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- FunVIP: Fungal Validation and Identification Pipeline based on phylogenetic analysis
- Chang Wan Seo, Shinnam Yoo, Yoonhee Cho, Ji Seon Kim, Martin Steinegger, Young Woon Lim
- J. Microbiol. 2025;63(4):e2411017. Published online April 29, 2025
- DOI: https://doi.org/10.71150/jm.2411017
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Abstract
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Supplementary Material -
The increase of sequence data in public nucleotide databases has made DNA sequence-based identification an indispensable tool for fungal identification. However, the large proportion of mislabeled sequence data in public databases leads to frequent misidentifications. Inaccurate identification is causing severe problems, especially for industrial and clinical fungi, and edible mushrooms. Existing species identification pipelines require separate validation of a dataset obtained from public databases containing mislabeled taxonomic identifications. To address this issue, we developed FunVIP, a fully automated phylogeny-based fungal validation and identification pipeline (
https://github.com/Changwanseo/FunVIP ). FunVIP employs phylogeny-based identification with validation, where the result is achievable only with a query, database, and a single command. FunVIP command comprises nine steps within a workflow: input management, sequence-set organization, alignment, trimming, concatenation, model selection, tree inference, tree interpretation, and report generation. Users may acquire identification results, phylogenetic tree evidence, and reports of conflicts and issues detected in multiple checkpoints during the analysis. The conflicting sample validation performance of FunVIP was demonstrated by re-iterating the manual revision of a fungal genus with a database with mislabeled sequences, Fuscoporia. We also compared the identification performance of FunVIP with BLAST and q2-feature-classifier with two mass double-revised fungal datasets, Sanghuangporus and Aspergillus section Terrei. Therefore, with its automatic validation ability and high identification performance, FunVIP proves to be a highly promising tool for achieving easy and accurate fungal identification.
- Predictive Modelling of Lactobacillus casei KN291 Survival in Fermented Soy Beverage
- Zieli&# , Koło&# , Goryl Antoni , Ilona Motyl
- J. Microbiol. 2014;52(2):169-178. Published online February 1, 2014
- DOI: https://doi.org/10.1007/s12275-014-3045-0
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- 8 Crossref
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Abstract
- The aim of the study was to construct and verify predictive growth and survival models of a potentially probiotic bacteria in fermented soy beverage. The research material included natural soy beverage (Polgrunt, Poland) and the strain of lactic acid bacteria (LAB) – Lactobacillus casei KN291. To construct predictive models for the growth and survival of L. casei KN291 bacteria in the fermented soy beverage we design an experiment which allowed the collection of CFU data. Fermented soy beverage samples were stored at various temperature conditions (5, 10, 15, and 20°C) for 28 days. On the basis of obtained data concerning the survival of L. casei KN291 bacteria in soy beverage at different temperature and time conditions, two non-linear models (r2= 0.68–0.93) and two surface models (r2=0.76–0.79) were constructed; these models described the behaviour of the bacteria in the product to a satisfactory extent. Verification of the surface models was carried out utilizing the validation data - at 7°C during 28 days. It was found that applied models were well fitted and charged with small systematic errors, which is evidenced by accuracy factor - Af, bias factor - Bf and mean squared error - MSE. The constructed microbiological growth and survival models of L. casei KN291 in fermented soy beverage enable the estimation of products shelf life period, which in this case is defined by the requirement for the level of the bacteria to be above 106 CFU/cm3. The constructed models may be useful as a tool for the manufacture of probiotic foods to estimate of their shelf life period.
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Citations
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- Modeling of Growth and Organic Acid Kinetics and Evolution of the Protein Profile and Amino Acid Content during Lactiplantibacillus plantarum ITM21B Fermentation in Liquid Sourdough
Mariaelena Di Biase, Yvan Le Marc, Anna Rita Bavaro, Stella Lisa Lonigro, Michela Verni, Florence Postollec, Francesca Valerio
Foods.2022; 11(23): 3942. CrossRef - A Predictive Growth Model for Pro-technological and Probiotic Lacticaseibacillus paracasei Strains Fermenting White Cabbage
Mariaelena Di Biase, Yvan Le Marc, Anna Rita Bavaro, Palmira De Bellis, Stella Lisa Lonigro, Paola Lavermicocca, Florence Postollec, Francesca Valerio
Frontiers in Microbiology.2022;[Epub] CrossRef - Modeling of Listeria monocytogenes survival and quality attributes of sliced mushroom (Agaricus bisporus) subjected to pulsed UV light
Gamze Koçer Alaşalvar, Nene Meltem Keklik
Journal of Food Process Engineering.2021;[Epub] CrossRef - Effect of Probiotic Soy Milk on Serum Levels of Adiponectin, Inflammatory Mediators, Lipid Profile, and Fasting Blood Glucose Among Patients with Type II Diabetes Mellitus
Sadegh Feizollahzadeh, Reza Ghiasvand, Abbas Rezaei, Hossein Khanahmad, Akram sadeghi, Mitra Hariri
Probiotics and Antimicrobial Proteins.2017; 9(1): 41. CrossRef - Recent research process of fermented plant extract: A review
Yanjun Feng, Min Zhang, Arun S. Mujumdar, Zhongxue Gao
Trends in Food Science & Technology.2017; 65: 40. CrossRef - Stability and functionality of synbiotic soy food during shelf-life
Olga Lucía Mondragón-Bernal, José Guilherme Lembi Ferreira Alves, Mariá Andrade Teixeira, Maria Fernanda Perina Ferreira, Francisco Maugeri Filho
Journal of Functional Foods.2017; 35: 134. CrossRef - Modeling the Effect of Inulin, pH and Storage Time on the Viability of Selected Lactobacillus in a Probiotic Fruity Yogurt Drink Using the Monte Carlo Simulation
Parang Nikmaram, Seyed Mohamad Mousavi, Hossein Kiani, Zahra Emamdjomeh, Seyed Hadi Razavi, Zeinab Mousavi
Journal of Food Quality.2016; 39(4): 362. CrossRef - A randomized, double-blind, placebo-controlled, clinical trial on probiotic soy milk and soy milk: effects on epigenetics and oxidative stress in patients with type II diabetes
Mitra Hariri, Rasoul Salehi, Awat Feizi, Maryam Mirlohi, Reza Ghiasvand, Nahal Habibi
Genes & Nutrition.2015;[Epub] CrossRef
- Modeling of Growth and Organic Acid Kinetics and Evolution of the Protein Profile and Amino Acid Content during Lactiplantibacillus plantarum ITM21B Fermentation in Liquid Sourdough
- Computational Detection of Prokaryotic Core Promoters in Genomic Sequences
- Ki-Bong Kim , Jeong Seop Sim
- J. Microbiol. 2005;43(5):411-416.
- DOI: https://doi.org/2282 [pii]
- 40 View
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Abstract
- The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non-adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.
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