Large Scale Parallelization Method of 16S rRNA Probe Design Algorithm on Distributed Architecture: Application to Grid Computing

2011 ◽  
Author(s):  
Mohieddine Missaoui ◽  
Faouzi Jaziri ◽  
Sebastien Cipiere ◽  
David R.C. Hill ◽  
Pierre Peyret
Keyword(s):  
16S Rrna ◽  
Grid Computing ◽  
Large Scale ◽  
Probe Design ◽  
Distributed Architecture ◽  
Design Algorithm

scholarly journals Microarray Analysis and Barcoded Pyrosequencing Provide Consistent Microbial Profiles Depending on the Source of Human Intestinal Samples

2011 ◽  
Vol 77 (6) ◽  
pp. 2071-2080 ◽  
Author(s):  
Bartholomeus van den Bogert ◽  
Willem M. de Vos ◽  
Erwin G. Zoetendal ◽  
Michiel Kleerebezem
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microarray Analysis ◽  
High Throughput ◽  
Large Scale ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
Probe Design ◽  
Rrna Gene ◽  
Small Intestinal

ABSTRACTLarge-scale and in-depth characterization of the intestinal microbiota necessitates application of high-throughput 16S rRNA gene-based technologies, such as barcoded pyrosequencing and phylogenetic microarray analysis. In this study, the two techniques were compared and contrasted for analysis of the bacterial composition in three fecal and three small intestinal samples from human individuals. As PCR remains a crucial step in sample preparation for both techniques, different forward primers were used for amplification to assess their impact on microbial profiling results. An average of 7,944 pyrosequences, spanning the V1 and V2 region of 16S rRNA genes, was obtained per sample. Although primer choice in barcoded pyrosequencing did not affect species richness and diversity estimates, detection ofActinobacteriastrongly depended on the selected primer. Microbial profiles obtained by pyrosequencing and phylogenetic microarray analysis (HITChip) correlated strongly for fecal and ileal lumen samples but were less concordant for ileostomy effluent. Quantitative PCR was employed to investigate the deviations in profiling between pyrosequencing and HITChip analysis. Since cloning and sequencing of random 16S rRNA genes from ileostomy effluent confirmed the presence of novel intestinal phylotypes detected by pyrosequencing, especially those belonging to theVeillonellagroup, the divergence between pyrosequencing and the HITChip is likely due to the relatively low number of available 16S rRNA gene sequences of small intestinal origin in the DNA databases that were used for HITChip probe design. Overall, this study demonstrated that equivalent biological conclusions are obtained by high-throughput profiling of microbial communities, independent of technology or primer choice.

scholarly journals General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

Metabolites ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 336
Author(s):  
Boštjan Murovec ◽  
Leon Deutsch ◽  
Blaž Stres
Keyword(s):  
16S Rrna ◽  
Metabolic Pathways ◽  
Large Scale ◽  
Enzymatic Reactions ◽  
Operational Taxonomic Units ◽  
Biochemical Pathways ◽  
Meaningful Information ◽  
Novel Biomarkers ◽  
Reproducible Analysis ◽  
Microbiome Profiling

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

scholarly journals Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Yufeng Zhang ◽  
Wenzhi Cui ◽  
Lu Liu ◽  
Jian Xu ◽  
...  
Keyword(s):  
16S Rrna ◽  
Large Scale ◽  
Low Cost ◽  
Human Microbiome ◽  
Amplicon Sequencing ◽  
Training Sample ◽  
Rrna Gene ◽  
16S Amplicon Sequencing ◽  
Cross Platform ◽  
Functional Profiles

Abstract Background Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results. Results Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS. Conclusions This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub (https://github.com/qibebt-bioinfo/meta-apo) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.

scholarly journals Phoenix 2: A locally installable large-scale 16S rRNA gene sequence analysis pipeline with Web interface

2013 ◽  
Vol 167 (4) ◽  
pp. 393-403 ◽  
Author(s):  
Jung Soh ◽  
Xiaoli Dong ◽  
Sean M. Caffrey ◽  
Gerrit Voordouw ◽  
Christoph W. Sensen
Keyword(s):  
Sequence Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Large Scale ◽  
Gene Sequence ◽  
Rrna Gene ◽  
Web Interface ◽  
Rrna Gene Sequence ◽  
Analysis Pipeline ◽  
Gene Sequence Analysis

scholarly journals Soil Denitrifier Community Size Changes with Land Use Change to Perennial Bioenergy Cropping Systems

2016 ◽  
Author(s):  
Karen A. Thompson ◽  
Bill Deen ◽  
Kari E. Dunfield
Keyword(s):  
Land Use ◽  
16S Rrna ◽  
Land Use Change ◽  
Crop Production ◽  
Large Scale ◽  
Perennial Grasses ◽  
N2o Emissions ◽  
Research Station ◽  
N2o Production ◽  
Nirs Gene

Abstract. Dedicated biomass crops are required for future bioenergy production. However, the effects of large-scale land use change (LUC) from traditional annual crops, such as corn-soybean rotations to the perennial grasses (PGs) switchgrass and miscanthus on soil microbial community functioning is largely unknown. Specifically, ecologically significant denitrifying communities, which regulate N2O production and consumption in soils, may respond differently to LUC due to differences in carbon (C) and nitrogen (N) inputs between crop types and management systems. Our objective was to quantify bacterial denitrifying gene abundances as influenced by corn-soybean crop production compared to PG biomass production. A field trial was established in 2008 at the Elora Research Station in Ontario, Canada (n = 30), with miscanthus and switchgrass grown alongside corn-soybean rotations at different N rates (0 and 160 kg N ha-1) and biomass harvest dates within PG plots. Soil was collected on four dates from 2011–2012 and quantitative PCR was used to enumerate the total bacterial community (16S rRNA), and communities of bacterial denitrifiers by targeting nitrite reductase (nirS) and N2O reductase (nosZ) genes. Miscanthus produced significantly larger yields and supported larger nosZ denitrifying communities than corn-soybean rotations regardless of management, indicating large-scale LUC from corn-soybean to miscanthus may be suitable in variable Ontario conditions while potentially mitigating soil N2O emissions. Harvesting switchgrass in the spring decreased yields in N-fertilized plots, but did not affect gene abundances. Standing miscanthus overwinter resulted in higher 16S rRNA and nirS gene copies than in fall-harvested crops. However, the size of the total (16S rRA) and denitrifying communities changed differently over time and in response to LUC, indicating varying controls on these communities.

scholarly journals Microbial Diversity Analysis of Sediment from Nakdong River Estuary in the Republic of Korea Using 16S rRNA Gene Amplicon Sequencing

2018 ◽  
Vol 7 (14) ◽  
Author(s):  
Kyunghoi Kim
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Diversity ◽  
Large Scale ◽  
Sediment Quality ◽  
River Estuary ◽  
Republic Of Korea ◽  
Rrna Gene ◽  
Nakdong River ◽  
The Republic

Deterioration of sediment quality has been found in the Nakdong River Estuary after large-scale reclamations. Here, I report microbial diversity in sediments of Nakdong River Estuary in the Republic of Korea based on 16S rRNA gene sequencing by next-generation sequencing (NGS) techniques.

Reliability and Performance Models for Grid Computing

2010 ◽  
pp. 219-245 ◽  
Author(s):  
Yuan-Shun Dai ◽  
Jack Dongarra
Keyword(s):  
Grid Computing ◽  
Resource Sharing ◽  
Large Scale ◽  
Optimization Problems ◽  
Data Dependence ◽  
Performance Models ◽  
Task Partitioning ◽  
Modeling And Analysis ◽  
Failure Correlation ◽  
And Performance

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. It is hard to analyze and model the Grid reliability because of its largeness, complexity and stiffness. Therefore, this chapter introduces the Grid computing technology, presents different types of failures in grid system, models the grid reliability with star structure and tree structure, and finally studies optimization problems for grid task partitioning and allocation. The chapter then presents models for star-topology considering data dependence and treestructure considering failure correlation. Evaluation tools and algorithms are developed, evolved from Universal generating function and Graph Theory. Then, the failure correlation and data dependence are considered in the model. Numerical examples are illustrated to show the modeling and analysis.

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