Improved enzyme annotation with EC-specific cutoffs using DETECT v2

Nursimulu, Nirvana; Xu, Leon L.; Wasmuth, James D.; Krukov, Ivan; Parkinson, John

doi:10.1093/bioinformatics/bty368

Cited by 20 publications

(20 citation statements)

References 11 publications

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“…We first built draft models of all strains using sequence similarity. Following that we added additional metabolic content identified through the use of DETECT v2 (Nursimulu et al, 2018), an enzyme annotation tool. This process allowed us to include additional metabolic processes unique to each of the strains.…”

Section: Resultsmentioning

confidence: 99%

Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Norsigian

Attia

Szubin

et al. 2019

Front. Cell. Infect. Microbiol.

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The emergence and spread of metallo-beta-lactamase–producing multidrug-resistant (MDR) Klebsiella pneumoniae is a serious public health threat, which is further complicated by the increased prevalence of colistin resistance. The link between antimicrobial resistance acquired by strains of Klebsiella and their unique metabolic capabilities has not been determined. Here, we reconstruct genome-scale metabolic models for 22 K. pneumoniae strains with various resistance profiles to different antibiotics, including two strains exhibiting colistin resistance isolated from Cairo, Egypt. We use the models to predict growth capabilities on 265 different sole carbon, nitrogen, sulfur, and phosphorus sources for all 22 strains. Alternate nitrogen source utilization of glutamate, arginine, histidine, and ethanolamine among others provided discriminatory power for identifying resistance to amikacin, tetracycline, and gentamicin. Thus, genome-scale model based predictions of growth capabilities on alternative substrates may lead to construction of classification trees that are indicative of antibiotic resistance in Klebsiella isolates.

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Section: Resultsmentioning

confidence: 99%

Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Norsigian

Attia

Szubin

et al. 2019

Front. Cell. Infect. Microbiol.

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“…EC Number Prediction Tools. Next, DeepEC was compared with the latest versions of 5 representative EC number prediction tools that are locally installable, including CatFam (13), DETECT v2 (19), ECPred (20), EFICAz2.5 (15), and PRIAM (11), with respect to their prediction performances. For a systematic comparison of prediction performances, 201 enzyme protein sequences were used as inputs, which were not used for the development of these 6 different tools; these enzyme protein sequences were obtained from the Swiss-Prot database released on August 2018.…”

Section: Comparison Of Prediction Performance Of Deepec With 5 Represmentioning

confidence: 99%

“…As of September 2018, 6,238 fourth-level EC numbers have been defined in the ExPASy database (10). Because of the importance of EC number prediction in understanding enzyme functions, a number of relevant computational methods have been developed: PRIAM (11), EzyPred (12), CatFam (13), EnzML (14), EFICAz2.5 (15), EnzDP (16), SVM-prot (17), DEEPre (18), DETECT v2 (19), and ECPred (20). However, prediction performances of these tools have room for further improvement with respect to computation time, precision, and coverage for the prediction of EC numbers.…”

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confidence: 99%

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

Ryu

Kim

Lee

2019

Proc. Natl. Acad. Sci. U.S.A.

163

128

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High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner. DeepEC takes a protein sequence as input and predicts EC numbers as output. DeepEC uses 3 convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers that cannot be classified by the CNNs. Comparative analyses against 5 representative EC number prediction tools show that DeepEC allows the most precise prediction of EC numbers, and is the fastest and the lightest in terms of the disk space required. Furthermore, DeepEC is the most sensitive in detecting the effects of mutated domains/binding site residues of protein sequences. DeepEC can be used as an independent tool, and also as a third-party software component in combination with other computational platforms that examine metabolic reactions.

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“…The former two tools rely on a non-redundant database of genome sequences, ChocoPhlAn 26 , while DIAMOND utilizes the NCBI non-redundant (NR) protein database 35 . For enzyme annotation, MetaPro relies on an ensemble approach involving DETECT 36 , PRIAM 37 and DIAMOND 34 searches against the Swiss-Prot database 38 . Due to its greater precision, MetaPro incorporates all DETECT predictions while only incorporating the union of the predictions obtained from both the PRIAM and DIAMOND searches.…”

Section: Resultsmentioning

confidence: 99%

“…The ability of each pipeline to accurately infer enzymatic functions of the datasets is essential for understanding the metabolic activity of a sample. Since annotations based on simple similarity searches can yield false positive rates of up to 50% 53 , MetaPro relies on a robust approach that combines predictions from DETECT 36 , with those from PRIAM 54 that are also confirmed by sequence similarity searches against the Swiss-Prot database 55 . This approach has been shown to significantly outperform sequence similarity searches and has been effectively applied in a number of settings 56-61…”

Section: Resultsmentioning

confidence: 99%

MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

Taj

Adeolu

Xiong

et al. 2021

Preprint

Self Cite

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Background Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles. Results Here we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single end or paired end reads) and processes them through a tiered series of filtering, assembly and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state of the art pipelines and demonstrate improved performance and functionality. Conclusion MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes is made freely available at https://github.com/ParkinsonLab/MetaPro. The software is freely available under the GNU general public license v3.

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Improved enzyme annotation with EC-specific cutoffs using DETECT v2

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 20 publications

References 11 publications

Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Comparative Genome-Scale Metabolic Modeling of Metallo-Beta-Lactamase–Producing Multidrug-Resistant Klebsiella pneumoniae Clinical Isolates

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

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