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Mammalian mitochondrial DNA accumulates insertions and deletions with age in energetically demanding tissues.

Code ↔ Paper

12 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Results › Distribution of indels along mtDNA as influenced by functional regions, the D-loop, and short tandem repeats ↔ 3. Plots/plot_figure4b_histogram_OriL.ipynb, lines 105–244 · score 0.81 · 5160–5191, 5681–5713, 5723–5780, OriL, mutated sites, bp
  2. [2] § Results › Distribution of indels along mtDNA as influenced by functional regions, the D-loop, and short tandem repeats ↔ 3. Plots/plot_table1_mtDNA_regions.ipynb, lines 332–440 · score 0.69 · rRNA, tRNA, genomic regions, Fisher, fold, mice
  3. [3] § Materials & methods › Statistical tests used in comparing indel frequencies ↔ 3. Plots/plot_tableS2_indel_freqs_3filterings.ipynb, lines 122–248 · score 0.62 · Mann Whitney, younger age, older age, FDR, S2, indel
  4. [4] § Results › Distribution of indels along mtDNA as influenced by functional regions, the D-loop, and short tandem repeats ↔ 3. Plots/plot_figure4b_histogram_OriL.ipynb, lines 105–244 · score 0.58 · Vertical dashed, OriL, allele frequencies, binned, Insertions, deletions
  5. [5] § Materials & methods › Simulated deletions ↔ utils/sim.py, lines 115–255 · score 0.57 · sequencing errors, mutation rate, wgsim, simulator, bp, indel
  6. [6] § Results › Distribution of indels along mtDNA as influenced by functional regions, the D-loop, and short tandem repeats ↔ 3. Plots/plot_figure4_histogram_loci_figS5-S6.ipynb, lines 128–173 · score 0.56 · Vertical dashed, OriL, allele frequencies, Figure 4, loop, tissue
  7. [7] § Materials & methods › Repeat detection ↔ 3. Plots/plot_tableS8_figureS8_Repeat_freqs.ipynb, lines 40–92 · score 0.55 · Repeat motifs, lenient definition, detection
  8. [8] § Results › De novo indels accumulate with age in somatic tissues more than in oocytes ↔ 2. Variant filtering, frequencies, and hotspots/A_process_and_filter_indels.ipynb, lines 336–416 · score 0.54 · germline mutations, somatic mutations, de novo, somatic tissues, filtering, variants
  9. [9] § Materials & methods › Enrichment analysis ↔ 3. Plots/plot_table1_mtDNA_regions.ipynb, lines 332–440 · score 0.53 · Benjamini Hochberg, contingency, Fisher, FDR, mouse, human
  10. [10] § Results › Deletions increase in frequency with age more than insertions in most cases ↔ 3. Plots/plot_tableS6_age_insertions_and_deletions.ipynb, lines 127–253 · score 0.51 · Mann Whitney, human blood, S6, insertion, deletion, MWU
  11. [11] § Materials & methods › Variant calling and filtering ↔ 2. Variant filtering, frequencies, and hotspots/B1_variant_hotspots_macaque.ipynb, lines 71–121 · score 0.51 · de novo mutations, variant hotspots, filtering, duplex, nucleotide, macaque
  12. [12] § Results › Samples and datasets ↔ 1. Du Novo pipeline and variant calling/utils/modifyINFO.sh, the whole file · a weak match · score 0.51 · Du Novo pipeline, variant calling, consensus, DNA, species

Paper

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The authors' code

Jupyter notebook · 275 lines · 9.6 KB · no license · 2 matches

  1. # %%
  2. import pandas as pd
  3. from matplotlib import pyplot as plt
  4. import matplotlib.patches as mpatches
  5. from matplotlib.lines import Line2D
  6. import seaborn as sns
  8. # Set the default font to Arial
  9. import matplotlib
  10. matplotlib.rcParams['font.family'] = 'Arial'
  12. # %% [markdown]
  13. # # Parameters
  15. # %%
  16. # The filtering based on hotspots.
  17. filter_version = 'excluding' # 'including', 'excluding', 'only'
  19. INDEL_PREFIX = {
  20. 'including':'indels/indels.denovo_',
  21. 'excluding':'indels/indels.exclHotspots.denovo_',
  22. 'only':'indels/indels.onlyHotspots.denovo_'
  23. }#[filter_version]
  25. FREQS_PREFIX = {
  26. 'including':'tables/freqs.denovo_indels.',
  27. 'excluding':'tables/freqs.exclHotspots.',
  28. 'only':'tables/freqs.onlyHotspots.'
  29. }#[filter_version]
  32. # The 3 species.
  33. list_species = ['mouse','macaque','human']
  34. # The alignment chosen.
  35. alignment = 'chrM'
  37. # %% [markdown]
  38. # # Import indels for 3 species
  40. # %%
  41. def get_indels(filter_version, alignment='chrM'):
  42. # File paths.
  43. INDEL_PREFIX = {
  44. 'including':'indels/indels.denovo_',
  45. 'excluding':'indels/indels.exclHotspots.denovo_',
  46. 'only':'indels/indels.onlyHotspots.denovo_'
  47. }
  48. # Test input.
  49. if filter_version not in INDEL_PREFIX.keys():
  50. ValueError('# Filter version not found')
  51. # The 3 species.
  52. list_species = ['mouse','macaque','human']
  53. # Combine indels for the three species.
  54. list_df = []
  55. for species in list_species:
  56. df = pd.read_table( f'{INDEL_PREFIX[filter_version]}{alignment}_{species}.tab' )
  57. list_df.append( df )
  58. df_indels = pd.concat(list_df)
  59. return df_indels
  61. df_indels = get_indels(filter_version, alignment)
  62. df_indels
  64. # %% [markdown]
  65. # # Histogram of indels along mtDNA
  67. # %%
  68. # D-loop coordinates in Mouse, Macaque, and Human.
  69. def coordinates_dloop(species):
  70. if species == 'mouse':
  71. coords_dloop = [ [15424,16300] ]
  72. coords_non = [ [1,15423] ]
  73. elif species == 'macaque':
  74. coords_dloop = [ [1,535],[16015,16564] ]
  75. coords_non = [ [536,16014] ]
  76. elif species == 'human':
  77. coords_dloop = [ [1,576],[16024,16569] ]
  78. coords_non = [ [577,16023] ]
  79. else:
  80. ValueError("### Species not recognized ###")
  81. return { 'D-loop':coords_dloop , 'non-D':coords_non}
  83. def size_of_marker(AC,intercept,exception):
  84. if AC > 20:
  85. return AC - exception
  86. else:
  87. return AC + intercept
  90. # %% [markdown]
  91. # ## Plot
  93. # %%
  94. import numpy as np
  95. import seaborn as sns
  96. from matplotlib.lines import Line2D
  97. from matplotlib.ticker import FuncFormatter
  99. import matplotlib.pyplot as plt
  100. import matplotlib.patches as mpatches
  102. def scale_marker_size(ac, unit=30):
  103. return unit * ac
  105. def plot_maf_mutations(filter_version, alignment='chrM', write=True):
  106. # data = df_mutations.copy()
  107. data = get_indels(filter_version, alignment=alignment)
  109. data['Allele count'] = data['AC'].astype(int)
  110. data['Allele frequency'] = data['AF']
  112. # Seaborn parameters.
  113. gridspec = dict(hspace=0.3, height_ratios=[1, 1, 1])
  114. fig, axes = plt.subplots(3, 1, sharex=False, figsize=[15, 10], gridspec_kw=gridspec)
  116. # Compute the global maximum log-transformed AC for consistent scaling.
  117. all_AC = data['Allele count']
  118. global_max_ac_log = np.log10(all_AC + 1).max()
  120. # Pre-compute global sizes dictionary.
  121. global_sizes = {ac: scale_marker_size(ac) for ac in all_AC.drop_duplicates()}
  123. for i, (ax, species) in enumerate(zip(axes, ['mouse', 'macaque', 'human'])):
  124. # Filter by species.
  125. df = data[data['Species'] == species].copy()
  127. # Change names.
  128. dict_tissues = {
  129. 'Oo': 'Oocytes', 'M': 'Skeletal muscle', 'Br': 'Brain',
  130. 'Li': 'Liver', 'Sa': 'Saliva', 'Bl': 'Blood'
  131. }
  132. df['Tissue'] = df['Tissue'].map(dict_tissues)
  133. df['Species'] = df['Species'].str.capitalize()
  135. # Mutations only considered once.
  136. df = df.drop_duplicates(subset=['Species', 'Tissue', 'POS', 'REF', 'ALT'])
  138. # D-loop coordinates for species.
  139. coords = coordinates_dloop(species)['D-loop']
  141. # OriL coordinates.
  142. dict_coords_ori = {
  143. 'mouse': [5160, 5191], 'macaque': [5681, 5713], 'human': [5723,5780]
  144. }
  145. coords_ori = dict_coords_ori[species]
  147. # Section of coordinates to show (oriL with flanks).
  148. flanks = 2000
  149. # flanks = 500
  150. df = df.loc[(df['POS'] >= coords_ori[0] - flanks) & (df['POS'] <= coords_ori[1] + flanks)]
  151. ax.set_xlim(coords_ori[0] - flanks, coords_ori[1] + flanks)
  152. # df = df.loc[(df['POS'] >= 3900) & (df['POS'] <= 6900)]
  153. # ax.set_xlim(3900, 6900)
  155. # HISTOGRAM
  156. sns.histplot(
  157. ax=ax, data=df, x="POS", hue="Tissue",
  158. hue_order=['Oocytes', 'Skeletal muscle', 'Brain', 'Liver', 'Saliva', 'Blood'],
  160. bins=130 if species != 'human' else 100,
  162. linewidth=1, multiple="stack",
  163. palette={
  164. 'Oocytes': '#0570b0', 'Skeletal muscle': '#66c2a4',
  165. 'Brain': '#fe9929', 'Liver': '#ae017e',
  166. 'Blood': '#d7301f', 'Saliva': '#cccccc'
  167. },
  168. legend=None
  169. )
  171. # Vertical dashed lines for OriL.
  172. for j,coord in enumerate(coords_ori):
  173. if coord not in [1, 16300, 16564, 16569]:
  174. ax.axvline(x=coord, color="black", linestyle='--', linewidth=3, alpha=0.6)
  175. # if j == 1:
  176. # ax.text(coord, ax.get_ylim()[1], 'OriL', color='black', fontsize=12, ha='right', va='bottom')
  178. legend_fontsize = 16
  180. # Change axis labels.
  181. ax.set_ylabel("Number of \n mutated sites\n", fontsize=legend_fontsize)
  182. ax.set_xlabel('')
  183. ax.tick_params(axis='both', labelsize=11) # For both x and y ticks
  185. # Adjust axis limits.
  186. if "chrM" in alignment:
  187. if 'including' in filter_version:
  188. pass
  189. elif 'excluding' in filter_version:
  190. if species == 'human':
  191. ax.set_ylim(0, 14)
  192. elif 'only' in filter_version:
  193. if species == 'human':
  194. ax.set_ylim(0, 9)
  196. # Subplot titles.
  197. ax.set_title(species.capitalize(), weight="bold", size=19)
  199. def format_with_commas(x, _):
  200. return f"{int(x):,}"
  202. # Apply the formatter to the x-axis
  203. axes[i].set_xlabel('')
  204. axes[i].xaxis.set_major_formatter(FuncFormatter(format_with_commas))
  205. axes[-1].set_xlabel('Position (bp)', size=legend_fontsize)
  207. dict_palettes = {
  208. 'Oocytes': '#0570b0', 'Skeletal muscle': '#66c2a4', 'Brain': '#fe9929',
  209. 'Liver': '#ae017e', 'Blood': '#d7301f', 'Saliva': '#cccccc',
  210. 'deletions': '#d7301f', 'insertions': '#2171b5'
  211. }
  214. # Add legend for the vertical dashed line representing OriL
  215. oriL_line = Line2D([0], [0], color='black', linestyle='--', linewidth=3, alpha=0.6, label='OriL')
  216. empty_patch = mpatches.Patch(color='white', label='')
  218. first_patch = mpatches.Patch(color=dict_palettes['Oocytes'], label='Oocytes')
  219. second_patch = mpatches.Patch(color=dict_palettes['Skeletal muscle'], label='Skeletal muscle')
  220. third_patch = mpatches.Patch(color=dict_palettes['Brain'], label='Brain')
  221. # axes[0].legend(handles=[first_patch, second_patch, third_patch], bbox_to_anchor=(1.2, 0.8), framealpha=0.0, fontsize=legend_fontsize)
  222. axes[0].legend(handles=[oriL_line, empty_patch, first_patch, second_patch, third_patch], bbox_to_anchor=(1.23, 1), framealpha=0.0, fontsize=legend_fontsize)
  224. first_patch = mpatches.Patch(color=dict_palettes['Oocytes'], label='Oocytes')
  225. second_patch = mpatches.Patch(color=dict_palettes['Skeletal muscle'], label='Skeletal muscle')
  226. third_patch = mpatches.Patch(color=dict_palettes['Liver'], label='Liver')
  227. axes[1].legend(handles=[first_patch, second_patch, third_patch], bbox_to_anchor=(1.23, 0.8), framealpha=0.0, fontsize=legend_fontsize)
  228. # axes[1].legend(handles=[first_patch, second_patch, third_patch, oriL_line], bbox_to_anchor=(1.2, 0.8), framealpha=0.0, fontsize=legend_fontsize)
  230. first_patch = mpatches.Patch(color=dict_palettes['Oocytes'], label='Oocytes')
  231. second_patch = mpatches.Patch(color=dict_palettes['Saliva'], label='Saliva')
  232. third_patch = mpatches.Patch(color=dict_palettes['Blood'], label='Blood')
  233. axes[2].legend(handles=[first_patch, second_patch, third_patch], bbox_to_anchor=(1.165, 0.8), framealpha=0.0, fontsize=legend_fontsize)
  234. # axes[2].legend(handles=[first_patch, second_patch, third_patch, oriL_line], bbox_to_anchor=(1.145, 0.8), framealpha=0.0, fontsize=legend_fontsize)
  237. print(filter_version)
  239. if write:
  240. fig_num = {'including': '4b', 'excluding': 'SX', 'only': 'SXX'}[filter_version]
  241. if filter_version == 'including':
  242. # Export as vectorized image.
  243. fig.savefig(f'plots/pdf/figure{fig_num}_histogram_OriL_{alignment}_{filter_version}Hotspots.pdf', bbox_inches='tight', format='pdf', dpi=1200)
  244. fig.savefig(f'plots/png/figure{fig_num}_histogram_OriL_{alignment}_{filter_version}Hotspots.png', bbox_inches='tight', format='png', dpi=1200)
  246. # Call the function
  247. plot_maf_mutations('including')
  249. # %% [markdown]
  250. # ## Excluding hotspots
  252. # %%
  253. plot_maf_mutations('excluding')
  255. # %% [markdown]
  256. # ## Only hotspots
  258. # %%
  259. plot_maf_mutations('only')
  261. # %%
  262. df_indels[['Species','AC']].max()
  263. df_indels[df_indels['AC']==2]
  264. # df_indels[['Species','AF']].max()
  265. # df_indels[df_indels['AF']>0.008]
  266. df_indels[df_indels['POS']==954]
  268. # %%
  269. df_indels[(df_indels['POS']>200)&(df_indels['POS']<300)&(df_indels['Species']=='macaque')].sort_values('POS')
  271. # %%
  272. df_indels['Mutation_ID'].value_counts()
  274. # %%

plot_figure4b_histogram_OriL.ipynb at commit 9db431b, no license · at the source

Overview

Authors: Edmundo Torres-Gonzalez1,2, Barbara Arbeithuber1,3, Nicholas Stoler4,5, Marzia A Cremona6,7,8, Omar Shebl3, Thomas Ebner3, Irene Tiemann-Boege9, Francisco J Diaz10, Francesca Chiaromonte6,11,12, Kateryna D Makova1,6
ORCID iDs: Edmundo Torres-Gonzalez, Barbara Arbeithuber, Nicholas Stoler, Marzia A Cremona, Omar Shebl, Thomas Ebner, Irene Tiemann-Boege, Francisco J Diaz, Francesca Chiaromonte, Kateryna D Makova
  1. Department of Biology, The Pennsylvania State University, University Park, PA, USA
  2. Huck Life Sciences Institutes, The Pennsylvania State University, University Park, PA, USA
  3. Department of Gynecology, Obstetrics, and Gynecological Endocrinology, Kepler Universitätsklinikum, Linz, Austria
  4. Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
  5. National Center for Biotechnology Information, The National Institutes of Health, Bethesda, MD, USA
  6. Center for Medical Genomics, The Pennsylvania State University, University Park, PA, USA
  7. Chair in Statistical Learning, Department of Operations and Decision Systems, Université Laval, Québec City, Québec, Canada
  8. Université Laval Research Center, CHU de Québec, Québec City, Québec, Canada
  9. Institute of Biophysics, Johannes Kepler University, Linz, Austria
  10. Department of Animal Science, Pennsylvania State University, University Park, PA, USA
  11. Department of Statistics, Pennsylvania State University, University Park, PA, USA
  12. Sant’Anna School of Advanced Studies, L’EMbeDS, Pisa, Italy
Institutions: Pennsylvania State University (United States); Johannes Kepler University of Linz (Austria); Kepler Universitätsklinikum (Austria); National Institutes of Health (United States); National Center for Biotechnology Information (United States); Université Laval (Canada); Centre de recherche du CHU de Québec-Université Laval (Canada); Scuola Superiore Sant'Anna (Italy)
Journal: Molecular biology and evolution, volume 43, issue 3, article msag035
Dates: received 21 February 2025; accepted 29 January 2026; published online 10 February 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/molbev/msag035 · PMID 41664480 · PMCID PMC12951523 · OpenAlex W7128539117
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), non-human primate (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: duplex sequencing, de novo mutations, indels, aging, mtDNA, replication slippage
MeSH: Aging*, DNA, Mitochondrial*, INDEL Mutation*, Animals, DNA Replication, Female, Humans, Macaca, Mice (* major topic)
Topic: Mitochondrial Function and Pathology (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIDA NIH HHS (T32 DA050560); Verne M. Willaman endowment fund; Austrian Science Fund FWF (J-4096, SFB F-8809-B, P30867000); NIH (R01GM116044, R35GM151945); PSU (T32GM102057); Eberly College of Sciences; NIH HHS (R35GM151945, R01GM116044); NSF Graduate Research Fellowship Program (DGE1255832); UMN (T32DA050560)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Mitochondrial function can be affected by mutations in mitochondrial DNA (mtDNA). However, detecting de novo mutations in mtDNA has been challenging due to its high copy number, particularly in germline cells, and the low accuracy of conventional next-generation sequencing technologies. Using highly accurate duplex sequencing, we study the frequency of de novo insertion and deletion (indel) mtDNA mutations across multiple age groups in somatic and germline tissues of three mammalian species—mouse, macaque, and human. We demonstrate that, similar to de novo nucleotide substitutions, indels accumulate rapidly with age in somatic tissues with high energetic demand (brain and skeletal muscle) or high proliferation (liver). However, in oocytes, indels accumulate slower with age than nucleotide substitutions (or do not accumulate at all). The increases in indel frequency with age are driven mostly by deletions. Short tandem repeats are highly enriched for indels, implicating DNA replication slippage as a major driver of indel formation in mtDNA. For some species and tissues, indels are depleted at protein-coding sequences; however, indels that are multiples of 3 bp are not overrepresented. Ours is the most detailed study of de novo small indels in mtDNA to date. It provides parameters for models of mtDNA evolution, informs molecular mechanisms for a multitude of human genetic diseases, and illuminates the accumulation of indel mutations with age. Such accumulation may have functional consequences, as it affects reproduction later in life and drives the decline of mitochondrial function during aging.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

galaxyproject/dunovo

License: GPL-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 365a6a816f72fd29412dfe229926bc6aa178e498, 15 February 2022
Languages: Python (27), Shell (10), C (3)
Size: 145 files, 40 scripts
Software Heritage: archived
Found in: the text, “Duplex sequence consensus pipeline”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Matplotlib (2 files), SAMtools (2 files), NetworkX (1 file), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
43 files
At the source: github.com/galaxyproject/dunovo/issues/32

makovalab-psu/indels-duplexSeq

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 9db431b4375da8fdc1e69c805d5216c7d1f6c065, 22 May 2026
Languages: Jupyter (23), Shell (2)
Size: 105 files, 25 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 23 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (21 files), Matplotlib (19 files), NumPy (16 files), seaborn (16 files), SciPy (11 files), statsmodels (11 files), BEDTools (3 files), Biopython (2 files), tidyverse (2 files), BCFtools (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
26 files
At the source: github.com/makovalab-psu/indels-duplexSeq

The paper's code and data availability statement is in the Data section.

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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Data links

Data availability

The data underlying this article are available in NCBI BioProjects at https://www.ncbi.nlm.nih.gov/bioproject, and can be accessed with PRJNA563921, PRJNA777828, and PRJNA1181658. These were published in Arbeithuber et al. (2020), Arbeithuber et al. (2022), and Arbeithuber et al. (2025), respectively. Supplementary code is available on GitHub at https://github.com/makovalab-psu/indels-duplexSeq.

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Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 6 keywords, 9 MeSH terms, 9 funders, 54 references.

Cite

This paper

Torres-Gonzalez, E., Arbeithuber, B., Stoler, N., Cremona, M. A., Shebl, O., Ebner, T., Tiemann-Boege, I., Diaz, F. J., Chiaromonte, F., & Makova, K. D. (2026). Mammalian mitochondrial DNA accumulates insertions and deletions with age in energetically demanding tissues. Molecular biology and evolution, 43(3), msag035. https://doi.org/10.1093/molbev/msag035

BibTeX

@article{torresgonzalez2026mammalian,
author = {Torres-Gonzalez, Edmundo and Arbeithuber, Barbara and Stoler, Nicholas and Cremona, Marzia A and Shebl, Omar and Ebner, Thomas and Tiemann-Boege, Irene and Diaz, Francisco J and Chiaromonte, Francesca and Makova, Kateryna D},
title = {{Mammalian mitochondrial DNA accumulates insertions and deletions with age in energetically demanding tissues}},
journal = {Molecular biology and evolution},
year = {2026},
month = mar,
volume = {43},
number = {3},
pages = {msag035},
publisher = {Oxford University Press},
issn = {0737-4038},
doi = {10.1093/molbev/msag035},
url = {https://doi.org/10.1093/molbev/msag035},
pmid = {41664480},
pmcid = {PMC12951523}
}

RIS

TY - JOUR
AU - Torres-Gonzalez, Edmundo
AU - Arbeithuber, Barbara
AU - Stoler, Nicholas
AU - Cremona, Marzia A
AU - Shebl, Omar
AU - Ebner, Thomas
AU - Tiemann-Boege, Irene
AU - Diaz, Francisco J
AU - Chiaromonte, Francesca
AU - Makova, Kateryna D
TI - Mammalian mitochondrial DNA accumulates insertions and deletions with age in energetically demanding tissues
T2 - Molecular biology and evolution
J2 - Mol Biol Evol
PY - 2026
DA - 2026/03/01
VL - 43
IS - 3
SP - msag035
SN - 0737-4038
PB - Oxford University Press
DO - 10.1093/molbev/msag035
UR - https://doi.org/10.1093/molbev/msag035
LA - en
ER -

CSL-JSON

{
"id": "10.1093/molbev/msag035",
"type": "article-journal",
"title": "Mammalian mitochondrial DNA accumulates insertions and deletions with age in energetically demanding tissues",
"container-title": "Molecular biology and evolution",
"author": [
{
"family": "Torres-Gonzalez",
"given": "Edmundo"
},
{
"family": "Arbeithuber",
"given": "Barbara"
},
{
"family": "Stoler",
"given": "Nicholas"
},
{
"family": "Cremona",
"given": "Marzia A"
},
{
"family": "Shebl",
"given": "Omar"
},
{
"family": "Ebner",
"given": "Thomas"
},
{
"family": "Tiemann-Boege",
"given": "Irene"
},
{
"family": "Diaz",
"given": "Francisco J"
},
{
"family": "Chiaromonte",
"given": "Francesca"
},
{
"family": "Makova",
"given": "Kateryna D"
}
],
"container-title-short": "Mol Biol Evol",
"volume": "43",
"issue": "3",
"page": "msag035",
"DOI": "10.1093/molbev/msag035",
"PMID": "41664480",
"PMCID": "PMC12951523",
"ISSN": "0737-4038",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/molbev/msag035",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

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