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A comprehensive collection of annotations to interpret sequence variation in human mitochondrial transfer RNAs



The abundance of biological data characterizing the genomics era is contributing to a comprehensive understanding of human mitochondrial genetics. Nevertheless, many aspects are still unclear, specifically about the variability of the 22 human mitochondrial transfer RNA (tRNA) genes and their involvement in diseases. The complex enrichment and isolation of tRNAs in vitro leads to an incomplete knowledge of their post-transcriptional modifications and three-dimensional folding, essential for correct tRNA functioning. An accurate annotation of mitochondrial tRNA variants would be definitely useful and appreciated by mitochondrial researchers and clinicians since the most of bioinformatics tools for variant annotation and prioritization available so far cannot shed light on the functional role of tRNA variations.


To this aim, we updated our MToolBox pipeline for mitochondrial DNA analysis of high throughput and Sanger sequencing data by integrating tRNA variant annotations in order to identify and characterize relevant variants not only in protein coding regions, but also in tRNA genes. The annotation step in the pipeline now provides detailed information for variants mapping onto the 22 mitochondrial tRNAs. For each mt-tRNA position along the entire genome, the relative tRNA numbering, tRNA type, cloverleaf secondary domains (loops and stems), mature nucleotide and interactions in the three-dimensional folding were reported. Moreover, pathogenicity predictions for tRNA and rRNA variants were retrieved from the literature and integrated within the annotations provided by MToolBox, both in the stand-alone version and web-based tool at the Mitochondrial Disease Sequence Data Resource (MSeqDR) website. All the information available in the annotation step of MToolBox were exploited to generate custom tracks which can be displayed in the GBrowse instance at MSeqDR website.


To the best of our knowledge, specific data regarding mitochondrial variants in tRNA genes were introduced for the first time in a tool for mitochondrial genome analysis, supporting the interpretation of genetic variants in specific genomic contexts.


The abundance of biological data characterizing the genomics era is contributing to a comprehensive understanding of human mitochondrial genetics. To date more than 30,000 complete human mitochondrial genomes have been sequenced [1] and lots of tools and databases are publicly available allowing to gather large amounts of information about mitochondrial DNA (mtDNA). Nevertheless many aspects are still unclear, specifically about the 22 human mitochondrial transfer RNAs (mt-tRNA).

Thanks to the “four-way wobble rule” and post transcriptional modifications at the first letters of tRNA anticodons [2], only 22 mt-tRNAs are sufficient in humans, as well as in other mammals, to translate all sense codons into 13 subunits of respiratory chain complexes encoded in each single copy of mtDNA [2]. mt-tRNAs could be considered hot spots of mutations [3]: among more than 600 disease associated mutations compiled to date, about 240 were mapped on mt-tRNA genes [4]. However, it is well known that clinical phenotypes appear only when the mutation load exceeds a certain threshold [5], considering the possible co-existence of different mtDNA genotypes within the same cell, tissue or individual, a condition known as heteroplasmy. Thus, if a mutation in an mt-tRNA gene has no consequences on mtDNA replication or transcription, it may instead affect biogenesis and functioning of tRNAs after their transcription [6]. For instance, post-transcriptional modifications by nuclear-encoded enzymes [7, 8] often occur in key positions for a correct tRNA functioning, including folding and codon-anticodon interaction [6, 9, 10]. As a consequence, the lack of a correct post-transcriptional process could cause pathological effects [11, 12].

Some features are shared among human and other mammalian mt-tRNAs, such as the low number of G–C pairs within stems of the 14 tRNAs encoded by the light DNA strand, due to a strong bias in nucleotide content (A, U and C-rich tRNAs), variable D-loop and T-loop sizes, and lack of conserved and semi-conserved signature motifs [13], thus the difficulties linked to the complex process of human tRNA purification and identification of modified nucleotides are often overpassed through predictions based on bovine models [2].

The availability of information about mt-tRNA genes and variants would support the interpretation of mtDNA variants and improve the understanding of molecular mechanisms of disease. However, most bioinformatics tools for variant annotation and prioritization available so far cannot shed light on the functional role of mt-tRNA variations, often focusing only on characterization of missense variants [14, 15].

To this aim, we updated our MToolBox pipeline [16] for mtDNA analysis of high throughput and Sanger sequencing data by integrating tRNA variants annotations in order to identify relevant variants not only in protein coding regions but also in tRNA genes. Pathogenicity predictions retrieved from the literature were added both for tRNA and rRNA gene variants, when available. These information were also provided as custom tracks which can be visualized in the GBrowse at the Mitochondrial Disease Sequence Data Resource (MSeqDR) website [17], conveniently allowing a deep insight into mitochondrial genomics.


Data collection from known databases, web-based resources and literature

All the information collected in this work and those previously collected and already implemented in the MToolBox pipeline [16], come from several resources and the literature about human mtDNA genomics and variation (Table 1). Nucleotide variability scores calculated by applying SiteVar algorithm [18] on 22,691 complete genomes from healthy individuals in the Human Mitochondrial Database, HmtDB (May 2014 update) [19], were reported for each position of the entire human mitochondrial genome; amino acid scores, calculated by MitVarProt algorithm [20] on the same dataset, were obtained for coding regions. Conservation scores calculated by PhyloP [21] and PhastCons [22] algorithms were retrieved from UCSC Genome Browser [23].

Table 1 Annotations by MToolBox pipeline

Somatic mutations and germline variants with reports of disease-associations were available in MITOMAP [4], with corresponding annotation of heteroplasmic/homoplasmic status (July 20, 2015 update of coding and control regions variants; July 29, 2015 update of somatic mutations and RNA genes variants). Other resources were exploited in order to facilitate clinical interpretation of variants, although they are not specialized for mitochondrial genome variant analysis, including OMIM [24], the Online Mendelian Inheritance in Man (August 4, 2015 update), dbSNP [25], a database for short genetic variations (release 144, May 26, 2015), and ClinVar [26], a public archive of reports of human variations and phenotypes reporting annotations of variants found in patient samples (January 21, 2015 update).

Moreover, specific annotations for tRNA variants were gathered from databases, such as Mamit-tRNA [13], mitotRNAdb [27] and MODOMICS [28], as well as from the literature. Specifically, a scoring system developed for 207 variants in tRNA genes considering functional evidence, conservation, frequency and heteroplasmy status in mutations reported in MITOMAP as “pathogenic”, was retrieved [29, 30] and normalized to a 0–1 range (Table 2). Recently published predictions of pathogenicity for DNA variants involving 12S mitochondrial rRNA (mt-rRNA) [31] were considered and adapted, too.

Table 2 RNA pathogenicity predictions in MToolBox with corresponding scores


MToolBox [16] is a bioinformatics pipeline recently developed for accurate and complete analysis of mitochondrial genome from high throughput sequencing. The tool includes several steps in the data analysis process, such as variant annotation and prioritization by exploiting several annotation resources, such as biological databases [4, 19] and pathogenicity prediction software [3234], proving to be very useful especially in the characterization of missense variants (Table 1). The pipeline was also developed as a web-based tool, hosted at MSeqDR website [17], a portal recently developed for supporting mitochondrial disease studies by providing both data and user-friendly tools specifically for mtDNA analysis.

Variant annotators

Both generic and mitochondrial-oriented tools were used for a comparison of variant annotation processes. The command line tools ANNOVAR (version date 2015-03-22) [35], dbNSFP (version 3.0b1a) [14], and SnpEff (version 4.1b) [36], although not specific for mtDNA analysis, were used to provide annotations for three mitochondrial mutations involving genes coding for an rRNA, a tRNA and a protein, respectively. Web-based versions of mit-o-matic [37], MitoBamAnnotator [38] and MitImpact 2.0 [15] tools were also applied to the same mutations to compare their performance in variant annotation.

GBrowse tracks at MSeqDR website

GBrowse instance at MSeqDR website [17] allows visualization and analysis of variations and other genomics data in a classic genome browser interface by hosting mtDNA specific annotation tracks containing data from some of the major mtDNA genomics resources, such as HmtDB_rCRSvariants and HmtDB_RSRSvariants, provided by our group [17]. Data collection for new tracks generation was manually curated in order to produce tab-delimited text files, then converted in the required format (General Feature Format version 3, GFF3). Variants were reported using the Human Genome Variation Society (HGVS) nomenclature [39].

Results and discussion

Annotations for mitochondrial DNA variants in RNA genes by MToolBox pipeline and data update

The MToolBox pipeline [16] was updated and enhanced with specific annotations regarding tRNA genes, introduced for the first time in a tool specific for mtDNA analysis.

New fields were added in the latest version of the MToolBox pipeline (Table 1): specific annotations for tRNA and rRNA genes, annotations from ClinVar database for disease-associated variants [26] and conservation scores for each site produced by PhyloP [21] and PhastCons [22] algorithms. Specifically, tRNA genes were characterized in each position with reports about tRNA structure including i) position in tRNA, following the Sprinzl standard nomenclature [27]; ii) tRNA type [40]; iii) cloverleaf-shaped secondary structure regions [27]; iv) mature nucleotide [2, 7, 28]; v) involvement of the specific position in tRNA folding [2, 7, 41] (Fig. 1). Each tRNA nucleotide was numbered from 1 to 73, CCA-ending excluded; the anticodon triplet was marked with nucleotides 34 to 36. The tRNA type indicates one of the four possible groups ranking human mt-tRNAs for their structural diversity and different tertiary interactions: type 0, the quasi-canonical cloverleaf structure, with standard D-loop/T-loop interaction; type II, the most common among mt-tRNAs, characterized by loss of D/T-loop interaction; type I and type III, each accounting one single tRNA with an atypical anticodon stem and lack of D-stem, respectively. The annotation of the typical cloverleaf pattern includes abbreviations of four loops (TL-TΨC Loop, VL-Variable Loop, CL-Anticodon Loop, DL-Dihydrouridine Loop), four stems (AS-Acceptor Stem, TS-TΨC Stem, CS-Anticodon Stem, DS-Dihydrouridine Stem), 3′ end (E) and junctions (-).

Fig. 1

Schematic representation of the four types of human mitochondrial tRNAs. The four types of human mt-tRNAs are shown. Green circles represent all the nucleotide positions involved in post-transcriptional modifications in each tRNA. Blue circles indicate nucleotide positions involved in tertiary folding with interactions represented by lines. Red circles represent nucleotide positions involved in tertiary folding and subject to post-transcriptional modifications. All the stems (A-stem, T-stem, C-stem, D-stem) and loops (T-loop, V-loop, C-loop, D-loop) of cloverleaf secondary regions are also shown

The mature nucleotide is meant as the nucleotide found in the tRNA molecule after post-transcriptional processes, predicted based on information of bovine and model organisms (bacteria, yeast, nematode) mt-tRNAs, and confirmed in 8 human mt-tRNAs [2, 8]. As a result of our data collection, we annotated 110 residues in the human mt-tRNA set involved in post-transcriptional modifications, with 16 different types of modified nucleotides. All the post-transcriptional modifications in mt-tRNAs and resulting mature nucleotides are listed in Table 3.

Table 3 Post-transcriptional modifications in mt-tRNAs

Indication of the involvement of a specific residue in tRNA folding could be now recovered through variant annotation by our updated version of MToolBox. The three-dimensional structure of mt-tRNA has a typical L-shape, due to the molecule folding back in itself forming two double helix segments through base pairing between T and D loop. Triplet interactions also occur in position 10-25-45, 9-23-12 and 13-22-46 in order to increase stability [7]. The strength of folding is also affected by base stacking interactions, interesting almost all the nucleotides [42].

As expected, we observed a relatively low frequency of disease associated mutations within the anticodon triplet (11/394 mutations) since its high conservation is required for a correct recognition of the messanger RNA. Specifically, position 36, corresponding to the third base within anticodon, is more subject to pathogenic mutations (7/11). Moreover we observed a quite homogeneous distribution of mutations with a deleterious effect in other tRNA regions, in line with an almost consistent involvement of all the regions in the three-dimensional folding.

Fortynine variants in rRNA genes [31] and 207 variants in tRNA genes [29, 30] were retrieved from the literature as validated mutations, hence inserted within the annotation mechanism used by MToolBox and integrated with pathogenicity predictions and scores. Original scores were normalized to a 0–1 range, with derived thresholds of 0.600 and 0.350 for rRNA and tRNA sequence variations, respectively (Table 2). Damaging effects could be observed for variants with a score above or equal to the chosen thresholds, while neutral variants should be associated with lower values.

Finally, several annotations previously collected [16] were accurately revised to provide users the most possible up-to-date pipeline for mitochondrial genome analysis, including updated variability data from HmtDB database [19], dbSNP identifiers [25], OMIM links to known variants [24], novel disease associated variants and somatic mutations reported in MITOMAP [4] (Table 1).

All the updates in MToolBox are available both in the command line version [43] and in the web-based resource at MSeqDR website [44]. New options to better manage input files are described in the readme file in the package. Moreover a summary is now produced reporting all the parameters chosen for the analysis and some basic statistics.

Annotation/prioritization tools comparison

In recent years lots of tools for variant prioritization were produced in order to help clinicians and researchers to recognize a few relevant mutations among the huge amount of variations detectable by NGS technologies. However, the annotation and prioritization processes carried out by these tools are often focused on missense variant characterization by providing pathogenicity predictions, dbSNP identifiers, frequency in known datasets such as the 1000 Genomes, conservation scores and region annotations (see Additional file 1). Among the most popular tools for variant prioritization, ANNOVAR [35], SnpEff [36] and dbNSFP [14] are commonly used both for nuclear DNA and mtDNA variations. Moreover mitochondrial-oriented tools have been recently developed, such as mit-o-matic [37], MitImpact [15] and MitoBamAnnotator [38] to ensure appropriate annotations mindful of mitochondrial genetics peculiarities, such as heteroplasmy. A comparison was performed among the aforementioned tools, showing pros and cons of each of them (Additional file 1). A few generic annotations regarding mt-tRNA variants were provided by some of the tested tools, while the MToolBox pipeline showed a wide range of annotations proving to be useful for any variant evaluation and not only missense variants (Table 4). Moreover, several input file formats can be used by MToolBox, proving a great efficiency for both high throughput sequencing and traditional FASTA data. Last but not least, the web-based version of the tool [44] ensures large usability also by non-expert users interested in mitochondrial genome analysis.

Table 4 Variant annotators comparison for a tRNA gene mutation

Mitochondrial variations tracks at MSeqDR

In order to facilitate the interpretation of genetic variants in a specific genomic context, four different custom tracks were produced in GFF3 file format displayable at MSeqDR GBrowse [45] (Fig. 2). The tracks included all the data used for the annotation step carried out by the MToolBox pipeline, providing users the possibility to analyze only variants or genomic positions with no need to provide input files. A track previously provided, called “Mitochondrial Pathogenicity Predictions” [17], was updated and split into two different tracks, “MT-patho.CDS” and “MT-patho.STOP” tracks. The first collects all the 24,202 possible non-synonymous variants within the 13 human mitochondrial protein encoding genes, identified using mtDNA-GeneSyn software [46]. Predictions and probabilities of pathogenicity were produced using five different software [16] and an overall disease score was also provided [47].

Fig. 2

Overview of the usage of mitochondrial tracks at MSeqDR GBrowse. MSeqDR website provides access to a GBrowse useful to visualize genomics data. Users can upload the four tracks generated in this work in the “Custom Tracks” section of the browser (a). For the sake of simplicity, the only “MT-patho.RNA” track is here shown, including data about pathogenic variants in mt-tRNA and mt-rRNA genes. The custom track can be selected, totally or partially (only transitions, transversions, insertions or deletions, b) and then visualized in the browser (c) where users can search for a specific genomic region of interest. Eventually, detailed information can be shown by clicking on a specific variant site (d)

The second track collects all the 1740 possible stop-gain and 77 possible stop-loss mutations, which could be damaging in the generation of the 13 human mitochondrial proteins.

The third track (“MT-patho.RNA”) is useful to show all the information currently available about pathogenicity of 392 variants in tRNA and 337 in rRNA genes, while the fourth track (“MT-RNA”) includes generic annotations reported for all the 1505 positions in genes encoding tRNAs and 2513 positions in genes encoding rRNAs, respectively. All the tracks were produced using the revised Cambridge Reference Sequence, rCRS (GenBank: J01415.2), as reference sequence.

Additional information from MITOMAP [4], ClinVar [26], Mamit-tRNA [13] dbSNP [25] and OMIM [24] databases were shown, when available, for all the four tracks, as well as variability data from HmtDB database [19] and conservation scores from UCSC Genome Browser [21, 22].

The tracks, can be uploaded in the “Custom Tracks” section of the MSeqDR website, selected, totally or partially (only transitions, transversions, insertions or deletions) and visualized in the GBrowse (Fig. 2).


To the best of our knowledge, specific data regarding mitochondrial variants in tRNA genes were introduced for the first time in a tool for mitochondrial genome analysis and then reported in custom tracks, which could be displayed at MSeqDR GBrowse. The availability of such data could be useful to support the interpretation of genetic variants in specific genomic contexts.



Acceptor stem


Anticodon loop


Anticodon stem


Dihydrouridine loop


Dihydrouridine Stem


General feature format version 3


Human genome variation society


Human mitochondrial database


Mitochondrial disease sequence data resource


Mitochondrial DNA


Mitochondrial ribosomal RNA


Mitochondrial transfer RNA


Revised Cambridge Reference Sequence


TΨC Loop


TΨC stem


Variable loop


  1. 1.

    GenBank. Accessed 22 Sept 2015.

  2. 2.

    Suzuki T, Suzuki T. A complete landscape of post-transcriptional modifications in mammalian mitochondrial tRNAs. Nucleic Acids Res. 2014;42:7346–57.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Yarham JW, Elson JL, Blakely EL, McFarland R, Taylor RW. Mitochondrial tRNA mutations and disease. Wiley Interdiscip Rev RNA. 2010;1:304–24.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Ruiz-Pesini E, Lott MT, Procaccio V, Poole JC, Brandon MC, Mishmar D, Yi C, Kreuziger J, Baldi P, Wallace DC. An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Res. 2007;35(Database issue):D823–828.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Rossignol R, Faustin B, Rocher C, Malgat M, Mazat J-P, Letellier T. Mitochondrial threshold effects. Biochem J. 2003;370(Pt 3):751–62.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Suzuki T, Nagao A, Suzuki T. Human mitochondrial diseases caused by lack of taurine modification in mitochondrial tRNAs. Wiley Interdiscip Rev RNA. 2011;2:376–86.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Suzuki T, Nagao A, Suzuki T. Human mitochondrial tRNAs: biogenesis, function, structural aspects, and diseases. Annu Rev Genet. 2011;45:299–329.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Powell CA, Nicholls TJ, Minczuk M. Nuclear-encoded factors involved in post-transcriptional processing and modification of mitochondrial tRNAs in human disease. Front Genet. 2015;6:79.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Helm M, Giegé R, Florentz C. A Watson-Crick base-pair-disrupting methyl group (m1A9) is sufficient for cloverleaf folding of human mitochondrial tRNALys. Biochemistry (Mosc). 1999;38:13338–46.

    CAS  Article  Google Scholar 

  10. 10.

    Sakurai M, Ohtsuki T, Watanabe K. Modification at position 9 with 1-methyladenosine is crucial for structure and function of nematode mitochondrial tRNAs lacking the entire T-arm. Nucleic Acids Res. 2005;33:1653–61.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Yasukawa T, Suzuki T, Ishii N, Ohta S, Watanabe K. Wobble modification defect in tRNA disturbs codon–anticodon interaction in a mitochondrial disease. EMBO J. 2001;20:4794–802.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Brandon MC, Lott MT, Nguyen KC, Spolim S, Navathe SB, Baldi P, Wallace DC. MITOMAP: a human mitochondrial genome database--2004 update. Nucleic Acids Res. 2005;33(Database issue):D611–613.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Pütz J, Dupuis B, Sissler M, Florentz C. Mamit-tRNA, a database of mammalian mitochondrial tRNA primary and secondary structures. RNA N Y N. 2007;13:1184–90.

    Article  Google Scholar 

  14. 14.

    Liu X, Jian X, Boerwinkle E. dbNSFP v2.0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat. 2013;34:E2393–402.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Castellana S, Rónai J, Mazza T. MitImpact: an exhaustive collection of pre-computed pathogenicity predictions of human mitochondrial non-synonymous variants. Hum Mutat. 2015;36:E2413–2422.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Calabrese C, Simone D, Diroma MA, Santorsola M, Guttà C, Gasparre G, Picardi E, Pesole G, Attimonelli M. MToolBox: a highly automated pipeline for heteroplasmy annotation and prioritization analysis of human mitochondrial variants in high-throughput sequencing. Bioinforma Oxf Engl. 2014;30:3115–7.

    CAS  Article  Google Scholar 

  17. 17.

    Falk MJ, Shen L, Gonzalez M, Leipzig J, Lott MT, Stassen APM, Diroma MA, Navarro-Gomez D, Yeske P, Bai R, Boles RG, Brilhante V, Ralph D, DaRe JT, Shelton R, Terry SF, Zhang Z, Copeland WC, van Oven M, Prokisch H, Wallace DC, Attimonelli M, Krotoski D, Zuchner S, Gai X, MSeqDR Consortium Participants. Mitochondrial Disease Sequence Data Resource (MSeqDR): A global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities. Mol Genet Metab. 2015;114(3):388–96.

  18. 18.

    Pesole G, Saccone C. A novel method for estimating substitution rate variation among sites in a large dataset of homologous DNA sequences. Genetics. 2001;157:859–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Rubino F, Piredda R, Calabrese FM, Simone D, Lang M, Calabrese C, Petruzzella V, Tommaseo-Ponzetta M, Gasparre G, Attimonelli M. HmtDB, a genomic resource for mitochondrion-based human variability studies. Nucleic Acids Res. 2012;40(Database issue):D1150–1159.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Horner DS, Pesole G. The estimation of relative site variability among aligned homologous protein sequences. Bioinforma Oxf Engl. 2003;19:600–6.

    CAS  Article  Google Scholar 

  21. 21.

    Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010;20:110–21.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, Weinstock GM, Wilson RK, Gibbs RA, Kent WJ, Miller W, Haussler D. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15:1034–50.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    UCSC Genome Browser. Accessed Aug 2015.

  24. 24.

    OMIM Online Mendelian Inheritance in Man. Accessed Aug 2015.

  25. 25.

    dbSNP. Accessed Aug 2015.

  26. 26.

    Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, Maglott DR. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(Database issue):D980–985.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Jühling F, Mörl M, Hartmann RK, Sprinzl M, Stadler PF, Pütz J. tRNAdb 2009: compilation of tRNA sequences and tRNA genes. Nucleic Acids Res. 2009;37(Database issue):D159–162.

    Article  PubMed  Google Scholar 

  28. 28.

    Dunin-Horkawicz S, Czerwoniec A, Gajda MJ, Feder M, Grosjean H, Bujnicki JM. MODOMICS: a database of RNA modification pathways. Nucleic Acids Res. 2006;34(Database issue):D145–149.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Yarham JW, Al-Dosary M, Blakely EL, Alston CL, Taylor RW, Elson JL, McFarland R. A comparative analysis approach to determining the pathogenicity of mitochondrial tRNA mutations. Hum Mutat. 2011;32:1319–25.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Blakely EL, Yarham JW, Alston CL, Craig K, Poulton J, Brierley C, Park S-M, Dean A, Xuereb JH, Anderson KN, Compston A, Allen C, Sharif S, Enevoldson P, Wilson M, Hammans SR, Turnbull DM, McFarland R, Taylor RW. Pathogenic mitochondrial tRNA point mutations: nine novel mutations affirm their importance as a cause of mitochondrial disease. Hum Mutat. 2013;34:1260–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Smith PM, Elson JL, Greaves LC, Wortmann SB, Rodenburg RJT, Lightowlers RN, Chrzanowska-Lightowlers ZMA, Taylor RW, Vila-Sanjurjo A. The role of the mitochondrial ribosome in human disease: searching for mutations in 12S mitochondrial rRNA with high disruptive potential. Hum Mol Genet. 2014;23:949–67.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Li B, Krishnan VG, Mort ME, Xin F, Kamati KK, Cooper DN, Mooney SD, Radivojac P. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinforma Oxf Engl. 2009;25:2744–50.

    CAS  Article  Google Scholar 

  33. 33.

    Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat. 2009;30:1237–44.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

    Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92.

    CAS  Article  Google Scholar 

  37. 37.

    Vellarikkal SK, Dhiman H, Joshi K, Hasija Y, Sivasubbu S, Scaria V. mit-o-matic: a comprehensive computational pipeline for clinical evaluation of mitochondrial variations from next-generation sequencing datasets. Hum Mutat. 2015;36:419–24.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Zhidkov I, Nagar T, Mishmar D, Rubin E. MitoBamAnnotator: a web-based tool for detecting and annotating heteroplasmy in human mitochondrial DNA sequences. Mitochondrion. 2011;11:924–8.

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    den Dunnen JT, Antonarakis SE. Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion. Hum Mutat. 2000;15:7–12.

    Article  Google Scholar 

  40. 40.

    Watanabe K. Unique features of animal mitochondrial translation systems. The non-universal genetic code, unusual features of the translational apparatus and their relevance to human mitochondrial diseases. Proc Jpn Acad Ser B Phys Biol Sci. 2010;86:11–39.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Cannone JJ, Subramanian S, Schnare MN, Collett JR, D’Souza LM, Du Y, Feng B, Lin N, Madabusi LV, Müller KM, Pande N, Shang Z, Yu N, Gutell RR. The comparative RNA web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinformatics. 2002;3:2.

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Li R, Ge HW, Cho SS. Sequence-dependent base-stacking stabilities guide tRNA folding energy landscapes. J Phys Chem B. 2013;117:12943–52.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    MToolBox. Accessed Aug 2015.

  44. 44.

    MToolBox pipeline at MSeqDR. Accessed Aug 2015.

  45. 45.

    MSeqDR GBrowse. Accessed Aug 2015.

  46. 46.

    Pereira L, Freitas F, Fernandes V, Pereira JB, Costa MD, Costa S, Máximo V, Macaulay V, Rocha R, Samuels DC. The diversity present in 5140 human mitochondrial genomes. Am J Hum Genet. 2009;84:628–40.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Santorsola M, Calabrese C, Girolimetti G, Diroma MA, Gasparre G, Attimonelli M. A multi-parametric workflow for the prioritization of mitochondrial DNA variants of clinical interest. Hum Genet. 2016;135:121–36.

    CAS  Article  PubMed  Google Scholar 

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The authors would like to thank Dr Claudia Calabrese, Dr Domenico Simone and Dr Mariangela Santorsola, co-developers of the MToolBox pipeline, for helpful discussions. The authors are also thankful to Dr Rosanna Clima, Dr Cristiano Guttà and Dr Roberto Preste for their contribution.


This article has been published as part of BMC Bioinformatics Vol 17 Suppl 12 2016: Italian Society of Bioinformatics (BITS): Annual Meeting 2015. The full contents of the supplement are available online at


Publication of this article was funded in part by the Bioinformatics Italian Society (BITS) and University of Bari funds (code ATTPRIN2009) to MA.

Availability of data and material

The pipeline supporting the results of this article is available in the GitHub repository The web-based version is available at Data supporting the results of this article are included within the article and its additional file. Tracks described and related documentation can be downloaded at

Authors’ contributions

Research study was conceived by MAD and PL. Data collection was carried out by PL. The bioinformatics pipeline was updated by MAD. GBrowse tracks at MSeqDR website were generated by MAD. Figure and table generation was performed by MAD and PL. MA coordinated and supervised the whole project. MAD, PL and MA drafted the manuscript and all authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

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Correspondence to Marcella Attimonelli.

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Additional file 1:

Variant annotation by 7 different tools. All the annotations provided by MToolBox, ANNOVAR, SnpEff, dbNSFP, MitImpact 2.0, MitoBamAnnotator and mit-o-matic are shown. Three variants were considered (m.879T>C, m.3436G>C, m.4450G>A), one for an rRNA gene (MT-RNR1), one for a tRNA gene (MT-TM) and one for a protein coding gene (MT-ND1). ANNOVAR and SnpEff tools use dbNSFP databases. Generally, all the tools provided an accurate annotation for the missense variant, although we were not able to obtain any information by mit-o-matic web-based software. MToolBox provided the most complete annotation for non protein coding regions. (XLSX 44 kb)

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Diroma, M.A., Lubisco, P. & Attimonelli, M. A comprehensive collection of annotations to interpret sequence variation in human mitochondrial transfer RNAs. BMC Bioinformatics 17, 338 (2016).

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  • Mitochondrial genomics
  • tRNA sequence variation
  • Annotation and prioritization tools
  • Bioinformatics analysis
  • NGS