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Laboratoire de Séquences Répétées et Centromères Humains, Institut de Génétique Humaine, Montpellier, France
3To whom correspondence should be addressed. E-mail: christoph.grunau{at}methdb.de.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSTRUCTURE OF THE METHYLATION...SEARCHING IN MethDBFUTURE ASPECTSLITERATURE CITED |
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KEY WORDS: • DNA methylation • MethDB • epigenetics • database • cancer • 5-methylcytosine
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSTRUCTURE OF THE METHYLATION...SEARCHING IN MethDBFUTURE ASPECTSLITERATURE CITED |
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), and the importance of DNA methylation for development and health of an organism was recognized early on (2
). During the past 20 y, the findings about DNA methylation have been accumulating, and the introduction of new techniques has led to an explosion of available data in recent years. Many new insights about the biological role of DNA methylation have been gained. In particular, the relationship between cancer development and abnormal methylation might bring about promising new approaches in diagnosis and therapy (3
). Despite this, many aspects of DNA methylation remain enigmatic, and the combined expertise of researchers in many fields will be necessary to clarify its biological role. Even for experts the comparison of experimental results and the extraction of common motifs often are difficult because experimental strategies and techniques differ considerably. In addition general publication standards for data representation have never been established. To find the related information, bibliographic databases are helpful in many cases, but a user who endeavors to scan the literature to find methylation data for a particular gene, tissue or disease will face a labor-intensive and time-consuming task. Moreover, many of the original findings have never been published due to the limitations and the character of paper journals. For this reason we established a database that is exclusively dedicated to DNA methylation: MethDB (4
) (http://www.methdb.net). For the moment the thematic focus of MethDB lies on the fifth base in eukaryotic cells, 5-methylcytosine (5-MC).4
A description of the structure of MethDB has been published before (4
), and in this article we propose strategies for making effective use of the database. |
STRUCTURE OF THE METHYLATION DATA |
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TOPABSTRACTINTRODUCTIONSTRUCTURE OF THE METHYLATION...SEARCHING IN MethDBFUTURE ASPECTSLITERATURE CITED |
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SEARCHING IN METHDB |
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TOPABSTRACTINTRODUCTIONSTRUCTURE OF THE METHYLATION...SEARCHING IN MethDBFUTURE ASPECTSLITERATURE CITED |
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).
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Users who are interested in total 5-MC content data will follow the corresponding link on the search page that brings them to a query mask (Fig. 2
). Here, the desired species, sex, tissue and gene or locus can be entered. For the phenotype there exists a basic choice between tumor samples, healthy wild-type samples or both. The DNA type can be total DNA, nonrepetitive genomic regions (unique sequences), repetitive sequences and single genomic fragments. Generally, single genomic fragments are parts of genes, regulatory regions or specific hybridization loci. There are many experimental ways to determine the degree of DNA methylation, and accordingly we find a rich choice of absolute and relative units for the 5-MC content. Some of these are in principle convertible into each other (e.g., "mol% of all nucleotides" and "mol% of 5-MC + cytosine") as long as the common data basis is known (e.g., guanine plus cytosine content). This is not the case for many data sets. Besides, comparison of data from different sources should be performed with care because the systematic errors apparently can be considerable. Generally, the policy of MethDB is to provide the raw data as they were published or submitted by the author and leave the interpretation to the user of the database. Further choices for the 5-MC content units are " DMH" for "differential methylation hybridization" and "MSP" for "methylation-sensitive polymerase chain reaction." Both are experimental methods used to determine the methylation degree in specific DNA fragments. They deliver relative units and values between 0 and 1.
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A user who wants to find sequence-specific methylation patterns or profiles must follow the corresponding link on the search page, which gives access to another query mask (Fig. 3
). Here again the requested species, sex, tissue, gene and phenotype can be entered. Every sequence for which methylation data exist in MethDB has a unique identification number (sequence ID). If this number is known (e.g., as a reference in a publication), the data also can be accessed directly by searching for this number. As for the 5-MC content data, the result of the query is a table in which each row represents one experiment. The Details link brings the user to the data for this experiment, including a graphic representation of the methylation patterns or of the methylation profile, respectively (Fig. 4
). The raw data are accessible via the hyperlinked pattern/profile ID numbers. A click on this ID number gives the user access to either a table for the methylation profiles or a guanine-adenine-thymine-cytosine-5-MC sequence for the methylation patterns. Clicking on the sequence ID provides the user with information about the analyzed sequence including exon-intron structure and regulatory sequences in the analyzed region. Another link leads to a description of the method that was used to generate the methylation data. If polymerase chain reaction was used, the reaction conditions and primer sequences are available. The structure of MethDB allows several experiment entries to exist for one sample entry. It is imaginable that for a certain sequence, the degree of methylation was determined by methylation-sensitive polymerase chain reaction and additionally that the methylation profile was generated with bisulfite genomic sequencing. Sample and experiment therefore are placed in different tables in the database. On the experimental details page, the sample ID link gives access to data of the sample used in this experiment. Different sequence fragments are treated as different samples. The same is true for the complement DNA strand if a strand-specific method was used to determine the distribution of 5-MC (e.g., bisulfite genomic sequencing). If the investigated sequence section has related 5' or 3' neighbors, links are available to the corresponding sample entries.
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), drugs (6
) and other environmental agents (7
) can influence the methylation state of DNA. Therefore, a table was incorporated in MethDB that contains descriptions of environmental conditions to which some of the samples were exposed. This table can be searched (Fig. 5
), and matching entries are shown as a list of experiments comparable with the scheme described above. It also is conceivable that users who have determined the 5-MC content in a certain sample wish to compare their results with entries in the database. For this purpose the phenotype search mask was created: users can enter their own values (or a value range) and find experiments that have delivered similar values for a similar type of sample.
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MethDB is a public database, and the scientific community is invited not only to submit data but also to comment on its design and accessibility and to suggest improvements. Data can be sent to an annotator by E-mail (submit{at}methdb.de). An E-mail submission form is available on the submission page. Methylation profiles should be submitted as tables of three comma-separated values: position in a mother-sequence, degree of methylation at this site between 0 (no methylation) and 1 (complete methylation), and standard error (if available). Methylation patterns are sequence files in a modified FASTA format with all nucleotide bases except 5-MC written in lower case and 5-MC residues written in upper case C. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater than (>) symbol in the first column (8
). For bisulfite genomic sequencing data (9
), methylation patterns in the above file format can be conveniently generated with the MethTools Software Suite (10
). For help with the conversion of data or for submitting data in a different format, please send a message to help{at}methdb.de.
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FUTURE ASPECTS |
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TOPABSTRACTINTRODUCTIONSTRUCTURE OF THE METHYLATION...SEARCHING IN MethDBFUTURE ASPECTSLITERATURE CITED |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Part of this work was supported by the Klaus Tschira Foundation (Heidelberg, Germany).
4 Abbreviations used: 5-MC, 5-methylcytosine; MethDB, DNA Methylation Database; PCR, polymerase chain reaction.
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LITERATURE CITED |
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TOPABSTRACTINTRODUCTIONSTRUCTURE OF THE METHYLATION...SEARCHING IN MethDBFUTURE ASPECTSLITERATURE CITED |
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1. Hotchkiss, R. D. (1948) The quantitative separation of purines, pyrimidines, and nucleosides by paper chromatography. J. Biol. Chem. 175:315-332.
2. Holliday, R. & Pugh, J. E. (1975) DNA modification mechanisms and gene activity during development. Science 187:226-232.
3. Costello, J. F. & Plass, C. (2001) Methylation matters. J. Med. Genet. 38:285-303.
4. Grunau, C., Renault, E., Rosenthal, A. & Roizes, G. (2001) MethDB: a public database for DNA methylation data. Nucleic Acids Res. 29:270-274.
5. Poirier, L.A. (1986) The role of methionine carcinogenesis in vivo. Adv. Exp. Med. Biol. 206:269-282.[Medline]
6. Creusot, F., Acs, G. & Christman, J.K. (1982) Inhibition of DNA methyltransferase and induction of Friend erythroleukemia cell differentiation by 5-azacytidine and 5-aza-2'-deoxycytidine. J. Biol. Chem. 257:2041-2048.
7. Cox, R. (1985) Selenite: a good inhibitor of rat-liver DNA methylase. Biochem. Int. 10:63-69.[Medline]
8. Pearson, W. R. & Lipman, D. J. (1988) Improved tools for biological sequence comparison. Proc. Natl. Acad. Sci. USA 85:2444-2448.
9. Frommer, M., McDonald, L. E., Millar, D. S., Collis, C. M., Watt, F., Grigg, G. W., Molloy, P. L. & Paul, C. L. (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc. Natl. Acad. Sci. USA 89:1827-1831.
10. Grunau, C., Schattevoy, R., Mache, N. & Rosenthal, A. (2000) MethTools: a toolbox to visualize and analyze DNA methylation data. Nucleic Acids Res. 28:1053-1058.
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