Literature DB >> 22013967

The reference human nuclear mitochondrial sequences compilation validated and implemented on the UCSC genome browser.

Domenico Simone1, Francesco Maria Calabrese, Martin Lang, Giuseppe Gasparre, Marcella Attimonelli.   

Abstract

BACKGROUND: Eukaryotic nuclear genomes contain fragments of mitochondrial DNA called NumtS (Nuclear mitochondrial Sequences), whose mode and time of insertion, as well as their functional/structural role within the genome are debated issues. Insertion sites match with chromosomal breaks, revealing that micro-deletions usually occurring at non-homologous end joining loci become reduced in presence of NumtS. Some NumtS are involved in recombination events leading to fragment duplication. Moreover, NumtS are polymorphic, a feature that renders them candidates as population markers. Finally, they are a cause of contamination during human mtDNA sequencing, leading to the generation of false heteroplasmies.
RESULTS: Here we present RHNumtS.2, the most exhaustive human NumtSome catalogue annotating 585 NumtS, 97% of which were here validated in a European individual and in HapMap samples. The NumtS complete dataset and related features have been made available at the UCSC Genome Browser. The produced sequences have been submitted to INSDC databases. The implementation of the RHNumtS.2 tracks within the UCSC Genome Browser has been carried out with the aim to facilitate browsing of the NumtS tracks to be exploited in a wide range of research applications.
CONCLUSIONS: We aimed at providing the scientific community with the most exhaustive overview on the human NumtSome, a resource whose aim is to support several research applications, such as studies concerning human structural variation, diversity, and disease, as well as the detection of false heteroplasmic mtDNA variants. Upon implementation of the NumtS tracks, the application of the BLAT program on the UCSC Genome Browser has now become an additional tool to check for heteroplasmic artefacts, supported by data available through the NumtS tracks.

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Year:  2011        PMID: 22013967      PMCID: PMC3228558          DOI: 10.1186/1471-2164-12-517

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


Background

Human mitochondrial DNA (mtDNA) is widely used for phylogenetic, forensic and clinical studies and many features like maternal inheritance, absence of recombination and lack of efficient repair systems are well known and extensively studied. Recent advances in genetics provide researchers with mitochondrial DNA sequences located within the nuclear genome, thus allowing the investigation of intriguing aspects of genome organization. Fragments of mtDNA that give rise to nuclear mitochondrial sequences (NumtS) are found in many eukaryotic nuclear genomes and believed to derive from damaged mitochondria [1]. The discovery of these genomic elements dates back to 1967 through hybridization experiments on mouse liver between mtDNA and nuclear genome [2]. NumtS generation may have started soon after the endosymbiontic event [3] although the underlying mechanisms are still unclear and time and mode of arrival from mitochondria to nucleus have not been defined. As far as the mode, the most credited hypothesis suggests that in presence of mutagenic agents or under stress conditions, fragments of mtDNA may escape the organelles, reach the nucleus and likely insert into nuclear DNA during double-strand breaks (DSB) repair by the non-homologous end joining (NHEJ) machinery, although other mechanisms have been proposed [4,5]. It is commonly accepted that most human NumtS have originated before modern man, although evidences of NumtS recent insertions as well as their duplication in human genomes have been reported [6-15]. As a consequence, some NumtS display a highly polymorphic behaviour, as they can occur in homo- or heterozygosis, or be absent in different individuals at specific loci. These features render them candidates as population markers, as already suggested [16]. Nonetheless, several criteria must be fulfilled before human NumtS can ascend to the status of evolutionary markers. NumtS identification, quantification and mapping must be completed and refined in the attempt to finally define the asset of mitochondrial sequences within the human nuclear genome, i.e. the human NumtSome. Indeed, despite scattered data on NumtS evolution and mechanisms of insertions being published [10,11,13], these results are based on pools of raw data that are not readily available. Moreover, it must be considered that accurate mapping is highly dependent on the quality of the genome assembly for each organism and on algorithms and parameters applied in silico. The need to provide a well-characterized, exhaustive database containing the human NumtSome has led us to generate the Reference Human NumtS compilation (RHNumtS), based on i) database similarity searching programs where the human mtDNA reference sequence (rCRS [17]) is compared to the human nuclear genome sequences (build hg18 available both at NCBI and UCSC sites) and ii) the comparison with other already published compilations [1,5,10,13]. Because of a great discrepancy among data obtained upon implementation of different protocols, stringent parameters were utilized during the production of the first release, RHNumtS.1 [18]. However in the process of in vitro validation, of the first release, evidences of additional mitochondrial fragments (Figure 1) not reported in RHNumtS.1 prompted a continuous work of protocol revision that has led to an extended and deeply validated second release, RHNumtS.2. The RHNumtS.2 also includes revisited data from smaller, scattered previously published compilations [1,5,10,13,18]. Here we report the optimized protocol that allows the detection of human NumtS. Upon a thorough validation by amplification and sequencing of the human NumtSome, here completed for the first time, both nuclear and mitochondrial human NumtS tracks have been created and made available through the UCSC Genome Browser.
Figure 1

BLAST2seq dotplot output. The dotplot shows the results obtained by applying BLAST2seq between rCRS and chromosome 1 region containing HSA_NumtS_021 (release RHNumtS.1). The thin line refers to the part of the HSP_NumtS_021 already annotated in the RHNumtS.1 compilation. NumtS added in RHNumtS.2 are marked with thick lines and highlighted by red arrows.

BLAST2seq dotplot output. The dotplot shows the results obtained by applying BLAST2seq between rCRS and chromosome 1 region containing HSA_NumtS_021 (release RHNumtS.1). The thin line refers to the part of the HSP_NumtS_021 already annotated in the RHNumtS.1 compilation. NumtS added in RHNumtS.2 are marked with thick lines and highlighted by red arrows.

Results

The RHNumtS.2 compilation

The RHNumtS.2 compilation was obtained through an in silico hybridization between each human chromosome (build hg18) and the reference human mitochondrial genome rCRS [GenBank:NC_012920]. The process returned 766 High Scoring Pairs (HSPs), i.e. mitochondrial fragments similar to nuclear sequences, hereafter named HSP_NumtS, whose alignment lengths ranged from 31 to 14904 bp. The similarity percentage of each fragment versus the rCRS sequence ranged from 63% to 100%. HSP_NumtS showing evident neighbourhood on both nuclear and mitochondrial genomes were merged in a single NumtS (assembled NumtS) according to the criteria described in the Methods section. NumtS covering the D-loop region were returned by BLASTN as different HSPs, as in the rCRS EMBL/GenBank/DDBJ databank entry the D-loop is split into the end (positions 16024-16569) of the sequence followed by the start (positions 1-576). Therefore, HSP_NumtS close in the nuclear genome and mapping on the D-loop region, were also merged in a single assembled NumtS: this device fitted our joining protocol to the circularity of the mitochondrial genome. Overall, RHNumtS.2 annotates 766 human HSP_NumtS corresponding to 585 assembled Human NumtS inclusive of the 190 annotated in RHNumtS.1 [18]. Covered genome amounts to 627410 bases. The complete RHNumtS.2 compilation is reported here in the additional file 1 RHNumtS.2.xls. A NumtS ID was assigned to each assembled NumtS with a format HSA_NumtS_xxx, where HSA stands for H. sapiens and xxx is a three-digit code. Detailed statistics on the NumtS length and similarity distributions are shown in Table 1. Mapping of NumtS (Figure 2) showed that chromosome 2 and chromosome 18 are respectively the most and least densely populated by this class of genomic elements in terms of base pairs overall length (Table 2). A statistical correlation showed that there is a linear relationship between the number of NumtS located on each chromosome and the relative chromosome length (Pearson's r = 0.82). On the other hand, the value obtained by correlating the number of NumtS for each chromosome and the genic density (0.22) suggests that, at chromosome level, there is no evidence of NumtS distribution biased by the presence of genes. NumtS mtDNA coverage (Figure 3) highlights that the fragments add up to several copies of the mitochondrial genome. Over-represented regions encompass many tRNAs, the two ribosomal genes 12S and 16S, large portions of COX1, COX3, CYTB genes and short fragments of ND1, ND5 and ND6 genes. On the other hand, D-loop region, the whole ATP8 gene and most of ND1, ND4 and ND6 genes are under-represented.
Table 1

HSPs statistics concerning the Blast2seq application of the rCRS sequence (J01415)

HSP similaritiesHSP lengths (% of mt genome)ΔHSP-span differencesDistances between concatenated HSPs
min63.5231 (0.1)0-8
1st quartile73.7494 (0.5)0215.25
median78.41215 (1)1320
3rd quartile84.62619 (3)5585.75
max10014836 (89)1606129

For each HSP, the ΔHSP-span difference is the absolute value of the difference between the HSP length on nuclear genome and the HSP length on the mitochondrial genome. The distance of -8 bp reported indicates overlapping of two concatenated HSPs on nuclear genome.

Figure 2

Mapping of the RHNumtS elements on the human karyotype. Vertical bars refer to the NumtS location. NumtS that are close together merge at this zoom level.

Table 2

NumtS number and percentage for each chromosome

Chr nameNumtS per ChrChr length (bp)Total NumtS span (bp)NumtS bp % per Chr
146247249719576700.023
2922429511491083170.045
339199501827242030.012
438191273063361790.019
524180857866401630.022
631170899992117080.007
735158821424513560.032
837146274826468050.032
928140273252368540.026
1033135374737214450.016
1128134452384266400.020
124013234953485510.006
1319114142980111590.010
14810636858589910.008
151210033891590020.009
161188827254172050.019
171778774742242850.031
1877611715313400.002
191563811651193730.030
20106243596459050.009
2184694432372100.015
2284969143290250.018
X13154913754308450.020
Y1257772954115100.020

Chromosome 2 displays the highest bp percentage of NumtS, while chromosome 18 the lowest. Chr: chromosome.

Figure 3

Coverage of human mtDNA sites on the whole human nuclear genome. Different genes are marked as follows: ATP6 and ATP8, ATPase subunits 6 and 8; COXI, COXII, COXIII, cytochrome c oxidase subunits I, II, III; CYTB, cytochrome b; ND1-6 and 4L, NADH dehydrogenase subunits; tRNA genes are indicated according to the transported amino acid. L1, Leu (UUR); L2, Leu (CUN); S1, Ser (UCN); S2, Ser (AGY). Horizontal axis = nucleotide position on mitochondrial genome; vertical axis = number of occurrences in the nuclear genome.

HSPs statistics concerning the Blast2seq application of the rCRS sequence (J01415) For each HSP, the ΔHSP-span difference is the absolute value of the difference between the HSP length on nuclear genome and the HSP length on the mitochondrial genome. The distance of -8 bp reported indicates overlapping of two concatenated HSPs on nuclear genome. Mapping of the RHNumtS elements on the human karyotype. Vertical bars refer to the NumtS location. NumtS that are close together merge at this zoom level. NumtS number and percentage for each chromosome Chromosome 2 displays the highest bp percentage of NumtS, while chromosome 18 the lowest. Chr: chromosome. Coverage of human mtDNA sites on the whole human nuclear genome. Different genes are marked as follows: ATP6 and ATP8, ATPase subunits 6 and 8; COXI, COXII, COXIII, cytochrome c oxidase subunits I, II, III; CYTB, cytochrome b; ND1-6 and 4L, NADH dehydrogenase subunits; tRNA genes are indicated according to the transported amino acid. L1, Leu (UUR); L2, Leu (CUN); S1, Ser (UCN); S2, Ser (AGY). Horizontal axis = nucleotide position on mitochondrial genome; vertical axis = number of occurrences in the nuclear genome.

Bench and in silico validation of RHNumtS.2

The in silico hybridization was based on a consensus human genome (build hg18) derived from the DNA sequencing from 6 different samples. Due to the polymorphic nature of human NumtS [13] and to the technical difficulties posed by repetitive sequences during assembly of the consensus genome, we proceeded to validate RHNumtS.2 on an individual of European origin, through amplification and sequencing (bench validation). HSP_NumtS not present in the European sample (HSA_NumtS_009, HSA_NumtS_426, HSA_NumtS_522) were validated in an Ethiopian sample. Additionally, in silico validation was carried out on genomic annotations from eight HapMap samples. Design of 355 specific primer pairs was performed, providing amplicons of molecular weight (MW) expected in presence of the NumtS, as confirmed by gel electrophoresis, allowing the validation of 354 HSP_NumtS. Overall 339/354 were successfully sequenced on both strands. Despite yielding an amplicon of expected MW in the case of NumtS presence, the remaining 15 PCR products contained long homopolymeric stretches or heterozygous repetitive satellite-like sequences, which rendered sequencing incomplete. Thus 44% of the total HSP_NumtS (339/766) were sequenced, which is the largest set of NumtS sequenced to date, to the best of our knowledge. Each obtained sequence was multi-aligned to the corresponding mtDNA fragment of the same individual, to the hg18 corresponding nuclear DNA fragment as well as to the rCRS mtDNA fragment. The multi-alignments are available in the additional file 2 Validated_NumtS_multial.txt. HSP_NumtS sequences were submitted to the EMBL databank through the WebIN submission tool (http://www.ebi.ac.uk/webin) available through the INSDC (International Nucleotide Sequence Database Collaboration) organization. To this purpose, a human NumtS EMBL flat-file format template was designed (Figure 4). The entry structure reports in the "Features" table the "misc_feature" data which allow the end-user accessing the INSDC databases to obtain the NumtS location on the nuclear genome and the mtDNA fragments of origin. The accession numbers are reported in additional file 3 NumtS_validation.xls. Overall, 279 assembled NumtS were validated with this approach (Table 3), i.e. those NumtS for which a specific primer pair could be designed.
Figure 4

EMBL flat-file template. The template was designed for the WebIN submission of the sequenced NumtS.

Table 3

Validation statistics

Validation testValidated Assembled NumtS (%)
PCR and HapMap5 (0,85%)
PCR and Seq26 (4,44%)
PCR, Seq and HapMap247 (42,22%)
HapMap only279 (47,69%)
PCR only1 (0,17%)
Not validated27 (4,62%)
Total585 (100%)

Number and percentage of validated assembled NumtS according to the three different validation tests: PCR, sequencing and HapMap FES data analysis.

EMBL flat-file template. The template was designed for the WebIN submission of the sequenced NumtS. Validation statistics Number and percentage of validated assembled NumtS according to the three different validation tests: PCR, sequencing and HapMap FES data analysis. To further improve and complete the RHNumtS.2 validation, we compared the NumtS map versus the individual Fosmid End Sequences (FES) annotations related to eight HapMap samples. Table 4 reports the number of FES positive to the presence of NumtS and the number of NumtS mapped in the library of each individual. The 558 NumtS mapped on the FES are listed in additional file 3 NumtS_validation.xls. As a further confirmation, 252 of our 279 bench-validated NumtS were also retrieved from FES analysis. The presence of 27 NumtS (5%) could not be investigated and ascertained. Overall, 95.4% of the RHNumtS.2 was hence validated.
Table 4

In silico validation on HapMap FES data

HapMap identifierGeographical originFES with NumtSValidated NumtS
NA18517 (ABC7)Yoruba417202
NA18507 (ABC8)Yoruba709307
NA18956 (ABC9)Japan494210
NA19240 (ABC10)Yoruba480214
NA18555 (ABC11)China490228
NA12878 (ABC12)CEPH533229
NA19129 (ABC13)Yoruba504230
NA12156 (ABC14)CEPH574245

For each individual, discrepancy between the number of FES with NumtS and the corresponding number of validated NumtS is due to the different coverage of NumtS loci in FES.

In silico validation on HapMap FES data For each individual, discrepancy between the number of FES with NumtS and the corresponding number of validated NumtS is due to the different coverage of NumtS loci in FES.

The UCSC human NumtS tracks

In order to facilitate browsing of NumtS sequences, we implemented human NumtS tracks using the UCSC Genome Browser tools upon mapping on the hg18 build. Four different NumtS tracks were designed and implemented under the section 'Variation and Repeats'. The 'NumtS' track shows the mapping of the HSPs returned by BLASTN on the nuclear genome, namely the HSP_NumtS. Items shading reflects the similarity score obtained by BLASTN and the arrows direction is concordant with the strand on which the NumtS is aligned. A link to mtDNA mapping is also provided. The 'NumtS assembled' track shows items obtained by assembling HSPs annotated in the 'NumtS' track fulfilling the conditions described in the Methods section. The 'NumtS on mitochondrion' track shows mapping of the HSP_NumtS on the mitochondrial genome. Items shading reflects the similarity returned by BLASTN, and the direction of the arrows is concordant with the alignment strand. For every item, a link pointing to the nuclear mapping is provided. The 'NumtS on mitochondrion with chromosome placement' track shows the mapping of the HSP_NumtS on the mitochondrial genome, but the items are depicted according to the colours assigned to each human chromosome by the UCSC Genome Browser. For every item, a link pointing to the nuclear mapping is available. The four NumtS tracks annotate the entire RHNumtS.2 compilation. A screenshot example of the browsing of the UCSC NumtS tracks is reported in Figure 5 and 6, while a brief explanation of their surveying is provided in additional file 4 UCSC_NumtS_browsing.pdf.
Figure 5

NumtS UCSC Genome Browser tracks (I): nuclear tracks. NumtS browsing on UCSC Genome Browser can be performed by displaying a genomic region of interest or by directly searching a NumtS ID in the Genome Browser gateway. The graph shows the genomic region where the HSA_NumtS_014 is located (in the 'NumtS assembled' track) and the ten constituent HSP_NumtS (in the 'NumtS' track). Details about each item can be displayed by clicking on it, as pointed out by the red arrows. The red-framed "browser" link in the HSA_NumtS_014_b5 detail page links to the mitochondrial NumtS tracks shown in Figure 6. An essential flowchart describing NumtS tracks browsing is given in additional file 4 UCSC_NumtS_browsing.pdf.

Figure 6

NumtS UCSC Genome Browser tracks (II): mitochondrial tracks. The mitochondrial genome region where HSA_NumtS_014_b5 maps is displayed, and all the HSP_NumtS mapping entirely or partially on the same region are reported within the 'NumtS on mitochondrion' track and the 'NumtS on mitochondrion with chromosome placement' track. The HSA_NumtS_014_b5 ID is highlighted in both tracks. An essential flowchart describing NumtS tracks browsing is given in additional file 4 UCSC_NumtS_browsing.pdf.

NumtS UCSC Genome Browser tracks (I): nuclear tracks. NumtS browsing on UCSC Genome Browser can be performed by displaying a genomic region of interest or by directly searching a NumtS ID in the Genome Browser gateway. The graph shows the genomic region where the HSA_NumtS_014 is located (in the 'NumtS assembled' track) and the ten constituent HSP_NumtS (in the 'NumtS' track). Details about each item can be displayed by clicking on it, as pointed out by the red arrows. The red-framed "browser" link in the HSA_NumtS_014_b5 detail page links to the mitochondrial NumtS tracks shown in Figure 6. An essential flowchart describing NumtS tracks browsing is given in additional file 4 UCSC_NumtS_browsing.pdf. NumtS UCSC Genome Browser tracks (II): mitochondrial tracks. The mitochondrial genome region where HSA_NumtS_014_b5 maps is displayed, and all the HSP_NumtS mapping entirely or partially on the same region are reported within the 'NumtS on mitochondrion' track and the 'NumtS on mitochondrion with chromosome placement' track. The HSA_NumtS_014_b5 ID is highlighted in both tracks. An essential flowchart describing NumtS tracks browsing is given in additional file 4 UCSC_NumtS_browsing.pdf.

Discussion

The number of NumtS reported in this release, RHNumtS.2, has increased about by three fold over the first one [18], while the amount of bases covered has increased about by 1.6 fold. More details about the extension of the previous release are provided in the additional file 1 RHNumtS.2.xls (sheet "RHNumtS.1_extension"). RHNumtS.2 includes all the NumtS annotated in RHNumtS.1; 79 of them weren't extended at all, while the median value of the extension ratio is 1.05. For ten NumtS, the extension was quite considerable (extension ratio > 6). Indeed the protocol designed for the production of the first release was aimed to produce a reference compilation, i.e. a collection of sequences located on the reference human genome build and showing strong evidences allowing to define them as "NumtS". The less stringent protocol here applied has allowed us to recognize 585 NumtS, over 95% of which have been validated here for the first time, either by bench approaches (PCR and sequencing) or in silico on eight samples collected within the international HapMap project. The novelty here presented is the implementation of the NumtS annotations in the UCSC Genome Browser. The "NumtS Sequence" tracks are available in the hg18 release of the human genome, in the track section "Variation and Repeats": this feature broadens the examination potential of NumtS within their genomic context, as they can be intuitively displayed and merged with annotations of other genomic elements available in the Genome Browser. Browsing of the 1000 bp-long flanking regions of NumtS shows that about 97% of NumtS reside in loci associated to repeated elements of various types. This evidence led us to suppose that amplification might be difficult because of the risk to obtain non-specific amplification. Notwithstanding this evidence, 279 NumtS were amplified, 15% of which fell within highly repeated regions (Figure 7). As a complementary approach, FES analysis revealed to be informative with respect to the validation of in silico detected NumtS located within duplicated regions. As marked in Table 3 and annotated in additional file 3 NumtS_validation.xls (sheet "Non-validated NumtS"), only 27 NumtS have not been validated, i.e. those mainly involved in duplication events.
Figure 7

Cumulative distributions of NumtS flanking regions repetitive elements. The cumulative distribution of repetitive elements (RE) and segmental duplications (SegDups) content in the 1000 bp 5' and 3' flanking regions of NumtS is reported as calculated on: a) the whole collection of NumtS; b) the PCR-validated NumtS; c) the NumtS not validated by PCR. The graph shows that 60% of the PCR-validated NumtS contained less than 41% RE and SegDups in their flanking regions, while this percentage increased (72%) when considering NumtS not validated by PCR.

Cumulative distributions of NumtS flanking regions repetitive elements. The cumulative distribution of repetitive elements (RE) and segmental duplications (SegDups) content in the 1000 bp 5' and 3' flanking regions of NumtS is reported as calculated on: a) the whole collection of NumtS; b) the PCR-validated NumtS; c) the NumtS not validated by PCR. The graph shows that 60% of the PCR-validated NumtS contained less than 41% RE and SegDups in their flanking regions, while this percentage increased (72%) when considering NumtS not validated by PCR. A result that it is worth commenting concerns the coverage of the mitochondrial genome provided by NumtS. mtDNA fragments related to tRNAs and extended portions of the two ribosomal genes as well as of COX1, COX3 and CYTB genes are over-represented, whereas the D-loop region and other genes such as ATP8, ND1, ND4 and ND6 were the least represented within the nuclear genome. These data are in agreement with the conservation degree of the mitochondrial genes in mammals reported in [19]. The diversified observed frequencies of mtDNA fragments could be justified by taking into account that the RHNumtS.2 compilation results from an in silico hybridization between the modern human mtDNA and the human nuclear genome, therefore more evolutionary conserved mitochondrial regions may have higher chances to be detected with such strategy. Further bioinformatics analyses based on different approaches could also contribute to recognize more ancestral events that had led mitochondrial fragments to insert into nuclear genome. Finally, with respect to the apparently unbalanced distribution of the NumtS on the different chromosomes, although preliminary evidence showed a preferential NumtS localization in non-coding regions, a more detailed analysis of each chromosome and NumtS is required to explain the insertion bias towards specific chromosomal regions.

Conclusions

In this paper we present the most exhaustive overview on the human NumtSome implemented in the RHNumtS.2 compilation, a resource whose aim is to support different research applications ranging from studies concerning human structural variation, diversity, and disease, as well as the detection of false heteroplasmic mtDNA variants.

Methods

Blasting of the human nuclear genome versus the mitochondrial genome

BLAST2seq implements the BLASTN program (release 2.2.19 of the BLAST suite) [20] applied to the comparison between two sequences. The run was performed on a local server. Twenty-four runs were launched, one for each human chromosome sequence available through the hg18 build. The human mtDNA reference sequence rCRS [GenBank:NC_012920] was used as query. Scoring parameters were fixed as follows: 2 for match reward, -3 for mismatch penalty; -5 for gap opening, -2 for gap extension. The e-value was fixed to 1e-03. The hg18 chromosome sequences were fetched using the get-genome program of the GMAP package [21]. Each fragment of each chromosome aligned with the J01415.2 mtDNA whose e-value was lower than the fixed threshold produced an HSP (High Scoring Pair).

NumtS assembling

The assembling of the HSPs was performed with spreadsheet interpolation and manual inspection, strongly supported by graphical display of the HSP_NumtS with a custom annotation tool available on the UCSC Genome Browser. HSP_NumtS located less than 2000 bp apart on a specific chromosome and corresponding to two mtDNA fragments, not more than 2000 bp apart and oriented in the same direction, were merged in a single NumtS and here named as 'assembled NumtS'. The fragment joining protocol was slightly modified for HSPs interposed by long repetitive elements (see HSA_NumtS_014 in Figure 5 for an example).

Primer design

Primers were designed with primer BLAST software (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) using as a template each NumtS extended by 1000 nucleotides from the 5' and 3' ends, and specifically locating primers in NumtS flanking regions. The NumtS sequences with their flanking regions were extracted from the UCSC Genome Browser (http://genome.ucsc.edu/) [22]. Primers were designed to ensure amplification, even in case of NumtS absence. For NumtS longer than 1200 bp, external/internal, internal/external, and/or internal/internal primer pairs were designed. To exclude co-amplification of mtDNA, primers were validated through BLAST analysis, by comparison with the mitochondrial sequence of the European individual [18]. Moreover, in order to avoid primers self-hybridization, the Oligo Analysis tool available at Operon web site (http://www.operon.com/technical/toolkit.aspxis), was used. Sequences of primer pairs are provided in additional file 3 NumtS_validation.xls (sheet "Validated_NumtS").

PCR amplification and sequencing

The validation of RHNumtS.2 was carried out on DNA extracted from blood of an individual of European origin belonging to a typical European mitochondrial haplogroup H2b. NumtS were amplified and sequenced as previously described [18]. HSP_NumtS not present in the European sample were validated in an Ethiopian sample whose mtDNA belonged to the L0 haplogroup. NumtS sequences were submitted to the EMBL databank and hence to the three Nucleic Acids databases joint in the international collaboration INSDC. The WebIn tool available on-line was utilized (http://www.ebi.ac.uk/embl/Submission/). Accession numbers are provided in additional file 3 NumtS_validation.xls (sheet "Validated_NumtS").

Multi-alignment

NumtS sequences were multialigned to the corresponding hg18 sequence inclusive of the NumtS flanking region and to the mtDNA fragments from rCRS and from the validation sample. The multi-alignment has been produced using the ClustalW program (http://www.ebi.ac.uk/Tools/ClustalW/) [23]. The whole data set of the multialigned NumtS is available in the additional file 2 Validated_NumtS_multial.txt. Multi-alignment of the sequences already published in [18] have been included in the dataset.

Comparison of the RHNumtS.2 sequences versus the FES

The HapMap consortium [24] has made available 270 samples from Nigeria, China, Japan and North/West Europe. With the aim to study human structural variation, eight HapMap samples (Table 4) were selected and their genomic DNA was cloned using a fosmid subcloning strategy [25]. For each individual library the paired ends were sequenced and the obtained FES (Fosmid End Sequences) data were made publicly available (http://hgsv.washington.edu/). The in silico validation of NumtS was based on a merging protocol carried out using the Galaxy package available at http://main.g2.bx.psu.edu/. For each sample, only clones with a single best concordant placement according to the FES-pair analysis previously described [25] were considered.

NumtS tracks implementation

The NumtS tracks and the external links were produced starting from the RHNumtS.2 compilation spreadsheet, with manual manipulation and by using in-house shell and Python scripts. The human mitochondrial reference genome at the UCSC Genome Browser derives from an African individual [GenBank:NC_001807] and shows three insertions with respect to the rCRS. Therefore, the mitochondrial coordinates annotated in the additional file 1 RHNumtS.2.xls were re-mapped to NC_001807. The tracks have been released in bed format, one of the formats allowing the display of the tracks at the UCSC Genome Browser. Templates of the bed file format and the chromosome colour key are available on the UCSC Genome Browser help pages (http://genome.ucsc.edu).

Authors' contributions

DS carried out the bioinformatics analyses aimed at extending the compilation, validating it by analysing the HapMap data and designed and implemented the UCSC tracks in collaboration with the UCSC staff. FMC designed primers for bench validation, produced the multialignments and submitted the produced sequences to INSDC; ML carried out the PCR amplification and the sequencing; GG and MA coordinated and supervised the whole project and wrote the manuscript. All authors read and approved the final manuscript.

Additional file 1

The RHNumtS.2 compilation. The table reports 766 Human HSP_ NumtS fragments of which 150 have been assembled in a single NumtS according to the rules reported in the Results section. Each line refers to an HSP_NumtS and reports the NumtS ID, the HSP_NumtS ID, a code tracing the compilation updating procedure, the chromosome where the NumtS is located and the strand where BLAST mapped the NumtS, the positions on the hg18 chromosome and the rCRS. In the case of assembled NumtS, in the right hand side of the table, detailed information on each fragment is available. The last column reports the identity percentage as reported in the BLAST output for each HSP. Click here for file

Additional file 2

Multi-alignments of sequenced NumtS. This archive contains multi-alignments of sequenced NumtS with the corresponding nuclear and mitochondrial regions. Each file reports the multi-alignment of reference (hg18) NumtS sequence, the sequenced NumtS, the corresponding mitochondrial H2b sequence and the corresponding rCRS region. For the three NumtS not present in the European individual (HSA_NumtS_009, HSA_NumtS_426, HSA_NumtS_522), the sequence validated on the Ethiopian individual (L0) was multialigned with the reference (hg18) NumtS sequence and the sequence produced in the European sample; the gap present in the latter sequence matches with the nuclear region where the NumtS maps. Click here for file

Additional file 3

The RHNumtS.2 Human NumtS validated by bench and/or . The table reports two sheets, one for the validated NumtS and the other one for non-validated NumtS. In the sheet "Validated NumtS", for each validated NumtS the following data are reported: the NumtS ID code, the HSP_NumtS_ID, the accession number hold until published code referred to he INSDC submission, in the case when the NumtS has been sequenced, otherwise the flag 'not sequenced' is annotated, the flags (Y or N) indicating successful response to PCR, sequencing and in silico Hapmap sample validation; and finally the primer code and the forward and reverse primer sequences. In the sheet "Non-validated NumtS", the list of non-validated NumtS (neither by PCR/sequencing, nor by in silico approach) is provided, along with a possible explanation for validation failure. Click here for file

Additional file 4

Browsing NumtS tracks on UCSC Genome Browser. This file provides an essential guide to the browsing of the NumtS tracks available at UCSC Genome Browser. Click here for file
  25 in total

1.  Mitochondrial pseudogenes: evolution's misplaced witnesses.

Authors:  D Bensasson; D -X. Zhang; D L. Hartl; G M. Hewitt
Journal:  Trends Ecol Evol       Date:  2001-06-01       Impact factor: 17.712

2.  The human genome browser at UCSC.

Authors:  W James Kent; Charles W Sugnet; Terrence S Furey; Krishna M Roskin; Tom H Pringle; Alan M Zahler; David Haussler
Journal:  Genome Res       Date:  2002-06       Impact factor: 9.043

Review 3.  Protein database searches using compositionally adjusted substitution matrices.

Authors:  Stephen F Altschul; John C Wootton; E Michael Gertz; Richa Agarwala; Aleksandr Morgulis; Alejandro A Schäffer; Yi-Kuo Yu
Journal:  FEBS J       Date:  2005-10       Impact factor: 5.542

4.  Multiple sequence alignment using ClustalW and ClustalX.

Authors:  Julie D Thompson; Toby J Gibson; Des G Higgins
Journal:  Curr Protoc Bioinformatics       Date:  2002-08

5.  Meta-analysis of gross insertions causing human genetic disease: novel mutational mechanisms and the role of replication slippage.

Authors:  Jian-Min Chen; Nadia Chuzhanova; Peter D Stenson; Claude Férec; David N Cooper
Journal:  Hum Mutat       Date:  2005-02       Impact factor: 4.878

6.  Nonsyndromic recessive deafness DFNB18 and Usher syndrome type IC are allelic mutations of USHIC.

Authors:  Zubair M Ahmed; Tenesha N Smith; Saima Riazuddin; Tomoko Makishima; Manju Ghosh; Sirosh Bokhari; Puthezhath S N Menon; Dilip Deshmukh; Andrew J Griffith; Sheikh Riazuddin; Thomas B Friedman; Edward R Wilcox
Journal:  Hum Genet       Date:  2002-05-03       Impact factor: 4.132

7.  A comparative approach shows differences in patterns of numt insertion during hominoid evolution.

Authors:  M I Jensen-Seaman; J H Wildschutte; I D Soto-Calderón; N M Anthony
Journal:  J Mol Evol       Date:  2009-05-27       Impact factor: 2.395

8.  Mapping and sequencing of structural variation from eight human genomes.

Authors:  Jeffrey M Kidd; Gregory M Cooper; William F Donahue; Hillary S Hayden; Nick Sampas; Tina Graves; Nancy Hansen; Brian Teague; Can Alkan; Francesca Antonacci; Eric Haugen; Troy Zerr; N Alice Yamada; Peter Tsang; Tera L Newman; Eray Tüzün; Ze Cheng; Heather M Ebling; Nadeem Tusneem; Robert David; Will Gillett; Karen A Phelps; Molly Weaver; David Saranga; Adrianne Brand; Wei Tao; Erik Gustafson; Kevin McKernan; Lin Chen; Maika Malig; Joshua D Smith; Joshua M Korn; Steven A McCarroll; David A Altshuler; Daniel A Peiffer; Michael Dorschner; John Stamatoyannopoulos; David Schwartz; Deborah A Nickerson; James C Mullikin; Richard K Wilson; Laurakay Bruhn; Maynard V Olson; Rajinder Kaul; Douglas R Smith; Evan E Eichler
Journal:  Nature       Date:  2008-05-01       Impact factor: 49.962

9.  Continued colonization of the human genome by mitochondrial DNA.

Authors:  Miria Ricchetti; Fredj Tekaia; Bernard Dujon
Journal:  PLoS Biol       Date:  2004-09-07       Impact factor: 8.029

10.  The RHNumtS compilation: features and bioinformatics approaches to locate and quantify Human NumtS.

Authors:  Daniela Lascaro; Stefano Castellana; Giuseppe Gasparre; Giovanni Romeo; Cecilia Saccone; Marcella Attimonelli
Journal:  BMC Genomics       Date:  2008-06-03       Impact factor: 3.969

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  32 in total

1.  Polymorphic NumtS trace human population relationships.

Authors:  Martin Lang; Marco Sazzini; Francesco Maria Calabrese; Domenico Simone; Alessio Boattini; Giovanni Romeo; Donata Luiselli; Marcella Attimonelli; Giuseppe Gasparre
Journal:  Hum Genet       Date:  2011-12-08       Impact factor: 4.132

Review 2.  Defining the momiome: Promiscuous information transfer by mobile mitochondria and the mitochondrial genome.

Authors:  Bhupendra Singh; Josephine S Modica-Napolitano; Keshav K Singh
Journal:  Semin Cancer Biol       Date:  2017-05-11       Impact factor: 15.707

Review 3.  Decoding the rosetta stone of mitonuclear communication.

Authors:  Justin English; Jyung Mean Son; Maria Dafne Cardamone; Changhan Lee; Valentina Perissi
Journal:  Pharmacol Res       Date:  2020-08-23       Impact factor: 7.658

4.  Population-level expression variability of mitochondrial DNA-encoded genes in humans.

Authors:  Gang Wang; Ence Yang; Ishita Mandhan; Candice L Brinkmeyer-Langford; James J Cai
Journal:  Eur J Hum Genet       Date:  2014-01-08       Impact factor: 4.246

5.  GWAS and ExWAS of blood mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia.

Authors:  Michael Chong; Pedrum Mohammadi-Shemirani; Nicolas Perrot; Walter Nelson; Robert Morton; Sukrit Narula; Ricky Lali; Irfan Khan; Mohammad Khan; Conor Judge; Tafadzwa Machipisa; Nathan Cawte; Martin O'Donnell; Marie Pigeyre; Loubna Akhabir; Guillaume Paré
Journal:  Elife       Date:  2022-01-13       Impact factor: 8.140

Review 6.  Numtogenesis as a mechanism for development of cancer.

Authors:  Keshav K Singh; Aaheli Roy Choudhury; Hemant K Tiwari
Journal:  Semin Cancer Biol       Date:  2017-05-13       Impact factor: 15.707

7.  Maternal age effect and severe germ-line bottleneck in the inheritance of human mitochondrial DNA.

Authors:  Boris Rebolledo-Jaramillo; Marcia Shu-Wei Su; Nicholas Stoler; Jennifer A McElhoe; Benjamin Dickins; Daniel Blankenberg; Thorfinn S Korneliussen; Francesca Chiaromonte; Rasmus Nielsen; Mitchell M Holland; Ian M Paul; Anton Nekrutenko; Kateryna D Makova
Journal:  Proc Natl Acad Sci U S A       Date:  2014-10-13       Impact factor: 11.205

8.  Extensive pathogenicity of mitochondrial heteroplasmy in healthy human individuals.

Authors:  Kaixiong Ye; Jian Lu; Fei Ma; Alon Keinan; Zhenglong Gu
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-07       Impact factor: 11.205

9.  Nuclear genetic regulation of the human mitochondrial transcriptome.

Authors:  Aminah T Ali; Lena Boehme; Guillermo Carbajosa; Vlad C Seitan; Kerrin S Small; Alan Hodgkinson
Journal:  Elife       Date:  2019-02-18       Impact factor: 8.140

Review 10.  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.

Authors:  Marni J Falk; Lishuang Shen; Michael Gonzalez; Jeremy Leipzig; Marie T Lott; Alphons P M Stassen; Maria Angela Diroma; Daniel Navarro-Gomez; Philip Yeske; Renkui Bai; Richard G Boles; Virginia Brilhante; David Ralph; Jeana T DaRe; Robert Shelton; Sharon F Terry; Zhe Zhang; William C Copeland; Mannis van Oven; Holger Prokisch; Douglas C Wallace; Marcella Attimonelli; Danuta Krotoski; Stephan Zuchner; Xiaowu Gai
Journal:  Mol Genet Metab       Date:  2014-12-04       Impact factor: 4.797

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