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      Complete mitochondrial genomes from transcriptomes: assessing pros and cons of data mining for assembling new mitogenomes

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          Abstract

          Thousands of eukaryotes transcriptomes have been generated, mainly to investigate nuclear genes expression, and the amount of available data is constantly increasing. A neglected but promising use of this large amount of data is to assemble organelle genomes. To assess the reliability of this approach, we attempted to reconstruct complete mitochondrial genomes from RNA-Seq experiments of Reticulitermes termite species, for which transcriptomes and conspecific mitogenomes are available. We successfully assembled complete molecules, although a few gaps corresponding to tRNAs had to be filled manually. We also reconstructed, for the first time, the mitogenome of Reticulitermes banyulensis. The accuracy and completeness of mitogenomes reconstruction appeared independent from transcriptome size, read length and sequencing design (single/paired end), and using reference genomes from congeneric or intra-familial taxa did not significantly affect the assembly. Transcriptome-derived mitogenomes were found highly similar to the conspecific ones obtained from genome sequencing (nucleotide divergence ranging from 0% to 3.5%) and yielded a congruent phylogenetic tree. Reads from contaminants and nuclear transcripts, although slowing down the process, did not result in chimeric sequence reconstruction. We suggest that the described approach has the potential to increase the number of available mitogenomes by exploiting the rapidly increasing number of transcriptomes.

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            Comparative population genomics in animals uncovers the determinants of genetic diversity.

            Genetic diversity is the amount of variation observed between DNA sequences from distinct individuals of a given species. This pivotal concept of population genetics has implications for species health, domestication, management and conservation. Levels of genetic diversity seem to vary greatly in natural populations and species, but the determinants of this variation, and particularly the relative influences of species biology and ecology versus population history, are still largely mysterious. Here we show that the diversity of a species is predictable, and is determined in the first place by its ecological strategy. We investigated the genome-wide diversity of 76 non-model animal species by sequencing the transcriptome of two to ten individuals in each species. The distribution of genetic diversity between species revealed no detectable influence of geographic range or invasive status but was accurately predicted by key species traits related to parental investment: long-lived or low-fecundity species with brooding ability were genetically less diverse than short-lived or highly fecund ones. Our analysis demonstrates the influence of long-term life-history strategies on species response to short-term environmental perturbations, a result with immediate implications for conservation policies.
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              Big trees from little genomes: mitochondrial gene order as a phylogenetic tool.

              Gene arrangement comparisons are a powerful tool for phylogenetic studies, especially those focused on ancient relationships. Recent reports using metazoan mitochondrial genomes address evolutionary relationships as well as rates and mechanisms of rearrangement. Mitochondrial systems serve as a model for larger-scale comparisons of whole organismal genomes and a stimulus for developing methods for reconstructing the patterns of rearrangement.
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                Author and article information

                Contributors
                andrea.luchetti@unibo.it
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 October 2019
                15 October 2019
                2019
                : 9
                : 14806
                Affiliations
                [1 ]ISNI 0000 0004 1757 1758, GRID grid.6292.f, Department of Biological, , Geological and Environmental Sciences - University of Bologna, ; via Selmi 3, 40126 Bologna, Italy
                [2 ]ISNI 0000 0000 9805 2626, GRID grid.250464.1, Okinawa Institute of Science & Technology Graduate University, ; 1919-1 Tancha, Onna-son, Okinawa 904–0495 Japan
                [3 ]ISNI 0000 0001 2238 631X, GRID grid.15866.3c, Faculty of Forestry and Wood Sciences, , Czech University of Life Sciences, ; Prague, Czech Republic
                [4 ]ISNI 0000 0004 1936 7910, GRID grid.1012.2, School of Animal Biology, , University of Western Australia, ; Perth, WA 6009 Australia
                [5 ]ISNI 0000 0004 1755 6224, GRID grid.424414.3, Agrarian Entomology, , Research and Innovation Centre, Fondazione Edmund Mach (FEM), ; Via E. Mach 1, 38010 San Michele all’Adige, TN Italy
                Author information
                http://orcid.org/0000-0002-2986-721X
                Article
                51313
                10.1038/s41598-019-51313-7
                6794255
                31616005
                18f04fa9-2fb1-4365-8d4c-c7b0ceff8646
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 March 2019
                : 25 September 2019
                Funding
                Funded by: Czech Science Foundation (project No. 15-07015Y)
                Funded by: Canziani Funding
                Funded by: Canziani funding
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                data mining,molecular evolution,phylogenetics,transcriptomics
                Uncategorized
                data mining, molecular evolution, phylogenetics, transcriptomics

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