Allendorf, F. W., Hohenlohe, P. A. & Luikart, G. Genomics and the future of conservation genetics. Nat. Rev. Genet. 11, 697–709 (2010).
Article CAS PubMed Google Scholar
Athanasopoulou, K., Boti, M. A., Adamopoulos, P. G., Skourou, P. C. & Scorilas, A. Third-generation sequencing: the spearhead towards the radical transformation of modern genomics. Life 12, 30 (2022).
Fiedler, P. L. et al. Seizing the moment: the opportunity and relevance of the California Conservation Genomics Project to state and federal conservation policy. J. Hered. 113, 589–596 (2022).
Article CAS PubMed PubMed Central Google Scholar
Hu, T., Chitnis, N., Monos, D. & Dinh, A. Next-generation sequencing technologies: an overview. Hum. Immunol. 82, 801–811 (2021).
Article CAS PubMed Google Scholar
Pompanon, F., Bonin, A., Bellemain, E. & Taberlet, P. Genotyping errors: causes, consequences and solutions. Nat. Rev. Genet. 6, 847–859 (2005). This review summarizes the sources of many common types of sequencing errors and provides some laboratory and bioinformatic ways to mitigate them.
Article CAS PubMed Google Scholar
Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genom. Bioinform. 3, lqab019 (2021).
Article PubMed PubMed Central Google Scholar
Fountain, E. D., Pauli, J. N., Reid, B. N., Palsbøll, P. J. & Peery, M. Z. Finding the right coverage: the impact of coverage and sequence quality on single nucleotide polymorphism genotyping error rates. Mol. Ecol. Resour. 16, 966–978 (2016).
Article CAS PubMed Google Scholar
O’Leary, S. J., Puritz, J. B., Willis, S. C., Hollenbeck, C. M. & Portnoy, D. S. These aren’t the loci you’re looking for: principles of effective SNP filtering for molecular ecologists. Mol. Ecol. 27, 3193–3206 (2018). This helpful review discusses the effects of missing data, MAC and other filters on genotyping error rates for RADseq data.
Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).
Article CAS PubMed Google Scholar
Ahrens, C. W. et al. Regarding the F-word: the effects of data filtering on inferred genotype–environment associations. Mol. Ecol. Resour. 21, 1460–1474 (2021).
Andrews, K. R. & Luikart, G. Recent novel approaches for population genomics data analysis. Mol. Ecol. 23, 1661–1667 (2014).
Shafer, A. B. A. et al. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol. Evol. 8, 907–917 (2017). This study demonstrates the effects of different filtering and alignment choices on several downstream statistics and demographic reconstruction in RADseq data.
Larson, W. A., Isermann, D. A. & Feiner, Z. S. Incomplete bioinformatic filtering and inadequate age and growth analysis lead to an incorrect inference of harvested-induced changes. Evol. Appl. 14, 278–289 (2021).
Article CAS PubMed Google Scholar
Nazareno, A. G. & Knowles, L. L. There is no ‘rule of thumb’: genomic filter settings for a small plant population to obtain unbiased gene flow estimates. Front. Plant Sci. 12, 677009 (2021). This comprehensive analysis of empirical data demonstrates how missing data and MAF thresholds affect estimates of gene flow.
Article PubMed PubMed Central Google Scholar
Sethuraman, A. et al. Continued misuse of multiple testing correction methods in population genetics — a wake-up call? Mol. Ecol. Resour. 19, 23–26 (2019).
Allendorf, F. W. et al. Conservation and the Genomics of Populations (Oxford Univ. Press, 2022).
Gervais, L. et al. RAD-sequencing for estimating genomic relatedness matrix-based heritability in the wild: a case study in roe deer. Mol. Ecol. Resour. 19, 1205–1217 (2019).
Article CAS PubMed Google Scholar
Crow, J. F. & Kimura, M. An Introduction to Population Genetics Theory (Scientific Publishers, 2017).
Van Etten, J., Stephens, T. G. & Bhattacharya, D. A k-mer-based approach for phylogenetic classification of taxa in environmental genomic data. Syst. Biol. 72, 1101–1118 (2023).
Article CAS PubMed Google Scholar
Todd, E. V., Black, M. A. & Gemmell, N. J. The power and promise of RNA-seq in ecology and evolution. Mol. Ecol. 25, 1224–1241 (2016).
Article CAS PubMed Google Scholar
Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).
Article PubMed PubMed Central Google Scholar
Olofsson, D., Preußner, M., Kowar, A., Heyd, F. & Neumann, A. One pipeline to predict them all? On the prediction of alternative splicing from RNA-seq data. Biochem. Biophys. Res. Commun. 653, 31–37 (2023).
Article CAS PubMed Google Scholar
Upton, R. N. et al. Design, execution, and interpretation of plant RNA-seq analyses. Front. Plant Sci. 14, 1135455 (2023).
Article PubMed PubMed Central Google Scholar
Rehn, J. et al. RaScALL: rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL). PLOS Genet. 18, e1010300 (2022).
Article CAS PubMed PubMed Central Google Scholar
Boshuizen, H. C. & te Beest, D. E. Pitfalls in the statistical analysis of microbiome amplicon sequencing data. Mol. Ecol. Resour. 23, 539–548 (2023).
Combrink, L. et al. Best practice for wildlife gut microbiome research: a comprehensive review of methodology for 16S rRNA gene investigations. Front. Microbiol. 14, 1092216 (2023).
Article PubMed PubMed Central Google Scholar
Cheng, Z. et al. Transcriptomic analysis of circulating leukocytes obtained during the recovery from clinical mastitis caused by Escherichia coli in Holstein dairy cows. Animals 12, 2146 (2022).
Article PubMed PubMed Central Google Scholar
Yang, L. & Chen, J. Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. Brief. Bioinformatics 24, bbac607 (2023).
Patin, N. V. & Goodwin, K. D. Capturing marine microbiomes and environmental DNA: a field sampling guide. Front. Microbiol. 13, 1026596 (2023).
Article PubMed PubMed Central Google Scholar
Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).
Deyneko, I. V. et al. Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes. BMC Bioinformatics 23, 488 (2022).
Article CAS PubMed PubMed Central Google Scholar
Giusti, A., Malloggi, C., Magagna, G., Filipello, V. & Armani, A. Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin? A systematic review. Compr. Rev. Food Sci. Food Saf. 23, 1–21 (2024).
da Fonseca, R. R. et al. Next-generation biology: sequencing and data analysis approaches for non-model organisms. Mar. Genomics 30, 3–13 (2016).
Zhao, M. et al. Exploring conflicts in whole genome phylogenetics: a case study within manakins (Aves: Pipridae). Syst. Biol. 72, 161–178 (2023).
Article CAS PubMed Google Scholar
Koboldt, D. C. Best practices for variant calling in clinical sequencing. Genome Med 12, 91 (2020).
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