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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]
 2014-10-25T12:00:00Z 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.
 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 http://research.nhgri.nih.gov/software/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.


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BMC Genomics - Latest Articles   [more] [xml]
 2014-10-26T00:00:00Z RNA sequencing provides evidence for functional variability between naturally co-existing Alteromonas macleodii lineages
Background: Alteromonas macleodii is a ubiquitous gammaproteobacterium shown to play a biogeochemical role in marine environments. Two A. macleodii strains (AltDE and AltDE1) isolated from the same sample (i.e., the same place at the same time) show considerable genomic differences. In this study, we investigate the transcriptional response of these two strains to varying growth conditions in order to investigate differences in their ability to adapt to varying environmental parameters. Results: RNA sequencing revealed transcriptional changes between all growth conditions examined (e.g., temperature and medium) as well as differences between the two A. macleodii strains within a given condition. The main inter-strain differences were more marked in the adaptation to grow on minimal medium with glucose and, even more so, under starvation. These differences suggested that AltDE1 may have an advantage over AltDE when glucose is the major carbon source, and co-culture experiments confirmed this advantage. Additional differences were observed between the two strains in the expression of ncRNAs and phage-related genes, as well as motility. Conclusions: This study shows that the genomic diversity observed in closely related strains of A. macleodii from a single environment result in different transcriptional responses to changing environmental parameters. This data provides additional support for the idea that greater diversity at the strain level of a microbial community could enhance the community's ability to adapt to environmental shifts.


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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]
 2014-10-24 [Editorial] China's private universities
China's expansion of universities has not been on a level playing field. Earlier this year, Education Minister Guiren Yuan declared that the government must treat both public and private universities equally. As founder and president of one of China's largest private universities, I wholeheartedly agree. China's private universities can help usher in new opportunities for social and economic development, but they must be enabled to launch robust education programs and compete for research grants. Unless the government loosens restrictions on such endeavors, private universities could enter a tailspin, and such an erosion of higher education could threaten social stability. Author: Huiqing Jin

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