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      MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          DNA methylation plays a crucial role in tumorigenesis and tumor progression, sparking substantial interest in the clinical applications of cancer DNA methylation biomarkers. Cancer-related whole-genome bisulfite sequencing (WGBS) data offers a promising approach to precisely identify these biomarkers with differentially methylated regions (DMRs). However, currently there is no dedicated resource for cancer DNA methylation biomarkers with WGBS data. Here, we developed a comprehensive cancer DNA methylation biomarker database (MethMarkerDB, https://methmarkerdb.hzau.edu.cn/), which integrated 658 WGBS datasets, incorporating 724 curated DNA methylation biomarker genes from 1425 PubMed published articles. Based on WGBS data, we documented 5.4 million DMRs from 13 common types of cancer as candidate DNA methylation biomarkers. We provided search and annotation functions for these DMRs with different resources, such as enhancers and SNPs, and developed diagnostic and prognostic models for further biomarker evaluation. With the database, we not only identified known DNA methylation biomarkers, but also identified 781 hypermethylated and 5245 hypomethylated pan-cancer DMRs, corresponding to 693 and 2172 genes, respectively. These novel potential pan-cancer DNA methylation biomarkers hold significant clinical translational value. We hope that MethMarkerDB will help identify novel cancer DNA methylation biomarkers and propel the clinical application of these biomarkers.

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          The Sequence Alignment/Map format and SAMtools

          Heng Li, Bob Handsaker, Alec Wysoker … (2009)
          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
          Article 0 comments Cited 13698 times – based on 0 reviews     Review now
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            BEDTools: a flexible suite of utilities for comparing genomic features

            Aaron Quinlan, Ira Hall (2010)
            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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              NCBI GEO: archive for functional genomics data sets—update

              Tanya Barrett, Stephen Wilhite, Pierre Ledoux … (2012)
              The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
              Article 0 comments Cited 2456 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Zhixian Zhu
                Qiangwei Zhou
                Yuanhui Sun
                Fuming Lai
                Zhenji Wang
                Zhigang Hao
                Guoliang Li:
                ORCID: https://orcid.org/0000-0003-1601-6640
                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 05 January 2024
                Publication date (Electronic): 27 October 2023
                Publication date PMC-release: 27 October 2023
                Volume: 52
                Issue: D1
                Pages: D1380-D1392
                Affiliations
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University , Wuhan 430070, China
                Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University , Wuhan 430070, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 27 87285078; Fax: +86 27 87286876; Email: guoliang.li@ 123456mail.hzau.edu.cn

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0003-1601-6640
                Article
                Publisher ID: gkad923
                DOI: 10.1093/nar/gkad923
                PMC ID: 10767949
                PubMed ID: 37889076
                SO-VID: b62bc5ae-f158-41b7-9d44-4fce74996789
                Copyright © © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 10 October 2023
                Date revision received : 21 September 2023
                Date received : 15 August 2023
                Page count
                Pages: 13
                Funding
                Funded by: National Key Research and Development Program of China, DOI 10.13039/501100012166;
                Award ID: 2021YFC2701201
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 31 970 590
                Categories
                Subject: AcademicSubjects/SCI00010
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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