SSREnricher: a computational approach for large-scale identification of polymorphic microsatellites based on comparative transcriptome analysis

Pipeline for developing polymorphic microsatellites in species without reference genomes

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; Megalobrama terminalis 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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03313-0.

AutomAted RepeaT Identifier (AARTI): A tool to identify common, polymorphic, and unique microsatellites

Here we are presenting an automated computational pipeline used to mine 5976 mitochondrial genomes to identify common, polymorphic, and unique microsatellites also known as simple sequence repeats (SSRs). Microsatellites are repetitive motifs of 1-6 bases in a DNA sequence. Due to their abundance and highly polymorphic nature, microsatellites have become one of the widely used molecular/genetic markers valuable for many studies including gene tagging, genetic diversity, and species identification. Several computational tools dedicated to mine and categorize microsatellites in nucleotide sequences were developed; however, there is no tool which can identify unique, common and polymorphic microsatellites between each pair of nucleotide sequences. To explore such microsatellites, we have developed a fully automated computational pipeline named AutomAted RepeaT Identifier (AARTI). The AARTI is the only tool till date, which identifies common, polymorphic, and unique microsatellites between each pair of nucleotide sequences. The computational pipeline was constructed using the PERL programming language and the web server for the pipeline was developed with the help of PHP, HTML, CSS, and JavaScript. It was successfully tested to reproduce the results of previous study on 7 mitochondrial genomes of genus Orthotrichum. Moreover, the pipeline was also applied on 5846 (Metazoa) and 130 (Viridiplantae) mitochondrial genomes. The AARTI is freely available at https://lms.snu.edu.in/aarti/ and will certainly accelerate the studies of length variation in microsatellites between species. Additionally, it will be useful in comparative genomic studies, for the computational characterization of microsatellites, and has the potential to be a routine genome analysis pipeline for mitochondrial genomes.

Automating microsatellite screening and primer design from multi-individual libraries using Micro-Primers

Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, as high levels of inbreeding could lead into whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies but discovering highly variable regions across species' genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify, and design PCR primers for amplification of SSR loci from a multi-individual microsatellite library. By combining commonly used programs for data cleaning and microsatellite mining, this pipeline easily generates, from a fastq file produced by high-throughput sequencing, standard information about the selected microsatellite loci, including the number of alleles in the population subset, and the melting temperature and respective PCR product of each primer set. Additionally, potential polymorphic loci can be identified based on the allele ranges observed in the population, to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers significantly reduces processing time in comparison to manual analysis while keeping the same quality of the results. The elapsed times at each step can be longer depending on the number of sequences to analyze and, if not assisted, the selection of polymorphic loci from multiple individuals can represent a major bottleneck in population studies.