Multilocus genotype principal coordinates analysis was conducted using GenAlEx version 6.1 add-in software (Peakall and Smouse, 2006) for Microsoft Excel to provide a schematic indication of the degree of inbreeding. The average number of alleles per locus, observed heterozygosities (Ho), and unbiased estimates of expected heterozygosity (He) were calculated using Arlequin version 3.11 software (Nei, 1978 , Excoffier et al., 2005) . Data were defined as 'standard' rather than 'microsatellite' because the loci did not adhere to the stepwise mutation model. Exact tests for Hardy-Weinberg equilibrium were tested per locus using Fisher's exact probability test based on contingency tables (Raymond and Rousset, 1995) , where P-values <0.05 were taken as evidence of significant deviation. Significance levels were estimated using 100,000 Markov chain steps. Pairwise linkage disequilibrium was tested for using a likelihood-ratio test (Slatkin and Excoffier, 1996) . For each locus, estimates of inbreeding (Fis) were calculated using an algorithm based on the formula (He -Ho)/ He. Pairwise Fst values were calculated using Arlequin version 3.11 software. Analysis of Molecular Variance (AMOVA) was performed to test for population differentiation of samples at various levels, locus by locus using the Arlequin version 3.11 software.
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Arlequin version 3.5 is a software application designed for genetic data analysis. It provides a suite of tools for population genetics and molecular ecology studies. The core function of this software is to perform various statistical analyses on genetic data, including estimating genetic diversity, population structure, and demographic parameters.
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65 protocols using version 3
1
Multilocus Genotype Analysis for Inbreeding
2
Estimating Chloroplast DNA Diversity
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Five indices were used to estimate cpDNA diversity: (1) Na, the number of haplotypes found in a given population; (2) h, haplotype diversity (Nei, 1987); (3) π, nucleotide diversity; (4) hS, average within-population diversity; and (5) hT, total genetic diversity. The h and π indices were calculated for each population using Arlequin version 3.5 [42 ]; hS and hT were estimated using PERMUT 2.0[43 (link)] with 2000 permutations.
To identify patterns of genetic variation within and between populations, we performed hierarchical AMOVA for all populations using Arlequin version 3.5 [42 ]. In addition, hierarchical analyses dividing populations into different geographic groups were performed. We also compared two measurements of genetic differentiation between populations, GST and NST, using PERMUT 2.0[43 (link)] with 2000 permutations. The pairwise population NST matrix was calculated in DnaSP version 5.00.05 [44 (link)] for all populations and separately for the southern and northern populations.
To identify patterns of genetic variation within and between populations, we performed hierarchical AMOVA for all populations using Arlequin version 3.5 [42 ]. In addition, hierarchical analyses dividing populations into different geographic groups were performed. We also compared two measurements of genetic differentiation between populations, GST and NST, using PERMUT 2.0[43 (link)] with 2000 permutations. The pairwise population NST matrix was calculated in DnaSP version 5.00.05 [44 (link)] for all populations and separately for the southern and northern populations.
3
Genetic Structure of Spartina alterniflora
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The collecting sites in each state of the United States were combined to one population for each state: NC1, NC2, and NC3 were combined to NC; GA1, GA2, and GA3 were combined to GA; and FL1, FL2, and FL3 were combined to FL (Table 3 ). ARLEQUIN version 3.5 (Excoffier and Lischer, 2010 (link)) was performed to estimate the total percentage variation attributable to genetic differentiation within and among populations of S. alterniflora in China and America. The genetic differentiation index between populations, pairwise FST, was calculated using ARLEQUIN version 3.5 (Excoffier and Lischer, 2010 (link)), using 10,000 permutations. The significances of correlations between the genetic differentiation index (FST) and geographic distance (in kilometers) of invasive and native populations were assessed using the Mantel test (Mantel, 1967 (link)) with 9,999 random permutations using GENALEX 6.5 software (Peakall and Smouse, 2006 (link), 2012 (link)).
4
Genetic Diversity Analysis Protocol
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For each population, the mean number of alleles per locus (Na), observed heterozygosity (HO), and expected heterozygosity (HE) were calculated using the program GenAlEx version 6 [43 (link)]. Allelic richness (AR) and private allele richness (PAR) were estimated using HP-RARE 1.1 software [44 (link)]. Genetic differentiation in all pair wise combinations of the populations was estimated from the fixation index (FST, [45 (link)] for nuclear simple sequence repeats (nrSSRs) with 1000 permutations using Arlequin version 3.5 software [46 (link)]. Tests for deviation from the Hardy-Weinberg equilibrium (HWE) at each locus were conducted with Arlequin version 3.5 software using 10,000 permutations [46 (link)]. Tests for the linkage disequilibrium (LD) of all combinations of the loci for each population were conducted using GENEPOP 4.2 software [47 (link)]. LD can be induced by genetic admixture between divergent gene pools [35 (link), 36 (link)] and will decay if gene flow occurs between the divergent gene pools [37 (link)], thus, if no pair wise LD was detected among loci, we would expect that the hybrid population would not be maintained by ongoing gene flow.
5
Genetic Diversity Analysis of Nematode Parasites
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The sequences obtained here at the mtDNA cox2 gene were aligned using Clustal X version 2.0 software [6 (link)]. The number of alleles found at the SSR loci (A), the observed heterozygosity (Ho), the expected heterozygosity (He), the Hardy–Weinberg exact test [4 ], and the fixation indices (FIS, FIT and the Fst) [25 (link)] inferred from the SSRs genetic data sets were evaluated using ARLEQUIN version 3.5 software [2 (link)]. Because of the discontinuous range of distribution of the species A. berlandi and its genetic sub-structuring in the Pacific Ocean, as previously detected by other nuclear markers (allozymes) [12 (link)], we preferred to maintain the nematode samples collected from the two individuals of G. melas as separate sub-populations for the analysis of molecular variance (AMOVA). AMOVA was used to determine variance among individuals from the two definitive hosts, locus by locus, using ARLEQUIN version 3.5 [2 (link)], with 1000 permutations. Cavalli-Sforza and Edwards’s chord distance [1 (link)] and Nei’s distance values [17 ] were calculated from the SSR allele frequency estimates, using BIOSYS 2.0 software [22 ]. An unweighted pair group method with arithmetic mean (UPGMA) was generated using PHYLIP software [3 ], based on Nei’s [17 ] distance values.
6
Phylogenetic Relationships and Population Dynamics
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Polymorphic sites of the sequences obtained from each specimen were screened and specific haplotypes were identified for clades by considering the polymorphism divergence between individuals. In this study, we used haplotypes that were acquired from combined data to detect the relation between specimens using the median-joining method (NETWORK version 4.6; Bandelt et al., (1999) ). Analyses of molecular variance (AMOVA) was computed in ARLEQUIN version 3.5 (Excoffier & Lischer, 2010) to detect the amount of the shared genetic variation within and among the clades in Turkey.
ARLEQUIN version 3.5 (Excoffier & Lischer, 2010) was also operated to compute the neutrality analysis as Tajima's D (Tajima, 1989 ), Fu's Fs (Fu, 1997) . DnaSP version 5.1 (Rozas, 2009) was used to estimate the haplotype and nucleotide diversities, and the mismatch distributions. MtDNA combined data set was also analyzed in BEAST (Drummond et al., 2012) using a Bayesian skyline plot model to reconstruct changes in population sizes through time. Each major clade was run for Markov Chain Monte Carlo (MCMC) chain lengths of 10 million generations using 2% evolution rate (Brown et al., 1979) .
ARLEQUIN version 3.5 (Excoffier & Lischer, 2010) was also operated to compute the neutrality analysis as Tajima's D (Tajima, 1989 ), Fu's Fs (Fu, 1997) . DnaSP version 5.1 (Rozas, 2009) was used to estimate the haplotype and nucleotide diversities, and the mismatch distributions. MtDNA combined data set was also analyzed in BEAST (Drummond et al., 2012) using a Bayesian skyline plot model to reconstruct changes in population sizes through time. Each major clade was run for Markov Chain Monte Carlo (MCMC) chain lengths of 10 million generations using 2% evolution rate (Brown et al., 1979) .
7
Multilocus Genetic Differentiation Analysis
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Samples were pooled in three ways: (i) by geographical origin, (ii) by host plant, and (iii) by year of collection. Population structure was assessed by calculating multilocus FST values 38 for pairwise comparisons of samples using ARLEQUIN version 3.11. 37 The null distribution of pairwise FST values under the hypothesis of no difference between the populations is obtained by permuting diploid multilocus genotypes between populations. The P value of the test is the proportion of 100 000 permutations leading to an FST value larger than or equal to the observed one. The structure of the data was also investigated by analysis of molecular variance 39 (AMOVA) using ARLEQUIN version 3.11. A permutation non-parametric approach was used for the significance of fixation indices described in Excoffier et al. 39 . Allelic differentiation between populations was examined using GENEPOP version 3.4. An unbiased estimate of the P value of the Fisher exact test was made using a Markov chain method described in Raymond and Rousset. 40 For microsatellite markers, analyses were performed without clonal copies, i.e., with the data reduced to a single representative of each multilocus genotype (MLGM) per population, because the clonal amplification of genotypes inevitably leads to deviations from genetic equilibria. 41, 42
8
Genetic Diversity Analysis of Insect Populations
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The number and size of alleles for each locus, observed (Ho) and expected heterozygosity (He) and Hardy-Weinberg equilibrium (HW) were determined using Arlequin version 3.1 [61 (link), 62 (link)]. The occurrence of null alleles was checked with the software Micro-Checker 2.2.3; these frequencies were calculated for each locus by population with GENEPOP [63 (link), 64 (link)]. Cluster presence was assessed by STRUCTURE [65 (link)].
AMOVA was used to determine the variance components and proportions of global, interpopulation and intrapopulation variability within total variation. In addition, the following fixation indices were estimated: FST (among populations), FSC (among individuals within populations) and FCT (within individuals). We evaluated the genotypic diversity between each sample pair (pairwise FST) and intrapopulation (FIS). The tests were run with a 5% significance level and a maximum loss of 5% of amplified alleles. The Mantel test was also carry out in the cluster research. All these analyses were performed with Arlequin version 3.1 [61 (link), 62 (link)]. To evaluated the reinfestation of Cachoeira do Júlio, a UPGMA tree also was built, based on genetic distance (pairwise FST) (POPTREEW) [66 (link)].
AMOVA was used to determine the variance components and proportions of global, interpopulation and intrapopulation variability within total variation. In addition, the following fixation indices were estimated: FST (among populations), FSC (among individuals within populations) and FCT (within individuals). We evaluated the genotypic diversity between each sample pair (pairwise FST) and intrapopulation (FIS). The tests were run with a 5% significance level and a maximum loss of 5% of amplified alleles. The Mantel test was also carry out in the cluster research. All these analyses were performed with Arlequin version 3.1 [61 (link), 62 (link)]. To evaluated the reinfestation of Cachoeira do Júlio, a UPGMA tree also was built, based on genetic distance (pairwise FST) (POPTREEW) [66 (link)].
9
Mitochondrial Diversity Analysis Across Populations
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The two mitochondrial fragments were analysed separately (cytochrome b accession numbers: MG496663—MG496927; control region accession numbers: MG496398—MG496662) as well as in concert (1819 bp); the results were largely congruent between fragments and the concatenated segments. Summary statistics (number of haplotypes, haplotype diversity and nucleotide diversity) for each population was calculated in Arlequin version 3.5 [53 (link)]. Possible fluctuations in population size were investigated using Fu’s Fs in DnaSP 5.10.01 [54 (link)]. To determine a measure of effective population sizes, Θ (theta) values for populations were calculated in Migrate 3.6.4 [55 ] using a Bayesian search strategy. Result were based on averaging over three replicates of 15 000 000 generations each (burnin = 3 000 000).
10
Genetic Diversity Analysis in Populations
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Statistical analyses were carried out using DnaSP v6 [26 (link)], DnaSP v5 [27 (link)] and GenAlEx v6.5. software [28 (link), 29 (link)]. The number of alleles (H), segregating sites (Ss), allele diversity (Hd), singletons (Si), nucleotide diversity (Pi), average number of nucleotide differences (K), synonymous (Syn) and non-synonymous (N-Syn) mutations were calculated to infer population diversity.
Sliding window analysis of nucleotide diversity (Pi), with a window size of 10 bp and step size of 5 bp, was performed for all the populations (Papua New Guinea represented by only 4 samples and Thailand with only 1 allele were excluded from the analysis). Tajima’s D, Fu and Li’s D* and F* test statistics, carried out using DnaSP v5, were used to assess the selective pressure on the gene.
Pairwise genetic differentiation and its significance was estimated for all populations by the Wright fixation index (Fst) using Arlequin version 3.5 [30 (link)]. We set the number of permutations to 110 and the P-value significance level to P < 0.05.
Sliding window analysis of nucleotide diversity (Pi), with a window size of 10 bp and step size of 5 bp, was performed for all the populations (Papua New Guinea represented by only 4 samples and Thailand with only 1 allele were excluded from the analysis). Tajima’s D, Fu and Li’s D* and F* test statistics, carried out using DnaSP v5, were used to assess the selective pressure on the gene.
Pairwise genetic differentiation and its significance was estimated for all populations by the Wright fixation index (Fst) using Arlequin version 3.5 [30 (link)]. We set the number of permutations to 110 and the P-value significance level to P < 0.05.
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