scholarly journals mockrobiota: a public resource for microbiome bioinformatics benchmarking

2016 ◽  
Author(s):  
Nicholas A Bokulich ◽  
Jai Ram Rideout ◽  
William G Mercurio ◽  
Benjamin Wolfe ◽  
Corinne F Maurice ◽  
...  
Keyword(s):  
Sequence Data ◽  
Marker Gene ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Community Members ◽  
Mock Community ◽  
Public Resource ◽  
Source Data ◽  
Community Data ◽  
Mock Communities

Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at https://github.com/caporaso-lab/mockrobiota. The materials contained in mockrobiota include dataset and sample metadata, expected composition data, which are annotated based on one or more reference taxonomies, links to raw data (e.g., raw sequence data) for each mock community dataset, and optional reference sequences for mock community members. mockrobiota does not supply physical sample materials directly, but the dataset metadata included for each mock community indicate whether physical sample materials are available (and associated contact information). At the time of this writing, mockrobiota contains 11 mock community datasets with known species compositions (including bacterial, archaeal, and eukaryotic mock communities), analyzed by high-throughput marker-gene sequencing. The availability of standard, public mock community data will facilitate ongoing methods optimizations; comparisons across studies that share source data; greater transparency and access; and eliminate redundancy. This dynamic resource is intended to expand and evolve to meet the changing needs of the ‘omics community.

scholarly journals mockrobiota: a public resource for microbiome bioinformatics benchmarking

2016 ◽  
Author(s):  
Nicholas A Bokulich ◽  
Jai Ram Rideout ◽  
William G Mercurio ◽  
Benjamin Wolfe ◽  
Corinne F Maurice ◽  
...  
Keyword(s):  
Sequence Data ◽  
Marker Gene ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Community Members ◽  
Mock Community ◽  
Public Resource ◽  
Source Data ◽  
Community Data ◽  
Mock Communities

Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at https://github.com/caporaso-lab/mockrobiota. The materials contained in mockrobiota include dataset and sample metadata, expected composition data, which are annotated based on one or more reference taxonomies, links to raw data (e.g., raw sequence data) for each mock community dataset, and optional reference sequences for mock community members. mockrobiota does not supply physical sample materials directly, but the dataset metadata included for each mock community indicate whether physical sample materials are available (and associated contact information). At the time of this writing, mockrobiota contains 11 mock community datasets with known species compositions (including bacterial, archaeal, and eukaryotic mock communities), analyzed by high-throughput marker-gene sequencing. The availability of standard, public mock community data will facilitate ongoing methods optimizations; comparisons across studies that share source data; greater transparency and access; and eliminate redundancy. This dynamic resource is intended to expand and evolve to meet the changing needs of the ‘omics community.

scholarly journals mockrobiota: a Public Resource for Microbiome Bioinformatics Benchmarking

mSystems ◽  
2016 ◽  
Vol 1 (5) ◽  
Author(s):  
Nicholas A. Bokulich ◽  
Jai Ram Rideout ◽  
William G. Mercurio ◽  
Arron Shiffer ◽  
Benjamin Wolfe ◽  
...  
Keyword(s):  
Marker Gene ◽  
Microbial Community Analysis ◽  
Mock Community ◽  
Data Set ◽  
Public Resource ◽  
Source Data ◽  
Community Data ◽  
Dynamic Resource ◽  
And Training ◽  
Mock Communities

ABSTRACT The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community. Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/ . The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community.

scholarly journals Consistent, comprehensive and computationally efficient OTU definitions

2014 ◽  
Author(s):  
Jai Ram Rideout ◽  
Yan He ◽  
Jose Antonio Navas-Molina ◽  
William A Walters ◽  
Luke K Ursell ◽  
...  
Keyword(s):  
16S Rrna ◽  
De Novo ◽  
Sequence Data ◽  
Marker Gene ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Reference Database ◽  
Data Sets ◽  
Computationally Efficient ◽  
Sequencing Platforms

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

scholarly journals Consistent, comprehensive and computationally efficient OTU definitions

2014 ◽  
Author(s):  
Jai Ram Rideout ◽  
Yan He ◽  
Jose Antonio Navas-Molina ◽  
William A Walters ◽  
Luke K Ursell ◽  
...  
Keyword(s):  
16S Rrna ◽  
De Novo ◽  
Sequence Data ◽  
Marker Gene ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Reference Database ◽  
Computationally Efficient ◽  
Highly Correlated ◽  
Sequencing Platforms

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because parts of our algorithm can be run in parallel, it makes open-reference OTU picking tractable on massive amplicon sequence data sets. We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “legacy” open-reference OTU picking, where less of the process can be parallelized, through comparisons on three well-studied datasets. We therefore recommend that subsampled open-reference OTU picking always be applied in favor of “legacy” open-reference OTU picking. An implementation of this algorithm is provided in the popular QIIME software package. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters.

scholarly journals Consistent, comprehensive and computationally efficient OTU definitions

2014 ◽  
Author(s):  
Jai Ram Rideout ◽  
Yan He ◽  
Jose Antonio Navas-Molina ◽  
William A Walters ◽  
Luke K Ursell ◽  
...  
Keyword(s):  
16S Rrna ◽  
De Novo ◽  
Sequence Data ◽  
Marker Gene ◽  
Community Analysis ◽  
Microbial Community Analysis ◽  
Reference Database ◽  
Data Sets ◽  
Computationally Efficient ◽  
Sequencing Platforms

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

scholarly journals Microbial Community Analysis Using MiSeq Sequencing and Pathway of Methane Production in Tehran WWTP: A Full-Scale Anaerobic Digester

2020 ◽  
Author(s):  
Reza Barati Rashvanlou ◽  
Mahdi Farzadkia ◽  
Abbas Ali Moserzadeh ◽  
Asghar Riazati ◽  
Chiang Wei ◽  
...  
Keyword(s):  
Microbial Community ◽  
Methane Production ◽  
Community Analysis ◽  
Full Scale ◽  
Microbial Community Analysis ◽  
Miseq Sequencing ◽  
Sequencing Method ◽  
Community Data ◽  
Rank Abundance ◽  
Made In

Introduction: One of biological wastewater treatment methods that utilizes to both digesting waste activated sludge and methane production is anaerobic digestion (AD). It is believed to be most effective solution in terms of energy crisis and environmental pollution issues. Materials and Methods: In this study the sludge was digested anaerobically sampled from a full-scale WWTP, located at south of Tehran, Iran for evaluation. To study the microbial community within the sludge the MiSeq Sequencing method utilized. Based on our field data (data not shown) and microbial community data, a schematic diagram of probable leading pathways was made in the studied digester. Results: At first, the community variety in the bulk sludge and richness were enhanced followed by loading increasing. Meanwhile, the loading change enhanced the community richness and variety of the sludge. By comparing the rank-abundance distributions, a shallow gradient would show high evenness since the abundances of diverse species are alike. The results showed all the communities were extremely diverse and 15 phyla were distinguished in the sludge sample. The dominant phyla of the community were Bacteroidetes and Firmicutes and quantity of the two phyla were 21% and 11%, respectively. Anaerobaculum, Acinetobacter, Syntrophomonas, and Coprothermobacter were the chief genera for the microbial communities and the sum of four genera were 7%, 3%, 3%, and 2%, respectively. Conclusion: It was shown that syntrophic acetate oxidizing bacterias (SAOBs) metabolized acetate through hydrogen trophic methanogenesis in the digester. Generally, the findings may be useful to help the wastewater operators to utilize an effective method that able to treat waste sludge plus methane production, simultaneously.

scholarly journals CCMetagen: comprehensive and accurate identification of eukaryotes and prokaryotes in metagenomic data

2019 ◽  
Author(s):  
Vanessa R. Marcelino ◽  
Philip T.L.C. Clausen ◽  
Jan P. Buchmann ◽  
Michelle Wille ◽  
Jonathan R. Iredell ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Sequence Data ◽  
Fold Increase ◽  
Community Analysis ◽  
Biological Data ◽  
Microbial Community Analysis ◽  
Metagenomic Data ◽  
Reference Database ◽  
Comprehensive Overview ◽  
Accurate Identification

AbstractHigh-throughput sequencing of DNA and RNA from environmental and host-associated samples (metagenomics and metatranscriptomics) is a powerful tool to assess which organisms are present in a sample. Taxonomic identification software usually align individual short sequence reads to a reference database, sometimes containing taxa with complete genomes only. This is a challenging task given that different species can share identical sequence regions and complete genome sequences are only available for a fraction of organisms. A recently developed approach to map sequence reads to reference databases involves weighing all high scoring read-mappings to the data base as a whole to produce better-informed alignments. We used this novel concept in read mapping to develop a highly accurate metagenomic classification pipeline named CCMetagen. Using simulated fungal and bacterial metagenomes, we demonstrate that CCMetagen substantially outperforms other commonly used metagenome classifiers, attaining a 3 – 1580 fold increase in precision and a 2 – 922 fold increase in F1 scores for species-level classifications when compared to Kraken2, Centrifuge and KrakenUniq. CCMetagen is sufficiently fast and memory efficient to use the entire NCBI nucleotide collection (nt) as reference, enabling the assessment of species with incomplete genome sequence data from all biological kingdoms. Our pipeline efficiently produced a comprehensive overview of the microbiome of two biological data sets, including both eukaryotes and prokaryotes. CCMetagen is user-friendly and the results can be easily integrated into microbial community analysis software for streamlined and automated microbiome studies.

scholarly journals Taxon Disappearance from Microbiome Analysis Reinforces the Value of Mock Communities as a Standard in Every Sequencing Run

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Yi-Chun Yeh ◽  
David M. Needham ◽  
Ella T. Sieradzki ◽  
Jed A. Fuhrman
Keyword(s):  
Method Development ◽  
Pcr Amplification ◽  
Unknown Origin ◽  
Community Analysis ◽  
Sequencing Analysis ◽  
Mock Community ◽  
Microbiome Analysis ◽  
Biological Studies ◽  
Sequencing Platforms ◽  
Mock Communities

ABSTRACT Mock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification and sequencing and to optimize pipeline outputs. Nevertheless, the strong value of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed >2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded “normal” results. Although obvious from the strange mock community results, we could have easily missed the problem had we not used the mock communities because of natural variation of microbiomes at our site. The “normal” results were validated over four MiSeqPE300 runs and three HiSeqPE250 runs, and run-to-run variation was usually low. While validating these “normal” results, we also discovered that some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We strongly advise the use of mock communities in every sequencing run to distinguish potentially serious aberrations from natural variations. The mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed to detect problems that show up only in some taxa and also to help validate clustering. IMPORTANCE Despite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be detected by the use of mock communities.

scholarly journals Taxon disappearance from microbiome analysis indicates need for mock communities as a standard in every sequencing run

2017 ◽  
Author(s):  
Yi-Chun Yeh ◽  
David M. Needham ◽  
Ella T. Sieradzki ◽  
Jed A. Fuhrman
Keyword(s):  
Method Development ◽  
Pcr Amplification ◽  
Unknown Origin ◽  
Community Analysis ◽  
Sequencing Analysis ◽  
Mock Community ◽  
Microbiome Analysis ◽  
Biological Studies ◽  
Sequencing Platforms ◽  
Mock Communities

AbstractMock communities have been used in microbiome method development to help estimate biases introduced in PCR amplification, sequencing, and to optimize pipeline outputs. Nevertheless, the necessity of routine mock community analysis beyond initial method development is rarely, if ever, considered. Here we report that our routine use of mock communities as internal standards allowed us to discover highly aberrant and strong biases in the relative proportions of multiple taxa in a single Illumina HiSeqPE250 run. In this run, an important archaeal taxon virtually disappeared from all samples, and other mock community taxa showed >2-fold high or low abundance, whereas a rerun of those identical amplicons (from the same reaction tubes) on a different date yielded “normal” results. Although obvious from the strange mock community results, due to natural variation of microbiomes at our site, we easily could have missed the problem had we not used the mock communities. The “normal” results were validated over 4 MiSeqPE300 runs and 3 HiSeqPE250 runs, and run-to-run variation was usually low (Bray-Curtis distance was 0.12±0.04). While validating these “normal” results, we also discovered some mock microbial taxa had relatively modest, but consistent, differences between sequencing platforms. We suggest that using mock communities in every sequencing run is essential to distinguish potentially serious aberrations from natural variations. Such mock communities should have more than just a few members and ideally at least partly represent the samples being analyzed, to detect problems that show up only in some taxa, as we observed.ImportanceDespite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs, and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be avoided by the use of suitable mock communities.

scholarly journals Optimization of 16S amplicon analysis using mock communities: implications for estimating community diversity

2016 ◽  
Author(s):  
Andrew Krohn ◽  
Bo Stevens ◽  
Adam Robbins-Pianka ◽  
Matthew Belus ◽  
Gerard J Allan ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Marker Gene ◽  
Community Diversity ◽  
Sequencing Data ◽  
Mock Community ◽  
Taxonomic Assignment ◽  
Data Set ◽  
Environmental Diversity ◽  
Quality Filtering ◽  
Mock Communities

The diversity of complex microbial communities can be rapidly assessed by high-throughput DNA sequencing of marker gene (e.g., 16S) PCR amplicon pools, often yielding many thousands of DNA sequences per sample. However, analysis of such community amplicon sequencing data requires multiple computational steps which affect the outcome of a final data set. Here we use mock communities to describe the effects of parameter adjustments for raw sequence quality filtering, picking operational taxonomic units (OTUs), taxonomic assignment, and OTU table filtering as implemented in the popular microbial ecology analysis package, QIIME 1.9.1. We demonstrate a workflow optimization based upon this exploration, which we also apply to environmental samples. We found that quality filtering of raw data and filtering of OTU tables had large effects on observed OTU diversity. While all taxonomy assignment programs performed with similar accuracy, an appropriate choice of similarity threshold for defining OTUs depended on the method used for OTU picking. Our “default” analysis in QIIME overestimated mock community OTU diversity by at least a factor of ten. Our optimized analysis correctly characterized mock community taxonomic composition and improved the OTU diversity estimate, reducing overestimation to a factor of about two. Though observed relative abundances of mock community member taxa were approximately correct, most were still represented by multiple OTUs. Low-frequency OTUs conspecific to constituent mock community taxa were characterized by multiple substitution and indel errors and the presence of a low-quality base call resulting in sequence truncation during quality filtering. Low-quality base calls were observed at “G” positions most of the time, and were also associated with a preceding “TTT” trinucleotide motif. Environmental diversity estimates were reduced by about 40% from 2508 to 1533 OTUs when comparing output from the default and optimized workflows. We attribute this reduction in observed diversity to the removal of erroneous sequences from the data set. Our results indicate that both strict quality filtering of raw sequencing data and careful filtering of raw OTU tables are important steps for accurately estimating microbial community diversity.

Sign in / Sign up

Export Citation Format

Share Document