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      Transcriptome profiling implicated in beneficiary actions of kimchi extracts against Helicobacter pylori infection

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          Abstract

          Dietary intervention to prevent Helicobacter pylori ( H. pylori)-gastric cancer might be ideal because of no risk of bacterial resistance, safety, and rejuvenating action of atrophic gastritis. We have published data about the potential of fermented kimchi as nutritional approach for H. pylori. Hence recent advances in RNAseq analysis lead us to investigate the transcriptome analysis to explain these beneficiary actions of kimchi. gastric cells were infected with either H. pylori or H. pylori plus kimchi. 943 genes were identified as significantly increased or decreased genes according to H. pylori infection and 68 genes as significantly changed between H. pylori infection and H. pylori plus kimchi ( p<0.05). Gene classification and Medline database showed DLL4, FGF18, PTPRN, SLC7A11, CHAC1, FGF21, ASAN, CTH, and CREBRF were identified as significantly increased after H. pylori, but significantly decreased with kimchi and NEO1, CLDN8, KLRG1, and IGFBP1 were identified as significantly decreased after H. pylori, but increased with kimchi. After KEGG and STRING-GO analysis, oxidative stress, ER stress, cell adhesion, and apoptosis genes were up-regulated with H. pylori infection but down-regulated with kimchi, whereas tissue regeneration, cellular anti­oxidative response, and anti-inflammation genes were reversely regulated with kimchi ( p<0.01). Conclusively, transcriptomes of H. pylori plus kimchi showed significant biological actions.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              KEGG: new perspectives on genomes, pathways, diseases and drugs

              KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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                Author and article information

                Journal
                J Clin Biochem Nutr
                J Clin Biochem Nutr
                JCBN
                Journal of Clinical Biochemistry and Nutrition
                the Society for Free Radical Research Japan (Kyoto, Japan )
                0912-0009
                1880-5086
                September 2021
                27 March 2021
                : 69
                : 2
                : 171-187
                Affiliations
                [1 ]Daejeon University School of Oriental Medicine , Daejeon, 34520, Korea
                [2 ]Seoul Center, Korea Basic Science Institute , Seoul, 02456, Korea
                [3 ]CJ Food Research Center , Suwon, 16471, Korea
                [4 ]CHA Cancer Preventive Research Center , CHA Bio Complex, Pangyo, 13497, Korea
                [5 ]Medpacto Research Institute, Medpacto , Seoul, 06668, Korea
                Author notes
                [* ]To whom correspondence should be addressed. E-mail: hahmkb@ 123456hotmail.com
                Article
                DN/JST.JSTAGE/jcbn/20-116 20-116
                10.3164/jcbn.20-116
                8482382
                34616109
                cec628c8-2b95-407d-bba4-8f4d6e3d7d29
                Copyright © 2021 JCBN

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 14 July 2020
                : 15 November 2020
                Categories
                Original Article

                Biochemistry
                h. pylori,rnaseq,transcriptome,biomarker,kimchi
                Biochemistry
                h. pylori, rnaseq, transcriptome, biomarker, kimchi

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