Current Issue : April - June Volume : 2012 Issue Number : 2 Articles : 7 Articles
Background: With the rapid development of the next generation sequencing (NGS) technology, large quantities of\r\ngenome sequencing data have been generated. Because of repetitive regions of genomes and some other factors,\r\nassembly of very short reads is still a challenging issue.\r\nResults: A novel strategy for improving genome assembly from very short reads is proposed. It can increase\r\naccuracies of assemblies by integrating de novo contigs, and produce comparative contigs by allowing multiple\r\nreferences without limiting to genomes of closely related strains. Comparative contigs are used to scaffold de novo\r\ncontigs. Using simulated and real datasets, it is shown that our strategy can effectively improve qualities of\r\nassemblies of isolated microbial genomes and metagenomes.\r\nConclusions: With more and more reference genomes available, our strategy will be useful to improve qualities of\r\ngenome assemblies from very short reads. Some scripts are provided to make our strategy applicable at http://\r\ncode.google.com/p/cd-hybrid/....
Bioinspired technologies have inspired the researchers, inventors and developers towards the world of Bio inspired innovations .The modern era of Science created a wide platform for the innovations, immerging from the active participation of living culture. Such Bio inspired involvements have key benefits of being the fruitful outcome of undiscovered or sometimes underestimated potentials of the traditional treatments of any subjects.This paper highlights the Bioelectrochemical response of plant Mimosa Pudica, also known as Touch-me-not, humble plant, Lajjawanti (Sanskrit) or Chhuimui (Hindi), and its vicinity to be introduced as a smart bio inspired sensor. Paper emphasizes on the response or movement of plant under applied damaging or nondamaging stimulus. Finally, paper concludes with the prospective study of the ability of Mimosa Pudica as Smart Sensor....
Background: Several preprocessing methods are available for the analysis of Affymetrix Genechips arrays. The most\npopular algorithms analyze the measured fluorescence intensities with statistical methods. Here we focus on a\nnovel algorithm, AffyILM, available from Bioconductor, which relies on inputs from hybridization thermodynamics\nand uses an extended Langmuir isotherm model to compute transcript concentrations. These concentrations are\nthen employed in the statistical analysis. We compared the performance of AffyILM and other traditional methods\nboth in the old and in the newest generation of GeneChips.\nResults: Tissue mixture and Latin Square datasets (provided by Affymetrix) were used to assess the performances\nof the differential expression analysis depending on the preprocessing strategy. A correlation analysis conducted\non the tissue mixture data reveals that the median-polish algorithm allows to best summarize AffyILM\nconcentrations computed at the probe-level. Those correlation results are equivalent to the best correlations\nobserved using popular preprocessing methods relying on intensity values. The performances of each tested\npreprocessing algorithm were quantified using the Latin Square HG-U133A dataset, thanks to the comparison of\ndifferential analysis results with the list of spiked genes. The figures of merit generated illustrates that the\nperformances associated to AffyILM(medianpolish), inferred from the present statistical analysis, are comparable to\nthe best performing strategies previously reported.\nConclusions: Converting probe intensities to estimates of target concentrations prior to the statistical analysis,\nAffyILM(medianpolish) is one of the best performing strategy currently available. Using hybridization theory, probelevel\nestimates of target concentrations should be identically distributed. In the future, a probe-level multivariate\nanalysis of the concentrations should be compared to the univariate analysis of probe-set summarized expression\ndata....
Background: Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many\r\nAffymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver\r\ninconsistent or even contradictory measurements. Therefore, obtaining an unambiguous expression estimate of a\r\npre-specified gene can be a nontrivial but essential task.\r\nResults: We developed scoring methods to assess each probe set for specificity, splice isoform coverage, and\r\nrobustness against transcript degradation. We used these scores to select a single representative probe set for each\r\ngene, thus creating a simple one-to-one mapping between gene and probe set. To test this method, we evaluated\r\nconcordance between protein measurements and gene expression values, and between sets of genes whose\r\nexpression is known to be correlated. For both test cases, we identified genes that were nominally detected by\r\nmultiple probe sets, and we found that the probe set chosen by our method showed stronger concordance.\r\nConclusions: This method provides a simple, unambiguous mapping to allow assessment of the expression levels\r\nof specific genes of interest....
Background: Our goal was to examine how various aspects of a gene signature influence the success of\r\ndeveloping multi-gene prediction models. We inserted gene signatures into three real data sets by altering the\r\nexpression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold\r\nincrease of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the\r\nnumber of samples perturbed. Prediction models were trained to identify which cases had been perturbed.\r\nPerformance was estimated using Monte-Carlo cross validation.\r\nResults: Signature strength had the greatest influence on predictor performance. It was possible to develop almost\r\nperfect predictors with as few as 10 features if the fold difference in mean expression values were > 2 even when\r\nthe spiked samples represented 10% of all samples. We also assessed the gene signature set size and strength for\r\n9 real clinical prediction problems in six different breast cancer data sets.\r\nConclusions: We found sufficiently large and strong predictive signatures only for distinguishing ER-positive from\r\nER-negative cancers, there were no strong signatures for more subtle prediction problems. Current statistical\r\nmethods efficiently identify highly informative features in gene expression data if such features exist and accurate\r\nmodels can be built with as few as 10 highly informative features. Features can be considered highly informative if\r\nat least 2-fold expression difference exists between comparison groups but such features do not appear to be\r\ncommon for many clinically relevant prediction problems in human data sets....
Background: Modern data generation techniques used in distributed systems biology research projects often\r\ncreate datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing\r\nthose large quantitative datasets and maximise the biological information extracted from them, a sound\r\ninformation system is required. Ease of integration with data analysis pipelines and other computational tools is a\r\nkey requirement for it.\r\nResults: We have developed openBIS, an open source software framework for constructing user-friendly, scalable\r\nand powerful information systems for data and metadata acquired in biological experiments. openBIS enables users\r\nto collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be\r\nextended and has been customized for different data types acquired by a range of technologies.\r\nConclusions: openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric\r\nmeasurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies.\r\nThe attributes that make it interesting to a large research community involved in systems biology projects include\r\nversatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and\r\nextensibility to the needs of any research domain....
Bioinformatics, for its very nature, is devoted to a set of targets that constantly evolve. Training is probably the best response to\r\nthe constant need for the acquisition of bioinformatics skills. It is interesting to assess the effects of training in the different sets of\r\nresearchers that make use of it. While training bench experimentalists in the life sciences, we have observed instances of changes\r\nin their attitudes in research that, if well exploited, can have beneficial impacts in the dialogue with professional bioinformaticians\r\nand influence the conduction of the research itself....
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