Skip to main content
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

1

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

1 result for “MitoFinder”

Learn how ShareScore rates datasets ↗
zenodo32/100

MitoFinder: efficient automated large-scale extraction of mitogenomic data in target enrichment phylogenomics

<p><strong>MitoFinder: efficient automated large-scale extraction of mitogenomic data in target enrichment phylogenomics</strong></p> <p>R&eacute;mi Allio<sup>1</sup>, Alex Schomaker-Bastos<sup>2,&dagger;</sup>, Jonathan Romiguier<sup>1</sup>, Francisco Prosdocimi<sup>2</sup>, Benoit Nabholz<sup>1</sup>, and Fr&eacute;d&eacute;ric Delsuc<sup>1</sup></p> <p><sup>1</sup><em>Institut des Sciences de l&rsquo;Evolution de Montpellier (ISEM), CNRS, EPHE, IRD, Universit&eacute; de Montpellier, Montpellier, France.</em></p> <p><sup>2</sup><em>Laborat&oacute;rio Multidisciplinar para An&aacute;lise de Dados (LAMPADA), Instituto de Bioqu&iacute;mica M&eacute;dica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil.</em></p> <p><sup>&dagger;</sup><em> In Memoriam (08/01/2015) </em></p> <p>&nbsp;</p> <p><em><strong>Correspondence</strong></em></p> <p>R&eacute;mi Allio</p> <p>Email: <a href="mailto:remi.allio@umontpelier.fr">remi.allio@umontpellier.fr</a></p> <p>Fr&eacute;d&eacute;ric Delsuc</p> <p>Email: <a href="mailto:frederic.delsuc@umontpellier.fr">frederic.delsuc@umontpellier.fr</a></p> <p>&nbsp;</p> <p><strong><em>Running head</em></strong></p> <p>Mitochondrial signal from UCE capture data</p> <p>&nbsp;</p> <p><strong>Abstract</strong><strong> </strong></p> <p>Thanks to the development of high-throughput sequencing technologies, target enrichment sequencing of nuclear ultraconserved DNA elements (UCEs) now allows routinely inferring phylogenetic relationships from thousands of genomic markers. Recently, it has been shown that mitochondrial DNA (mtDNA) is frequently sequenced alongside the targeted loci in such capture experiments. Despite its broad evolutionary interest, mtDNA is rarely assembled and used in conjunction with nuclear markers in capture-based studies. Here, we developed MitoFinder, a user-friendly bioinformatic pipeline, to efficiently assemble and annotate mitogenomic data from hundreds of UCE libraries. As a case study, we used ants (Formicidae) for which 501 UCE libraries have been sequenced whereas only 29 mitogenomes are available. We compared the efficiency of four different assemblers (IDBA-UD, MEGAHIT, MetaSPAdes, and Trinity) for assembling both UCE and mtDNA loci. Using MitoFinder, we show that metagenomic assemblers, in particular MetaSPAdes, are well suited to assemble both UCEs and mtDNA. Mitogenomic signal was successfully extracted from all 501 UCE libraries allowing confirming species identification using COI barcoding. Moreover, our automated procedure retrieved 296 cases in which the mitochondrial genome was assembled in a single contig, thus increasing the number of available ant mitogenomes by an order of magnitude. By leveraging the power of metagenomic assemblers, MitoFinder provides an efficient tool to extract complementary mitogenomic data from UCE libraries, allowing testing for potential mito-nuclear discordance. Our approach is potentially applicable to other sequence capture methods, transcriptomic data, and whole genome shotgun sequencing in diverse taxa.</p> <p>&nbsp;</p> <p><strong><em>Figures &amp; Tables</em></strong></p> <p><strong>Figure 1.</strong> Conceptualization of the pipeline used to assemble and extract UCE and mitochondrial signal from ultraconserved element sequencing data.</p> <p><strong>Figure 2</strong>. Comparison of the efficiency of the assemblers in terms of: A) computational time, B) number of potentially mitochondrial contigs identified, and C) number of mitochondrial genes annotated. Violin plots reflect the data distribution with a horizontal line indicating the median. Note that for the three metagenomic assemblers, 5 CPUs were used compared to 35 CPUs for Trinity. Plots were obtained using PlotsOfData (Postma &amp; Goedhart 2019).</p> <p><strong>Figure 3.</strong> Phylogenomic relationships of ants (Formicidae). AA) Mito-nuclear phylogenetic differences among subfamily relationships based on the UCE and mtDNA supermatrices obtained with the assembler MetaSPAdes assembler. Clades corresponding to subfamilies were collapsed. Inter-subfamily relationships with UFBS &lt; 95% were collapsed. Non-maximal node support values are reported. B) The topology obtained reflects the results of phylogenetic analyses based on the amino acid mitochondrial supermatrix (using MetaSPAdes as assembler). Histograms reflect the percent of UCEs (light grey) and mitochondrial genes (dark grey) recovered for each species. Illustrative pictures (*): <em>Diacamma sp</em>. (Ponerinae; top left), <em>Formica sp</em>. (Formicinae; top right), and <em>Messor barbarus </em>(Myrmicinae; bottom right).</p> <p><strong>Table 1. </strong>Summary statistics on assembly results according to the assembler used. The values are averages over the 501 assemblies, except for the assembly time, which is a median value. The two tables report specific statistics for A) ultraconserved elements data, and B) mitochondrial data. Note that 35 CPUs were used for Trinity whereas 5 CPUs were used for other assemblers.</p> <p><strong>Table 2.</strong> Statistical comparison between the performances of the different assemblers. Statistical significance was estimated with a paired non parametric test (paired wilcoxon test). *** = <em>p</em>&lt;0.001; ** = <em>p</em>&lt;0.01; * = <em>p</em>&lt;0.05; NS = <em>p</em>&gt;0.05; and (+)/(-) is the result of the comparison between the row and the column.</p> <p>&nbsp;</p> <p><strong><em>Appendices</em></strong></p> <p><strong>Appendix S1.</strong> List of the 501 UCE libraries (SRA accessions) and associated metadata.</p> <p><strong>Appendix S2.</strong> Summary statistics on mitochondrial signal recovered per species and depending on the assembler used. The table provides the number of contigs and genes recovered with MitoFinder and the size of each annotated gene.</p> <p><strong>Appendix S3.</strong> Summary statistics of barcoding analyses. Detailed results for both BOLDsystem and Megablast analyses are provided for each CO1 recovered with MitoFinder using MetaSPAdes.</p> <p><strong>Appendix S4.</strong> Detailed results of tree distance analyses realized with Dquad (Ranwez, Criscuolo, &amp; Douzery 2010). Trees obtained with each assembler with mitochondrial amino acid supermatrix, mitochondrial nucleotide supermatrix, and UCE nucleotide supermatrix were compared with each others.</p> <p><strong>Appendix S5</strong>. List of Genbank accession numbers for newly generated mitchondrial contigs.</p> <p>&nbsp;</p> <p><strong><em>Zenodo supplementary files</em></strong></p> <p><strong>Assembly_results.tar.gz</strong> Contains all contigs obtained for each species with the different assemblers implemented in MitoFinder.</p> <p><strong>MitoFinder_annotations.tar.gz</strong> Contains MitoFinder annotations for each species. (based on the contigs obtained with MetaSPAdes)</p> <p><strong>UCE_results.tar.gz</strong> Contains all annotated UCE obtained for each species after UCE identification with PHYLUCE. (MetaSPAdes)</p> <p><strong>Final_mtDNA_alignments.tar.gz</strong> Contains the final mitochondrial gene&nbsp;alignments. (MetaSPAdes)</p> <p><strong>Final_UCE_alignments.tar.gz</strong> Contains the final UCE alignments. (MetaSPAdes)</p> <p><strong>Final_mtDNA_matrices.tar.gz</strong> Contains the final mi&nbsp; tochondrial supermatrices (AA and NT) used for the phylogenetic analyses. (MetaSPAdes)</p> <p><strong>Metaspades_final_UCE_matrix.phy</strong> The final UCE supermatrix used for the phylogenetic analyses. (MetaSPAdes)</p>

opencc-by-4.0Sep 2019View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record