MITOMASTER: A Bioinformatics Tool for the Analysis of
Mitochondrial DNA Sequences
Marty C. Brandon,1–3Eduardo Ruiz-Pesini,3Dan Mishmar,3Vincent Procaccio,3Marie T. Lott,3Kevin Cuong Nguyen,1,3
Syawal Spolim,3Upen Patil,3Pierre Baldi,1,2and Douglas C. Wallace2,3?
1Department of Information and Computer Science, University of California, Irvine, Irvine, California
2Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, California
3The Center for Molecular and Mitochondrial Medicine and Genetics (MAMMAG), University of California, Irvine, Irvine, California
Communicated by Richard G.H. Cotton
Received 25 June 2007; accepted revised manuscript 7 March 2008.
Published online 19 June 2008 in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/humu.20801
ABSTRACT: We have developed a computer system,
MITOMASTER, to make analysis of human mitochon-
drial DNA (mtDNA) sequences efficient, accurate, and
easily available. From imported sequences, the system
identifies nucleotide variants, determines the haplogroup,
rules out possible pseudogene contamination, identifies
novel DNA sequence variants, and evaluates the
potential biological significance of each variant. This
system should be beneficial for mtDNA analyses of
biomedical physicians and investigators, population
biologists and forensic scientists. MITOMASTER can
be accessedat http://mammag.web.uci.edu/twiki/bin/
Hum Mutat 30, 1–6, 2009.
& 2008 Wiley-Liss, Inc.
KEY WORDS: mitochondria;
mtDNA; variation; bioinformatics; clinical analysis;
There is a rapidly growing requirement for analyzing human
mitochondrial DNA (mtDNA) variation for a wide variety of
applications, including: determining its contribution to rare and
common genetic diseases; identification and interpretation of
acquired variants in cancer, aging, and age-related diseases;
analyzing variation for lineage and populations studies; and
making individual identifications in forensics [Coskun et al.,
2003;Wallace, 2005b, 2005a]. Consequently, large quantities of
mtDNA sequence data are being acquired using automated
sequencing [Sarzi et al., 2007], heteroduplex screening using
denaturing gradient gels [Wong et al., 2002], Surveyor Endonu-
clease digestions [Bannwarth et al., 2005], and chip-based
sequencing [Maitra et al., 2004; van Eijsden et al., 2006].
The 16,569-nucleotide (nt) circular mtDNA encodes a 12S and
16S rRNA, 22 tRNAs, 13 polypeptides, and a 1,121-nt control
region. The 13 polypeptides encompass critical components of the
mitochondrial energy production pathway, oxidative phosphor-
ylation (OXPHOS): seven subunits (ND1, 2, 3, 4L, 4, 5, and 6) of
complex I, one subunit (cytochrome b, cytb) of complex III, three
subunits (COI, II, and III) of complex IV, and two subunits (ATP6
and 8) of complex V [Wallace, 2007].
The maternally inherited mtDNA is present in thousands of
copies per cell and has a very high mutation rate. New mutations
arise in cells mixed among normal mtDNAs (heteroplasmy), and
segregate randomly during cytokinesis. Therefore, clinical pheno-
types are determined by the type and severity of the mutations and
the percentage of heteroplasmy [Wallace, 2005a; Wallace et al.,
The mtDNA nucleotide positions are numbered according to
the modified Cambridge Reference Sequence (mCRS) (accession
number: AC_000021.2 gi:115315570) [Anderson et al., 1981;
Howell et al., 1992; Ruiz-Pesini et al., 2007]. Hence, a new
sequence can be described relative to the mCRS by specifying only
the nucleotide position and deviant base. Deviant bases fall into
four categories: recent pathogenic mutations, ancient polymorph-
isms, age-related somatic mutations, or altered bases in nuclear
DNA (nDNA)-encoded mtDNA pseudogenes.
New pathogenic mtDNA mutations have been estimated at a
frequency of 1 in 5,000 individuals [Schaefer et al., 2004; Wallace
et al., 2007]. Hundreds of putative pathogenic mtDNA variants
have been identified [Wallace et al., 2007]. Mild pathogenic
mutations cause disease when homoplasmic, while severe muta-
tions can be pathogenic when heteroplasmic [Wallace et al., 2007].
Ancient mtDNA polymorphisms are not only common in the
mtDNA, but occur in discrete groups of related haplotypes, known
as haplogroups. This is because the mtDNA is exclusively maternally
inherited, so that the mtDNAs can only change by the sequential
accumulation of mutations [Cann et al., 1987; Ingman et al., 2000;
Johnson et al., 1983; Merriwether et al., 1991; Mishmar et al., 2003;
Wallace et al., 1999]. The geographical association of mtDNA
haplogroups is a consequence of the presence of adaptive variants
that permitted the associated mtDNAs to become established in
specific environments [Mishmar et al., 2003; Ruiz-Pesini et al., 2004;
Ruiz-Pesini and Wallace, 2006]. Today, these same variants can
influence longevity and predisposition to disease [Baudouin et al.,
2005; Chagnon et al., 1999; De Benedictis et al., 1999; Ivanova et al.,
1998; McMahon et al., 2000; Niemi et al., 2003; Ross et al., 2001; van
der Walt et al., 2004, 2003].
& 2008 WILEY-LISS, INC.
Contract grant sponsor: National Institutes of Health (NIH); Grant number:
NS21328, AG24373, DK73691, AG13154.
?Correspondence to: Douglas C. Wallace, Ph.D., Donald Bren Professor of
Molecular Medicine and Director, Center for Molecular and Mitochondrial Medicine
and Genetics, University of California, Irvine, Irvine, CA 92697-3940. E-mail:
Mutations in the mtDNA also arise in somatic tissues as a
component of the aging process [Michikawa et al., 1999; Murdock
et al., 2000; Wang et al., 2001; Zhang et al., 2003] and have been
proposed to be the aging clock [Coskun et al., 2004; Wallace,
2005a]. Somatic variants also arise in cancerous cells [Brandon
et al., 2006; Petros et al., 2005].
Finally, throughout animal cellular evolution, fragments of the
mtDNA sequence have been transferred to the nDNA to generate
nuclear mtDNA (NUMTs) pseudogene sequences [Mishmar et al.,
2004; Wallace, 2007]. These can be mistaken for ‘‘heteroplasmic’’
pathogenic mtDNA variants [Davis and Parker, 1998; Davis et al.,
1997; Hirano et al., 1997; Wallace et al., 1997].
All of these considerations make interpreting the biological
significance of mtDNA sequence variants particularly challenging.
Not only must a large and diverse literature be known and employed
in the analysis, but multiple interacting factors must be considered.
For a clinical sequence, not only is it necessary to identify putative
new mutations, relative to the mCRS, the background mtDNA
variation must be identified and the functional significance of
previously observed, as well as novel, variants must be evaluated.
Potentially spurious NUMT variants must also be identified and the
contribution of mtDNA variants evaluated.
Therefore, simple catalogs, such as all known pathogenic
mtDNA mutations and common haplogroup polymorphisms,
are no longer adequate for interpreting mtDNA sequence
variation (www.mitomap.org). Such databases must now be
extended to include intelligent analytical systems that can
immediately bring to bear other relevant information on the
interpretation of new mtDNA sequences. This is the purpose of
the analytical system, MITOMASTER (http://mammag.web.uci.
edu/twiki/bin/view/Mitomaster), described in this work.
Results and Procedures
Logic of the MITOMASTER mtDNA Sequence Analysis
MITOMASTER permits the automatic evaluation of mtDNA
sequence variation through comparisons of a series of comple-
mentary data sets, each encapsulating the known information
about one of the classes of mtDNA variants: reference sequence,
functional domains, haplogroup variants, pathogenic mutations,
First, the mtDNA sequence is aligned with the mCRS to identify
all deviant nucleotides specified by nucleotide numbers (Fig. 1). We
use the mCRS, instead of a nodal African sequence from haplogroup
L0, because current human mtDNA sequence numbers are based on
the original CRS and small insertion-deletion mutants in another
sequence could misalign the MITOMASTER sequence analyses
relative to the literature. The sequence variants are then compared to
our library of 247 NUMTs to determine if the variant might be the
product of a contaminating nuclear pseudogene sequence. The
sequence variants are also compared to our library of haplogroups.
This can be done in two ways: 1) recognition of haplogroup-specific
polymorphisms; or 2) comparison of the sequence to the
2,452mtDNA sequences in our mtDNA phylogenetic tree and
identifying the most similar sequence and its haplotype [Ruiz-Pesini
et al., 2007]. Once the haplogroup has been determined, the ancient
haplogroup-specific mtDNA sequence variants are subtracted. The
remaining sequence variants must be recent, and thus potentially
pathogenic. Each of the recent variants is then classified according to
the region of the mtDNA in which it occurs: polypeptide, tRNA,
rRNA, or control region. Protein coding gene variants are further
characterized by codon position, the affect on the amino acid
sequence, and the interspecific species conservation index (CI) of the
mutated amino acid [Mishmar et al., 2003]. Similarly, tRNA and
rRNA variants are localized within the secondary structural models
of RNAs [Ruiz-Pesini and Wallace, 2006] and the CI of the affected
base is calculated.
MITOMASTER interaction of three elements: database, analy-
sis, and interface (Fig. 2). Though these parts are generally
intended to be used together as an online tool, the database and
code library that implements the analyses can function indepen-
dent of the interface. As such, they provide an extensible
framework for bioinformaticians to issue queries directly to the
database or write problem-specific programs. Each of the
components is discussed in more detail below.
The primary data set is the 16,569-nt mCRS, a haplogroup H
subject [Anderson et al., 1981] that has been corrected for errors,
but with insertion-deletion errors maintained to sustain the
original nucleotide numbering system [Howell et al., 1992].
Hence, each mCRS nucleotide number is a unique identifier
permitting indexing of nucleotides. The sequence has been
annotated to encompass all of the known mtDNA functional
domains from the MITOMAP database (www.mitomap.org).
The second data set encompasses the 247 known human
NUMT region sequences [Mishmar et al., 2004]. These NUMTs
have been aligned with the reference sequence to determine the
area spanned and the nt position and altered base for all deviants
relative to mCRS. Since there is an overlap between mtDNA
variants found in legitimate mtDNA sequences and those found in
certain NUMTs, we have elected to provide the user with an
accounting of the proportion of NUMTs that align with the query
sequence region that also harbors the variant of interest.
The third data set encompasses the curated mtDNA sequences
of 2,452 human subjects from around the globe. These sequences
have been prealigned with the mCRS, and tables have been
generated that list all of the observed nucleotide differences. The
current collection of mtDNA sequences encompasses 1,920
complete mtDNA sequences plus 532 coding region mtDNA
sequences. These sequences encompass 3,512 total population
variants including 453 noncoding, 2,563 mRNA, 216 tRNA, and
280 rRNA variant sites [Brandon et al., 2003]. Each of the
sequences has also been assigned to an mtDNA haplogroup.
The fourth data set is the information or pathogenic mtDNA
mutations that have been accumulated over the past 14 years in
our MITOMAP database. This database is curated to assure the
most internally consistent and accurate data possible. Currently,
MITOMAP encompasses 271 nt variants that have been
documented as potentially pathogenic mutations, plus a list of
over 800 unpublished mtDNA sequence variants submitted to the
The fifth data set encompasses multiple amino acid sequence
alignments of the various mtDNA polypeptide genes from an
array of species. The current default number that we query
includes 39 species. This comparison permits calculation of the
interspecific CI of any mutant amino acid.
The final data set involves the structure and common
polymorphisms of the human mtDNA tRNAs and rRNAs
genes [Ruiz-Pesini and Wallace, 2006]. This data set permits
interspecies tRNA and rRNA sequence alignments for functional
analysis and calculation of the CI, with the current default of 17
HUMAN MUTATION, Vol. 30, No. 1, 1–6, 2009
mtDNA Sequence Data Analysis
The algorithms supporting the analysis are implemented within
an object-oriented class library written in Perl. The goal is to create a
reusable code base, tightly integrated with the database, and having a
flexible range of functions for analyzing mitochondrial data.
Analysis by the MITOMASTER system is initiated by the
submission of a new mtDNA sequence under investigation. The
sequence is optimally aligned to the mCRS and the variants are
found. These variants are assigned position numbers based upon
commonly accepted conventions, and the collection of variants is
used to assign the sequence to a haplogroup.
The NUMTsequences encompassed by the patient sequence are
then searched for the same sequence variants. If the variant is
found, the number and region encompassed by the NUMT are
listed for further evaluation.
If NUMTs are eliminated, then the effect of the variant on the
mtDNA gene structure and function is investigated. For polypep-
tide variants, the codon change is identified and its amino acid
and CI are provided. For alterations of tRNAs or an rRNA, the
specific structural location affected is marked and displayed upon
the appropriate diagram.
Once the variant nucleotides have been identified and the
characteristics of the variants ascertained, then the variant
nucleotide is compared to lists of known pathogenic mtDNA
mutations and neutral or adaptive polymorphisms.
MITOMASTER provides an interactive interface through which
users submit an mtDNA sequence, and the sequence variants are
returned with links to the relevant information about the
evaluation. The web page interface is distributable to anyone with
a web browser and can be used to submit or retrieve mtDNA
sequences, to perform clinical analyses, and query the significance
of single mtDNA nucleotide changes.
Because each submitted sequence undergoes a lengthy pre-
processing that includes alignment, haplogrouping, and simulated
transcription and translation, users must wait following submis-
sion of their sequence until the initial processing is completed.
The user is then notified by e-mail when the system has finished
preprocessing. Afterward, the user can access the various outputs
by making specific inquiries.
mtDNA sequence. Variants are extracted by alignment with a reference sequence; the group of extracted variants is used to determine the
haplogroup of the sequence and each individual variant is checked within the database and analyzed to determine its biological effect; variation
within coding loci is further analyzed to determine its coding effect and interspecies rate of conservation.
The algorithm used by biologist, and implemented within MITOMASTER, to identify possible clinical variants within a patient’s
HUMAN MUTATION, Vol. 30, No. 1, 1–6, 2009
The MITOMASTER system also uses an application program-
ming interface (API) that facilitates the implementation of more
specialized analyses. The class API facilitates the extension and
development of software that can use MITOMASTER’s database
for new types of analyses.
Database and Algorithm Design
Unlike typical sequence databases, MITOMASTER models its
data at the individual nucleotide level (Fig. 3). When this level of
granularity is combined with the structured query language
(SQL), many types of biological analyses become possible simply
by querying the database. One such example is the computation of
the average number of amino acid variants found within the
cytochrome oxidase I locus for all the sequences stored in the
database (Table 1).
This query computes an average from two sets of data, those
sequences with an amino acid substitution and those without. The
top query, from ‘‘SELECT seqid, COUNT(peptide) AS pepcount’’
through ‘‘GROUP BY seqid,’’ identifies all of the sequences in the
database that have a variant amino acid. However, some sequences
do not have a variant amino acid, so these need to be assigned a
zero value, which is accomplished by the query from ‘‘SELECT
sequence.id, 0 AS pepcount’’ to ‘‘WHERE locusid516)) AS
FOO.’’ These two datasets are combined by ‘‘UNION’’ and
averaged by the ‘‘SELECT AVG (pepcount)’’ function.
Though powerful, implementing relational models to capture
all the nuances of biological systems can make them complicated.
MITOMASTER uses virtual tables, i.e., ‘‘views,’’ to create a
simplified meta-layer to the data model when needed. Views are
conceptualizations of the set of data output from queries.
MITOMASTER’s views facilitate easy retrieval of data about many
different aspects of the biology of each mtDNA nucleotide; e.g.,
entries made in the dna table. During simulated transcription and translation processes, all variation (relative to the mCRS) is entered into the
rna and aa tables, respectively. Some variation involves multiple base alterations and these cases are grouped in the ‘‘variant’’ table. The
aggregated variants that define the known haplogroups are represented in the haplogroups data set, the nodes are the branch points of the
mtDNA phylogenetic tree.
Simplified schema diagram for MITOMASTER’s database. DNA sequences are decomposed into their constituent nucleic acids and
Number of Amino Acid Variants Found Within the Cytochrome
Oxidase I Locus
MITOMASTER SQL Query That Computes the Average
SELECT seqid, COUNT(peptide) AS pepcount
GROUP BY seqid
SELECT sequence.id, 0 AS pepcount
sequence.id NOT IN
(SELECT DISTINCT seqid
WHERE locusid516))AS FOO
interface allows users to submit either files of whole mtDNA
sequences or individual variants for analysis. Programs within the
analysis component compare the submission with the mCRS, extract
the variants found within submitted sequences, perform NUMT
screening on the variants extracted, assign the sequences to a
haplogroup, and determine the predicted functional effect of each
MITOMASTER’s functional components. A website
HUMAN MUTATION, Vol. 30, No. 1, 1–6, 2009
reference sequence nucleotide, somatic mutation, pathological
mutation, population polymorphism, etc.
Data on mtDNA sequence variation has been collected under
the auspices of the MITOMAP curation team using Excel
(Microsoft, Redmond, WA) spreadsheets, and is available at the
MITOMAP website (www.mitomap.org). Data from spreadsheets
is transferred into PostgreSQL tables using the Perl module
Spreadsheet::ParseExcel. Submitted sequences can be analyzed
from fasta (complete nucleotide sequence) formatted text files.
Data is then extracted (parsed) using bioperl [Stajich et al., 2002].
MITOMASTER is implemented on a dual-processor, 2-GHz
Pentium IV Linux server running Apache 2.2 and PostgreSQL 8.1.
cascading style sheets to make the users interaction with
MITOMASTER more dynamic. The HTML::Mason Perl module
is used to embed subroutines into MITOMASTER that will
instantaneously provide updated values for changing information.
Web server programs are implemented with mod_perl 2.0 to
increase efficiency and security. Use of the CGI Perl module
facilitates efficient application development, while use of the DBI
Perl module provides database connectivity.
In this report, we describe a new computational tool, MITO-
MASTER, to assist in the management and analysis of the
increasing quantity and complexity of mtDNA data of relevance to
mitochondrial medicine, human population genetics, and foren-
sics. MITOMASTER is designed to accept new human mtDNA
sequences and to then apply to the sequence analysis a sequential
array of analytical functions with domain-specific factual
information. Through this strategy, the program follows a similar
logic process that might be used by an expert investigator to draw
on the extensive data embedded in the literature for interpreting a
new mtDNA sequence. As a result, the nonmitochondrial expert
can interpret the occasional mtDNA sequence with confidence.
Because the initial steps in analysis of DNA sequences are often
the same, the basic architecture of MITOMASTER should be
readily extendable for use in the analysis of other types of
sequence data. Therefore, it is our hope that the architecture we
have developed using the human mtDNA as a model system will
pave the way for the development of similar sequence knowledge
bases for other genomic regions.
This work was supported by an NIH training grant, to P.F.B and D.C.W,
and by NIH grants NS21328, AG24373, DK73691, and AG13154, to
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