MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis

Rodrigues, João F. Matias; Schmidt, Thomas; Tackmann, Janko; Mering, Christian von

doi:10.1093/bioinformatics/btx517

Cited by 117 publications

(86 citation statements)

References 7 publications

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“…The HMP data set consisted of 5514 samples from the body sites Oral, Gastrointestinal tract, Urogenital tract, Skin and Airways. Samples were mapped to OTUs at 96% 16S rRNA identity using MAPseq (version v1.0 [55] , confidence > 0.5) and the full-length 16S reference provided with MAPseq. The data set was further filtered for OTUs present in > 20 samples.…”

Section: Speed Benchmarksmentioning

confidence: 99%

“…Keywords of these samples were further checked for terms not related to gut, followed by manual review of matching samples via the SRA web service and removal in case of non-gut origin. The final set of samples was downloaded and mapped to OTUs at 98% 16S rRNA identity using MAPseq (version v1.0 [55] , confidence > 0.5) and the full-length 16S reference provided with MAPseq (hierarchically clustered with HPC-CLUST [56] ; average linkage). We removed samples with less than 100 mapped reads and OTUs found in less than 200 samples (see Table S2 for SRA accessions of the final sample set).…”

Section: Data Set Creationmentioning

confidence: 99%

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Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

Tackmann¹,

Rodrigues²,

Mering³

2018

Preprint

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The recent explosion of metagenomic sequencing data opens the door towards the modeling of microbial ecosystems in unprecedented detail. In particular, co-occurrence based prediction of ecological interactions could strongly benefit from this development. However, current methods fall short on several fronts: univariate tools do not distinguish between direct and indirect interactions, resulting in excessive false positives, while approaches with better resolution are so far computationally highly limited. Furthermore, confounding variables typical for cross-study data sets are rarely addressed. We present FlashWeave, a new approach based on a flexible Probabilistic Graphical Models framework to infer highly resolved direct microbial interactions from massive heterogeneous microbial abundance data sets with seamless integration of metadata. On a variety of benchmarks, FlashWeave outperforms state-of-the-art methods by several orders of magnitude in terms of speed while generally providing increased accuracy. We apply FlashWeave to a cross-study data set of 69 818 publicly available human gut samples, resulting in one of the largest and most diverse models of microbial interactions in the human gut to date. Prediction performance on a variety of synthetic data setsSince experimentally verified biological interactions between microbes are scarcely available, we first employed previously published frameworks that generate synthetic data with ecological structure. We compared the quality of networks inferred by FlashWeave "sensitive" (-S) and "fast" (-F) ( Fig. 1 C) to three competing univariate inference methods (SparCC [30] , eLSA [17] and CoNet [20] ) and three conditional methods (mLDM [21] and SpiecEasi [15] with neighborhood selection (MB) and inverse covariance selection (GL)).

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Section: Speed Benchmarksmentioning

confidence: 99%

Section: Data Set Creationmentioning

confidence: 99%

Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

Tackmann¹,

Rodrigues²,

Mering³

2018

Preprint

Self Cite

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Section: Discussionmentioning

confidence: 99%

“…Analysis of the bacterial V3-V4 region of 16S rRNA regions has a limited resolution in terms of identification of bacterial species 17,25,33 . Current 16S rRNA gene amplicon sequencing generally capture reliable taxonomic classification at the genus level 33 . However, several recent analyses indicate that many taxonomic associations might be presented only at levels subordinate to species 17,34,35 .…”

Section: Discussionmentioning

confidence: 99%

Identification of gut microbiome markers for schizophrenia delineates a potential role of Streptococcus

Zhu

Wang

et al. 2019

Preprint

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46Emerging evidence has linked the gut microbiota to schizophrenia. However, the 47 functional changes in the gut microbiota and the biological role of individual bacterial 48 species in schizophrenia have not been explored systematically. Here, we 49 characterized the gut microbiota in schizophrenia using shotgun metagenomic 50 sequencing of feces from a discovery cohort of 90 drug-free patients and 81 controls, 51 as well as a validation cohort of 45 patients taking antipsychotics and 45 controls. We 52 screened 83 schizophrenia-associated bacterial species and constructed a classifier 53 comprising 26 microbial biomarkers that distinguished patients from controls with a 54 0.896 area under the receiver operating characteristics curve (AUC) in the discovery 55 cohort and 0.765 AUC in the validation cohort. Our analysis of fecal metagenomes 56 revealed that schizophrenia-associated gut-brain modules included short-chain fatty 57 acids synthesis, tryptophan metabolism, and synthesis/degradation of 58 neurotransmitters including glutamate, γ-aminobutyric acid, and nitric oxide. The 59 schizophrenia-enriched gut bacterial species include several oral cavity-resident 60 microbes, such as Streptococcus vestibularis. We transplanted Streptococcus 61 vestibularis into the gut of the mice with antibiotic-induced microbiota depletion to 62 explore its functional role. We observed that this microbe transiently inhabited the 63 mouse gut and this was followed by hyperactivity and deficit in social behaviors, 64 accompanied with altered neurotransmitter levels in peripheral tissues. In conclusion, 65 our study identified 26 schizophrenia-associated bacterial species representing 66 potential microbial targets for future treatment, as well as gut-brain modules, some of 67 which may give rise to new microbial metabolites involved in the development of 68 schizophrenia. 69 70 Schizophrenia is a severe psychiatric disorder associated with hallucinations, 71 delusions, and thought disorders perturbing perception and social interaction 1 . The 72 etiology of schizophrenia is not elucidated, but assumed to be multifactorial involving 73 genetic and environmental factors. Abnormalities of neurotransmitter systems have 74 been extensively studied especially focusing on aberration of signaling involving 75 dopamine, glutamate, and γ-aminobutyric acid (GABA) 2-4 . Increasing evidence 76 indicates that schizophrenia may be a systemic disorder with neuropsychiatric 77 conditions in addition to psychosis 5 . Furthermore, the importance of inflammation 6 78 and the involvement of the gastrointestinal system 7 in schizophrenia have received 79 attention. 80 The gut microbiota is reported to play an important role in neurogenerative 81 processes, and perturbation of the microbiota and microbial products have been 82 demonstrated to affect behavior 8-10 . Changes in the gut microbiota have been 83 associated with neurological 11 and neurodevelopmental disorders 12,13 , including 84 schizophrenia 14 . It was recently reported that fecal tr...

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“…In 2018, Almeida et al performed benchmark tests comparing QIIME 2 to its predecessor, QIIME 1, and to two additional 16S classification tools, MAPseq [32] and mothur [33]. Almeida et al evaluated the performance of each tool by classifying 16S rRNA reads that were simulated from bacteria known to be present in human gut, soil, and ocean microbiomes.…”

Section: S Classificationmentioning

confidence: 99%

Ultrafast and accurate 16S microbial community analysis using Kraken 2

Salzberg

2020

Preprint

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For decades, 16S ribosomal RNA sequencing has been the primary means for identifying the bacterial species present in a sample with unknown composition. One of the most widely-used tools for this purpose today is the QIIME (Quantitative Insights Into Microbial Ecology) package. Recent results have shown that the newest release, QIIME 2, has higher accuracy than QIIME, MAPseq, and mothur when classifying bacterial genera from simulated human gut, ocean, and soil metagenomes, although QIIME 2 also proved to be the most computationally expensive method. Kraken, first released in 2014, has been shown to provide exceptionally fast and accurate classification for shotgun metagenomics sequencing projects. Bracken, released in 2016, then provided users with the ability to accurately estimate species or genus abundances using Kraken classification results. Kraken 2, which matches the accuracy and speed of Kraken 1, now supports 16S rRNA databases, allowing for direct comparisons to QIIME and similar systems. Here we show that, using the same simulated 16S rRNA metagenomic data as previous studies, Kraken 2 and Bracken are up to 300 times faster and also more accurate at 16S profiling than QIIME 2.

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MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis

Cited by 117 publications

References 7 publications

Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

Identification of gut microbiome markers for schizophrenia delineates a potential role of Streptococcus

Ultrafast and accurate 16S microbial community analysis using Kraken 2

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