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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]
 2014-10-22T00:00:00Z 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.
 2014-10-14T00:00:00Z Interactively Illustrating Polymerization using Three-level Model Fusion
Background: Research in cell biology is steadily contributing new knowledge about many aspects of physiologicalprocesses, both with respect to the involved molecular structures as well as their related function.Illustrations of the spatio-temporal development of such processes are not only used in biomedicaleducation, but also can serve scientists as an additional platform for in-silico experiments. Results: In this paper, we contribute a new, three-level modeling approach to illustrate physiological processesfrom the class of polymerization at different time scales. We integrate physical and empirical modeling,according to which approach best suits the different involved levels of detail, and we additionallyenable a form of interactive steering, while the process is illustrated. We demonstrate the suitabilityof our approach in the context of several polymerization processes and report from a first evaluationwith domain experts. Conclusion: We conclude that our approach provides a new, hybrid modeling approach for illustrating the processof emergence in physiology, embedded in a densely filled environment. Our approach of a complementaryfusion of three systems combines the strong points from the different modeling approachesand is capable to bridge different spatial and temporal scales.
 2014-10-13T00:00:00Z trieFinder: an efficient program for annotating digital gene expression (DGE) tags
Background: Quantification of a transcriptional profile is a useful way to evaluate the activity of a cell at a given point in time. Although RNA-Seq has revolutionized transcriptional profiling, the costs of RNA-Seq are still significantly higher than microarrays, and often the depth of data delivered from RNA-Seq is in excess of what is needed for simple transcript quantification. Digital Gene Expression (DGE) is a cost-effective, sequence-based approach for simple transcript quantification: by sequencing one read per molecule of RNA, this technique can be used to efficiently count transcripts while obviating the need for transcript-length normalization and reducing the total numbers of reads necessary for accurate quantification. Here, we present trieFinder, a program specifically designed to rapidly map, parse, and annotate DGE tags of various lengths against cDNA and/or genomic sequence databases. Results: The trieFinder algorithm maps DGE tags in a two-step process. First, it scans FASTA files of RefSeq, UniGene, and genomic DNA sequences to create a database of all tags that can be derived from a predefined restriction site. Next, it compares the experimental DGE tags to this tag database, taking advantage of the fact that the tags are stored as a prefix tree, or "trie," which allows for linear-time searches for exact matches. DGE tags with mismatches are analyzed by recursive calls in the data structure. We find that, in terms of alignment speed, the mapping functionality of trieFinder compares favorably with Bowtie. Conclusions: trieFinder can quickly provide the user an annotation of the DGE tags from three sources simultaneously, simplifying transcript quantification and novel transcript detection, delivering the data in a simple parsed format, obviating the need to post-process the alignment results. trieFinder is available at https://github.com/grenaud/trieFinder.
 2014-10-05T00:00:00Z An algorithm of discovering signatures from DNA databases on a computer cluster
Background: Signatures are short sequences that are unique and not similar to any other sequence in a databasethat can be used as the basis to identify different species. Even though several signature discoveryalgorithms have been proposed in the past, these algorithms require the entirety of databases to beloaded in the memory, thus restricting the amount of data that they can process. It makes thosealgorithms unable to process databases with large amounts of data. Also, those algorithms usesequential models and have slower discovery speeds, meaning that the efficiency can be improved. Results: In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discoveryand have proposed a parallel signature discovery algorithm on a computer cluster. The algorithmapplies the divide-and-conquer strategy to solve the problem posed to the existing algorithms wherethey are unable to process large databases and uses a parallel computing mechanism to effectivelyimprove the efficiency of signature discovery. Even when run with just the memory of regular personalcomputers, the algorithm can still process large databases such as the human whole-genome ESTdatabase which were previously unable to be processed by the existing algorithms. Conclusions: The algorithm proposed in this research is not limited by the amount of usable memory and canrapidly find signatures in large databases, making it useful in applications such as Next GenerationSequencing and other large database analysis and processing. The implementation of the proposedalgorithm is available at http://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm.
 2014-10-04T00:00:00Z Systematic exploration of guide-tree topology effects for small protein alignments
Background: Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods. Results: We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments. Conclusions: Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.
 2014-10-04T00:00:00Z Systematic identification of transcriptional and post-transcriptional regulations in human respiratory epithelial cells during influenza A virus infection
Background: Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks. Results: Here we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs. Conclusion: Compared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection.
 2014-10-04T00:00:00Z Detecting temporal protein complexes from dynamic protein-protein interaction networks
Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization. Results: In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points. Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-10-23T00:00:00Z A flexible Bayesian method for detecting allelic imbalance in RNA-seq data
Background: One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data. Results: Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test. Conclusions: To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.


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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]
 2014-10-17 [Special Issue Report] On the prevalence of small-scale twist in the solar chromosphere and transition region
The solar chromosphere and transition region (TR) form an interface between the Sun’s surface and its hot outer atmosphere. There, most of the nonthermal energy that powers the solar atmosphere is transformed into heat, although the detailed mechanism remains elusive. High-resolution (0.33–arc second) observations with NASA’s Interface Region Imaging Spectrograph (IRIS) reveal a chromosphere and TR that are replete with twist or torsional motions on sub–arc second scales, occurring in active regions, quiet Sun regions, and coronal holes alike. We coordinated observations with the Swedish 1-meter Solar Telescope (SST) to quantify these twisting motions and their association with rapid heating to at least TR temperatures. This view of the interface region provides insight into what heats the low solar atmosphere. Authors: B. De Pontieu, L. Rouppe van der Voort, S. W. McIntosh, T. M. D. Pereira, M. Carlsson, V. Hansteen, H. Skogsrud, J. Lemen, A. Title, P. Boerner, N. Hurlburt, T. D. Tarbell, J. P. Wuelser, E. E. De Luca, L. Golub, S. McKillop, K. Reeves, S. Saar, P. Testa, H. Tian, C. Kankelborg, S. Jaeggli, L. Kleint, J. Martinez-Sykora

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