Genome data mining for everyone

Lee, Gir-Won; Kim, Sang Soo

doi:10.5483/bmbrep.2008.41.11.757

Cited by 4 publications

(1 citation statement)

References 38 publications

(25 reference statements)

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“…Such action is called data mining (DM) and can include simple, complex and/or combinational queries. Consequently, numerous techniques of genomic DM have been created for experimental and computational biologists [9]. DM methods can be used in bioinformatics studies because bioinformatics is data-rich, while no comprehensive theory of life organization can be detected at the molecular level [8].…”

Section: Introductionmentioning

confidence: 99%

Intelligent mining of large-scale bio-data: Bioinformatics applications

Hashemi

Ismail

Rafii

et al. 2017

Biotechnology & Biotechnological Equipment

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Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given.

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Section: Introductionmentioning

confidence: 99%

Intelligent mining of large-scale bio-data: Bioinformatics applications

Hashemi

Ismail

Rafii

et al. 2017

Biotechnology & Biotechnological Equipment

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Discovery: Computational Systems Biology (CSB) in Health and Disease I

Prokop

Michelson

2012

Systems Biology in Biotech &Amp; Pharma

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Identification and characterization of a mesophilic phytase highly resilient to high-temperatures from a fungus-garden associated metagenome

Tan

Xie

et al. 2015

Appl Microbiol Biotechnol

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Phytases are enzymes degrading phytic acid and thereby releasing inorganic phosphate. While the phytases reported to date are majorly from culturable microorganisms, the fast-growing quantity of publicly available metagenomic data generated in the last decade has enabled bioinformatic mining of phytases in numerous data mines derived from a variety of ecosystems throughout the world. In this study, we are interested in the histidine acid phosphatase (HAP) family phytases present in insect-cultivated fungus gardens. Using bioinformatic approaches, 11 putative HAP phytase genes were initially screened from 18 publicly available metagenomes of fungus gardens and were further overexpressed in Escherichia coli. One phytase from a south pine beetle fungus garden showed the highest activity and was then chosen for further study. Biochemical characterization showed that the phytase is mesophilic but possesses strong ability to withstand high temperatures. To our knowledge, it has the longest half-life time at 100 °C (27 min) and at 80 °C (2.1 h) as compared to all the thermostable phytases publicly reported to date. After 100 °C incubation for 15 min, more than 93 % of the activity was retained. The activity was 3102 μmol P/min/mg at 37 °C and 4135 μmol P/min/mg at 52.5 °C, which is higher than all the known thermostable phytases. For the high activity level demonstrated at mesophilic temperatures as well as the high resilience to high temperatures, the phytase might be promising for potential application as an additive enzyme in animal feed.

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Genome data mining for everyone

Cited by 4 publications

References 38 publications

Intelligent mining of large-scale bio-data: Bioinformatics applications

Intelligent mining of large-scale bio-data: Bioinformatics applications

Discovery: Computational Systems Biology (CSB) in Health and Disease I

Identification and characterization of a mesophilic phytase highly resilient to high-temperatures from a fungus-garden associated metagenome

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