 150 papillomavirus genomes have been sequenced to date. We analyzed a representative sample of 53 PVes genomes using maximum likelihood, Bayesian inference, maximum parsimony, and distance-based methods both on nucleotide (nt) and on amino acid (aa) alignments. When the 4 genes were analyzed separately, aa-inferred phylogenies contradicted each other less than nt-inferred trees (judged by partition homogeneity tests). In particular, gene combinations including the L2 gene generated significant incongruence (P < 0.001). Combined analyses of the remaining genes E1-E2-L1 produced a well-supported phylogeny including supertaxon beta + gamma + pi + xi-PVes (infecting Artiodactyla, Carnivora, Primates, and Rodentia) and supertaxon kappa + lambda + mu + nu + sigma-PVes (infecting Carnivora, Lagomorpha, Primates, and Rodentia). Based on the tree topology, host-linked evolution appears plausible at shallow, rather than deeper, taxonomic levels. Diversification within PVes may also involve adaptive radiation establishing different niches (within a single-host species) and recombination events (within single-host cells). Heterogeneous groups of closely related PVes infecting, for example, humans and domestic animals such as hamster, dog, and cattle suggest multiple infections across species borders. Additional evolutionary phenomena such as strong codon usage preferences, and computational biases including reconstruction artifacts and insufficient taxon sampling, may contribute to the incomplete resolution of deep phylogenetic nodes. The molecular data globally supports a complex evolutionary scenario for PVes, which is driven by multiple mechanisms but not exclusively by coevolution with corresponding hosts.