Open Access

The RHNumtS compilation: Features and bioinformatics approaches to locate and quantify Human NumtS

  • Daniela Lascaro1,
  • Stefano Castellana1,
  • Giuseppe Gasparre2,
  • Giovanni Romeo2,
  • Cecilia Saccone1 and
  • Marcella Attimonelli1Email author
BMC Genomics20089:267

DOI: 10.1186/1471-2164-9-267

Received: 14 September 2007

Accepted: 03 June 2008

Published: 03 June 2008

Abstract

Background

To a greater or lesser extent, eukaryotic nuclear genomes contain fragments of their mitochondrial genome counterpart, deriving from the random insertion of damaged mtDNA fragments. NumtS (Nuclear mt Sequences) are not equally abundant in all species, and are redundant and polymorphic in terms of copy number. In population and clinical genetics, it is important to have a complete overview of NumtS quantity and location. Searching PubMed for NumtS or Mitochondrial pseudo-genes yields hundreds of papers reporting Human NumtS compilations produced by in silico or wet-lab approaches. A comparison of published compilations clearly shows significant discrepancies among data, due both to unwise application of Bioinformatics methods and to a not yet correctly assembled nuclear genome. To optimize quantification and location of NumtS, we produced a consensus compilation of Human NumtS by applying various bioinformatics approaches.

Results

Location and quantification of NumtS may be achieved by applying database similarity searching methods: we have applied various methods such as Blastn, MegaBlast and BLAT, changing both parameters and database; the results were compared, further analysed and checked against the already published compilations, thus producing the Reference Human Numt Sequences (RHNumtS) compilation. The resulting NumtS total 190.

Conclusion

The RHNumtS compilation represents a highly reliable reference basis, which may allow designing a lab protocol to test the actual existence of each NumtS. Here we report preliminary results based on PCR amplification and sequencing on 41 NumtS selected from RHNumtS among those with lower score. In parallel, we are currently designing the RHNumtS database structure for implementation in the HmtDB resource. In the future, the same database will host NumtS compilations from other organisms, but these will be generated only when the nuclear genome of a specific organism has reached a high-quality level of assembly.

Background

In greater or lesser abundance, eukaryotic nuclear genomes contain fragments of their mitochondrial (mt) genome counterpart, deriving from "random" insertion of damaged mtDNA fragments [1]. The discovery of these genomic "elements" dates back to 1967, when du Buy and Riley [2] discovered mtDNA sequences in the nuclear genome by means of hybridization experiments on mouse liver. The presence of mtDNA in the nuclear genome was confirmed in 1983, in yeast, locust, fungi, sea urchin, man, maize and rat [39]. In 1994 Lopez et al. [10] called these fragments numt, in this paper renamed NumtS, Nuclear mt Sequences. One hypothesis on the mechanism of their generation suggests that fragments of mtDNA may escape from mitochondria to avoid mutagenic agents or other forms of cellular stress, reach the nucleus and, during repair of chromosomal breaks, insert into the nuclear DNA [11]. Papers published so far report that NumtS loci do not show a common feature at integration sites [12]. The NumtS generation process may have started soon after endosymbiosis. It seems obvious that the genomic region where the mt sequence is inserted may be involved in further recombination events, thus generating duplication of the mt fragment. In some organisms, such as primates, the same mt region occurs several times along the nuclear genome, but only detailed evolutionary analysis may help in identifying "duplicated" NumtS, because recombination and mutation occurring after duplication may well mask the latter event. Once this problem is solved, each NumtS may be associated with a given copy number, although this may differ even among tissues or cells of the same individual. NumtS have been shown in fact to be polymorphic: a specific NumtS may be present in heterozygosis in the same individual or may be totally absent in a specific tissue or individual. The first evidence of the polymorphic nature of NumtS was reported by Zischler et al. [13], in which an insert of 540 bp (reverse positions (59–16089) of the revised Cambridge Reference Sequence (rCRS) [14]), located on chromosome 11 and detected on total DNA extracted from sperm, was screened in various populations using primers designed on the sequences flanking the insertion. Among the screened individuals, some were homozygous, some were heterozygous and some did not show the insertion. When present, the inserted sequence was highly conserved in all populations, thus revealing a "nuclear fossil". The inserted sequence with its nuclear flanking region is available through GenBank entry S80333 but, when this sequence is blasted against Human nuclear genome Build 36.2, the resulting hit matches only the flanking region. This means that the samples used for sequencing the Human Genome did not harbour the "insertion". Further examples of polymorphic NumtS are reported in "The case of siblings" [15] and the Ricchetti compilation [16].

NumtS are not equally abundant in all species. For instance a much higher number of NumtS occurs in plants with respect to Metazoa. Within Metazoan, NumtS are more abundant in mammals and birds, but a very small number can be found in Plasmodium, Caenorhabditis and Drosophila. The debate about the presence or absence of NumtS in fugu is open [17, 18]. The great abundance of NumtS in Apis mellifera, comparable to that in plants, has been published recently [19]. A complete knowledge of Human NumtS is of fundamental importance in the study of human population migrations, which utilize mtDNA as a phylogenetic marker, and also in the study of mitochondrial diseases. NumtS are in fact a potential source of contamination when PCR is used to study mtDNA. This is particularly important in the case of ancient DNA or tissue with a reduced quantity of mtDNA copy number, in both physiological (sperm) and pathological states [20, 21]. Bensasson et al. [1] presented an exhaustive vademecum suggesting how to check and avoid NumtS contamination. A final consideration concerns the observation that since NumtS reside in the nucleus, they should evolve much more slowly than their functional counterparts in the nucleus, so that they represent nuclear fossils, "snapshots" of mtDNA at the time of transfer. This allows them to be used as outgroups in phylogenetic studies [2225]. Searching PubMed for NumtS or Mitochondrial pseudogenes in November 2006 yielded hundreds of papers, 113 of which on humans. Many of them report the compilation of Human NumtS and other Eukaryotic Genomes [1, 16, 23, 2631], and usually mention the location and length of each NumtS. Such data were mainly obtained by in silico approaches and only a minority derived from a wet-lab approach, sequencing or nDNA-mtDNA hybridizations [32, 16]. Parr et al. [32] demonstrated by sequencing that the entire mitochondrial genome is present within the nuclear genome in multiple copies: the "pseudo-mitochondrial genome". A comparison of published compilations highlights great discrepancies among data (Figure 1). The reason for this lies in two important facts: incautious usage of Bioinformatics methods and application of methods to a still not yet correctly assembled nuclear genome. However, the trends common to all papers are that the number of NumtS varies among species and that the human genome apparently contains the highest number of NumtS within Metazoan. But the data are still incomplete and imperfect. NumtS quantification needs revision, particularly starting from Human data. The present paper, describing the bioinformatics approaches used to optimize quantification and localization of NumtS, reports the Consensus Reference Human NumtS Compilation (RHNumtS) and the results of the amplification and sequencing approach applied to 41 selected NumtS
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-267/MediaObjects/12864_2007_Article_1460_Fig1_HTML.jpg
Figure 1

Number of NumtS reported in a selected group of published compilations. Horizontal axis reports the reference number in this paper of the analysed compilation.

Results

The location and quantification of NumtS may be achieved by applying database similarity searching methods, comparing the human mt DNA sequence with human nuclear genome sequences. The goal of database similarity searching methods is to seek for regions showing statistically meaningful similarity, but these methods are too sensitive: slight changes in parameters and/or in both query and database sequences may give rise to considerable changes in the resulting hits. In this study the available human mt DNA sequences total at present more than 3000, of which about 90% represent different haplotypes, thus choosing the query sequence is the first problem. Moreover available human nuclear genome sequences are consensus sequences, obtained from the DNA of five different individuals, and suffer from physiological assembly limits, due to great repetition in the genome. This means that there are slight differences which may lead to poorly reproducible results, due to mitochondrial polymorphic sites and to a still not completely defined nuclear genome. Thus, changing methods or parameters or applying the method to different human genome sequence collections produce diverse results. Here, we suggest the usage of different approaches and the comparison of the obtained results, in order to minimize both false positive and negative results with the aim to optimize quantification and localization of human NumtS. Our overall proposal is to apply several bioinformatics approaches, to compare the results, to produce a consensus compilation and then validate the results through PCR and sequencing analysis. This last step is not the primary scope of this work although we report some preliminary results. Available bioinformatics programs for this type of analysis are BLAST, BLAT and FASTA. We used only BLAST [33, 34] and BLAT [35], the performance of which better suited our needs. BLAST is implemented in many versions, Blastn, Blastx, tBLastn, MegaBlast, etc. We used Blastn and MegaBlast, the versions most frequently adopted in reports on human NumtS compilation.

Blastn results

Tables 1 and 2 summarise the results obtained with Blastn, searching the revised Reference Cambridge Sequence [14] (J01415.2) similarity versus various human nucleotide sequence datasets by changing limits by Entrez and limits on number of positive hits to be displayed (see Methods section). The data in the tables clearly show how easy it is to obtain false positive or negative hits. Among the various results, we chose the one producing 2145 hits, comparing the J01415.2 sequence against the Chromosome Genome database with the Entrez limit "homo sapiens [ORGN] NOT (mitochondrion OR mitochondrial) [ALL]" since in this case hits on the mitochondrial genome were not obtained (query #19 in Table 2). The data shown here were produced in March 2007, when the old version of Blast was available. At present, some of the Blast options we used are no longer available.
Table 1

Differences in Blastn hit numbers.

Limit by Entrez Query

Descr#, Aligment#, Graph#

Hits#found

1.-nothing

100, 100, 50

435

2.-nothing

5000, 5000, 1000

4903

3.-nothing

10000, 10000, 1000

4903

4.-Homo sapiens BUT NOT mitochondrion

100, 100, 50

116

5.-Homo sapiens BUT NOT mitochondrion

5000, 5000, 1000

2497

Data are obtained by comparing reference Human mt Genomes (J01415.2 in GenBank) with Human Nuclear DNA sequences in differing conditions. Maximum fixed Description number, Graphic display number and Alignments number do not fit Hits# obtained; thus, true hit number is that obtained when set values are higher than number of obtained hits.

Table 2

Differences in Blastn hit numbers by changing human genome searched datasets.

Limit by Entrez query

Hits#

Type of selected data reported in the Blastn output

6. – nothing

4903

Human Complete mt genomes, Human D-loop, other species

7. – Homo sapiens BUT NOT mitochondrion

2497

Genomic DNA, cDNA, D-loop also from other species

8. – Homo sapiens [ORGN]

4903

Human Complete mt genomes, D-loop,

9. – Homo sapiens [ORGN] NOT mitochondrion [PROP]

4903

Human Complete mt genomes, D-loop, other species

10. – Homo sapiens [ORGN] AND genomic DNA [MOLTYPE] NOT mitochondrion [PROP]

2154

Human Genomic DNA, cDNA, 2 complete mt genomes

11. – Homo sapiens [ORGN] NOT mitochondrion [ALL]

2497

Human Genomic DNA, cDNA, D-loop

12. – Homo sapiens [ORGN] AND genomic DNA [MOLTYPE] NOT mitochondrion [ALL]

123

Human Genomic DNA, cDNA

13. – Homo sapiens [ORGN] AND genomic DNA [MOLTYPE] NOT (mitochondrion OR mitochondrial) [ALL]

119

Human Genomic DNA

14. – nothing

16350

Genomic, D-loop, mt genomes other organisms

15. – Homo sapiens NOT mitochondrion

2097

Genomic DNA, D-loop, other organisms

16. – Homo sapiens [ORGN]

2106

Human Genomic DNA, D-loop, mt complete genomes

17. – Homo sapiens [ORGN] NOT (mitochondrion OR mitochondrial) [ALL]

2097

Human Genomic DNA, Human D-loop

18. – Homo sapiens [ORGN] NOT mitochondrion [PROP]

2154

2 human mt genomes, Genomic DNA

19. – Homo sapiens [ORGN] NOT (mitochondrion OR mitochondrial) [ALL]

2145

HGPC+Celera+Assemblychr7

20. – Homo sapiens [ORGN] NOT mitochondrion [ALL]

2145

HGPC+Celera

Different Hits number and different class of selected entries obtained by changing subject sequence datasets through "Limits by Entrez query" function. Resulting subject sequences are subsets of non-redundant nucleotide database (query 6 to 13), ref_seq genome database (query 14 to 17), Chromosome human genome database (query 18 to 20) all available through Blastn at NCBI. E-value set at 0.001. Description#, graphic display# and alignments# were set at maximum values allowed. Runs 19 and 20, resulting in 2145 hits, were those most suited to our needs, i.e., to select completely assembled human nuclear sequences.

MegaBlast results

We used MegaBlast to compare the J01415.2 Human mt sequence against the last assembled Human genome database and Human Genome Reference sequence sets (Build 36.2, January 2007): when the E threshold was set at 0.001, the resulting hits were 288 and 186 respectively.

BLAT results

We applied the BLAT program by submitting the J01415.2 Human mt sequence to the four available Human Builds, obtaining 118, 122, 124 and 117 hits for the hg15, hg16, hg17 and hg18 human genome assemblies. In parallel, we applied the same runs with the NC_001807.4 human mt genome, reported by NCBI as the reference human mt genome sequence. There are 22 differences between these two genomes, which cause differing results (data not shown). This is a further explanation of the differing results obtained in published compilations in which different query sequences were used.

The RHNumtS compilation production

Comparisons of our results with the published ones allowed us to produce the RHNumtS compilation. The resulting NumtS total 190. Each NumtS in the compilation refers to an mtDNA fragment; thus, if 2 mtDNA regions are contiguous on nuclear DNA, they are considered as two distinct NumtS. This is the case, for example, of the repeated NumtSs 41–54 that was here validated experimentally (see Figure 2a). Table 3 reports RHNumtS data for NumtS whose mt fragment length is greater than 2000 bp. The complete compilation is available in Additional file 1. The rationale used to produce the compilation is described here. The reference results are those obtained through BlastN (code 19 in Table 2). Hits less than 2000 nucleotides from each other, on both nuclear and mt genomes were merged. The results were compared with those from Megablast [Human Genome all assemblies (Build 36.2, January 2007)] and BLAT (Assembling hg18): the more reproducible the results obtained with the various methods, the higher the probability that NumtS exists in the Human Genome. Each NumtS in RHNumtS is identified by a numeric code (RHNumtS identifier); only three were identified by a letter. To each NumtS we associated: chromosome and strand location, both mt and nuclear coordinates of the NumtS ("mt start" and "mt end", "chr start" and "chr end"); mitochondrial and nuclear fragment lengths and differences between mitochondrial and nuclear fragment lengths; the longer the NumtS, the higher the number of gaps within the NumtS, thus indicating that it likely underwent several modifications since the time of its insertion in the nuclear genome. Additional file 2 reports the comparison of Blastn obtained data vs BLAT and Megablast results. Of the 190 NumtS available through RHNumtS, 122 (64%) were matched with both Blastn and BLAT, 60 (32%) with Blastn only and eight (4%) with Blastn, but located on the Celera assembly instead of the public Human Genome consortium. The compilation was compared with some published compilations. For each NumtS, Additional file 3 lists its presence (OK) or absence (-) in some of the published compilations. Question marks (?) indicate ambiguous cases. With respect to the Parr compilation [32], the sequenced NumtS are all present in RHNumtS; this validates our results, although some sequenced NumtS could not be present in our compilation, due to their polymorphic features. The same applies to the two NumtS sequenced by Collura [37], located on chromosome 7. In order to quantify the strength of our approach, RHNumtS quality scores were assigned to each NumtS for each program applied and for each match with the selected published compilations, according to defined criteria: 0.25 or 0.50 for an ambiguous or perfect match with each of the selected published compilations [16, 23, 26, 28], 2.00 for matches with Parr and Collura sequenced NumtS, 0.25 for ambiguous matches with Megablast hits, 1.00 for perfect matches with MegaBlast and BLAT hits, and 0.75 for NumtS not directly identified by BLAT. Score values range from 6 to 0.25. The last column of Additional file 1 and Table 3 lists the total score for each NumtS. NumtS with scores higher than 3 (16.3% of the total Reference Human NumtS compilation) are highlighted. Perfect matches with published compilations are at most 12%: this is the case of the comparison with the Wallace compilation [26].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-267/MediaObjects/12864_2007_Article_1460_Fig2_HTML.jpg
Figure 2

PCR amplification of 41 selected NumtS. PCR amplification of (a) 27 selected NumtS in 4 healthy subjects from different ethnic groups (haplogroups H2b (Europe), L2a1c1 (North Africa), I3a (Latin America) and G1a1a (Japan)); (b) 14 in the H2b sample only. Primers were designed with PRIMER3 software, testing the amplification of the full NumtS (external-external primers) or part of it (external-internal primers or internal-internal primers). In NumtS 41–54, samples H2b and L2a1c1 have shorter amplicons, due to a lower number of repetitions. Triple band in NUMTS 12 was due to aspecific amplification, subsequently reduced by increasing stringency of primers annealing. Abbreviations: ext for external, int for internal; the number below each band refers to the NumtS code assigned within the RHNumtS compilation.

Table 3

Longest NumtS of Reference Human NumtS compilation (RHNumtS)

NumtS Code

Chr

Strand

Mt Start

Mt End

Mt fragment length

Nuc Start

Nuc End

Chr fragment length

Difference

Quality Score

1

1

+

3914

9755

5842

554327

560167

5840

3

4

3

1

-

6060

9316

3257

107146786

107150029

3243

15

1.25

4

1

-

1051

3162

2112

120286496

120288780

2284

173

0.75

9

1

-

9782

13593

3812

233768514

233772288

3774

39

1.75

10

1

-

636

6189

5554

236170699

236176250

5551

4

1.75

11

1

-

12218

16563

4346

236177249

236181582

4333

14

1.75

14

2

+

12220

16475

4256

82896241

82900506

4265

10

1.75

19

2

+

596

5892

5297

117495259

117500547

5288

10

3.25

20

2

+

9196

13574

4379

120685762

120690928

5166

788

1.75

22

2

+

3799

15354

11556

130745853

130757329

11476

81

2

23

2

-

10657

15398

4742

131843104

131847799

4695

48

1.75

24

2

-

3799

10519

6721

131853669

131860205

6536

186

1.5

25

2

-

598

5892

5295

140691291

140698242

6951

1657

3.5

27

2

-

9166

16563

7398

143566386

143574013

7627

230

1.75

30

2

+

11801

15067

3267

155875844

155879111

3267

1

2

33

2

-

10440

13131

2692

201785264

201787949

2685

8

2

34

2

+

6966

11240

4275

203187200

203191742

4542

268

2

36

2

-

596

3105

2510

212346765

212349578

2813

304

1.75

37

2

+

4854

7590

2737

212350179

212352885

2706

32

1.5

58

3

-

6604

9316

2713

89718693

89721366

2673

41

2

61

3

-

9787

12340

2554

108095676

108098627

2951

398

1.25

62

3

+

13536

15573

2038

108100533

108101514

981

1058

1.5

71

4

-

9781

12301

2521

25328634

25331437

2803

283

1.75

76

4

-

9485

16561

7077

65155336

65160181

4845

2233

1.75

79

4

+

596

3105

2510

117438367

117440855

2488

23

1.75

81

4

-

672

15325

14654

156592474

156607061

14587

68

3.5

88

5

-

341

2697

2357

79981597

79983943

2346

12

4.75

89

5

+

12662

16124

3463

93928917

93932379

3462

2

5

91

5

-

6117

15183

9067

99409541

99418648

9107

41

4.5

94

5

-

10270

15488

5219

134286898

134292116

5218

2

5

97

6

+

8437

10622

2186

92493159

92493750

591

1596

1.5

100

6

+

7451

11649

4199

154028400

154032608

4208

10

1.75

102

7

+

8505

15238

6734

57238827

57245471

6644

91

3.75

103

7

+

3819

15924

12106

57257414

57269467

12053

54

6

104

7

-

3117

11880

8764

63201998

63210482

8484

281

1.5

105

7

-

5513

8246

2734

68433640

68436926

3286

553

1.75

106

7

+

13065

15369

2305

111799937

111802234

2297

9

2

107

7

-

2793

6553

3761

141147677

141151744

4067

307

2

108

7

+

600

3095

2496

142052596

142055088

2492

5

2.25

110

8

+

636

4888

4253

32988565

32992739

4174

80

1.5

113

8

-

656

4880

4225

47858273

47861837

3564

662

4

114

8

-

9176

16569

7394

68655653

68662552

6899

496

1.75

117

8

+

1013

7114

6102

104164459

104171823

7364

1263

3.5

120

9

+

1294

13574

12281

5082095

5100699

18604

6324

1.5

121

9

+

598

3093

2496

33646633

33649128

2495

2

1.5

125

9

-

4773

6873

2101

82368550

82370501

1951

151

1.75

126

9

+

9202

11598

2397

93911111

93913772

2661

265

3.25

128

10

+

2417

4831

2415

20075681

20077114

1433

983

2.25

131

10

+

636

3105

2470

57027643

57030440

2797

328

1.5

132

10

-

3821

7698

3878

71020912

71025687

4775

898

1.75

134

11

-

577

2972

2396

10486010

10488403

2393

4

6

140

11

+

9820

15243

5424

80940264

80945683

5419

6

1.75

142

11

-

724

9666

8943

102778067

102786933

8866

78

3.25

150

13

+

13052

16472

3421

95142796

95146598

3802

382

1.5

156

14

+

11367

15325

3959

83708940

83713093

4153

195

1.75

158

15

+

9786

15318

5533

56229853

56235023

5170

364

1.5

159

16

-

2468

7683

5216

3357487

3362068

4581

636

3.5

160

16

-

8688

15327

6640

10720543

10726494

5951

690

1.5

164

17

-

596

5979

5384

19442485

19449425

6940

1557

3.5

165

17

+

14365

16569

2205

21942648

21944853

2205

1

3.75

166

17

+

1

11112

11112

21944854

21955968

11114

3

3.5

171

20

-

649

4038

3390

55366111

55369449

3338

53

3.5

174

X

-

581

5892

5312

55221910

55227180

5270

43

4

175

X

+

1049

3161

2113

61976282

61978565

2283

171

1.75

182

X

+

1054

4415

3362

142345841

142349570

3729

368

1.75

184

Y

+

596

4477

3882

8294669

8300289

5620

1739

1.25

Each NumtS was assigned an identifying numeric code, according to increasing values starting from chromosome 1; a letter code (A, B, or C) was assigned to only 3 NumtS, because they were located later, when all other NumtS had already been characterised. Chromosome and strand location is listed for each NumtS; both mt and nuclear coordinates of NumtS ("mt start" and "mt end", "chr start" and "chr end"); mitochondrial and nuclear fragment lengths; "difference" between mitochondrial and nuclear fragment lengths; and RHNumtS quality score are also reported. Additional file 1 reports the complete RHNumtS compilation: there, NumtS exclusively identified by Blastn are shown in grey and NumtS exclusively identified by Blastn, but only on Human Genome Celera Assembly, shown in black in columns "Nuc start" and "Nuc end"; repeated NumtS in bold type and underlined in columns "Mt start" and "Mt end". NumtS with scores higher than 3 are shown in grey in column "score".

Human NumtS Sequencing

The assigned scores are theoretical indicators of the quality of our prediction, but experimental validation of the predicted RHNumtS compilation is definitely a must and this is our goal in the immediate future. Starting from the RHNumtS compilation, we propose to test the real presence of each NumtS in a set of different healthy subjects belonging to different geographic areas and various haplogroups, in order to verify if the NUMTS presence/absence may be different in various phylogenetic lineages. Experimental validation will be based on the amplification and sequencing of the NumtS. This requires a great effort in terms of manpower and funds. However such project is currently ongoing and here we report only preliminary results obtained for 41 of the 190 NumtS, selected among the NumtS with lower scores. Indeed, the lower the score, the higher the probability that the NumtS is a false positive. Table 4 reports the list of the analyzed NumtS with information about samples where amplification and sequencing has been successful. Figure 2 reports the PCR amplification of (a) 27 of the selected NumtS in 4 healthy subjects from different ethnic groups and (b) 14 NumtS in a European sample. With respect to NumtS 41–54, the H2b and L2a1c1 samples have shorter amplicons, due to the presence of a lower number of repeats, as confirmed in the multi-alignment (Additional file 4).
Table 4

Amplified and sequenced NumtS

NumtS Code

Amplified Haplogroup samples

Sequenced Haplogroup samples

2

H2B

H2B

12

H2B

H2B

13

H2B, L2a1c1,G1a1a, I3a

H2B

28

H2B

H2B

38

H2B, L2a1c1,G1a1a, I3a

H2B

41

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

42

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

43

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

44

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

45

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

46

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

47

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

48

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

49

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

50

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

51

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

52

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

53

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

54

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

63

H2B, L2a1c1,G1a1a, I3a

H2b

72

H2B

H2B

73

H2B, L2a1c1,G1a1a, I3a

H2B

75

H2B, L2a1c1,G1a1a, I3a

H2B

77

H2B

H2B

82

H2B, L2a1c1,G1a1a, I3a

H2B

87

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a, I3a

101

H2B, L2a1c1,G1a1a, I3a

H2B

109

H2B

H2B

112

H2B

H2B

115

H2B

H2B

122

H2B, L2a1c1,G1a1a, I3a

H2B, L2a1c1,G1a1a

133

H2B

H2B

139

H2B, L2a1c1,G1a1a, I3a

H2B

151

H2B

H2B

153

H2B

H2B

157

H2B

H2B

169

H2B, L2a1c1,G1a1a, I3a

H2B

170

H2B, L2a1c1,G1a1a, I3a

sequencing failed

179

H2B

sequencing failed

187

H2B

sequencing failed

C

H2B, L2a1c1,G1a1a, I3a

H2B

For each of the 41 analysed NumtS, the mt haplogroup code of the sample, if amplified and if sequenced, is reported. NumtS 170, 179 and 187 in the H2b sample and 122 in the I3a sample have not been sequenced because primers were not optimal for sequencing.

The 122 NumtS sequence of the Latin American sample and the European sample of NumtS 170, 178 and 187 were not obtained. For NumtS 87, 122 and 41–54 Additional file 4 reports the nucleotide multi-alignment of the amplified and sequenced NumtS in the samples from the 4 different haplogroups, compared with the NumtS sequence as extracted from the Human Genome build 36.2 through the UCSC genome browser (hg18 release), and the sequences of the corresponding mitochondrial region for the same samples. As it appears from the multi-alignment there is a high conservation of the NumtS among the different subjects, although heterozygous sites can be observed (nucleotide ambiguity letter such as Y for C/T, R for A/G, etc.). NumtS sites reporting ambiguous nucleotides do in fact refer to sites where two alleles are evidenced in the sequence. The comparison of NumtS sequence with the corresponding mtDNA also clearly shows divergence among them. Additional file 5 reports the multialignments of the other 21 NumtS whose sequences have been so far produced for the European sample only. The NumtS sequence is aligned with the hg18 and mitochondrial corresponding sequences.

NumtS features

The resulting compilation was further analyzed, in order to qualify and quantify the process of transfer of Human mtDNA into the nuclear genome.

NumtS distribution along the genome

Chromosome 2 hosts the largest amount of NumtS, whereas chromosomes 19 and 22 do not show to harbour NumtS although, due to their polymorphic features, some individuals may present them on the latter chromosomes. Generally speaking, the longer the chromosome, the greater the chance of locating NumtS, partly because, as reported below, selection operates in the direction of avoiding NumtS inside genes, so that shorter chromosomes with a higher density of genes are less prone to hosting NumtS. In addition, with respect to the chromosome region where NumtS are located, no preference between euchromatin or etherochromatin regions has been observed. However, for each NumtS chromosome band, Additional file 6 lists information contributing to an overview about where NumtS are integrated.

NumtS dimensions

For each NumtS Additional file 1 also lists the mt and chromosomal fragment lengths, besides the mt and nuclear coordinates. These values do not coincide since, after insertion, NumtS undergo rearrangements such as deletions, insertions, and single nucleotide substitutions. The older the NumtS, the greater the difference between mitochondrial and chromosomal fragments (see difference column in Table 3 and additional file 1). The longest mt fragment located on the nuclear genome is NumtS no. 81, 14654 mt bp, located on chromosome 4 and highly compacted: the 81 NumtS chromosomal fragment is in fact 14587 bp long. The shortest NumtS is 45 bp long. More than 30% of NumtS derive from mt fragments longer than 2000 mt bp (Table 3). NumtS chromosomal fragment length is listed in Additional File 6: the longest one is NumtS 120, 18604 chromosomal BP, containing a mt fragment 12281 bp long, located on chromosome 9, with an RHNumtS score of 1.5. NumtS 103, 12053 bp long and derived from an mt fragment of 12106 nucleotides, located on chromosome 7, received a score of 6, because it was entirely sequenced from both Parr [32] and Collura [35].

Estimation of similarity

Although Blastn, Megablast and BLAT provide scores and percentages of identity, the values are approximate for each hit, due to the heuristic algorithms implemented in these programs. We thus further analysed each NumtS by applying both the Needleman and Wunsch algorithm for global alignment and the Waterman and Smith algorithm for local ones. Additional file 7 shows alignment scores compared with BLAT scores. The highest score is that of NumtS 81, which contains the longest mt fragment. Half of the NumtS have similarity values between 99% and 80%, thus showing a high degree of conservation from the time of their insertion in the nucleus.

NumtS in nuclear genes

Once the NumtS had been located through the UCSC Genome Browser [38] and NCBI Map viewer [39], we checked their location in nuclear genes. For NumtS located in genomic regions coding for genes, Additional file 6 lists both the gene name and the region of the gene where the NumtS are mapped. There are 16 NumtS inside genes; they are always located inside introns, and only two (13 and 88) are located in 5'UTR regions.

NumtS and isochores

Isochores are large DNA segments (> 300 kb on average) characterized by an internal variation in GC well below the full variation observed in the mammalian genome [40]. The previous definition of human isochores, based on ultracentrifugation in Cs2SO4 density gradients, has recently been revised by simply scanning the GC% content along the entire genome, and the five isochore families L1, L2, H1, H2 and H3, were defined according to GC content, values increasing from L1 to H3. We mapped the NumtS on isochores according to the data published in [41]. Results are listed in Additional file 6. Only 9% of total NumtS in RHNumtS were not located within isochores; 5% maps with the highest GC dense isochores (H2, H3); 30% on isochore H1, and 33% and 23% on isochores L2 and L1, respectively. Thus, NumtS prefer locations with low GC contents, corresponding to poor gene-containing regions. Indeed, the presence of a NumtS inside a gene may cause loss of function, so selection may act to clean out the genome from disrupting NumtS insertion events. This is also confirmed by the fact that, when NumtS are located in a gene, the gene region is always an intron and in some rare cases a UTR.

Mapping of Human NumtS along Human mt genome

As already reported by Parr et al. [32], human NumtS are made up of mt fragments covering the entire human mt genome, "the pseudo-mitochondrial human genome". For each human mt gene, Figure 3 shows the RHNumtS identifiers containing it: all mt regions are present in the NumtS, but the number of NumtS containing any mt gene is highly variable. Moreover, not all NumtS contain an mt gene entirely, because the locus may be located partially at the 5' and 3' ends of the NumtS or because it may have been truncated after insertion by rearrangement events. The mt genes most frequently present in the "pseudo-human mt-genome" are the two ribosomal RNAs, ND5 and COI genes; the least represented is the D-loop region. At present, no explanation for such preferences can be made. Certainly, the higher the number of locus copies in the nuclear genome, the higher the risk of co-amplification of mt-nuclear DNA, in any study on mtDNA variations.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-267/MediaObjects/12864_2007_Article_1460_Fig3_HTML.jpg
Figure 3

Mapping of Human NumtS on Human mitochondrial genome. Column 1 reports mt gene name, column 2 and 3 report location of the mt gene inside the genome and, starting from column 4, the RHNumtS identifiers of the NumtS containing the mt gene are shown. An RHNumtS identifier present in three contiguous genes indicates that NumtS contains the centrally located gene in its entire length; otherwise, it is partially contained. Green: regulatory regions; yellow: tRNA genes; red: protein coding genes; pink: rRNA genes.

Conclusions

The RHNumtS compilation proposed here results from the application of several bioinformatics approaches and from comparisons of resulting data with previously published Human NumtS compilations. It thus represents a highly reliable reference basis on which to start designing a lab protocol to test the truthfulness of each NumtS. Two experimental procedures are proposed: nDNA-mtDNA hybridisation, as already done with the Canis familiaris genome [42], or by amplifying and sequencing NumtS themselves. The latter approach, here adopted to validate 25% of the NumtS whose score in our compilation is lower than 3, confirms the quality of our bioinformatics approach; however, a systematic and complete experimental validation will be designed. In parallel, we are already designing the RHNumtS database structure for implementation in the HmtDB resource [43]. This database will report the NumtS sequences annotated with the attributes derived from both in silico and in vitro analyses. This work will be important because until now no database concerning NumtS has ever been published, so that we will be able to offer an updated reference for consultation on-line to the scientific community interested in analysis of foreign DNA integration. In the future, the same database will host NumtS compilations from other organisms, but these will be generated only when the nuclear genome of a specific organism has reached a high-quality level of assembly. Once the Reference Compilations for other organisms have been produced, pairwise NumtS compilation comparisons, based on the UCSC Genome Browser Alignment net option, will be used to define orthologous NumtS. This procedure has been already implemented in [31] in the Human-Chimp comparison. Lastly, some features of Human NumtS will be investigated. We will also analyze SNPs located inside NumtS through dbSNP at NCBI. These data may offer new perspectives in population mitochondrial genetics, preferentially in those NumtS that comparative genomics may indicate as being conserved. Lastly, with respect to the NumtS insertion site, we have started some preliminary pattern analysis by applying the WORDUP program [44] to the NumtS flanking region, although no significant results have yet been found (data not shown). This should mean that NumtS integration is not guided by specific DNA signals or does not generate any specific pattern, as is the case for retrotransposons, in which LTR sequences are generated after integration. Gherman et al. have recently confirmed the randomness of NumtS site integration [12].

Methods

Blast

Blastn compares a nucleotide query sequence against a nucleotide database. It can produce differing results if the parameters selected among those available differ. The input data for a Blastn run are: query sequence, sequence set to be searched, Expected number of chance matches in a random model (E-value), maximum number of hits to be displayed, maximum number of aligned sequences to be displayed, size of the string to be searched in pairwise comparison (word size), scoring parameters, and filtering and masking options. In addition, within the "sequence set to be searched" section, a specific sequence subset can be selected with the Limits option, available through the Entrez retrieval system [45]. We submitted several runs, changing: a) the sequence set to be searched ("chromosome", "nr" (not redundant nucleotide sequences) or "refseq_genome"), with and without Limits by Entrez; b) Maximum number of Hits to be displayed (egg. Description# = 1000, Graphic display# = 500, Alignments view# = 1000). The threshold E-value was always fixed at 0.001. The query sequence was that of the revised Reference Cambridge Sequence (GenBank Accession J01415.2, [14]). As already stated above, if the reference human mitochondrial genome is changed, the Blast result also changes.

MegaBlast

As reported in the NCBI Handbook [36], "MEGABLAST is specifically designed to efficiently find long alignments between very similar sequences and thus it is the best tool to find the identical match to the query sequence. In addition to the expected value significance cut-off, MEGABLAST also provides an adjustable percent identity cut-off that overrides the significance threshold."

BLAT

BLAT (BLAST-Like Alignment Tool) [37] is a very fast sequence alignment tool similar to BLAST. On DNA queries, BLAT is designed to find quickly sequences with 95% or greater similarity 40 BP long or more. It may miss genomic alignments that are more divergent or shorter than these minima, although it will find perfect sequence matches of 33 bases and sometimes as few as 22. The tool is capable of aligning sequences containing large intron sequences. Thus, because NumtS, after insertion in the nuclear genome, undergoes further arrangements, losing or acquiring new interspersed fragments and/or single nucleotides, BLAT is definitely a good tool for locating them. Instead, Blast locates single fragments of the entire NumtS. As both approaches are useful for a complete view of NumtS, this is the reason for our using both methods. The BLAT program available at the UCSC site has the great advantage of allowing the comparison of a query sequence against a repertoire of four different Human Builds, starting from April 2004 up to October 2006. Each Human Build also reports the absolute coordinate for each Chromosome, thus ensuring a good referencing system for locating NumtS in the genome. Each hit in BLAT corresponds to a wide region where several blocks are located. These can be displayed and analysed starting from the BLAT output page and clicking on "details", so that the sequences of each block and their alignment appear. Thus, aligned blocks with gaps less than or equal to 8 BP are merged, when only one sequence has a gap or when gaps in both sequences are of the same size. This implies that the identity percentage is the sum of the matches in each block divided by the sum of the block lengths.

Amplification and Sequencing

In order to carry out a preliminary validation of the compilation, we selected 41 NumtS whose score is lower than 3 and submitted them to PCR amplification and sequencing. The 41 NumtS were amplified on DNA extracted from the blood of a European individual available in G. Romeo laboratory. Moreover 27 among the 41 NumtS were also amplified from DNA extracted from the blood of 3 healthy subjects belonging to different geographic areas and different haplogroups, in order to verify the NumtS presence/absence in phylogenetic lineages. Samples selected for analysis were individuals coming from Japan (A. Torroni laboratory), Latin America and North Africa (V. Carelli laboratory), and belonged respectively to haplogroups H2b, G1a1a, I3a and L2a1c1. Among the 27 NumtS, 16 were sequenced in all the samples: NumtS 87, 122 and 41–54. NumtSs 41–54, because they were tandemly repeated, were amplified and sequenced all together. For some of the amplified NumtS, the sequencing failed. These are marked in Table 4. PCR conditions were not always equal. Primers were designed with PRIMER3 software, testing the amplification of the full NumtS (external-external primers) or part of it (external-internal primers or internal-internal primers) as reported in Table 4 and Figures 2a and 2b. Before the application of the PRIMER3 program to the NumtS sequence and its flanking regions, the sequence was submitted to further bioinformatics test by blasting it against the J01415.2 reference mt sequence using the Blast2 program [49].

This produces new results allowing a refinement of the RHNumtS compilation as far as it concerns NumtS region extension.

Primer sequences are available on request. The sequences were produced starting from the amplified fragments. Sequencing was performed with BigDye v3.1 (Applied Biosystems, Foster City, CA), according to the manufacturer's instructions, on an AB3730 capillary analyzer. The produced sequences have been multialigned by applying MAFFT [46] and MUSCLE [47] programs both available at [48] in the tools section.

Declarations

Acknowledgements

We thank students G. Macchia, N. Quaranta, M.P. Tomasino and G. Girolimetti who, while preparing their degree theses, contributed to the compilation; the student S. Carlucci for Primers selection. This work was supported by the University of Bari, "PROGETTO DI RICERCA MIUR-PNR FIRB "Laboratorio Internazionale di Bioinformatica"", and PRIN 2006 (project # 2006064992_003). We thank also Prof. A. Torroni (University of Pavia, Italy) and Prof. V. Carelli (University of Bologna, Italy) for kindly giving us the DNA samples used in the NumtS validation experiments.

Authors’ Affiliations

(1)
Dipartimento di Biochimica e Biologia Molecolare "E. Quagliariello", Università di Bari
(2)
Unità di Genetica Medica, Policlinico Universitario S. Orsola-Malpighi, Università di Bologna

References

  1. Bensasson D, Zhang D, Hartl DL, Hewitt GM: Mitochondrial pseudogenes: evolution's misplaced witnesses. Trends Ecol Evol. 2001, 16 (6): 314-321. 10.1016/S0169-5347(01)02151-6.View Article
  2. Du Buy HG, Riley FL: Hybridization between the nuclear and kinetoplast DNAs of Leishmania enriettii and between nuclear and mitochondrial DNAs of mouse liver. Proc Nat Acad Sci USA. 1967, 57: 790-797. 10.1073/pnas.57.3.790.PubMed CentralView Article
  3. Farrelly F, Butow RA: Rearranged mitochondrial genes in the yeast nuclear genome. Nature. 1983, 301: 296-301. 10.1038/301296a0.View Article
  4. Gellissen G, Bradfield JY, White BN, Wyatt GR: Mitochondrial DNA sequences in the nuclear genome of a locust. Nature. 1983, 301: 631-634. 10.1038/301631a0.View Article
  5. Wright RM, Cummings DJ: Integration of mitochondrial gene sequences within the nuclear genome during senescence in a fungus. Nature. 1983, 302: 86-88. 10.1038/302086a0.View Article
  6. Jacobs HT, Posakony JW, Grula JW, Roberts JW, Xin JH, Britten RJ, Davidson EH: Mitochondrial DNA sequences in the nuclear genome of Strongylocentrotus purpuratus. J Mol Biol. 1983, 165: 609-632. 10.1016/S0022-2836(83)80270-8.View Article
  7. Tsuzuki T, Nomiyama H, Setoyama C, Maeda S, Shimada K: Presence of mitochondrial DNA like sequences in the human nuclear DNA. Gene. 1983, 25: 223-229. 10.1016/0378-1119(83)90226-3.View Article
  8. Kemble RJ, Mans RJ, Gabay-Laughnan S, Laughnan JR: Sequences homologous to episomal mitochondrial DNAs in the maize nuclear genome. Nature. 1983, 304: 744-747. 10.1038/304744a0.View Article
  9. Hadler HI, Dimitrijevic B, Mahalingam R: Mitochondrial DNA and nuclear DNA from normal rat liver have a common sequence. Genomics. 1983, 22: 487-489.
  10. Lopez JV, Yuhki N, Masuda R, Modi W, O'brien SJ: Numt a recent transfer and tandem amplification of mitochondrial DNA to the nuclear genome of the domestic cat. J Mol Evol. 1994, 39: 174-190.
  11. Bravi CM, Parson W: Numts Revisited. Human Mitochondrial DNA and the Evolution of Homo Sapiens. Edited by: Bandelt V, Macaulay M, Richards HJ. 2006, Springer-Verlag, Berlin Heidelberg, chapter 3 (Part 1): 31-
  12. Gherman A, Chen PE, Teslovich TM, Stankiewicz P, Withers M, Kashuk CS, Chakravarti A, Lupski JR, Cutler DJ, Katsanis N: Population Bottlenecks as a Potential Major Shaping Force of Human Genome Architecture. PLoS Genet. 2007, 3 (7): e119-10.1371/journal.pgen.0030119.PubMed CentralView Article
  13. Zischler H, von Haeseler A, Paabo S: A nuclear fossil of the mitochondrial D-loop and the origin of the modern humans. Nature. 1995, 378: 489-492. 10.1038/378489a0.View Article
  14. Andrews RM, Kubacka I, Chinnery PF, Lightowlers RN, Turnbull DM, Howell N: Reanalysis and revision of the Cambridge reference sequence for human mitochondrial DNA. Nat Genet. 1999, 23 (2): 147-10.1038/13779.View Article
  15. Yuan JD, Shi JX, Meng GX, An LG, Hu GX: Nuclear pseudogenes of mitochondrial DNA as a variable part of the human genome. Cell Res. 1999, 9: 281-290. 10.1038/sj.cr.7290027.View Article
  16. Ricchetti M, Tekaia F, Dujon B: Continued colonization of the Human Genome by mitochondrial DNA. Plos Biology. 2004, 2 (9): e273-10.1371/journal.pbio.0020273.PubMed CentralView Article
  17. Antunes A, Ramos MJ: Discovery of a large number of previously unrecognised mitochondrial pseudogenes in fish genomes. Genomics. 2005, 86 (6): 708-717. 10.1016/j.ygeno.2005.08.002.View Article
  18. Venkatesh B, Dandona N, Brenner S: Fugu genome does not contain mitochondrial pseudogenes. Genomics. 2006, 87: 307-310. 10.1016/j.ygeno.2005.11.007.View Article
  19. Behura SK: Analysis of nuclear copies of mitochondrial sequences in honey bee Apis mellifera genome. Mol Biol and Evol. 2007, 24 (7): 1492-505. 10.1093/molbev/msm068.View Article
  20. Woodward SR, Weyand NJ, Bunnell M: DNA sequence from Cretaceous period bone fragments. Science. 1995, 268: 1194-10.1126/science.268.5214.1194.View Article
  21. Wallace DC, Stugard C, Murdock D, Schurr T, Brown MD: Ancient mtDNA sequences in the human nuclear genome: a potential source of errors in identifying pathogenic mutations. Proc Natl Acad Sci USA. 1997, 94 (26): 14900-14905. 10.1073/pnas.94.26.14900.PubMed CentralView Article
  22. Adcock GJ, Dennis ES, Easteal S, Huttley GA, Jermiin LS, Peacock WJ, Thorne A: Mitochondrial DNA sequences in ancient Australians: Implications for modern human origins. Proc Natl Acad Sci USA. 2001, 98 (2): 537-42. 10.1073/pnas.98.2.537.PubMed CentralView Article
  23. Hazkani-Covo E, Sorek R, Graur D: Evolutionary dynamics of large NUMTs in the Human Genome: rarity of independent insertions and Abundance of Post-insertion duplications. J Mol Evol. 2003, 56: 169-174. 10.1007/s00239-002-2390-5.View Article
  24. Schmitz J, Piskurek O, Zischler H: Forty million years of independent evolution: a mitochondrial gene and its corresponding nuclear pseudogene in primates. J Mol Evol. 2005, 61 (1): 1-11. 10.1007/s00239-004-0293-3.View Article
  25. Lopez JV, Culver M, Stephens JC, Johnson WE, O'Brien SJ: Rates of nuclear and cytoplasmic mitochondrial DNA sequence divergence in mammals. Mol Biol Evol. 1997, 14 (3): 277-86.View Article
  26. Mishmar D, Ruiz-Pesini E, Brandon M, Wallace DC: Mitochondrial DNA like sequences in the Nucleus (NUMTs):Insights onto our african origins and the mechanism of foreign DNA integration. Human mutation. 2004, 23: 125-133. 10.1002/humu.10304.View Article
  27. Tourmen Y, Baris O, Dessen P, Jacques C, Malthiery Y, Reynier P: Structure and chromosomal distribution of human mitochondrial pseudogenes. Genomics. 2002, 80 (1): 71-77. 10.1006/geno.2002.6798.View Article
  28. Mourier T, Hansen AJ, Willerslev E, Arctander P: The Human Genome Project reveals a continuous transfer of Large Mitochondrial Fragments to the Nucleus. Mol Biol and Evol. 2001, 18: 1833-1837.View Article
  29. Woischnick M, Moraes CT: Pattern of organization of human mitochondrial pseudogenes in the nuclear genome. Genome Res. 2002, 12: 885-893. 10.1101/gr.227202. Article published online before print in May 2002.View Article
  30. Bensasson D, Feldman MW, Petrov DA: Rates of DNA duplication and Insertion in the Human Genome. J Mol Evol. 2004, 57 (3): 343-354. 10.1007/s00239-003-2485-7.View Article
  31. Hazkani-Covo E, Graur D: A Comparative Analysis of Numt Evolution in Human and Chimpazee. Mol Biol Evol. 2007, 24 (1): 13-8. 10.1093/molbev/msl149.View Article
  32. Parr RL, Maki J, Reguly B, Dakubo GD, Aguirre A, Wittock R, Robinson K, Jakupciak JP, Thayer RE: The pseudomitochondrial genome influences mistakes in heteroplasmy interpretation. BMC Genomics. 2006, 7: 185-10.1186/1471-2164-7-185.PubMed CentralView Article
  33. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. J Mol Biol. 1990, 215 (3): 403-410.View Article
  34. Altschul SF, Boguski MS, Gish W, Wootton JC: Issues in searching molecular sequence databases. Nature Genet. 1994, 6: 119-129. 10.1038/ng0294-119.View Article
  35. Kent WJ: BLAT – The BLAST-Like Alignment Tool. Genome Res. 2002, 12 (4): 656-664. 10.1101/gr.229202. Article published online before March 2002.PubMed CentralView Article
  36. Madden T: The BLAST sequence analysis tool. NCBI Handbook part 3. [http://www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=handbook]
  37. Collura R, Stewart CB: Insertions and duplications of mtDNA in the nuclear genomes of Old World Monkeys and Hominoids. Nature. 1995, 378: 485-489. 10.1038/378485a0.View Article
  38. UCSC Genome Browser. [http://genome.ucsc.edu/]
  39. NCBI Map viewer. [http://www.ncbi.nlm.nih.gov/mapview]
  40. Bernardi G: Isochores and the evolutionary genomics of vertebrates. Gene. 2000, 241 (1): 3-17. 10.1016/S0378-1119(99)00485-0.View Article
  41. Costantini M, Clay O, Auletta F, Bernardi G: An isochore map of human chromosomes. Genome Res. 2006, 16 (4): 536-541. 10.1101/gr.4910606.PubMed CentralView Article
  42. Ishiguro N, Nakajima A, Horiuchi M, Shinagawa M: Multiple nuclear pseudogenes of mitochondrial DNA exist in the canine genome. Mamm Genome. 2002, 13 (7): 365-72. 10.1007/s00335-001-2139-2.View Article
  43. Attimonelli M, Accetturo M, Santamaria M, Lascaro D, Scioscia G, Pappada G, Russo L, Zanchetta L, Tommaseo-Ponzetta M: HmtDB, a Human Mitochondrial Genomic Resource Based on Variability Studies Supporting Population Genetics and Biomedical Research. BMC Bioinformatics. 2005, 6 (Suppl 4): S4-10.1186/1471-2105-6-S4-S4.PubMed CentralView Article
  44. Pesole G, Prunella N, Liuni S, Attimonelli M, Saccone C: WordUP: an efficient algorithm for discovering statistically significant patterns in DNA sequences. Nucl Acids Res. 1992, 20 (11): 2871-2875. 10.1093/nar/20.11.2871.PubMed CentralView Article
  45. Entrez home. [http://www.ncbi.nlm.nih.gov/Entrez]
  46. Katoh K, Misawa1 K, Kuma K, Miyata T: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research. 2002, 30 (14): 3059-3066. 10.1093/nar/gkf436.PubMed CentralView Article
  47. Edgar RC: MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004, 5: 113-10.1186/1471-2105-5-113.PubMed CentralView Article
  48. EBI home. [http://www.ebi.ac.uk]
  49. Blast2 site. [http://www.ncbi.nlm.nih.gov/blast/bl2seq/wblast2.cgi]

Copyright

© Lascaro et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement