{"id":1220,"date":"2015-06-04T16:22:05","date_gmt":"2015-06-04T14:22:05","guid":{"rendered":"http:\/\/bioinfo2.ugr.es\/NGSmethDB\/?page_id=1220"},"modified":"2019-05-15T16:57:51","modified_gmt":"2019-05-15T14:57:51","slug":"manual","status":"publish","type":"page","link":"https:\/\/bioinfo2.ugr.es\/NGSmethDB\/manual\/","title":{"rendered":"Manual"},"content":{"rendered":"

{{How to cite NGSmethDB}}<\/strong><\/span><\/p>\n

Introduction<\/span><\/h2>\n

NGSmethDB is a dedicated database to store whole-genome methylation maps or methylomes (1\u20133). Methylomes are obtained by single-cytosine methylation profiling based on high-throughput sequencing (NGS) of sodium-bisulfite treated DNA (4, 5). Furthermore, NGSmethDB<\/em> includes two additional datasets: 1) methylation segments (i.e. genome regions of homogeneous methylation); and 2) differentially methylated single-cytosines.<\/span><\/p>\n

<\/a>Methylation profiling<\/span><\/h2>\n

Publicly available short-read data sets from NGS bisulfite sequencing projects for different cell lines, primary and pathological tissues are downloaded mainly from NCBI GEO (6) and the ROADMAP project (7).<\/span><\/p>\n

The same pipeline, MethFlow<\/em> (8), was used to produce high-quality methylomes under uniform conditions, which enables comparative downstream analyses. MethFlow<\/em> integrates the stringent quality controls of MethylExtract<\/em> (9) with several other third-part scripts. A first step was to run Trimmomatic<\/em> (10) for adapter trimming and removing of low quality 3\u2019 ends. The alignment to a three letter genome was made by means of Bismark<\/em> (11) which uses Bowtie2<\/em> (12) as aligner. The next step was running BSeQC<\/em> (13) for the elimination of known technical artefacts. And finally, MethylExtract<\/em> was run for methylation calling and genotyping. MethylExtract<\/em> minimizes several important error sources like sequencing errors, bisulfite failure, clonal reads, and single nucleotide variants. The result of the entire process is a high-quality, whole-genome methylation map.<\/span><\/p>\n

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<\/a>Database back-end<\/span><\/h2>\n

Single-cytosine methylation and differential methylation data are stored hierarchically in MongoDB<\/em> (https:\/\/www.mongodb.com\/<\/a>), a NoSQL<\/em> database with JSON<\/em>-formatted documents and dynamic schemas. This makes the joining and the comparison of data of different samples easier and faster. Each assembly is stored in a database and inside every database there is collection for each chromosome. Within the collection, each JSON<\/em>-like document represents a cytosine and contains hierarchically all genotype, differential methylation and methylation data of all individuals and samples. The first level is the data type (genotype, methylation or differential methylation), the second is the individual, the third is the sample and the fourth are the data themselves.<\/span><\/p>\n

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<\/a>Data access and visualization<\/span><\/h2>\n

The data stored in NGSmethDB can be accessed as follows.<\/span><\/p>\n

Downloading database dumps for entire methylomes<\/span><\/h3>\n

Complete methylomes zipped with bzip2<\/em> (http:\/\/www.bzip.org\/<\/a>) can be downloaded from the Database dumps page. Once unzipped, you get a tab-delimited file\u00a0which can be directly open in any spreadshet for downstream analyses.<\/span><\/p>\n

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Using the web form<\/span><\/h3>\n

On the database web form (Database access -> Web access) you can easily select for a chromosome or chromosome region to get a table in BED format (https:\/\/genome.ucsc.edu\/FAQ\/FAQformat.html#format1<\/a>) with the corresponding cytosine methylation levels. The table can be downloaded and directly open in any spreadsheet\u00a0for downstream analyses.<\/span><\/p>\n

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Using track hubs<\/span><\/h3>\n

A third way to access NGSmethDB<\/em> data is through standard track hubs (14), which provide an efficient mechanism for visualizing remotely hosted Internet-accessible collections of genome annotations. Hub datasets can then be fully integrated into the University of California Santa Cruz (UCSC) Genome Browser<\/strong> (15). In this way, NGSmethDB<\/em> data can be visualized and compared<\/strong> to a plethora of third-part annotations. In addition, UCSC tools, as the Table Browser<\/strong> or Data Integrator<\/strong>, provide a way to 1) retrieve detailed NGSmethDB<\/em> datasets from any genome, chromosome, genome region, gene, SNP or whatever other genome element; 2) combine methylation data and any other third-part annotation into a single set of data based on a specific join criteria \u2013for example, this can be used to find the methylation state of cytosines that intersect with CpG islands; and 3) directly upload NGSmethDB<\/em> datasets to public bioinformatics platforms as Galaxy<\/em> (16), GenomeSpace<\/em> (17) or GREAT<\/em> (18) for further downstream analyses.<\/span><\/p>\n

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Programmatic access to the database: the NGSmethDB API server<\/span><\/h3>\n

A Node.js<\/em> (https:\/\/nodejs.org\/en\/<\/a>) application (NGSmethDB API server<\/em>) has been implemented on our server, which provides access to the MongoDB<\/em> database via a RESTful API<\/em> (19). This allows for three interactive or programmatic ways to access NGSmethDB<\/em> data:<\/span><\/p>\n

HTTP access<\/span><\/h4>\n

A first way to access de NGSmethDB API Server<\/em> is by issuing simple HTTP queries on the navigation bar of a web browser. The server responds by sending you a JSON file. Examples of the available commands follow.<\/span><\/p>\n

This access method is recommended only to retrieve data on a single position, or regions of moderate size as exons, genes, etc. Querying larger regions can bother your browser; if needed use instead the track hubs or the programmatic access.<\/span><\/pre>\n
Get API server content information<\/span><\/h5>\n