scholarly journals DeepMicrobes: taxonomic classification for metagenomics with deep learning

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Qiaoxing Liang ◽  
Paul W Bible ◽  
Yu Liu ◽  
Bin Zou ◽  
Lai Wei
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Taxonomic Classification ◽  
Reference Database ◽  
Computational Framework ◽  
Bowel Diseases ◽  
Comparable Accuracy ◽  
Inflammatory Bowel ◽  
Genome Assemblies ◽  
Taxonomic Tree

Abstract Large-scale metagenomic assemblies have uncovered thousands of new species greatly expanding the known diversity of microbiomes in specific habitats. To investigate the roles of these uncultured species in human health or the environment, researchers need to incorporate their genome assemblies into a reference database for taxonomic classification. However, this procedure is hindered by the lack of a well-curated taxonomic tree for newly discovered species, which is required by current metagenomics tools. Here we report DeepMicrobes, a deep learning-based computational framework for taxonomic classification that allows researchers to bypass this limitation. We show the advantage of DeepMicrobes over state-of-the-art tools in species and genus identification and comparable accuracy in abundance estimation. We trained DeepMicrobes on genomes reconstructed from gut microbiomes and discovered potential novel signatures in inflammatory bowel diseases. DeepMicrobes facilitates effective investigations into the uncharacterized roles of metagenomic species.

scholarly journals DeepMicrobes: taxonomic classification for metagenomics with deep learning

2019 ◽  
Author(s):  
Qiaoxing Liang ◽  
Paul W. Bible ◽  
Yu Liu ◽  
Bin Zou ◽  
Lai Wei
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Genomic Sequence ◽  
Taxonomic Classification ◽  
Sequencing Data ◽  
Computational Framework ◽  
Genome Wide ◽  
Disease Diagnostics ◽  
Genomic Sequence Analysis ◽  
Microbial Genomic

AbstractTaxonomic classification is a crucial step for metagenomics applications including disease diagnostics, microbiome analyses, and outbreak tracing. Yet it is unknown what deep learning architecture can capture microbial genome-wide features relevant to this task. We report DeepMicrobes (https://github.com/MicrobeLab/DeepMicrobes), a computational framework that can perform large-scale training on > 10,000 RefSeq complete microbial genomes and accurately predict the species-of-origin of whole metagenome shotgun sequencing reads. We show the advantage of DeepMicrobes over state-of-the-art tools in precisely identifying species from microbial community sequencing data. Therefore, DeepMicrobes expands the toolbox of taxonomic classification for metagenomics and enables the development of further deep learning-based bioinformatics algorithms for microbial genomic sequence analysis.

scholarly journals Kaiju: Fast and sensitive taxonomic classification for metagenomics

2015 ◽  
Author(s):  
Peter Menzel ◽  
Kim Lee Ng ◽  
Anders Krogh
Keyword(s):  
Large Scale ◽  
Taxonomic Classification ◽  
Greedy Heuristic ◽  
Reference Database ◽  
The Novel ◽  
Sequencing Technologies ◽  
A Genome ◽  
Reference Databases ◽  
Genome Exclusion ◽  
Higher Sensitivity

The constantly decreasing cost and increasing output of current sequencing technologies enable large scale metagenomic studies of microbial communities from diverse habitats. Therefore, fast and accurate methods for taxonomic classification are needed, which can operate on increasingly larger datasets and reference databases. Recently, several fast metagenomic classifiers have been developed, which are based on comparison of genomic k-mers. However, nucleotide comparison using a fixed k-mer length often lacks the sensitivity to overcome the evolutionary distance between sampled species and genomes in the reference database. Here, we present the novel metagenome classifier Kaiju for fast assignment of reads to taxa. Kaiju finds maximum exact matches on the protein-level using the Borrows-Wheeler transform, and can optionally allow amino acid substitutions in the search using a greedy heuristic. We show in a genome exclusion study that Kaiju can classify more reads with higher sensitivity and similar precision compared to fast k-mer based classifiers, especially in genera that are underrepresented in reference databases. We also demonstrate that Kaiju classifies more than twice as many reads in ten real metagenomes compared to programs based on genomic k-mers. Kaiju can process up to millions of reads per minute, and its memory footprint is below 6 GB of RAM, allowing the analysis on a standard PC. The program is available under the GPL3 license at: http://bioinformatics-centre.github.io/kaiju

scholarly journals Effects of Wine Components in Inflammatory Bowel Diseases

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5891
Author(s):  
Josip Vrdoljak ◽  
Marko Kumric ◽  
Tina Ticinovic Kurir ◽  
Ivan Males ◽  
Dinko Martinovic ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Subjects ◽  
Current Evidence ◽  
Biological Drugs ◽  
Biological Compounds ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Anti Inflammatory ◽  
Natural Foods ◽  
The Impact

With the rising prevalence of Inflammatory bowel disease (IBD) worldwide, and the rising cost of treatment with novel biological drugs, there is an increasing interest in various diets and natural foods as a potential way to control/modulate IBD. As recent data indicates that diet can modify the metabolic responses essential for the resolution of inflammation, and as wine compounds have been shown to provide substantial anti-inflammatory effect, in this review we aimed to discuss the current evidence concerning the impact of biological compounds present in wine on IBD. A number of preclinical studies brought forth strong evidence on the mechanisms by which molecules in wine, such as resveratrol or piceatannol, provide their anti-inflammatory, anti-oxidative, anti-tumor, and microbiota-modulation effects. However, concerning the effects of alcohol, it is still unclear how the amount of ethanol ingested within the framework of moderate wine consumption (1–2 glasses a day) affects patients with IBD, as human studies regarding the effects of wine on patients with IBD are scarce. Nevertheless, available evidence justifies the conductance of large-scale RCT trials on human subjects that will finally elucidate whether wine can offer real benefits to the IBD population.

Localization and Activity of Inflammatory Bowel Disease Using 111ln Leukocyte Imaging

1988 ◽  
Vol 27 (03) ◽  
pp. 83-86 ◽  
Author(s):  
B. Briele ◽  
F. Wolf ◽  
H. J. Biersack ◽  
F. F. Knapp ◽  
A. Hotze
Keyword(s):  
Inflammatory Bowel Disease ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Disease Activity ◽  
Bowel Disease ◽  
Cell Uptake ◽  
Disease Extent ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Leukocyte Imaging

A prospective study was initiated to compare the clinically proven results concerning localization/extent and activity of inflammatory bowel diseases with those of 111ln-oxine leukocyte imaging. All patients studied were completely examined with barium enema x-ray, clinical and laboratory investigations, and endoscopy with histopathology. A total of 31 leukocyte scans were performed in 15 patients (12 with Crohn’s disease, 3 with ulcerative colitis). The scans were graded by comparing the cell uptake of a lesion (when present) and a bone marrow area providing a count ratio (CR). The inflammatory lesions were correctly localized on 26 leukocyte scans, and in 21 scans the scintigraphically estimated extent of disease was identical to endoscopy. In 5 cases the disease extent was underestimated, 4 scans in patients with relapse of Crohn’s disease were falsely negative, and in one patient with remission truly negative. The scintigraphically assessed disease activity was also in a good agreement with clinical disease activity based on histopathology in all cases. We conclude that leukocyte imaging provides valuable information about localization and activity of inflammatory bowel disease.

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