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BMC Structural Biology - Latest Articles   [more] [xml]
 2014-10-18T00:00:00Z A PDB-wide, evolution-based assessment of protein¿protein interfaces
Background: Thanks to the growth in sequence and structure databases, more than 50 million sequences are now available in UniProt and 100,000 structures in the PDB. Rich information about protein?protein interfaces can be obtained by a comprehensive study of protein contacts in the PDB, their sequence conservation and geometric features. Results: An automated computational pipeline was developed to run our Evolutionary Protein?Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces. This allows the analysis of interface data on a PDB-wide scale. Two large benchmark datasets of biological interfaces and crystal contacts, each containing about 3000 entries, were automatically generated based on criteria thought to be strong indicators of interface type. The BioMany set of biological interfaces includes NMR dimers solved as crystal structures and interfaces that are preserved across diverse crystal forms, as catalogued by the Protein Common Interface Database (ProtCID) from Xu and Dunbrack. The second dataset, XtalMany, is derived from interfaces that would lead to infinite assemblies and are therefore crystal contacts. BioMany and XtalMany were used to benchmark the EPPIC approach. The performance of EPPIC was also compared to classifications from the Protein Interfaces, Surfaces, and Assemblies (PISA) program on a PDB-wide scale, finding that the two approaches give the same call in about 85% of PDB interfaces. By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB. Additionally, we developed a PyMOL plugin for direct download and easy visualization of EPPIC interfaces for any PDB entry. Both the datasets and the PyMOL plugin are available at http://www.eppic-web.org/ewui/\#downloads. Conclusions: Our computational pipeline allows us to analyze protein?protein contacts and their sequence conservation across the entire PDB. Two new benchmark datasets are provided, which are over an order of magnitude larger than existing manually curated ones. These tools enable the comprehensive study of several aspects of protein?protein contacts in the PDB and represent a basis for future, even larger scale studies of protein?protein interactions.


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BMC Bioinformatics - Latest Articles   [more] [xml]
 2014-10-31T00:00:00Z CIG-P: Cicular Interaction Graph for Proteomics
Background: A typical affinity purification coupled to mass spectrometry (AP-MS) experiment includes the purification of a target protein (bait) using an antibody and subsequent mass spectrometry analysis of all proteins co-purifying with the bait (aka prey proteins). Like any other systems biology approach, AP-MS experiments generate a lot of data and visualization has been challenging, especially when integrating AP-MS experiments with orthogonal datasets. Results: We present Circular Interaction Graph for Proteomics (CIG-P), which generates circular diagrams for visually appealing final representation of AP-MS data. Through a Java based GUI, the user inputs experimental and reference data as file in csv format. The resulting circular representation can be manipulated live within the GUI before exporting the diagram as vector graphic in pdf format. The strength of CIG-P is the ability to integrate orthogonal datasets with each other, e.g. affinity purification data of kinase PRPF4B in relation to the functional components of the spliceosome. Further, various AP-MS experiments can be compared to each other. Conclusions: CIG-P aids to present AP-MS data to a wider audience and we envision that the tool finds other applications too, e.g. kinase - substrate relationships as a function of perturbation. CIG-P is available under: http://sourceforge.net/projects/cig-p/
 2014-10-30T00:00:00Z SketchBio: a scientist's 3D interface for molecular modeling and animation
Background: Because of the difficulties involved in learning and using 3D modeling and rendering software,many scientists hire programmers or animators to create models and animations. This both slowsthe discovery process and provides opportunities for miscommunication. Working with multiplecollaborators, a tool was developed (based on a set of design goals) to enable them to directly constructmodels and animations. Results: SketchBio is presented, a tool that incorporates state-of-the-art bimanual interaction and drop shadowsto enable rapid construction of molecular structures and animations. It includes three novel features:crystal-by-example, pose-mode physics, and spring-based layout that accelerate operations commonin the formation of molecular models. Design decisions and their consequences are presented,including cases where iterative design was required to produce effective approaches. Conclusions: The design decisions, novel features, and inclusion of state-of-the-art techniques enabled SketchBioto meet all of its design goals. These features and decisions can be incorporated into existing and newtools to improve their effectiveness.
 2014-10-29T12:00:00Z A p-Median approach for predicting drug response in tumour cells
Background: The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. Results: The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs. Conclusion: The proposed learning framework represents a promising approach predicting drug response in tumour cells.
 2014-10-25T12:00:00Z Improving the accuracy of expression data analysis in time course experiments using resampling
Background: As time series experiments in higher eukaryotes usually obtain data from different individuals collected at the different time points, a time series sample itself is not equivalent to a true biological replicate but is, rather, a combination of several biological replicates. The analysis of expression data derived from a time series sample is therefore often performed with a low number of replicates due to budget limitations or limitations in sample availability. In addition, most algorithms developed to identify specific patterns in time series dataset do not consider biological variation in samples collected at the same conditions. Results: Using artificial time course datasets, we show that resampling considerably improves the accuracy of transcripts identified as rhythmic. In particular, the number of false positives can be greatly reduced while at the same time the number of true positives can be maintained in the range of other methods currently used to determine rhythmically expressed genes. Conclusions: The resampling approach described here therefore increases the accuracy of time series expression data analysis and furthermore emphasizes the importance of biological replicates in identifying oscillating genes. Resampling can be used for any time series expression dataset as long as the samples are acquired from independent individuals at each time point.
 2014-10-24T00:00:00Z FastMG: a simple, fast, and accurate maximum likelihood procedure to estimate amino acid replacement rate matrices from large data sets
Background: Amino acid replacement rate matrices are a crucial component of many protein analysis systems such as sequence similarity search, sequence alignment, and phylogenetic inference. Ideally, the rate matrix reflects the mutational behavior of the actual data under study; however, estimating amino acid replacement rate matrices requires large protein alignments and is computationally expensive and complex. As a compromise, sub-optimal pre-calculated generic matrices are typically used for protein-based phylogeny. Sequence availability has now grown to a point where problem-specific rate matrices can often be calculated if the computational cost can be controlled. Results: The most time consuming step in estimating rate matrices by maximum likelihood is building maximum likelihood phylogenetic trees from protein alignments. We propose a new procedure, called FastMG, to overcome this obstacle. The key innovation is the alignment-splitting algorithm that splits alignments with many sequences into non-overlapping sub-alignments prior to estimating amino acid replacement rates. Experiments with different large data sets showed that the FastMG procedure was an order of magnitude faster than without splitting. Importantly, there was no apparent loss in matrix quality if an appropriate splitting procedure is used. Conclusions: FastMG is a simple, fast and accurate procedure to estimate amino acid replacement rate matrices from large data sets. It enables researchers to study the evolutionary relationships for specific groups of proteins or taxa with optimized, data-specific amino acid replacement rate matrices. The programs, data sets, and the new mammalian mitochondrial protein rate matrix are available at http://fastmg.codeplex.com.
 2014-10-24T00:00:00Z Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies
Background: Infections are often associated to comorbidity that increases the risk of medical conditions whichcan lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but thecomorbidity is the key aspect to underline their different impacts. One UK doctor says "I'd rather haveHIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However,HIV has a comorbidity impact on the diabetes. Results: We present a quantitative framework to compare and explore comorbidity between diseases. By usingneighbourhood based benchmark and topological methods, we have built comorbidity relationshipsnetwork based on the OMIM and our identified significant genes. Then based on the gene expression,PPI and signalling pathways data, we investigate the comorbidity association of these 2 infectivepathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerativedisorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured bycalculating both the Relative Risk as the quantified measures of comorbidity tendency of two diseasepairs and the ¿-correlation to measure the robustness of the comorbidity associations. The differentialgene expression profiling strongly suggests that the response of SARS affected patients seems tobe mainly an innate inflammatory response and statistically dysregulates a large number of genes,pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes),breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationshipwith many other diseases, particularly strong correlation with the neurological, cancer, metabolicand immunological diseases. Similar comorbidities risk is observed from the clinical information.Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV andSARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes arecommon between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immuneresponsiveness implicated in susceptibility to infectious and inflammatory diseases. Conclusions: Our method presents a ripe opportunity to use data-driven approaches for advancing our currentknowledge on disease mechanism and predicting disease comorbidities in a quantitative way.
 2014-10-22T00:00:00Z SummonChimera infers integrated viral genomes with nucleotide precision from NGS data
Background: Viral integration into a host genome is defined by two chimeric junctions that join viral and host DNA. Recently, computational tools have been developed that utilize NGS data to detect chimeric junctions. These methods identify individual viral-host junctions but do not associate chimeric pairs as an integration event. Without knowing the chimeric boundaries of an integration, its genetic content cannot be determined. Results: Summonchimera is a Perl program that associates chimera pairs to infer the complete viral genomic integration event to the nucleotide level within single or paired-end NGS data. SummonChimera integration prediction was verified on a set of single-end IonTorrent reads from a purified Salmonella bacterium with an integrated bacteriophage. Furthermore, SummonChimera predicted integrations from experimentally verified Hepatitis B Virus chimeras within a paired-end Whole Genome Sequencing hepatocellular carcinoma tumor database. Conclusions: SummonChimera identified all experimentally verified chimeras detected by current computational methods. Further, SummonChimera integration inference precisely predicted bacteriophage integration. The application of SummonChimera to cancer NGS accurately identifies deletion of host and viral sequence during integration. The precise nucleotide determination of an integration allows prediction of viral and cellular gene transcription patterns.
 2014-10-21T00:00:00Z A comparative study of cell classifiers for image-based high-throughput screening
Background: Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells. Results: For the HT29 data set we find that gentle boosting, SVM (linear) and SVM (RBF) are close in performance but SVM (linear) is faster than gentle boosting and SVM (RBF). For the HT29 data set the average performance difference between SVM (RBF) and SVM (linear) is 0.42%. For the HeLa data set we find that SVM (RBF) outperforms other classification methods and is on average 1.41% better in performance than SVM (linear). Conclusions: Our study proposes SVM (linear) for iterative improvement of the training data and SVM (RBF) for the final classifier to classify all unlabeled cells in the whole data set.
 2014-10-18T00:00:00Z Bison: bisulfite alignment on nodes of a cluster
Background: DNA methylation changes are associated with a wide array of biological processes. Bisulfite conversion of DNA followed by high-throughput sequencing is increasingly being used to assess genome-wide methylation at single-base resolution. The relative slowness of most commonly used aligners for processing such data introduces an unnecessarily long delay between receipt of raw data and statistical analysis. While this process can be sped-up by using computer clusters, current tools are not designed with them in mind and end-users must create such implementations themselves. Results: Here, we present a novel BS-seq aligner, Bison, which exploits multiple nodes of a computer cluster to speed up this process and also has increased accuracy. Bison is accompanied by a variety of helper programs and scripts to ease, as much as possible, the process of quality control and preparing results for statistical analysis by a variety of popular R packages. Bison is also accompanied by bison_herd, a variant of Bison with the same output but that can scale to a semi-arbitrary number of nodes, with concomitant increased demands on the underlying message passing interface implementation. Conclusions: Bison is a new bisulfite-converted short-read aligner providing end users easier scalability for performance gains, more accurate alignments, and a convenient pathway for quality controlling alignments and converting methylation calls into a form appropriate for statistical analysis. Bison and the more scalable bison_herd are natively able to utilize multiple nodes of a computer cluster simultaneously and serve to simplify to the process of creating analysis pipelines.
 2014-10-16T00:00:00Z Detection of internal exon deletion with exon Del
Background: Exome sequencing allows researchers to study the human genome in unprecedented detail. Among the many types of variants detectable through exome sequencing, one of the most over looked types of mutation is internal deletion of exons. Internal exon deletions are the absence of consecutive exons in a gene. Such deletions have potentially significant biological meaning, and they are often too short to be considered copy number variation. Therefore, to the need for efficient detection of such deletions using exome sequencing data exists. Results: We present ExonDel, a tool specially designed to detect homozygous exon deletions efficiently. We tested ExonDel on exome sequencing data generated from 16 breast cancer cell lines and identified both novel and known IEDs. Subsequently, we verified our findings using RNAseq and PCR technologies. Further comparisons with multiple sequencing-based CNV tools showed that ExonDel is capable of detecting unique IEDs not found by other CNV tools. Conclusions: ExonDel is an efficient way to screen for novel and known IEDs using exome sequencing data. ExonDel and its source code can be downloaded freely at https://github.com/slzhao/ExonDel.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-10-31T00:00:00Z Omics technologies provide new insights into the molecular physiopathology of equine osteochondrosis
Background: Osteochondrosis (OC(D)) is a juvenile osteo-articular disorder affecting several mammalian species. In horses, OC(D) is considered as a multifactorial disease and has been described as a focal disruption of endochondral ossification leading to the development of osteoarticular lesions. Nevertheless, OC(D) physiopathology is poorly understood. Affected horses may present joint swelling, stiffness and lameness. Thus, OC(D) is a major concern for the equine industry. Our study was designed as an integrative approach using omics technologies for the identification of constitutive defects in epiphyseal cartilage and/or subchondral bone associated with the development of primary lesions to further understand OC(D) pathology. This study compared samples from non-affected joints (hence lesion-free) from OC(D)-affected foals (n = 5, considered predisposed samples) with samples from OC-free foals (n = 5) considered as control samples. Consequently, results are not confounded by changes associated with the evolution of the lesion, but focus on altered constitutive molecular mechanisms. Comparative proteomics and micro computed tomography analyses were performed on predisposed and OC-free bone and cartilage samples. Metabolomics was also performed on synovial fluid from OC-free, OC(D)-affected and predisposed joints. Results: Two lesion subtypes were identified: OCD (lesion with fragment) and OC (osteochondral defects). Modulated proteins were identified using omics technologies (metabolomics and proteomics) in cartilage and bone from affected foals compare to OC-free foals. These were associated with cellular processes including cell cycle, energy production, cell signaling and adhesion as well as tissue-specific processes such as chondrocyte maturation, extracellular matrix and mineral metabolism. Of these, five had already been identified in synovial fluid of OC-affected foals: ACTG1 (actin, gamma 1), albumin, haptoglobin, FBG (fibrinogen beta chain) and C4BPA (complement component 4 binding protein, alpha). Conclusion: This study suggests that OCD lesions may result from a cartilage defect whereas OC lesions may be triggered by both bone and cartilage defects, suggesting that different molecular mechanisms responsible for the equine osteochondrosis lesion subtypes and predisposition could be due to a defect in both bone and cartilage. This study will contribute to refining the definition of OC(D) lesions and may improve diagnosis and development of therapies for horses and other species, including humans.


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BMC Biochemistry - Latest Articles   [more] [xml]
 2014-10-09T00:00:00Z Alleviation effect of arbutin on oxidative stress generated through tyrosinase reaction with l-tyrosine and l-DOPA
Background: Hydroxyl radical that has the highest reactivity among reactive oxygen species (ROS) is generated through l-tyrosine-tyrosinase reaction. Thus, the melanogenesis might induce oxidative stress in the skin. Arbutin (p-hydroxyphenyl-β-d-glucopyranoside), a well-known tyrosinase inhibitor has been widely used for the purpose of skin whitening. The aim of the present study was to examine if arbutin could suppress the hydroxyl radical generation via tyrosinase reaction with its substrates, l-tyrosine and l-DOPA. Results: The hydroxyl radical, which was determined by an electron spin resonance-spin trapping technique, was generated by the addition of not only l-tyrosine but l-DOPA to tyrosinase in a concentration dependent manner. Arbutin could inhibit the hydroxyl radical generation in the both reactions. Conclusion: It is presumed that arbutin could alleviate oxidative stress derived from the melanogenic pathway in the skin in addition to its function as a whitening agent in cosmetics.


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Nature   [more] [xml]
 2005-01-19 Einstein is dead
Until its next revolution, much of the glory of physics will be in engineering. It is a shame that the physicists who do so much of it keep so quiet about it.

Einstein is dead

Nature 433, 179 (2005). doi:10.1038/433179a

Until its next revolution, much of the glory of physics will be in engineering. It is a shame that the physicists who do so much of it keep so quiet about it.



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Science: Current Issue   [more] [xml]
 2014-10-31 [Editorial] Planet at the crossroads
When we think of nature in 2014, chances are that protected areas come to mind: Amazonian rainforests teeming with wildlife, the sweeping plains of the Serengeti, or an Alpine lake surrounded by glaciers. But the world's protected areas are at a crossroads, and next month, when the International Union for Conservation of Nature (IUCN) convenes its once-in-a-decade World Parks Congress in Sydney, Australia, nations will discuss how to address the challenges in protecting ecosystems across the world for the benefit of humanity. Author: Julia Marton-Lefèvre

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