다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.

/pdf/checkm-assessing-the-quality-of-microbial-genomes-recovered-2792hxp0ce.pdf

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

Supplementary Results Refinement for Gene Loss and Duplication Estimates under Opal Stop Codon Recodings Supplementary Methods Identification of Trusted Reference Genomes Refining Marker Sets for Lineage-specific Gene Loss and Duplication Determination of Coding Table Systematic Bias of Completeness and Contamination Estimates Supplemental Figure S1. Distribution of the 104 bacterial and 281 gammaproteobacterial marker genes around the E. coli K12 genome. Supplemental Figure S2. Error in completeness and contamination estimates on simulated genomes with varying levels of completeness and contamination generated under the random contig model. Supplemental Figure S3. Error in completeness and contamination estimates on simulated genomes with varying levels of completeness and contamination generated under the inverse length model. Supplemental Figure S4. Maximum-likelihood genome tree inferred from 5656 reference genomes. Supplemental Figure S5. Error in completeness and contamination estimates on simulated genomes with varying levels of completeness and contamination generated under the random fragment model using a window size of 20 kbp. Supplemental Figure S6. Error in completeness and contamination estimates on simulated genomes with varying levels of completeness and contamination generated under the inverse length model. Supplemental Figure S7. Error in completeness and contamination estimates on simulated genomes from different phyla. Supplemental Figure S8. Bias in completeness and contamination estimates when modelled as a binomial distribution. Supplemental Figure S9. GC-distribution plots of the HMP Capnocytophaga sp. oral taxon 329 genome. Supplemental Figure S10. Phylogenetic placement of the two genomes (Cluster 0 and Cluster 1) identified within the HMP Capnocytophaga sp. oral taxon 329 genome. Supplemental Figure S11. Completeness estimates for 90 putative population genomes recovered from an acetate-amended aquifer. Supplemental Figure S12. Contamination estimates for 90 putative population genomes recovered from an acetate-amended aquifer. Supplemental Figure S13. Identification of the 213 marker genes within the Meyerdierks et al. (2010) ANME-1 genome. Supplemental Figure S14. Refining a marker set for lineage-specific gene loss and duplication. Supplemental Tables Supplemental Table S1. Mean absolute error of completeness (comp.) and contamination (cont.) estimates determined using different universal- and domain-specific marker gene sets. Supplemental Table S2. Number of marker genes and marker sets for taxonomic groups with g 20 reference genomes. Supplemental Table S3. Mean absolute error of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker genes treated individually (IM) or organized into collocated marker sets (MS). Supplemental Table S4. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker genes treated individually (IM) or organized into collocated marker sets (MS). Supplemental Table S5. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker genes treated individually (IM) or organized into collocated marker sets (MS). Supplemental Table S6. Phylogenetically informative marker genes used to infer the reference genome tree along with matching PhyloSift genes. Supplemental Table S7. Phylogenetically informative genes used in PhyloSift without a matching CheckM gene. Supplemental Table S8. Mean absolute error of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker sets (dms), the lineage-specific marker set selected by CheckM (sms), and the best performing lineage-specific marker set (bms). Supplemental Table S9. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker sets (dms), the lineage-specific marker set selected by CheckM (sms), and the best performing lineage-specific marker set (bms). Supplemental Table S10. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker sets (dms), the lineage-specific marker set selected by CheckM (sms), and the best performing lineage-specific marker set (bms). Supplemental Table S11. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker sets (dms) and the lineage-specific marker set selected by CheckM (sms). Supplemental Table S12. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker sets (dms) and the lineage-specific marker sets selected by CheckM (sms). Supplemental Table S13. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates determined using domain-specific marker sets (dms) and the lineage-specific marker sets selected by CheckM (sms). Supplemental Table S14. Taxonomic rank of the selected lineage-specific marker set used for evaluating the quality of genomes at different degrees of taxonomic novelty. Supplemental Table S15. Mean absolute error and standard deviation of completeness (comp.) and contamination (cont.) estimates for simulated genomes at different degrees of taxonomic novelty. Supplemental Table S16. Lineage-specific completeness and contamination estimates for isolate genomes from large-scale sequencing initiatives. (see Excel file) Supplemental Table S17. Completeness and contamination estimates of the Lactobacillus gasseri MV-22 genome for increasingly basal lineage-specific marker sets. Supplemental Table S18. Bacterial marker genes identified within the HMP Lactobacillus gasseri genomes. Markers missing from a genome or present in multiple copies are highlighted with a grey background. Supplemental Table S19. Lineage-specific completeness and contamination estimates for genomes annotated as finished at IMG, along with predicted translation tables and calculated coding density. (see Excel file) Supplemental Table S20: Lineage-specific completeness and contamination estimates for single-cell genomes from the GEBA-MDM initiative along with traditional assembly statistics. (see Excel file) Supplemental Table S21: Lineage-specific completeness and contamination estimates for population genomes, plasmids, and phage recovered from metagenomic datasets along with traditional assembly statistics. (see Excel file) Supplemental Table S22: Completeness and contamination estimates for population genomes recovered from an acetate-amended aquifer determined using domain-level and lineage-specific marker sets. (see Excel file)

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes: supplemental material

The importance of the gut-brain axis in maintaining homeostasis has long been appreciated. However, the past 15 yr have seen the emergence of the microbiota (the trillions of microorganisms within ...

The Microbiota-Gut-Brain Axis

It has been well realized that model validation plays an important role in a modelling and simulation development process. One important validation approach is to directly compare the experimental data with the data produced by the simulation. A problem with this approach is that the residual used in the analysis is usually nonstationary and contaminated by unknown initial conditions. A new approach is proposed in this paper, in which a Luenberger observer or a Kalman filter is used to generate the residual. In this way, the generated residual conveys the information of modelling errors, and meanwhile the effect of unknown initial conditions upon it is minimized. Furthermore, the residual is Gaussian and white when no modelling errors exist, a property which can be easily tested. The approach is illustrated with the validation of a biological model, which is typically hard to validate.

An approach to validation of stochastic dynamic models with initial state uncertainty

ABSTRACT Simultaneous production of sulfide and methane by anaerobic sewer biofilms has recently been observed, suggesting that sulfate-reducing bacteria (SRB) and methanogenic archaea (MA), microorganisms known to compete for the same substrates, can coexist in this environment. This study investigated the community structures and activities of SRB and MA in anaerobic sewer biofilms (average thickness of 800 μm) using a combination of microelectrode measurements, molecular techniques, and mathematical modeling. It was seen that sulfide was mainly produced in the outer layer of the biofilm, between the depths of 0 and 300 μm, which is in good agreement with the distribution of SRB population as revealed by cryosection-fluorescence in situ hybridization (FISH). SRB had a higher relative abundance of 20% on the surface layer, which decreased gradually to below 3% at a depth of 400 μm. In contrast, MA mainly inhabited the inner layer of the biofilm. Their relative abundances increased from 10% to 75% at depths of 200 μm and 700 μm, respectively, from the biofilm surface layer. High-throughput pyrosequencing of 16S rRNA amplicons showed that SRB in the biofilm were mainly affiliated with five genera, Desulfobulbus, Desulfomicrobium, Desulfovibrio, Desulfatiferula, and Desulforegula, while about 90% of the MA population belonged to the genus Methanosaeta. The spatial organizations of SRB and MA revealed by pyrosequencing were consistent with the FISH results. A biofilm model was constructed to simulate the SRB and MA distributions in the anaerobic sewer biofilm. The good fit between model predictions and the experimental data indicate that the coexistence and spatial structure of SRB and MA in the biofilm resulted from the microbial types and their metabolic transformations and interactions with substrates.

Stratified Microbial Structure and Activity in Sulfide- and

This paper reports an integrated modelling methodology for simultaneously investigating the effects of network dosing of chemicals on sulfide control in sewers and N and P removal at the downstream WWTP. The changes in influent due to chemical dosing are considered in evaluating the WWTP performance. For the sewer system studied here, oxygen is the most cost-effective option without considering the impact on the WWTP. Once the impact on the WWTP is included, the operational cost for oxygen increases, as an extra carbon source is needed to improve the N removal performance. Ferric chloride becomes the most cost-effective option since it also enhances P removal.

Impact of chemical dosing of sewers on WWTP performance

Control of nitrate recirculation predentrification systems

The desorption of SO2 from sodium phosphate buffer solution was investigated using a laboratory-scale rotating packed bed (RPB). The effects of operating parameters such as the high-gravity factor (beta) of the RPB, gas-liquid ratio (G/L), sodium phosphate concentration (C-L), preheating temperature (t), and pH of SO2-rich solutions on the desorption efficiency (theta) of SO2 were investigated. The experimental results showed that all factors except C-L have significant effects on theta, and theta decreases with increasing rich solution pH and C-L, but increases with increasing beta, G/L, and t. Under the optimal conditions of beta = 60-80, pH <= 5, G/L approximate to 800 m(3)/m(3), C-L<2 mol/L, and t approximate to 90 degrees C, theta can exceed 90%. The obtained results imply great potential for RPB use in SO2-loaded solution desorption.

Regeneration of SO2-Loaded Sodium Phosphate Solution in Rotating Packed Bed

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