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Computer aided gene mining for gingerol biosynthesis.

ABSTRACT: Inspite of the large body of genomic data obtained from the transcriptome of Zingiber officinale, very few studies have focused on the identification and characterization of miRNAs in gingerol biosynthesis. Zingiber officinale transcriptome was analyzed using EST dataset (38169 total) deposited in public domains. In this paper computational functional annotation of the available ESTs and identification of genes which play a significant role in gingerol biosynthesis are described. Zingiber officinale transcriptome was analyzed using EST dataset (38169 total) from ncbi. ESTs were clustered and assembled, resulting in 8624 contigs and 8821 singletons. Assembled dataset was then submitted to the EST functional annotation workflow including blast, gene ontology (go) analysis, and pathway enrichment by kyoto encyclopedia of genes and genomes (kegg) and interproscan. The unigene datasets were further exploited to identify simple sequence repeats that enable linkage mapping. A total of 409 simple sequence repeats were identified from the contigs. Furthermore we examined the existence of novel miRNAs from the ESTs in rhizome, root and leaf tissues. EST analysis revealed the presence of single hypothetical miRNA in rhizome tissue. The hypothetical miRNA is warranted to play an important role in controlling genes involved in gingerol biosynthesis and hence demands experimental validation. The assembly and associated information of transcriptome data provides a comprehensive functional and evolutionary characterization of genomics of Zingiber officinale. As an effort to make the genomic and transcriptomic data widely available to the public domain, the results were integrated into a web-based Ginger EST database which is freely accessible at http://www.kaubic.in/gingerest/.

SUBMITTER: James P

PROVIDER: S-EPMC4512007 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Computer aided gene mining for gingerol biosynthesis.

James Priyanka P Baby Bincy B Charles SonaSona S Nair Lekshmysree Saraschandran LS Nazeem Puthiyaveetil Abdulla PA

Bioinformation 20150630 6

Inspite of the large body of genomic data obtained from the transcriptome of Zingiber officinale, very few studies have focused on the identification and characterization of miRNAs in gingerol biosynthesis. Zingiber officinale transcriptome was analyzed using EST dataset (38169 total) deposited in public domains. In this paper computational functional annotation of the available ESTs and identification of genes which play a significant role in gingerol biosynthesis are described. Zingiber offici ...[more]

PMID: 26229293

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Project description:BackgroundTuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information.ObjectiveThe main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients' database.MethodsTo accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database.ResultsThe performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods.ConclusionsThis paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients' database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

Project description:Recent data indicates limited awareness and compliance on infection prevention procedures by dental offices and by dental laboratories. Guidelines for infection prevention in dentistry have been published by Centres for Disease Control and Prevention since 2003; the section "IX-Special consideration" includes a subsection concerning the prevention in dental laboratories, but it has not been modernised in later versions to fit the needs of traditional and computer-aided technology. Traditional techniques required disinfecting items (impression, chewing waxes, and appliances) with well-suited products, which are also chosen for limiting impression changes or appliance deterioration. Effective procedures are available with difficulties. Some of these contain irritant or non-eco-friendly disinfectants. The transport of impression, to dental laboratories, is often delayed with limited precautions for limiting cross-infection. Gypsum casts are frequently contaminated mainly by bacteria and their antibiotic-resistant strains and even stored for long periods during dental implant supported restoration and orthodontic therapy, becoming a hidden source of infection. Nowadays, computer-aided design/computer-aided manufacturing technology seems to be an interesting way to promote both business and safety, being more comfortable for patients and more accurate than traditional technology. A further advantage is easier infection prevention since, for the most part, mainly digital impression and casts are not a source of cross-infection and the transport of contaminated items is reduced and limited to try-in stages. Nevertheless, a peculiar feature is that a digital electronic file is of course unalterable, but may be ruined by a computer virus. Additionally, the reconditioning of scanner tips is determinant for the optical characteristics and long term use of the scanner, but information for its reconditioning from producers is often limited. This study focuses on some critical points including (a) insufficient guidelines, (b) choice of proper procedure for scanner reconditioning, and (c) data protection in relation to patient privacy.

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Computer aided gene mining for gingerol biosynthesis.

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Computer aided gene mining for gingerol biosynthesis.

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