利用筛选的20对微卫星引物对海南省6个养殖罗非鱼群体进行遗传多样性研究，分析各群体内的遗传变异和各群体间的遗传关系，比较不同群体的等位基因数、有效等位基因数、观测杂合度、期望杂合度、Shannon’s 多态性指数、平均多态性信息含量等遗传参数，并利用遗传距离构建6个罗非鱼群体的系统聚类图。结果表明：1）6个罗非鱼群体等位基因数为68个，平均等位基因数为1.992 2～2.255 8，平均观测杂合度为0.5725～0.7450，平均期望杂合度为0.469 2～0.532 3， Shannon’s多样性指数为0.719 3～0.848 6，多态信息含量为0.25<PIC<0.5，其中莫桑比克罗非鱼的遗传多样性指数最高，奥尼罗非鱼最低。2）采用邻接法（NJ）对6个罗非鱼群体进行聚类分析，表明6个罗非鱼群体分为2大支，吉富罗非鱼、尼罗罗非鱼、红罗非鱼、莫桑比克罗非鱼聚为一大支，其中吉富罗非鱼和尼罗罗非鱼聚集为一小支，红罗非鱼与莫桑比克罗非鱼聚为一小支；奥尼罗非鱼和泰奥罗非鱼聚为一支。综上所述，海南省6个养殖罗非鱼群体的遗传多样性较高，但不同群体之间存在差异；奥尼罗非鱼与泰奥罗非鱼亲缘关系较近，红罗非鱼与莫桑比克罗非鱼亲缘关系较近。

本研究以东海带鱼转录组测序数据为基础，采用MISA软件进行SSR筛选，利用Samtools和GATK进行SNP筛选，并进行SSR和SNP的特征分析。结果共获得SSR 49311个，其中完整的微卫星共有44625个，复合型SSR数目为4687个。SSR片段长度11~374bp。重复长度小于20bp的序列，有30997条，其中长度在12～20bp的SSR为7523个，约占总数的29.35%，呈中等多态性，重复长度大于20bp长序列为13627个，约占总数的53.18％，呈较高多态性；共获得SNP 位点数876199 个，其中颠换335218个，转换540981个，转换、颠换比例为1.61：1。在非编码区的SNP 数量为578020个；在编码区的SNP总数298179个，在编码区的碱基转换类型中C-&gt;T占比最高，为22.09%。另本研究提供了SSR和SNP原始引物各10对。研究结果可为今后带鱼的分子标记开发、遗传连锁图谱构建以及种质资源保护等提供基础数据。

Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient.

Microsatellites, also known as simple sequence repeats (SSRs), are the preferred type of marker for many genetic applications. In conjunction with the ongoing development of next-generation sequencing, several bioinformatic tools have been developed for identifying SSRs from genomic or transcriptomic sequences. Although these tools are handy for generating polymorphic SSRs, their application almost always depends on an existing reference genome or self-assembly of the reference genome. With this in mind, we propose a pipeline for developing polymorphic SSRs that may be applied to species without reference genomes. Using a species without a reference genome (black Amur bream; Richardson, 1846) as a model, our pipeline was able to effectively discover polymorphic SSRs. Under different R parameters of a reference-free single nucleotide polymorphisms (SNPs) caller (ebwt2InDel), a total of 258, 208, 102, and 11 polymorphic SSRs were mined. To quantify the accuracy of the polymorphic SSRs detected using our pipeline, we analyzed 25 SSRs with PCR experiments. All primers were successfully amplified, and most SSRs (23 SSRs, 92%) were polymorphic. From the 36 individual black Amur bream, we acquired an average of 3.36 alleles per locus, ranging from one to 11. This demonstrates the effectiveness of our pipeline in identifying polymorphic SSRs and designing primers for SSR genotyping. Ultimately, our pipeline can effectively mine polymorphic SSRs for species without reference genomes, complementing SSR mining approaches based on reference genomes and helping to resolve biological issues that accompany these methods.The online version contains supplementary material available at 10.1007/s13205-022-03313-0.© King Abdulaziz City for Science and Technology 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

In [Prezza et al., AMB 2019], a new reference-free and alignment-free framework for the detection of SNPs was suggested and tested. The framework, based on the Burrows-Wheeler Transform (BWT), significantly improves sensitivity and precision of previous de Bruijn graphs based tools by overcoming several of their limitations, namely: (i) the need to establish a fixed value, usually small, for the order k, (ii) the loss of important information such as k-mer coverage and adjacency of k-mers within the same read, and (iii) bad performance in repeated regions longer than k bases. The preliminary tool, however, was able to identify only SNPs and it was too slow and memory consuming due to the use of additional heavy data structures (namely, the Suffix and LCP arrays), besides the BWT.In this paper, we introduce a new algorithm and the corresponding tool ebwt2InDel that (i) extend the framework of [Prezza et al., AMB 2019] to detect also INDELs, and (ii) implements recent algorithmic findings that allow to perform the whole analysis using just the BWT, thus reducing the working space by one order of magnitude and allowing the analysis of full genomes. Finally, we describe a simple strategy for effectively parallelizing our tool for SNP detection only. On a 24-cores machine, the parallel version of our tool is one order of magnitude faster than the sequential one. The tool ebwt2InDel is available at github.com/nicolaprezza/ebwt2InDel.Results on a synthetic dataset covered at 30x (Human chromosome 1) show that our tool is indeed able to find up to 83% of the SNPs and 72% of the existing INDELs. These percentages considerably improve the 71% of SNPs and 51% of INDELs found by the state-of-the art tool based on de Bruijn graphs. We furthermore report results on larger (real) Human whole-genome sequencing experiments. Also in these cases, our tool exhibits a much higher sensitivity than the state-of-the art tool.

The growing use of next-generation sequencing and enlarged sequencing throughput require efficient short-read alignment, where seeding is one of the major performance bottlenecks. The key challenge in the seeding phase is searching for exact matches of substrings of short reads in the reference DNA sequence. Existing algorithms, however, present limitations in performance due to their frequent memory accesses.This paper presents BWA-MEME, the first full-fledged short read alignment software that leverages learned indices for solving the exact match search problem for efficient seeding. BWA-MEME is a practical and efficient seeding algorithm based on a suffix array search algorithm that solves the challenges in utilizing learned indices for SMEM search which is extensively used in the seeding phase. Our evaluation shows that BWA-MEME achieves up to 3.45x speedup in seeding throughput over BWA-MEM2 by reducing the number of instructions by 4.60x, memory accesses by 8.77x, and LLC misses by 2.21x, while ensuring the identical SAM output to BWA-MEM2.The source code and test scripts are available for academic use at https://github.com/kaist-ina/BWA-MEME/.Supplementary data are available at Bioinformatics online.© The Author(s) (2022). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods.

GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G'(ST), G''(ST), Jost's D(est) and F'(ST) through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised.GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx.rod.peakall@anu.edu.au.

Genotypes are frequently used to identify parentage. Such analysis is notoriously vulnerable to genotyping error, and there is ongoing debate regarding how to solve this problem. Many scientists have used the computer program CERVUS to estimate parentage, and have taken advantage of its option to allow for genotyping error. In this study, we show that the likelihood equations used by versions 1.0 and 2.0 of CERVUS to accommodate genotyping error miscalculate the probability of observing an erroneous genotype. Computer simulation and reanalysis of paternity in Rum red deer show that correcting this error increases success in paternity assignment, and that there is a clear benefit to accommodating genotyping errors when errors are present. A new version of CERVUS (3.0) implementing the corrected likelihood equations is available at http://www.fieldgenetics.com.

We describe a new basis for the construction of a genetic linkage map of the human genome. The basic principle of the mapping scheme is to develop, by recombinant DNA techniques, random single-copy DNA probes capable of detecting DNA sequence polymorphisms, when hybridized to restriction digests of an individual's DNA. Each of these probes will define a locus. Loci can be expanded or contracted to include more or less polymorphism by further application of recombinant DNA technology. Suitably polymorphic loci can be tested for linkage relationships in human pedigrees by established methods; and loci can be arranged into linkage groups to form a true genetic map of "DNA marker loci." Pedigrees in which inherited traits are known to be segregating can then be analyzed, making possible the mapping of the gene(s) responsible for the trait with respect to the DNA marker loci, without requiring direct access to a specified gene's DNA. For inherited diseases mapped in this way, linked DNA marker loci can be used predictively for genetic counseling.

In the last few years microsatellites have become one of the most popular molecular markers used with applications in many different fields. High polymorphism and the relative ease of scoring represent the two major features that make microsatellites of large interest for many genetic studies. The major drawback of microsatellites is that they need to be isolated de novo from species that are being examined for the first time. The aim of the present paper is to review the various methods of microsatellite isolation described in the literature with the purpose of providing useful guidelines in making appropriate choices among the large number of currently available options. In addition, we propose a fast and easy protocol which is a combination of different published methods.

Short tandem repeats (STRs) are highly informative genetic markers that have been used extensively in population genetics analysis. They are an important source of genetic diversity and can also have functional impact. Despite the availability of bioinformatic methods that permit large-scale genome-wide genotyping of STRs from whole genome sequencing data, they have not previously been applied to sequencing data from large collections of malaria parasite field samples. Here, we have genotyped STRs using HipSTR in more than 3,000Plasmodium falciparumand 174Plasmodium vivaxpublished whole-genome sequence data from samples collected across the globe. High levels of noise and variability in the resultant callset necessitated the development of a novel method for quality control of STR genotype calls. A set of high-quality STR loci (6,768 fromP.falciparumand 3,496 fromP.vivax) were used to studyPlasmodiumgenetic diversity, population structures and genomic signatures of selection and these were compared to genome-wide single nucleotide polymorphism (SNP) genotyping data. In addition, the genome-wide information about genetic variation and other characteristics of STRs inP.falciparumandP.vivaxhave been available in an interactive web-based R Shiny application PlasmoSTR (https://github.com/bahlolab/PlasmoSTR).

Abundant human interspersed repetitive DNA sequences of the form (dC-dA)n.(dG-dT)n have been shown to exhibit length polymorphisms. Examination of over 100 human (dC-dA)n.(dG-dT)n sequences revealed that the sequences differed from each other both in numbers of repeats and in repeat sequence type. Using a set of precise classification rules, the sequences were divided into three categories: perfect repeat sequences without interruptions in the runs of CA or GT dinucleotides (64% of total), imperfect repeat sequences with one or more interruptions in the run of repeats (25%), and compound repeat sequences with adjacent tandem simple repeats of a different sequence (11%). Informativeness of (dC-dA)n.(dG-dT)n markers in the perfect sequence category was found to increase with increasing average numbers of repeats. PIC values ranged from 0 at about 10 or fewer repeats to above 0.8 for sequences with about 24 or more repeats. (dC-dA)n.(dG-dT)n polymorphisms in the imperfect sequence category showed lower informativeness than expected on the basis of the total numbers of repeats. The longest run of uninterrupted CA or GT repeats was found to be the best predictor of informativeness of (dC-dA)n.(dG-dT)n polymorphisms regardless of the repeat sequence category.

We summarize available data on the frequencies of alleles at microsatellite loci in human populations and compare observed distributions of allele frequencies to those generated by a simulation of the stepwise mutation model. We show that observed frequency distributions at 108 loci are consistent with the results of the model under the assumption that mutations cause an increase or decrease in repeat number by one and under the condition that the product Nu, where N is the effective population size and u is the mutation rate, is larger than one. We show that the variance of the distribution of allele sizes is a useful estimator of Nu and performs much better than previously suggested estimators for the stepwise mutation model. In the data, there is no correlation between the mean and variance in allele size at a locus or between the number of alleles and mean allele size, which suggests that the mutation rate at these loci is independent of allele size.

Microsatellites were isolated from P. monodon genomic libraries by direct sequencing of recombinant clones without probe screening. Forty-nine out of 83 clones sequenced contained 99 microsatellite arrays of three or more repeats. When five or more and ten or more repeats were considered, 28 and 14 microsatellites were detected, respectively. The 99 microsatellites were classified as perfect (75%), imperfect (6%), compound perfect (3%) and compound imperfect (16%). The abundance of di-, tri-, tetra- and hexanucleotide repeats were 67%, 20%, 9% and 3%, respectively. The dinucleotide repeats included 36 (CT)n, 31 (GT)n, 17(AT)n and 3 (CG)n. One octanucleotide repeat (ATTTATTC)5 was found within a large repeat sequence. Optimal annealing temperatures were determined for PCR using 11 primer sets encompassing 15 microsatellites. Ten primer sets provided successful amplifications with allele sizes generally ranging from 139 to 410 bp. All these primers amplified polymorphic loci with PIC values ranging from 0.63 to 0.96. Two primer sets amplified additional bands which can easily be distinguished from the bands of the main locus. Three out of 10 P. monodon microsatellites also amplified alleles in P. vannamei. The abundance and informative nature of P. monodon microsatellites and their potential for cross-species amplification make them useful for genetic studies.