MitoVariome: a variome database of human mitochondrial DNA
© Lee et al; licensee BioMed Central Ltd. 2009
Published: 3 December 2009
Mitochondrial sequence variation provides critical information for studying human evolution and variation. Mitochondrial DNA provides information on the origin of humans, and plays a substantial role in forensics, degenerative diseases, cancers, and aging process. Typically, human mitochondrial DNA has various features such as HVSI, HVSII, single-nucleotide polymorphism (SNP), restriction enzyme sites, and short tandem repeat (STR).
We present a variome database (MitoVariome) of human mitochondrial DNA sequences. Queries against MitoVariome can be made using accession numbers or haplogroup/continent. Query results are presented not only in text but also in HTML tables to report extensive mitochondrial sequence variation information. The variation information includes repeat pattern, restriction enzyme site polymorphism, short tandem repeat, disease information as well as single nucleotide polymorphism. It also provides a graphical interface as Gbrowse displaying all variations at a glance. The web interface also provides the tool for assigning haplogroup based on the haplogroup-diagnostic system with complete human mitochondrial SNP position list and for retrieving sequences that users query against by using accession numbers.
MitoVariome is a freely accessible web application and database that enables human mitochondrial genome researchers to study genetic variation in mitochondrial genome with textual and graphical views accompanied by assignment function of haplogrouping if users submit their own data. Hence, the MitoVariome containing many kinds of variation features in the human mitochondrial genome will be useful for understanding mitochondrial variations of each individual, haplogroup, or geographical location to elucidate the history of human evolution.
Human mitochondrial genome is 16,569 base pairs (bp) in length, a closed, circular molecule located within the cytoplasmic mitochondria . It has 37 genes: 22 transfer RNA genes, 13 protein coding genes, two ribosomal RNA genes, and one non-coding region containing the origin of replication. Twenty-eight of these genes are encoded by the heavy strand, and nine by the light strand.
Mitochondrial DNA is known for maternal clonal inheritance, rapid evolutionary rate, lack of introns, absence of recombination events, and haploidy [2–4]. Mitochondrial variations are linked to the origin of humans, and play a substantial role in forensics, degenerative diseases, cancers, and the aging process . These mitochondrial DNA roles caused the development of various mitochondrial databases, i.e. MITOMAP containing information related to human evolution, disease , mtDB having the human mitochondrial DNAs which have not been deposited in a publicly available database such as GenBank , Mitome containing data on whole mitochondrial genomes for metazoan species , and MitoRes containing data on nuclear-encoded mitochondrial-related products for metazoan species. However, these databases haven't provided the integrated variation information. The environment of these web-based databases typically lacks a user-friendly and interactive platform for presenting all kinds of variation information.
Here we present an extensive web-based database, MitoVariome. The database will help researchers to identify mitochondrial DNA sequence variation, which is useful for understanding human evolution. It has integrated information such as gene and disease annotation. It provides a user-friendly interface to accept user-defined rules and display graphical views of variations and gene structures.
Methods and results
The database consists of over 5,000 complete human sequences of mitochondrial DNA. They were obtained from the NCBI GenBank database, MITOMAP at http://www.mitomap.org, and mtDB at http://www.genpat.uu.se/mtDB/. The nucleotide position and gene annotation corresponding to those of rCRS (revised Cambridge Reference Sequence)  were used. Geographic origin and disease information of human mitochondrial DNA were parsed from the above mentioned databases.
Assignment of haplogroup
Each human mitochondrial DNA sequence was aligned with the rCRS by ClustalW software and haplogroups were assigned defined by sequence differences from the rCRS. The haplogroup-diagnostic nucleotide sequence variants were assembled from the information from the references [10, 11]. The distribution of each haplogroup is shown in statistics page http://variome.kobic.re.kr/MitoVariome/statistics.jsp. The mitochondrial haplogroups in this study are divided into twenty groups broadly. We defined mitochondrial DNA sequences sharing over 95 percent of SNPs of each haplogroup as each haplogroup.
Finding of short tandem repeats, restriction enzyme sites
We used mreps  tool to detect short tandem repeats in mitochondrial DNA. We found all the mitochondrial tandem repeats which have at least two bases in a repeated unit and two copies as the minimum threshold. From the tandem repeat analysis, we found that each human mitochondrial DNA had on average 929 tandem repeat regions. Also, around 31.2% of each mitochondrial genome was detected as tandem repeat regions.
In order to predict restriction enzyme cleavage sites in human mitochondrial DNA, we used the 'restrict' program from the EMBOSS package. These analyses used 19 restriction enzymes which are frequently used in several studies on mitochondrial genomes. Each human mitochondrial DNA had on average 506 restriction enzyme sites http://variome.kobic.re.kr/MitoVariome/view_enzymelist.jsp. On average there were 99 MseI restriction enzyme sites (TTAA) at the most and one XhoI restriction enzyme site (CTCGAG) at the least.
Mapping disease information
Mapping disease information to mitochondrial DNA was performed by using mitochondrial disease information (Leber Hereditary Optic Neuropathy, Alzeimer's Disease, Parkinsons's Disease, and so on) on coding and non-coding regions. This information was obtained from Pubmed and MITOMAP. There are forty-two reported mitochondrial DNA base substitution diseases in our database (disease listing in http://variome.kobic.re.kr/MitoVariome/view_diseaselist.jsp, and 235 sites were related to base substitution diseases.
Visualization of results
Tables and two-dimensional view are provided to the users as visualize the final resultants. The tables provide detailed information of genome/gene and diseases associated with variation data. This information linked to GenBank and Pubmed databases with ID number and users can download sequences in FASTA files. The two-dimensional view result is shown by a method developed using Gbrowse . This browser provides a graphical view of the mitochondrial genome/gene structure and the results obtained from above mentioned analyses.
There are two additional applications in the database for further study, Haplogrouping and Get sequences. In order to assign haplogroup with user provided data, MitoVariome has the Haplogrouping application. By entering a user provided SNPs list, MitoVariome is matching with SNP positions in each haplogroup. It provides a table with over 50% matched haplogroup(s); haplogroup name, matched SNPs, unmatched SNPs, the count of matched SNPs and SNPs in each group, and percentage of matched over SNPs in each group. The second application, Get sequence, is provided in MitoVariome. Users can download the human mitochondrial genome sequences by typing accession numbers.
MitoVariome was constructed as a database and analysis server for human mitochondrial DNA variation. It provides a platform to retrieve all kinds of variation information found in human mitochondrial DNA. MitoVariome provides assignment for the haplogroup with user provided SNP list by using rCRS information. MitoVariome database can be accessed at http://variome.kobic.re.kr/MitoVariome.
Other papers from the meeting have been published as part of BMC Bioinformatics Volume 10 Supplement 15, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.
The authors would like to thank Byun, Ha-na who designed the logo of MitoVariome. This research was supported by a grant from the Korean Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Program and the Korean Ministry of Science and Technology (MOST) under grant number (M10757020001-07N5702-00110). We thank Maryana Bhak for editing the manuscript.
This article has been published as part of BMC Genomics Volume 10 Supplement 3, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/10?issue=S3.
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