Software

Manufactured by Arlequin

Sourced in Switzerland

ARLEQUIN software is a data analysis tool designed to perform population genetic analyses. It provides a suite of statistical methods for the investigation of genetic diversity within and between populations.

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Prism 6, by GraphPad (1 mentions)

15 protocols using software

Genetic Diversity and Bottleneck Analysis

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Genetic diversity was measured by number of alleles (N_A), expected heterozygosity (H_E), and observed heterozygosity (H_O) using CERVUS software (Ver. 3.0)^{48 (link)}. Allelic richness (AR was analyzed using FSTAT software (Ver. 2.9.3)⁴⁹ to correct for population-level differences. Analyses of population inbreeding coefficient (F_IS and Hardy–Weinberg equilibrium (HWE deviations were performed using GENEPOP software (Ver. 4.2)^{50 (link)} and ARLEQUIN software (Ver. 3.5)^{51 (link)}.
Two methods were used to estimate bottlenecks. The first method used BOTTLENECK software (Ver. 1.2.02)^{52 (link)}, a program for estimating bottlenecks via heterozygous excess testing using the infinite allele model^{53 (link)}, two-phase model, and stepwise mutation model^{54 (link)}. Each model was run for 10,000 iterations, and significance was determined using the Wilcoxon signed-rank test^{55 (link)}. The second method used the M-ratio^{56 (link)}, which estimates bottlenecks by using the mean ratio of the range of allele numbers and allele sizes,this analysis was performed using ARLEQUIN (Ver. 3.5)^{51 (link)}. To determine the size of the effective population, LDNE software^{57 (link)} was used for linkage disequilibrium estimation.

Kim K.R., Kwak Y.H., Sung M.S., Cho S.J, & Bang I.C. (2023). Population structure and genetic diversity of the endangered fish black shinner Pseudopungtungia nigra (Cyprinidae) in Korea: a wild and restoration population. Scientific Reports, 13, 9692.

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Genetic Differentiation Analysis

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Pairwise F_ST was computed with 1,000 permutations using ARLEQUIN software, version 3.5 (Excoffier & Lischer, 2010). A phylogenetic tree was then generated from a matrix of pairwise F_ST estimates using Splits Tree software, version 4.13.1 (Huson & Bryant, 2006).

Makombu J.G., Stomeo F., Oben P.M., Tilly E., Stephen O.O., Oben B.O., Cheruiyot E.K., Tarekegn G.M., Zango P., Egbe A.E., Ndagyong A., Mialhe E., Ngueguim J.R, & Mujibi F.D. (2019). Morphological and molecular characterization of freshwater prawn of genus Macrobrachium in the coastal area of Cameroon. Ecology and Evolution, 9(24), 14217-14233.

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Genetic Structure of P. knowlesi in Malaysia

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To define genetic structure of the P. knowlesi parasite population in Malaysia based on the msp1p, STRUCTURE software (version 2.3.4) was used that deploys the Bayesian model based clustering approach. The most probable number of populations (K) was determined using an admixture model. Since the 19 kDa domain is largely conserved in all Plasmodium species, the 42 kDa domain (19 and 33 kDa) was used for population structure analysis. All sample data were run for values K = 1–6, each with a total of 15 iterations, 100,000 Markov Chain Monte Carlo (MCMC) generations for each run after a burn-in of 50,000 steps. The most likely number K in the data was estimated by calculating ΔK values and identifying the K value that maximizes the log probability of data, lnP(D). The most probable K value was then calculated according to Evanno’s method by using the webpage interface STRUCTURE Harvester. The ARLEQUIN software (version 3.5.1.3, University of Berne, Berne, Switzerland) was used to compute pairwise differences (F_ST) between populations (i.e., Sarikei, Betong, Kapit and Peninsular Malaysia) with 10,100 permutations. F_ST is a comparison of the sum of genetic variability within and between populations on the basis of the differences in allelic frequencies. F_ST values are interpreted as no (0), low (> 0–0.05), moderate (0.05–0.15), and high (0.15–0.25) genetic differentiation.

Ahmed M.A., Fauzi M, & Han E.T. (2018). Genetic diversity and natural selection of Plasmodium knowlesi merozoite surface protein 1 paralog gene in Malaysia. Malaria Journal, 17, 115.

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Statistical Analysis of Genetic Variants

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Statistical analysis was carried out using the computer packages EXCEL and SPSS^® for WINDOWS softwares (version 16.0; SPSS^® Inc, Chicago IL). Independent t-tests were used to analyse differences between the means of continuous variables. Discrete variables, genotype distribution and Hardy–Weinberg equilibrium (HWE) were tested using χ² test with Yates’s correction. Odds Ratios (OR’s) 95% confidence intervals (CI) and associated chi-squares were calculated for genotypes and alleles. Haplotype analysis was performed using Arlequin^® software (version 3.11) (Excoffier, Laval, & Schneider, 2005 ). Two-tailed p values of <0.05 were considered to be statistically significant.

Rai H., Sinha N., Finn J., Agrawal S, & Mastana S. (2016). Association of serum lipids and coronary artery disease with polymorphisms in the apolipoprotein AI-CIII-AIV gene cluster. Cogent Medicine, 3(1), 1266789.

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Statistical Analysis of Research Data

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All data obtained were analyzed with an IBM PC computer by using the SPSS for
Windows statistical program, version 17.0 of 2008. The following tests were
used: Tukey, chi-square (χ²), analysis of variance (F) and
Pearson correlation. The statistical significance level adopted was 5%.
Categorical variables were presented as absolute values and their respective
percentages. Continuous variables were presented as mean ± standard
deviation. To assess the distribution of the variables studied, skewness
analysis was used. Gene and haplotype frequencies were tested for Hardy-Weinberg
equilibrium, using ARLEQUIN software, version 2000.

Duque G.S., da Silva D.A., de Albuquerque F.N., Schneider R.S., Gimenez A., Pozzan R., Rocha R.M, & de Albuquerque D.C. (2016). Influence of Angiotensin-Converting-Enzyme Gene Polymorphism on Echocardiographic Data of Patients with Ischemic Heart Failure. Arquivos Brasileiros de Cardiologia, 107(5), 446-454.

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Genetic Differentiation Analysis of Malaysian Populations

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Even though Peninsular Malaysia and Malaysian Borneo were separated by the South China Sea, samples originating from these areas were considered as one for population differentiation analysis. The ARLEQUIN software (version 3.5.1.3) [44 (link)] was used to compute pairwise differences (F_ST) between populations, i.e., Thailand (n = 23), Malaysian Borneo (n = 42) and Peninsular Malaysia (n = 11) with 10,100 permutations. F_ST is a comparison of the sum of genetic variability within and between populations based on the allelic frequency differences. F_ST values are interpreted as no (0), low (> 0–0.05), moderate (0.05–0.15), and high (0.15–0.25) genetic differentiation.

Ahmed M.A., Chu K.B., Vythilingam I, & Quan F.S. (2018). Within-population genetic diversity and population structure of Plasmodium knowlesi merozoite surface protein 1 gene from geographically distinct regions of Malaysia and Thailand. Malaria Journal, 17, 442.

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Genetic Diversity Analysis in Population Samples

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Allele frequencies were obtained by direct counting and compared by 2X2
contingency tables using the chi-square test and Fisher's exact test. Only P
values < 0.05 were significant. Significant P values were corrected by
the number of specificities tested at each locus (Pc; Bonferroni correction).
Statistical analyses were carried out using the SISA statistics software
(http://www.quantitativeskills.com/sisa/). The Hardy-Weinberg
equilibrium was calculated through the Arlequin software.

Barbosa Â.M., Prestes-Carneiro L.E., Sobral A.R., Sakiyama M.J., Lemos B.C., de Abreu M.A., Martos L.L, & Moliterno R.A. (2016). Lack of association between alopecia areata and HLA class I and II in a southeastern Brazilian population. Anais Brasileiros de Dermatologia, 91(3), 284-289.

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Genetic Diversity and Bottleneck Analysis

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MICROCHECKER software (ver. 2.2.3) [29 (link)] was used to examine the presence or absence of scoring errors at the microsatellite loci. Genetic diversity was measured as the number of alleles (N_A), expected heterozygosity (H_E), and observed heterozygosity (H_O) using CERVUS software (ver. 3.0) [30 (link)]. Population inbreeding coefficient (F_IS) and Hardy–Weinberg equilibrium (HWE) variance analyses were performed using GENEPOP (ver. 4.2) [31 (link)] and ARLEQUIN software (ver. 3.5) [32 (link)]. Two methods were used to estimate the bottlenecks. These methods used BOTTLENECK software (ver. 1.2.02) [33 (link)], a program to estimate bottlenecks through excess heterozygosity testing, and an infinite allele model (IAM) [34 (link)]. A two-phase model (TPM) and stepwise mutation model (SMM) [35 (link)] were used for the estimation, and the TPM was performed with 10% variance and 90% SMM. In addition, each model had 10,000 iterations, and the significance was verified using the Wilcoxon signed-rank test [36 (link)]. Contemporary effective population size (Ne) was estimated for each population using NeEstimator ver. 2.1 software [37 (link)] with the Linkage Disequilibrium (LDNe) method under a random mating model [37 (link)].

Kim K.R., Sung M.S., Hwang Y., Jeong J.H, & Yu J.N. (2024). Assessment of the Genetic Diversity and Structure of the Korean Endemic Freshwater Fish Microphysogobio longidorsalis (Gobioninae) Using Microsatellite Markers: A First Glance from Population Genetics. Genes, 15(1), 69.

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Genetic Structure Analysis of Populations

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ARLEQUIN software (ver. 3.05) [34 (link)] was used to analyze the differences in genetics between groups, as well as AMOVA. STRUCTURE software (ver. 2.3) [40 (link)] was used to perform genetic structure clustering analysis based on the Bayesian method model. To estimate the most suitable population, we set K to 1–10, and a suitable admixture model was applied to the mixture of water systems. The burn-in period was repeated 10 times with 10,000 iterations, and Markov chain Monte Carlo simulation was used with 100,000 iterations. To estimate a population-appropriate constant, we analyzed a study by the cluster results corresponding to K using STRUCTURE SELECTOR [41 (link)]. A discriminant analysis of principal components (DAPC) of the microsatellite dataset was performed on the population using the R package ADEGENET (ver. 2.1.3) [42 (link)], a non-model-based genetic clustering method.

Kim K.R., Kim K.Y, & Song H.Y. (2024). Genetic Structure and Diversity of Hatchery and Wild Populations of Yellow Catfish Tachysurus fulvidraco (Siluriformes: Bagridae) from Korea. International Journal of Molecular Sciences, 25(7), 3923.

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Genetic Diversity Analysis of Pvs230 in Malaria Parasites

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The degree of genetic differentiation in Pvs230 among global isolates was estimated by calculating Wright’s fixation index (F_ST) using Arlequin software, version 3.5.2.2 [33 (link)]. Interpretation of F_ST values is defined as described previously [34 (link)], with no differentiation (0), low genetic differentiation (≤ 0.15), moderate genetic differentiation (0.15–0.25), and high differentiation (≥ 0.25). A P-value < 0.05 was considered significant difference. To investigate the genetic relatedness among the Pvs230 haplotypes, a haplotype network was constructed using the median-joining (MJ) method implemented in the PopART v1.7 software with a haplotype frequency > 1 [35 (link)]. A phylogenetic tree of Pvs230 sequences (nt 52–2862) from the global isolates [36 (link)] was constructed by the NJ method [37 (link)] with a bootstrap of 1000 replicates using MEGA 7.0 software [22 (link)]. Final trees were drawn and edited using iTOL version 6 (https://itol.embl.de/).

Zhao X., Hu Y., Zhao Y., Wang L., Wu Z., Soe M.T., Kyaw M.P., Cui L., Zhu X, & Cao Y. (2022). Genetic diversity in the transmission-blocking vaccine candidate Plasmodium vivax gametocyte protein Pvs230 from the China–Myanmar border area and central Myanmar. Parasites & Vectors, 15, 371.

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