- Low impact of different SNP panels from two building-loci pipelines on RAD-Seq. - Many building-loci pipelines have been developed to obtain robust single nucleotide polymorphism (SNPs) genotyping datasets using a de novo RAD-seq approach, i.e. - Genotyped vs missing data mismatches were the main genotyping difference detected between the two building-loci pipelines or between the de novo and reference genome comparisons.. - https://www.earthbiogenome.org. - It is not uncommon to find large differences in the number of SNPs (e.g. - of one order of magnitude) in some building-loci pipelines comparisons [17]. - From two building-loci pipelines, STACKS (STA panel onwards) and Meyer’s 2b-RAD v2.1 pipeline (ALT panel onwards), and two criteria for SNP selection, common SNPs (i.e.. - The number of filtered reads loaded into building-loci pipelines using the reference genome approach was lower than with the de novo approach. - -m 1 in Bowtie 1.1.2), the remaining reads failed to align with. - The number of initial SNPs, after the building-loci step with the de novo approach, ranged from 56,074 in brown trout (STA) and 125,823 in silver catfish (ALT) to 356,389 (STA) and 426,317 (ALT) in common cockle (Tables S1-S5). - There was a remarkable difference at the initial number of SNPs. - 2 Number of SNPs from the initial building-loci pipelines (blue bars) to the final panels (green bars) through the different SNPs filtering steps. - The final number of SNPs ranged from 479 (STA) and 956 (ALT) in Manila clam to 21,468 (STA) and 22,481 (ALT) in silver catfish. - number of population structure units detected using STRUCTURE (STR groups) are shown. - The number of SNPs obtained with reference genome (RG) approach was always lower than with both de novo building-loci pipelines (see Tables S1 and S3), however, the number of SNPs shared between RG and each of the de novo pipelines was similar (i.e. - (RG-STA/RG) and 80.9% (RG-ALT/RG) in brown trout, unlike the reverse way where the percentages of shared SNPs were lower due to the higher number of SNPs ob- tained with ALT: 28.8% (RG-STA/STA) and 11.4% (RG- ALT/ALT) for Manila clam with (Table S1) and 46.5%. - Some minor discrepancies in the number of suggestive outliers among panels were de- tected. - When a reference genome is available the number of po- tential loci obtained with different restriction enzymes (e.g. - Minor differences were found in the number of suggestive outliers detected in common cockle. - In this case, the number of suggestive outliers detected could be related to the total number of SNPs of each panel. - The number of initial and final SNPs obtained with reference genome was lower than obtained with a de novo approach. - The initial number of SNPs ob- tained with STA and ALT pipelines across the different species tested was rather similar except for the brown trout and the small-spotted catshark. - In cases with low coverage, a less demanding cri- terion to build loci can produce large differences in the initial number of SNPs. - For instance, the number of SNPs was markedly reduced through filtering steps and the highest difference in the percentage of retained SNPs was found among species.. - In the study by O’Leary et al. - to achieve the number of SNPs re- quired to meet the research goals. - In the study by Díaz-Arce et al. - Despite the differences ob- served in the number of SNPs among de novo approach panels, this seems not to affect dramatically the conclu- sions, at least in the biological scenarios managed in this study. - On one hand, different genotypes can be obtained due to the different building- loci pipeline parameters to call genotypes (e.g. - Furthermore, small differences in the building-loci pipe- line could have more influence in the number of missing genotypes when working with low coverage loci. - Building-loci pipelines: background. - STACKS 2.0 and Meyer’s 2b-RAD v2.1 were the building-loci pipelines chosen for comparing their per- formances using a de novo approach within the broad genome and population genetics species scenarios se- lected. - Meyer’s 2b-RAD v2.1 pipeline and STACKS building-loci pipelines have some similarities on their strategy. - Building-loci pipelines: analysis. - At the building-loci pipeline step, the parameters considered were: (1) minimum number of identical reads to create a stack (default values were used). - to say, when the frequency of the less frequent allele was lower than 0.1 the genotype was called as homozy- gous while frequencies higher than 0.2 were called as heterozygous. - number of SNPs) and population genomics metrics. - org/10.1186/s w.. - Number of SNPs and population metrics for Manila clam (Ruditapes philippinarum) samples (N = 110, four localities). - Number of SNPs and population metrics for common edible cockle (Cerastoderma edule) samples (N = 120, four localities). - Number of SNPs and population metrics for brown trout (Salmo trutta) samples (N = 52, three localities). - Number of SNPs and population metrics for silver catfish (Rhamdia quelen) samples (N = 21, two localities). - Number of SNPs and population metrics for small-spotted catshark (Scyliorhinus canicula) sam- ples (N = 28, two localities). - Building- loci pipelines options selected for process_radtags (A), STACKS 2 (B), and Meyer ’ s 2b-RAD v2.1 (C). - Differences in genotyping between common SNPs (COM panel) from both building-loci pipelines in each species. - Type of genotype differences between common SNPs (COM panel) from both building-loci pipelines in each species. - AC, FM, AB, MH ran the different building-loci pipelines. - https://doi.org . - https://doi.org/10.1038/nrg.2016.49.. - https://doi.org/10.1371/journal.pone.0003376.. - https://doi.org/1 0.1038/nrg3012.. - https://doi.org/10.1038/nrg.2015.28.. - https://doi.org/1 0.1038/nmeth.2023.. - These aren ’ t the loci you ’ re looking for: principles of effective SNP filtering for molecular ecologists. - https://doi.org/10.1111/mec.14792.. - https://doi.org/10.3389/fgene . - 2020.http://researcharchive.calacademy.org/research/. - https://doi.org/10.1073/pnas . - https://doi.org/10.1038/. - https://doi.org/10.1111/mec.12354.. - https://doi.org/10.1534/g . - https://doi.org/10.7717/peerj.431.. - doi.org/10.1186/s . - https://doi.org/10.1371/journal.pone.0090346.. - https://doi.org/10.1093/bioinforma tics/btz966.. - https://doi.. - https://doi.org X.12775.. - https://doi.org/10.1186/s y.. - https://doi.org/10.1007/s y.. - https://doi.org/10.1007/s x.. - https://doi.org/1 0.1534/g . - https://doi.org/10.3390/genes11010109.. - https://doi.org/10.1093/biolinnean/bly185.. - https://doi.org/10.1016/j.. - https://doi.org/10.1093/bfgp/elu042.. - https://doi.org/10.1186/s . - https://doi.org/10.1186/s z.. - https://doi.org/1 0.1098/rspb.2013.2881.. - https://doi.org/1 0.1038/ncomms4657.. - https://doi.org/10.1016/j.tree . - doi.org/10.1111/mec.13647.. - https://doi.org/10.1111/eva.12659.. - https://doi.org/10.1038/s . - https://doi.org/10.1016/j.foodchem . - https://doi.org/10.3390/genes10100760.. - https://doi.org/10.1046/. - https://doi.org/10.1111/j . - https://doi.org/10.1111/j x.. - https://doi.org/10.1016/S . - org/10.1111/j x.. - org/10.1093/bioinformatics/bts565.. - https://doi.org/10.1093/bioinformatics/btl158.. - https://doi.org/10.1093/. - https://doi.org/10.1093/bioinformatics/btr642.. - https://doi.org X.12067.. - https://doi.org/10.1371/journal.pone.0070651.. - https://doi.org/10.1007/s . - Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. - https://doi.org/10.1111/j.1365-294X x.. - https://doi.org/10.1534/genetics.108.
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