Quantification of miRNAs and Their Networks in the light of Integral Value Transformations

MicroRNAs (miRNAs) which are on average only 21-25 nucleotides long are key post-transcriptional regulators of gene expression in metazoans and plants. A proper quantitative understanding of miRNAs is required to comprehend their structures, functions, evolutions etc. In this paper, the nucleotide strings of miRNAs of three organisms namely Homo sapiens (hsa), Macaca mulatta (mml) and Pan troglodytes (ptr) have been quantified and classified based on some characterizing features. A network has been built up among the miRNAs for these three organisms through a class of discrete transformations namely Integral Value Transformations (IVTs), proposed by Sk. S. Hassan et al [1, 2]. Through this study we have been able to nullify or justify one given nucleotide string as a miRNA. This study will help us to recognize a given nucleotide string as a probable miRNA, without the requirement of any conventional biological experiment. This method can be amalgamated with the existing analysis pipelines, for small RNA sequencing data (designed for finding novel miRNA). This method would provide more confidence and would make the current analysis pipeline more efficient in predicting the probable candidates of miRNA for biological validation and filter out the improbable candidates

Proteomics in the Light of Integral Value Transformations

In this paper, Proteomics have been studied in the light of Integral Value Transformations (IVTs) which was introduced by Sk. S. Hassan et al in 2010. For case study, a Human olfactory receptor OR1D2 protein sequence has been taken and then different IVTs have been used to evolve OR1D2 into some other proteomic like sequences. It has been observed that some of the generated sequences have been mapped to another olfactory receptor in Human or in some other species. Also it has been corroborated through fractal dimension that some of the fundamental protein properties have been nearly intact, even after the mapping. This study will help to comprehend the proteomic evolutionary network with the help of IVTs

Semantically Resolving Type Mismatches in Scientific Workflows

Scientists are increasingly utilizing Grids to manage large data sets and execute scientific experiments on distributed resources. Scientific workflows are used as means for modeling and enacting scientific experiments. Windows Workflow Foundation (WF) is a major component of Microsoft’s .NET technology which offers lightweight support for long-running workflows. It provides a comfortable graphical and programmatic environment for the development of extended BPEL-style workflows. WF’s visual features ease the syntactic composition of Web services into scientific workflows but do nothing to assure that information passed between services has consistent semantic types or representations or that deviant flows, errors and compensations are handled meaningfully. In this paper we introduce SAWSDL-compliant annotations for WF and use them with a semantic reasoner to guarantee semantic type correctness in scientific workflows. Examples from bioinformatics are presented

Comparative proteomics of uropathogenic Escherichia coli during growth in human urine identify UCA-like (UCL) fimbriae as an adherence factor involved in biofilm formation and binding to uroepithelial cells

Uropathogenic Escherichia coli (UPEC) are the primary cause of urinary tract infection (UTI) in humans. For the successful colonisation of the human urinary tract, UPEC employ a diverse collection of secreted or surface-exposed virulence factors including toxins, iron acquisition systems and adhesins. In this study, a comparative proteomic approach was utilised to define the UPEC pan and core surface proteome following growth in pooled human urine. Identified proteins were investigated for subcellular origin, prevalence and homology to characterised virulence factors. Fourteen core surface proteins were identified, as well as eleven iron uptake receptor proteins and four distinct fimbrial types, including type 1, P, F1C/S and a previously uncharacterised fimbrial type, designated UCA-like (UCL) fimbriae in this study. These pathogenicity island (PAI)-associated fimbriae are related to UCA fimbriae of Proteus mirabilis, associated with UPEC and exclusively found in members of the E. coli B2 and D phylogroup. We further demonstrated that UCL fimbriae promote significant biofilm formation on abiotic surfaces and mediate specific attachment to exfoliated human uroepithelial cells. Combined, this study has defined the surface proteomic profiles and core surface proteome of UPEC during growth in human urine and identified a new type of fimbriae that may contribute to UTI

Initial steps towards a production platform for DNA sequence analysis on the grid

ABSTRACT: BACKGROUND: Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users. RESULTS: In this study we reused a platform that was developed in the VL-e project for the analysis of medical images. Data transfer, workflow execution and job monitoring are operated from one graphical interface. We developed workflows for two sequence alignment tools (BLAST and BLAT) as a proof of concept. The analysis time was signicantly reduced. All workflows and executables are available for the members of the Dutch Life Science Grid and the VL-e Medical virtual organizations. All components are open source and can be transported to other grid infrastructures. CONCLUSIONS: The availability of in-house expertise and tools facilitates the usage of grid resources by new users. Our first results indicate that this is a practical, powerful and scalable solution to address the capacity and collaboration issues raised by the deployment of next generation sequencers. We currently adopt this methodology on a daily basis for DNA sequencing and other applications. More information and source code is available via http://www.bioinformaticslaboratory.nl

Collaborative Computation in Self-Organizing Particle Systems

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms

Principal manifolds and graphs in practice: from molecular biology to dynamical systems

We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.Comment: 12 pages, 9 figure

Two intracellular and cell type-specific bacterial symbionts in the placozoan Trichoplax H2

Placozoa is an enigmatic phylum of simple, microscopic, marine metazoans(1,2). Although intracellular bacteria have been found in all members of this phylum, almost nothing is known about their identity, location and interactions with their host(3-6). We used metagenomic and metatranscriptomic sequencing of single host individuals, plus metaproteomic and imaging analyses, to show that the placozoan Trichoplax sp. H2 lives in symbiosis with two intracellular bacteria. One symbiont forms an undescribed genus in the Midichloriaceae (Rickettsiales)(7,8) and has a genomic repertoire similar to that of rickettsial parasites(9,10), but does not seem to express key genes for energy parasitism. Correlative image analyses and three-dimensional electron tomography revealed that this symbiont resides in the rough endoplasmic reticulum of its host's internal fibre cells. The second symbiont belongs to the Margulisbacteria, a phylum without cultured representatives and not known to form intracellular associations(11-13). This symbiont lives in the ventral epithelial cells of Trichoplax, probably metabolizes algal lipids digested by its host and has the capacity to supplement the placozoan's nutrition. Our study shows that one of the simplest animals has evolved highly specific and intimate associations with symbiotic, intracellular bacteria and highlights that symbioses can provide access to otherwise elusive microbial dark matter