Guia para genômica de populações aplicada a mamíferos Neotropicais
Do delineamento experimental às análises básicas com polimorfismos de nucleotídeo único (SNPs)
DOI:
https://doi.org/10.32673/bjm.vie92.120Palavras-chave:
ddRAD-Seq, ipyrad, Proechimys, Scripts em R, StacksResumo
Os polimorfismos de nucleotídeo único (SNPs) são marcadores genéticos que podem ser usados em estudos de genômica populacional de mamíferos Neotropicais. Este ensaio é um guia com sugestões e dicas para as principais etapas de um trabalho envolvendo SNPs e genômica populacional a partir de uma técnica de sequenciamento de representação reduzida ou RRS. Neste ensaio é abordado como pensar um delineamento experimental eficiente, desde a escolha das amostras, como preparar as bibliotecas genômicas até dicas para verificar a qualidade do sequenciamento e dos dados. Durante o texto são discutidos os pontos-chaves dos trabalhos com genômica de populações, com indicações de estudos e referências para fundamentar os projetos de pesquisa e o aprendizado. Vale ressaltar que o guia contém sugestões e não regras para realização desse tipo de pesquisa. No fim, é fornecido um banco de dados da espécie de roedor Proechimys steerei da família Echimyidae e uma pipeline para realizar na prática as etapas de filtragem de SNPs e algumas análises estatísticas básicas, usando pacotes de R. Espera-se que esse guia facilite a compreensão de alunos e pesquisadores que estão iniciando no campo da genômica. Mesmo sendo um guia usando exemplos de mamíferos Neotropicais e de RRS, boa parte do que é discutido neste guia poderá ser aplicado a qualquer banco de dados de SNPs gerado a partir de qualquer técnica de sequenciamento genômico e de qualquer grupo de organismo.
Referências
Abreu EF, Pavan SE, Tsuchiya MT, Wilson DE, Percequillo AR, Maldonado JE. 2020. Museomics of tree squirrels: A dense taxon sampling of mitogenomes reveals hidden diversity, phenotypic con-vergence, and the need of a taxonomic overhaul. BMC Evolutionary Biology 20(1): 1-25. https://doi.org/10.1186/s12862-020-01639-y.
Aguirre-Liguori JA, Luna-Sánchez JA, Gasca-Pineda J, Eguiarte LE. 2020. Evaluation of the minimum sampling design for population genomic and microsatellite studies: An analysis based on wild maize. Frontiers in Genetics 11: 870. https://doi.org/10.3389%2Ffgene.2020.00870.
Allendorf FW, Hohenlohe P, Luikart G. 2010. Genomics and the future of conservation genetics. Na-ture Reviews Genetics 11: 697-709. https://doi.org/10.1038/nrg2844.
Allendorf FW, Phelps SR. 1981. Use of allelic frequencies to describe population structure. Canadian Journal of Fisheries and Aquatic Sciences 38(12): 1507-1514.
Altmann A, Weber P, Bader D, Preui M, Binder EB, Müller-Myhsok B. 2012. A beginners guide to SNP calling from high-throughput DNA-sequencing data. Human Genetics 131: 1541-1554: https://doi.org/10.1007/s00439-012-1213-z.
Amiteye S. 2021. Basic concepts and methodologies of DNA marker systems in plant molecular breeding. Heliyon 7(10): e08093. https://doi.org/10.1016/j.heliyon.2021.e08093.
Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. 2016. Harnessing the power of RADseq for ecological and evolutionary genomics. Nature Reviews Genetics 17(2): 81-92. https://doi.org/10.1038/nrg.2015.28.
Andrews S. 2010. FastQC: a quality control tool for high throughput sequence data. Cambridge, UK. Versão: 0.12.0. Disponível em: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Acessado em: 10 de dezembro de 2023.
Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker EU, Cresko WA, et al. 2008. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PloS One 3(10): e3376. https://doi.org/10.1371/journal.pone.0003376.
Bartram AK, Lynch MD, Stearns JC, Moreno-Hagelsieb G, Neufeld JD. 2011. Generation of multimil-lion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end Illumina reads. Applied and Environmental Microbiology 77(11): 3846-3852. https://doi.org/10.1128/AEM.02772-10.
Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15): 2114-2120. https://doi.org/10.1093/bioinformatics/btu170.
Calderón-Acevedo CA, Bagley JC, Muchhala N. 2022. Genome-wide ultraconserved elements resolve phylogenetic relationships and biogeographic history among Neotropical leaf-nosed bats in the genus Anoura (Phyllostomidae). Molecular Phylogenetics and Evolution 167: 107356. https://doi.org/10.1016/j.ympev.2021.107356.
Capblancq T, Fitzpatrick MC, Bay RA, Exposito-Alonso M, Keller SR. 2020. Genomic prediction of (mal) adaptation across current and future climatic landscapes. Annual Review of Ecology, Evolu-tion, and Systematics 51: 245-269. https://doi.org/10.1146/annurev-ecolsys-020720-042553.
Caruccio N. 2011. Preparation of next-generation sequencing libraries using Nextera™ technology: Simultaneous DNA fragmentation and adaptor tagging by in vitro Transposition. Pp 241-255, In: Kwon Y, Ricke S (Eds.), High-throughput next generation sequencing. Humana Press, To-towa. https://doi.org/10.1007/978-1-61779-089-8_17.
Carvalho CS, Lanes ÉC, Silva AR, Caldeira CF, Carvalho-Filho N, Gastauer M, Imperatriz-Fonseca VL, Nascimento WJ, et al. 2019. Habitat loss does not always entail negative genetic consequenc-es. Frontiers in Genetics 10: 1011. https://doi.org/10.3389/fgene.2019.01101.
Caye K, Deist TM, Martins H, Michel O, François O. 2016. TESS3: Fast inference of spatial population structure and genome scans for selection. Molecular Ecology Resources 16(2): 540-548. https://doi.org/10.1111/1755-0998.12471.
Cerca J, Maurstad MF, Rochette NC, Rivera‐Colón AG, Rayamajhi N, Catchen JM, Struck TH. 2021. Removing the bad apples: A simple bioinformatic method to improve loci‐recovery in de novo RADseq data for non‐model organisms. Methods in Ecology and Evolution 12(5): 805-817. https://doi.org/10.1111/2041-210X.13562.
Chao S, Zhang W, Akhunov E, Sherman J, Ma Y, Luo M C, Dubcovsky J. 2009. Analysis of gene-derived SNP marker polymorphism in US wheat (Triticum aestivum L.) cultivars. Molecular Breeding 23: 23-33. https://doi.org/10.1007/s11032-008-9210-6.
DaCosta JM, Sorenson MD. 2014. Amplification biases and consistent recovery of loci in a double-digest RAD-seq protocol. PLoS One 9: e106713. https://doi.org/10.1371/journal.pone.0106713.
Dahn HA, Mountcastle J, Balacco J, Winkler S, Bista I, Schmitt AD, Pettersson OV, Formenti G, et al. 2022. Benchmarking ultra-high molecular weight DNA preservation methods for long read and long-range sequencing. GigaScience 11: giac068. https://doi.org/10.1093/gigascience/giac068.
Dalapicolla J, Prado JR, Percequillo AR, Knowles LL. 2021. Functional connectivity in sympatric spiny rats reflects different dimensions of Amazonian forest‐association. Journal of Biogeography 48(12): 3196-3209. https://doi.org/10.1111/jbi.14281.
Davey JW, Hohenlohe PA, Etter PD, Boone JO, Catchen JM, Blaxter ML. 2011. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics 12: 499-510. https://doi.org/10.1038/nrg3012.
Deamer D, Akeson M, Branton D. 2016. Three decades of nanopore sequencing. Nature Biotechnology 34: 518-524. https://doi.org/10.1038/nbt.3423.
Deininger, PL 1983. Random subcloning of sonicated DNA: application to shotgun DNA sequence anal-ysis. Analytical Biochemistry 129(1): 216-223. https://doi.org/10.1016/0003-2697(83)90072-6.
DeRaad DA. 2022. SNPfiltR: An R package for interactive and reproducible SNP filtering. Molecular Ecology Resources 22(6): 2443–2453. https://doi.org/10.1111/1755-0998.13618.
DiBattista JD, Saenz‐Agudelo P, Piatek MJ, Wang X, Aranda M, Berumen ML. 2017. Using a butterfly-fish genome as a general tool for RAD‐Seq studies in specialized reef fish. Molecular Ecology Resources 17(6): 1330-1341. https://doi.org/10.1111/1755-0998.12662.
Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR. 2014. NeEstimator v2: Re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Molecular Ecology Resources 14(1): 209-214. https://doi.org/10.1111/1755-0998.12157.
Dussex N, Taylor HR, Stovall WR, Rutherford K, Dodds KG, Clarke SM, Gemmell NJ. 2018. Reduced representation sequencing detects only subtle regional structure in a heavily exploited and rapidly recolonizing marine mammal species. Ecology and Evolution 8(17): 8736-8749. https://doi.org/10.1002/ece3.4411.
Dyer RJ, Nason JD. 2004. Population graphs: the graph theoretic shape of genetic structure. Molecular Ecology 13(7): 1713-1727. https://doi.org/10.1111/j.1365-294X.2004.02177.x.
Eaton DA, Overcast I. 2020. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinfor-matics 36(8): 2592-2594. https://doi.org/10.1093/bioinformatics/btz966.
Edwards D, Forster JW, Chagné D, Batley J. 2007. What are SNPs? Pp. 41-52, In: Oraguzie NC, Rikker-ink EHA, Gardiner SE, Silva HN (Eds.), Association mapping in plants. Springer, New York. https://doi.org/10.1007/978-0-387-36011-9_3.
Eizirik E, Ferran V, Sartor CC, Trindade FJ, Figueiró HV. 2023. Conservation genomics of Neotropical carnivores. Pp. 475-501, In: Galetti Jr PM. (Ed.), Conservation genetics in the Neotropics. Springer, Cham. https://doi.org/10.1007/978-3-031-34854-9_19.
Elleouet JS, Aitken SN. 2018. Exploring Approximate Bayesian Computation for inferring recent de-mographic history with genomic markers in nonmodel species. Molecular Ecology Resources 18(3): 525-540. https://doi.org/10.1111/1755-0998.12758.
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. 2011. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PloS One 6(5): e19379. https://doi.org/10.1371/journal.pone.0019379.
Eren AM, Vineis JH, Morrison HG, Sogin ML. 2013. A filtering method to generate high quality short reads using Illumina paired-end technology. PloS One 8(6): e66643. https://doi.org/10.1371/journal.pone.0066643.
Ewing B, Green P. 1998. Base-calling of automated sequencer traces using Phred. II. Error probabili-ties. Genome Research 8(3): 186-194. https://doi.org/10.1101/gr.8.3.186.
Ewing B, Hillier L, Wendl MC, Green P. 1998. Base-calling of automated sequencer traces using Phred. I. Accuracy assessment. Genome Research 8(3): 175-185. https://doi.org/10.1101/gr.8.3.175.
Excoffier L, Marchi N, Marques DA, Matthey-Doret R, Gouy A, Sousa VC. 2021. fastsimcoal2: Demo-graphic inference under complex evolutionary scenarios. Bioinformatics 37(24): 4882-4885. https://doi.org/10.1093/bioinformatics/btab468.
Fenderson LE, Kovach AI, Llamas B. 2020. Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time. Molecular Ecology 29(2): 218-246. https://doi.org/10.1111/mec.15315.
Ferran V, Figueiró HV, Trindade FJ, Smith O, Sinding MHS, Trinca CS, Lazzari GZ, Veron G, et al. 2022. Phylogenomics of the world’s otters. Current Biology 32(16): 3650-3658. https://doi.org/10.1016/j.cub.2022.06.036.
Flanagan SP, Jones AG. 2018. Substantial differences in bias between single‐digest and double‐digest RAD‐seq libraries: a case study. Molecular Ecology Resources 18(2): 264-280. https://doi.org/10.1111/1755-0998.12734.
Flanagan SP, Jones AG. 2019. The future of parentage analysis: from microsatellites to SNPs and be-yond. Molecular Ecology 28(3): 544-567. https://doi.org/10.1111/mec.14988.
Flesch E, Rotella J, Thomson J, Graves T, Garrot R. 2018. Evaluating sample size to estimate genetic management metrics in the genomics era. Molecular Ecology Resources 18: 1077-1091. https://doi.org/10.1111/1755-0998.12898.
Frichot E, Mathieu F, Trouillon T, Bouchard G, François O. 2014. Fast and efficient estimation of indi-vidual ancestry coefficients. Genetics 196(4): 973-983. https://doi.org/10.1534/genetics.113.160572.
Fuller CW, Middendorf LR, Benner SA, Church GM, Harris T, Huang X, Jovanovich SB, Nelson JR, et al. 2009. The challenges of sequencing by synthesis. Nature Biotechnology 27(11): 1013-1023. https://doi.org/10.1038/nbt.1585.
García-Dorado A, Caballero A. 2021. Neutral genetic diversity as a useful tool for conservation biolo-gy. Conservation Genetics 22: 541-545. https://doi.org/10.1007/s10592-021-01384-9.
Greenwood PJ. 1980. Mating systems, philopatry and dispersal in birds and mammals. Animal Behav-iour 28(4): 1140-1162. https://doi.org/10.1016/S0003-3472(80)80103-5
Gruber B, Georges, A. 2019. dartR: Importing and analysing SNP and silicodart data generated by ge-nome-wide restriction fragment analysis, R package. Disponível em: https://cran.r-project.org/package=dartR. Acessado em: 10 de Dezembro de 2023.
Gulcher J, Stefansson K. 1998. Population genomics: Laying the groundwork for genetic disease mod-elling and targeting. Clinical Chemistry and Laboratory Medicine 36(8): 523-527. https://doi.org/10.1515/CCLM.1998.089.
Gunasekera S, Abraham S, Stegger M, Pang S, Wang P, Sahibzada S, O’Dea M. 2021. Evaluating cov-erage bias in next generation sequencing of Escherichia coli. Plos One 16(6): e0253440. https://doi.org/10.1371/journal.pone.0253440.
Gyarmati P, Song Y, Hällman J, Käller M. 2013. Chemical fragmentation for massively parallel se-quencing library preparation. Journal of Biotechnology 168(1): 95-100. https://doi.org/10.1016/j.jbiotec.2013.08.020.
Harrison XA, Donaldson L, Correa-Cano ME, Evans J, Fisher DN, Goodwin CE, Robinson BS, Hodgson DJ, Inger R. 2018. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6: e4794. https://doi.org/10.7717/peerj.4794.
Hartl DL, Clark AG. 2010. Princípios de Genética de Populações. Artmed, Porto Alegre.
Helyar SJ, Hemmer‐Hansen J, Bekkevold D, Taylor MI, Ogden R, Limborg MT, Cariani A, Maes GE, et al. 2011. Application of SNPs for population genetics of nonmodel organisms: New opportuni-ties and challenges. Molecular Ecology Resources 11: 123-136. https://doi.org/10.1111/j.1755-0998.2010.02943.x.
Herrera S, Reyes-Herrera PH, Shank TM. 2015. Predicting RAD-seq marker numbers across the eukar-yotic tree of life. Genome Biology and Evolution 7(12): 3207-3225. https://doi.org/10.1093%2Fgbe%2Fevv210.
Höglund J. 2009. How to measure genetic variation. Pp. 18-36, In: Höglund J, Evolutionary conserva-tion genetics, Oxford University Press, Oxford. https://doi.org/10.1093/acprof:oso/9780199214211.003.0002.
Holderegger R, Kamm U, Gugerli F. 2006. Adaptive vs. neutral genetic diversity: Implications for land-scape genetics. Landscape Ecology 21: 797-807. https://doi.org/10.1007/s10980-005-5245-9.
Illumina. 2014. BaseSpace user guide: Supporting the NextSeq, MiSeq, and HiSeq sequencing sys-tems. Illumina, San Diego.
Illumina. 2020. Illumina DNA PCR-Free Prep, Tagmentation. Balancing sample coverage for whole-genome sequencing. Disponível em: https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/illumina-dna-pcr-free-data-sheet-m-gl-00679/illumina-dna-pcr-free-data-sheet-m-gl-00679.pdf. Acessado em: 10 de dezembro de 2023.
Illumina. 2021. Balancing sample coverage for whole-genome sequencing. Index correction strategies for Illumina DNA PCR-Free Prep. Disponível em: https://www.illumina.com/content/dam/illumina/gcs/assembled-assets/marketing-literature/illumina-dna-prep-pcr-free-index-correction-tech-note-m-gl-00005/illumina-dna-pcr-free-index-correction-tech-note-m-gl-00005.pdf. Acessado em: 10 de dezembro de 2023.
Kahl G, Mast A, Tooke N, Shen R, Boom D. 2005. Single nucleotide polymorphisms: detection tech-niques and their potential for genotyping and genome mapping. Pp. 75-107, In: Meksem K, Kahl G (Eds.), The handbook of plant genome mapping: Genetic and physical mapping. Wiley-Blackwell, Hoboken.
Kimura M. 1983. The neutral theory of molecular evolution. Cambridge University Press, Cambridge.
Kircher M, Sawyer S, Meyer M. 2012. Double indexing overcomes inaccuracies in multiplex sequenc-ing on the Illumina platform. Nucleic Acids Research 40(1): e3. https://doi.org/10.1093/nar/gkr771.
Knaus BJ, Grünwald NJ. 2017. vcfR: A package to manipulate and visualize variant call format data in R. Molecular Ecology Resources 17(1): 44-53. https://doi.org/10.1111/1755-0998.12549.
Korneliussen TS, Albrechtsen A, Nielsen R. 2014. ANGSD: Analysis of next generation sequencing da-ta. BMC Bioinformatics 15(1): 1-13. https://doi.org/10.1186/s12859-014-0356-4.
Kruglyak L. 1997. The use of a genetic map of biallelic markers in linkage studies. Nature Genetics 17: 21-24. https://doi.org/10.1038/ng0997-21.
Kwok PY, Gu Z. 1999. Single nucleotide polymorphism libraries: Why and how are we building them? Molecular Medicine Today 5(12): 538-543. https://doi.org/10.1016/S1357-4310(99)01601-9.
Landegren U, Nilsson M, Kwok PY. 1998. Reading bits of genetic information: Methods for single-nucleotide polymorphism analysis. Genome Research 8(8): 769-776. https://doi.org/10.1101/gr.8.8.769.
Larguinho M, Santos HM, Doria G, Scholz H, Baptista PV, Capelo JL. 2010. Development of a fast and efficient ultrasonic-based strategy for DNA fragmentation. Talanta 81(3): 881-886. https://doi.org/10.1016/j.talanta.2010.01.032.
Leaché AD, Oaks JR. 2017. The utility of single nucleotide polymorphism (SNP) data in phylogenetics. Annual Review of Ecology, Evolution, and Systematics 48: 69-84. https://doi.org/10.1146/annurev-ecolsys-110316-022645.
Ledergerber C, Dessimoz C. 2011. Base-calling for next-generation sequencing platforms. Briefings in Bioinformatics 12(5): 489-497. https://doi.org/10.1093/bib/bbq077.
Lemmon EM, Lemmon AR. 2013. High-throughput genomic data in systematics and phylogenetics. Annual Review of Ecology, Evolution, and Systematics 44: 99-121. https://doi.org/10.1146/annurev-ecolsys-110512-135822.
Lepais O, Weir JT. 2014. SimRAD: An R package for simulation-based prediction of the number of loci expected in RADseq and similar genotyping by sequencing approaches. Molecular Ecology Re-sources 14(6): 1314-1321. https://doi.org/10.1111/1755-0998.12273.
Linck E, Battey CJ. 2019. Minor allele frequency thresholds strongly affect population structure infer-ence with genomic data sets. Molecular Ecology Resources 19(3): 639-647. https://doi.org/10.1111/1755-0998.12995.
Liu J, Shen Q, Bao H. 2022. Comparison of seven SNP calling pipelines for the next-generation se-quencing data of chickens. Plos One 17(1): e0262574. https://doi.org/10.1371/journal.pone.0262574.
Liu Q, Guo Y, Li J, Long J, Zhang B, Shyr Y. 2012. Steps to ensure accuracy in genotype and SNP calling from Illumina sequencing data. BMC Genomics 13: 1-8. https://doi.org/10.1186/1471-2164-13-S8-S8.
Lo E, Bonizzoni M, Hemming-Schroeder E, Ford A, Janies DA, James AA, Afrane Y, Etemesi H, et al. 2018. Selection and utility of single nucleotide polymorphism markers to reveal fine-scale population structure in human malaria parasite Plasmodium falciparum. Frontiers in Ecology and Evolution 6: 145. https://doi.org/10.3389/fevo.2018.00145.
Lorenzana GP, Figueiró HV, Kaelin CB, Barsh GS, Johnson J, Karlsson E, Morato RG, Sana DA, et al. 2022. Whole-genome sequences shed light on the demographic history and contemporary ge-netic erosion of free-ranging jaguar (Panthera onca) populations. Journal of Genetics and Ge-nomics 49(1): 77-80. https://doi: 10.1016/j.jgg.2021.10.006.
Lotterhos KE. 2019. The effect of neutral recombination variation on genome scans for selection. G3: Genes, Genomes, Genetics 9(6): 1851-1867. https://doi.org/10.1534/g3.119.400088.
Lou RN, Jacobs A, Wilder AP, Therkildsen NO. 2021. A beginner's guide to low‐coverage whole ge-nome sequencing for population genomics. Molecular Ecology 30(23): 5966-5993. https://doi.org/10.1111/mec.16077.
Loureiro LO, Engstrom MD, Lim BK. 2020. Single nucleotide polymorphisms (SNPs) provide unprece-dented resolution of species boundaries, phylogenetic relationships, and genetic diversity in the mastiff bats (Molossus). Molecular Phylogenetics and Evolution 143: 106690. https://doi.org/10.1016/j.ympev.2019.106690.
Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK, Antolin MF, Storfer A. 2017. Breaking RAD: An evaluation of the utility of restriction site‐associated DNA sequencing for genome scans of adaptation. Molecular Ecology Resources 17(2): 142-152. https://doi.org/10.1111/1755-0998.12635.
Luikart G, England P, Tallmon D, Jordan S, Taberlet P. 2003. The power and promise of population genomics: From genotyping to genome typing. Nature Reviews Genetics 4: 981-994. https://doi.org/10.1038/nrg1226.
Luu K, Bazin E, Blum MG. 2017. pcadapt: An R package to perform genome scans for selection based on principal component analysis. Molecular Ecology Resources 17(1): 67-77. https://doi.org/10.1111/1755-0998.12592.
Maddison WP. 1997. Gene trees in species trees. Systematic Biology 46(3): 523-536. https://doi.org/10.1093/sysbio/46.3.523.
Manel S, Schwartz MK, Luikart G, Taberlet P. 2003. Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology & Evolution 18(4): 189-197. https://doi.org/10.1016/S0169-5347(03)00008-9.
Marandel F, Charrier G, Lamy JB, Le Cam S, Lorance P, Trenkel VM. 2020. Estimating effective popula-tion size using RADseq: Effects of SNP selection and sample size. Ecology and Evolution 10(4): 1929-1937. https://doi.org/10.1002/ece3.6016.
Mastretta-Yanes A, Arrigo N, Alvarez N, Jorgensen TH, Piñero D, Emerson BC. 2014. Restriction site‐associated DNA sequencing, genotyping error estimation and de novo assembly optimiza-tion for population genetic inference. Molecular Ecology Resources 15(1): 28-41. https://doi.org/10.1111/1755-0998.12291.
McCombie WR, McPherson JD, Mardis ER. 2019. Next-generation sequencing technologies. Cold Spring Harbor Perspectives in Medicine 9(11): a036798. https://doi.org/10.1101%2Fcshperspect.a036798.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K., Altshuler D, et al. 2010. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20(9): 1297-1303. https://doi.org/10.1101/gr.107524.110.
Meirmans PG. 2006. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60(11): 2399-2402. https://doi.org/10.1111/j.0014-3820.2006.tb01874.x.
Miller JM, Cullingham CI, Peery RM. 2020. The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method. Heredity 125(5): 269-280. https://doi.org/10.1038/s41437-020-0348-2.
Morgil H, Gercek YC, Tulum I. 2020. Single nucleotide polymorphisms (SNPs) in plant genetics and breeding.Pp. 1-12, In: Çalışkan M, Erol O, Cevahir-Öz G (Eds.), The recent topics in genetic pol-ymorphisms. IntechOpen., London. http://dx.doi.org/10.5772/intechopen.91886.
Morin PA, Martien KK, Taylor BL. 2009. Assessing statistical power of SNPs for population structure and conservation studies. Molecular Ecology Resources 9(1): 66-73. https://doi.org/10.1111/j.1755-0998.2008.02392.x.
Nadachowska‐Brzyska K, Konczal M, Babik W. 2021. Navigating the temporal continuum of effective population size. Methods in Ecology and Evolution 13(1): 22-41. https://doi.org/10.1111/2041-210X.13740.
Nagy M, Heckel G, Voigt CC, Mayer F. 2007. Female-biased dispersal and patrilocal kin groups in a mammal with resource-defense polygyny. Proceedings of the Royal Society B: Biological Sci-ences 274(1628): 3019-3025. https://doi.org/10.1098/rspb.2007.1008.
Naseri A, Zhi D, Zhang S. 2019. Multi-allelic positional Burrows-Wheeler transform. BMC Bioinfor-matics 20(11): 279. https://doi.org/10.1186/s12859-019-2821-6.
Nazareno AG, Bemmels JB, Dick CW, Lohmann LG. 2017. Minimum sample sizes for population ge-nomics: An empirical study from an Amazonian plant species. Molecular Ecology Resources 17(6): 1136-1147. https://doi.org/10.1111/1755-0998.12654.
Nery MF, Ramos EK, Souza, EMS. 2020. Present and prospects of research on neotropical mammals using genomic approaches. Mastozoología Neotropical 27: 101-119.
Nielsen R, Paul JS, Albrechtsen A, Song YS. 2011. Genotype and SNP calling from next-generation sequencing data. Nature Reviews Genetics 12(6): 443-451. https://doi.org/10.1038/nrg2986.
Ochoa A, Onorato DP, Fitak RR, Roelke-Parker ME, Culver M. 2017. Evolutionary and functional mito-genomics associated with the genetic restoration of the Florida panther. Journal of Heredity 108(4): 449-455. https://doi.org/10.1093/jhered/esx015.
O'Leary SJ, Puritz JB, Willis SC, Hollenbeck CM, Portnoy DS. 2018. These aren’t the loci you're looking for: Principles of effective SNP filtering for molecular ecologists. Molecular Ecology 27(16): 3193-3206. https://doi.org/10.1111/mec.14792.
Palsbøll PJ, Berube M, Allendorf FW. 2007. Identification of management units using population ge-netic data. Trends in Ecology & Evolution 22(1): 11-16. https://doi.org/10.1016/j.tree.2006.09.003.
Parada A, Hanson J, D’Elía G. 2021. Ultraconserved elements improve the resolution of difficult nodes within the rapid radiation of Neotropical sigmodontine rodents (Cricetidae: Sigmodontinae). Systematic Biology 70(6): 1090-1100. https://doi.org/10.1093/sysbio/syab023.
Paris JR, Stevens JR, Catchen JM. 2017. Lost in parameter space: A road map for Stacks. Methods in Ecology and Evolution 8(10): 1360-1373. https://doi.org/10.1111/2041-210X.12775.
Patterson N, Price AL, Reich D. 2006. Population structure and eigenanalysis. PLoS Genetics 2(12): e190. https://doi.org/10.1371/journal.pgen.0020190.
Patton JL, Leite RN. 2015. Genus Proechimys J. A. Allen, 1889. Pp. 950–989, In: Patton JL, Pardiñas UFJ, D’Elía G (Eds.), Mammals of South America, Volume 2: Rodents. University Of Chicago Press, Chicago.
Pearman WS, Urban L, Alexander A. 2022. Commonly used Hardy-Weinberg equilibrium filtering schemes impact population structure inferences using RADseq data. Molecular Ecology Re-sources 22(7): 2599-2613. https://doi.org/10.1111/1755-0998.13646.
Pecoraro C, Babbucci M, Villamor A, Franch R, Papetti C, Leroy B, Franch R, Papetti C, et al. 2016. Methodological assessment of 2b-RAD genotyping technique for population structure infer-ences in yellowfin tuna (Thunnus albacares). Marine Genomics 25: 43-48. https://doi.org/10.1016/j.margen.2015.12.002.
Pedersen BS, Brown JM, Dashnow H, Wallace AD, Velinder M, Tristani-Firouzi M, Schiffman JD, Tvrdik T, et al. 2021. Effective variant filtering and expected candidate variant yield in studies of rare human disease. NPJ Genomic Medicine 6(1): 60. https://doi.org/10.1038/s41525-021-00227-3.
Percequillo AR, Prado JR, Abreu EF, Dalapicolla J, Pavan AC, Chiquito EA, Brennand P, Steppan SJ, et al. 2021. Tempo and mode of evolution of oryzomyine rodents (Rodentia, Cricetidae, Sigmo-dontinae): a phylogenomic approach. Molecular Phylogenetics and Evolution 159: 107120. https://doi.org/10.1016/j.ympev.2021.107120.
Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. 2012. Double digest RADseq: An inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PloS One 7(5): e37135. https://doi.org/10.1371/journal.pone.0037135.
Petrazzini BO, Naya H, Lopez-Bello F, Vazquez G, Spangenberg L. 2021. Evaluation of different ap-proaches for missing data imputation on features associated to genomic data. BioData Mining 14(1): 1-13. https://doi.org/10.1186/s13040-021-00274-7.
Pirani RM, Werneck FP, Thomaz AT, Kenney ML, Sturaro MJ, Ávila‐Pires TC, Peloso PLV, Rodrigues MT, Knowles LL. 2019. Testing main Amazonian rivers as barriers across time and space with-in widespread taxa. Journal of Biogeography 46(11): 2444-2456. https://doi.org/10.1111/jbi.13676.
Prado JR, Knowles LL, Percequillo AR. 2021. New species boundaries and the diversification history of marsh rat taxa clarify historical connections among ecologically and geographically distinct wetlands of South America. Molecular Phylogenetics and Evolution 155: 106992. https://doi.org/10.1016/j.ympev.2020.106992.
Prado JR, Percequillo AR, Pirani RM, Thomaz AT. 2022. Phenotypic and genomic differences between biomes of the South America marsh rat, Holochilus brasiliensis. Biological journal of the Lin-nean Society 135(1): 98-116. https://doi.org/10.1093/biolinnean/blab132.
Prado JR, Percequillo AR, Thomaz AT, Knowles LL. 2019. Similar but different: Revealing the relative roles of species‐traits versus biome properties structuring genetic variation in South American marsh rats. Journal of Biogeography 46(4): 770-783. https://doi.org/10.1111/jbi.13529.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, et al. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81(3): 559-575. https://doi.org/10.1086/519795.
R Core Team. 2023. R: A language and environment for statistical computing. R Foundation for Statis-tical Computing, Vienna, Austria. Versão 4.3.0. Disponível em: https://www.R-project.org/.
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R. 2015. A practical guide to environmental association analysis in landscape genomics. Molecular Ecology 24(17): 4348-4370. https://doi.org/10.1111/mec.13322.
Rivera-Colón AG, Catchen J. 2022. Population genomics analysis with RAD, reprised: Stacks 2. Pp. 99-149, In: Verde C, Giordano D (Eds.), Marine Genomics: Methods and Protocols. Springer, New York. https://doi.org/10.1007/978-1-0716-2313-8_7.
Rivera‐Colón AG, Rochette NC, Catchen JM. 2021. Simulation with RADinitio improves RADseq exper-imental design and sheds light on sources of missing data. Molecular Ecology Resources 21(2): 363-378. https://doi.org/10.1111/1755-0998.13163.
Robledo-Ruiz D, Austin L, Amos J, Castrejón-Figureoa J, Magrath M, Sunnucks P, Pavlova A. 2023. Easy-to-use R functions to separate reduced-representation genomic datasets into sex-linked and autosomal loci and conduct sex-assignment. Molecular Ecology Resources. https://doi.org/10.1111/1755-0998.13844.
Rochette NC, Catchen JM. 2017. Deriving genotypes from RAD-seq short-read data using Stacks. Na-ture Protocols 12(12): 2640-2659. https://doi.org/10.1038/nprot.2017.123.
Rochette NC, Rivera‐Colón AG, Catchen JM. 2019. Stacks 2: Analytical methods for paired‐end se-quencing improve RADseq‐based population genomics. Molecular Ecology 28(21): 4737-4754. https://doi.org/10.1111/mec.15253.
Ross MG, Russ C, Costello M, Hollinger A, Lennon NJ, Hegarty R, Nusbaum C, Jaffe DB. 2013. Charac-terizing and measuring bias in sequence data. Genome Biology 14: 1-20. https://doi.org/10.1186/gb-2013-14-5-r51.
RStudio Team. 2022. RStudio: Integrated Development for R. RStudio, Boston, USA. Versão: 2022.02.3 Build 492. Disponível em: http://www.rstudio.com/.
Sato MP, Ogura Y, Nakamura K, Nishida R, Gotoh Y, Hayashi M, Hisatsune J, Sugai M, et al. 2019. Comparison of the sequencing bias of currently available library preparation kits for Illumina sequencing of bacterial genomes and metagenomes. DNA Research 26(5): 391-398. https://doi.org/10.1093/dnares/dsz017.
Savary P, Foltête JC, Moal H, Vuidel G, Garnier S. 2021. graph4lg: A package for constructing and ana-lysing graphs for landscape genetics in R. Methods in Ecology and Evolution 12(3): 539-547. https://doi.org/10.1111/j.1365-294X.2004.02177.x.
Schmieder R, Edwards R. 2011. Quality control and preprocessing of metagenomic datasets. Bioin-formatics 27(6): 863-864. https://doi.org/10.1093/bioinformatics/btr026.
Segatto ALA, Goetze M, Turchetto C. 2017. Marcadores moleculares baseados na análise de sequên-cias: utilização em filogenia e filogeografia. Pp. 77-93, In: Turchetto-Zolet ACT, Turchetto C, Zanella CM, Passaia G (Orgs.), Marcadores moleculares na era genômica: metodologias e aplicações. Sociedade Brasileira de Genética, Ribeirão Preto.
Shirk AJ, Landguth EL, Cushman SA. 2017. A comparison of individual‐based genetic distance metrics for landscape genetics. Molecular Ecology Resources 17(6): 1308-1317. https://doi.org/10.1111/1755-0998.12684.
Silk MJ, Harrison XA, Hodgson DJ. 2020. Perils and pitfalls of mixed-effects regression models in biol-ogy. PeerJ 8: e9522. https://doi.org/10.7717/peerj.9522.
Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. 2014. Sequencing depth and coverage: Key consider-ations in genomic analyses. Nature Reviews Genetics 15(2): 121-132. https://doi.org/10.1038/nrg3642.
Souza ÉMS, Freitas L, Ramos EK, Selleghin-Veiga G, Rachid-Ribeiro MC, Silva AF, Marmontel M, San-tos FR, et al. 2021. The evolutionary history of manatees told by their mitogenomes. Scientific Reports 11: 3564. https://doi.org/10.1038/s41598-021-82390-2.
Stange M, Sánchez-Villagra MR, Salzburger W, Matschiner M. 2018. Bayesian divergence-time esti-mation with genome-wide single-nucleotide polymorphism data of sea catfishes (Ariidae) supports Miocene closure of the Panamanian Isthmus. Systematic Biology 67(4): 681-699. https://doi.org/10.1093/sysbio/syy006.
Strandén I, Christensen OF. 2011. Allele coding in genomic evaluation. Genetics Selection Evolution 43: 1-11. https://doi.org/10.1186/1297-9686-43-25.
Streicher JW, Schulte JA, Wiens JJ. 2016. How should genes and taxa be sampled for phylogenomic analyses with missing data? An empirical study in iguanian lizards. Systematic Biology 65(1): 128-145. https://doi.org/10.1093/sysbio/syv058.
Tanaka N, Takahara A, Hagio T, Nishiko R, Kanayama J, Gotoh O, Mori S. 2020. Sequencing artifacts derived from a library preparation method using enzymatic fragmentation. PLoS One 15(1): e0227427. https://doi.org/10.1371/journal.pone.0227427.
Therkildsen NO, Palumbi SR. 2017. Practical low‐coverage genome wide sequencing of hundreds of individually barcoded samples for population and evolutionary genomics in non-model species. Molecular Ecology Resources 17(2): 194-208. https://doi.org/10.1111/1755-0998.12593.
Thomaz AT, Malabarba LR, Knowles LL. 2017. Genomic signatures of paleodrainages in a freshwater fish along the southeastern coast of Brazil: Genetic structure reflects past riverine properties. Heredity 119(4): 287-294. https://doi.org/10.1038/hdy.2017.46.
Tiffin P, Ross-Ibarra J. 2014. Advances and limits of using population genetics to understand local adaptation. Trends in Ecology & Evolution 29(12): 673-680. https://doi.org/10.1016/j.tree.2014.10.004.
Toonen RJ, Puritz JB, Forsman ZH, Whitney JL, Fernandez-Silva I, Andrews KR, Bird CE. 2013. ezRAD: A simplified method for genomic genotyping in non-model organisms. PeerJ 1: e203. https://doi.org/10.7717/peerj.203.
Turchetto-Zolet ACT, Turchetto C, Guzman F, Silva-Arias GA, Sperb-Ludwig F, Veto NM. 2017b. Poli-morfismo de nucleotídeo único (SNP): metodologias de identificação, análise e aplicações. Pp. 132-179, In: Turchetto-Zolet ACT, Turchetto C, Zanella CM, Passaia G (Orgs.), Marcadores mo-leculares na era genômica: metodologias e aplicações. Sociedade Brasileira de Genética, Ri-beirão Preto.
Turchetto-Zolet ACT, Turchetto C, Zanella CM, Passaia G. 2017a. Marcadores moleculares na era ge-nômica: metodologias e aplicações. Sociedade Brasileira de Genética, Ribeirão Preto.
Van Dijk EL, Jaszczyszyn Y, Thermes C. 2014. Library preparation methods for next-generation se-quencing: Tone down the bias. Experimental Cell Research 322(1): 12-20. https://doi.org/10.1016/j.yexcr.2014.01.008.
Vignal A, Milan D, SanCristobal M, Eggen A. 2002. A review on SNP and other types of molecular markers and their use in animal genetics. Genetics Selection Evolution 34(3): 275-305. http://dx.doi.org/10.1051/gse:2002009.
Waits LP, Storfer A. 2015. Basics of population genetics: Quantifying neutral and adaptive genetic variation for landscape genetic studies. Pp. 35-57, In: Balkenhol N, Cushman SA, Storfer A, Waits LP (Eds.), Landscape genetics: Concepts, methods, applications. Wiley-Blackwell, Hobo-ken.
Wang Y, Zhao Y, Bollas A, Wang Y, Au KF. 2021. Nanopore sequencing technology, bioinformatics and applications. Nature Biotechnology 39: 1348-1365. https://doi.org/10.1038/s41587-021-01108-x.
Waples RK, Larson WA, Waples RS. 2016. Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity 117(4): 233-240. https://doi.org/10.1038/hdy.2016.60.
Ward CM, To TH, Pederson SM. 2020. ngsReports: A Bioconductor package for managing FastQC re-ports and other NGS related log files. Bioinformatics 36(8): 2587-2588. https://doi.org/10.1093/bioinformatics/btz937.
Wickland DP, Battu G, Hudson KA, Diers BW, Hudson ME. 2017. A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy. BMC Bioinformatics 18: 1-12. https://doi.org/10.1186%2Fs12859-017-2000-6.
Willing EM, Hoffmann M, Klein JD, Weigel D, Dreyer C. 2011. Paired-end RAD-seq for de novo assem-bly and marker design without available reference. Bioinformatics 27(16): 2187-2193.https://doi.org/10.1093/bioinformatics/btr346.
Zhang YM, Williams JL, Lucky A. 2019. Understanding UCEs: a comprehensive primer on using ultra-conserved elements for arthropod phylogenomics. Insect Systematics and Diversity 3(5): 3. https://doi.org/10.1093/isd/ixz016.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2023 Brazilian Journal of Mammalogy
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.