471 research outputs found

    Law of Genome Evolution Direction : Coding Information Quantity Grows

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    The problem of the directionality of genome evolution is studied. Based on the analysis of C-value paradox and the evolution of genome size we propose that the function-coding information quantity of a genome always grows in the course of evolution through sequence duplication, expansion of code, and gene transfer from outside. The function-coding information quantity of a genome consists of two parts, p-coding information quantity which encodes functional protein and n-coding information quantity which encodes other functional elements except amino acid sequence. The evidences on the evolutionary law about the function-coding information quantity are listed. The needs of function is the motive force for the expansion of coding information quantity and the information quantity expansion is the way to make functional innovation and extension for a species. So, the increase of coding information quantity of a genome is a measure of the acquired new function and it determines the directionality of genome evolution.Comment: 16 page

    ADC Histograms from Routine DWI for Longitudinal Studies in Cerebral Small Vessel Disease: A Field Study in CADASIL.

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    Diffusion tensor imaging (DTI) histogram metrics are correlated with clinical parameters in cerebral small vessel diseases (cSVD). Whether ADC histogram parameters derived from simple diffusion weighted imaging (DWI) can provide relevant markers for long term studies of cSVD remains unknown. CADASIL patients were evaluated by DWI and DTI in a large cohort study overa6-year period. ADC histogram parameters were compared to those derived from mean diffusivity (MD) histograms in 280 patients using intra-class correlation and Bland-Altman plots. Impact of image corrections applied to ADC maps was assessed and a mixed effect model was used for analyzing the effects of scanner upgrades. The results showed that ADC histogram parameters are strongly correlated to MD histogram parameters and that image corrections have only limited influence on these results. Unexpectedly, scanner upgrades were found to have major effects on diffusion measures with DWI or DTI that can be even larger than those related to patients' characteristics. These data support that ADC histograms from daily used DWI can provide relevant parameters for assessing cSVD, but the variability related to scanner upgrades as regularly performed in clinical centers should be determined precisely for longitudinal and multicentric studies using diffusion MRI in cSVD

    Reply to Guy et al.: Support for a bottleneck in the 2011 Escherichia coli O104:H4 outbreak in Germany

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    In our paper (1), we analyzed isolates from the Escherichia coli O104:H4 outbreaks in Germany and France in May to July 2011. We concluded that, although the German outbreak was larger, the German isolates represent a clade within the greater diversity of the French outbreak. We proposed several hypotheses to explain these findings, including that the lineage leading to the German outbreak went through a narrow bottleneck that purged diversity. Guy et al. (2) report the genomes of eight additional E. coli O104:H4 isolates sampled from the German outbreak. By focusing on the numbers of SNPs in their samples, they suggest that the German outbreak is more diverse than we reported and is similar to the French outbreak. In fact, Guy et al.’s data (2) strongly support our conclusion that the German outbreak represents a clade within the diversity

    LOCAS – A Low Coverage Assembly Tool for Resequencing Projects

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    Motivation: Next Generation Sequencing (NGS) is a frequently applied approach to detect sequence variations between highly related genomes. Recent large-scale re-sequencing studies as the Human 1000 Genomes Project utilize NGS data of low coverage to afford sequencing of hundreds of individuals. Here, SNPs and micro-indels can be detected by applying an alignment-consensus approach. However, computational methods capable of discovering other variations such as novel insertions or highly diverged sequence from low coverage NGS data are still lacking. Results: We present LOCAS, a new NGS assembler particularly designed for low coverage assembly of eukaryotic genomes using a mismatch sensitive overlap-layout-consensus approach. LOCAS assembles homologous regions in a homologyguided manner while it performs de novo assemblies of insertions and highly polymorphic target regions subsequently to an alignment-consensus approach. LOCAS has been evaluated in homology-guided assembly scenarios with low sequence coverage of Arabidopsis thaliana strains sequenced as part of the Arabidopsis 1001 Genomes Project. While assembling the same amount of long insertions as state-of-the-art NGS assemblers, LOCAS showed best results regarding contig size, error rate and runtime. Conclusion: LOCAS produces excellent results for homology-guided assembly of eukaryotic genomes with short reads and low sequencing depth, and therefore appears to be the assembly tool of choice for the detection of novel sequenc

    Methods for Minimizing the Confounding Effects of Word Length in the Analysis of Phonotactic Probability and Neighborhood Density

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    This is the author's accepted manuscript. The original is available at http://jslhr.pubs.asha.org/article.aspx?articleid=1781521&resultClick=3Recent research suggests that phonotactic probability (the likelihood of occurrence of a sound sequence) and neighborhood density (the number of words phonologically similar to a given word) influence spoken language processing and acquisition across the lifespan in both normal and clinical populations. The majority of research in this area has tended to focus on controlled laboratory studies rather than naturalistic data such as spontaneous speech samples or elicited probes. One difficulty in applying current measures of phonotactic probability and neighborhood density to more naturalistic samples is the significant correlation between these variables and word length. This study examines several alternative transformations of phonotactic probability and neighborhood density as a means of reducing or eliminating this correlation with word length. Computational analyses of the words in a large database and reanalysis of archival data supported the use of z scores for the analysis of phonotactic probability as a continuous variable and the use of median transformation scores for the analysis of phonotactic probability as a dichotomous variable. Neighborhood density results were less clear with the conclusion that analysis of neighborhood density as a continuous variable warrants further investigation to differentiate the utility of z scores in comparison to median transformation scores. Furthermore, balanced dichotomous coding of neighborhood density was difficult to achieve, suggesting that analysis of neighborhood density as a dichotomous variable should be approached with caution. Recommendations for future application and analyses are discussed

    Comprehensive comparative analysis of strand-specific RNA sequencing methods

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    Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but no consensus exists as to how to choose between them. Here we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library-construction protocols, including both published and our own methods. We found marked differences in strand specificity, library complexity, evenness and continuity of coverage, agreement with known annotations and accuracy for expression profiling. Weighing each method's performance and ease, we identified the dUTP second-strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.Howard Hughes Medical InstituteUnited States-Israel Binational Science Foundatio

    Application of Diffusion Tensor Imaging Parameters to Detect Change in Longitudinal Studies in Cerebral Small Vessel Disease.

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    Cerebral small vessel disease (SVD) is the major cause of vascular cognitive impairment, resulting in significant disability and reduced quality of life. Cognitive tests have been shown to be insensitive to change in longitudinal studies and, therefore, sensitive surrogate markers are needed to monitor disease progression and assess treatment effects in clinical trials. Diffusion tensor imaging (DTI) is thought to offer great potential in this regard. Sensitivity of the various parameters that can be derived from DTI is however unknown. We aimed to evaluate the differential sensitivity of DTI markers to detect SVD progression, and to estimate sample sizes required to assess therapeutic interventions aimed at halting decline based on DTI data. We investigated 99 patients with symptomatic SVD, defined as clinical lacunar syndrome with MRI confirmation of a corresponding infarct as well as confluent white matter hyperintensities over a 3 year follow-up period. We evaluated change in DTI histogram parameters using linear mixed effect models and calculated sample size estimates. Over a three-year follow-up period we observed a decline in fractional anisotropy and increase in diffusivity in white matter tissue and most parameters changed significantly. Mean diffusivity peak height was the most sensitive marker for SVD progression as it had the smallest sample size estimate. This suggests disease progression can be monitored sensitively using DTI histogram analysis and confirms DTI's potential as surrogate marker for SVD
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