GMATA: An Integrated Software Package for Genome-Scale SSR Mining, Marker Development and Viewing

Abstract

Simple sequence repeats (SSRs), also referred to as microsatellites, are highly variable tandem DNAs that are widely used as genetic markers. The increasing availability of whole-genome and transcript sequences provides information resources for SSR marker development. However, efficient software is required to efficiently identify and display SSR information along with other gene features at a genome scale. We developed novel software package Genome-wide Microsatellite Analyzing Tool Package (GMATA) integrating SSR mining, statistical analysis and plotting, marker design, polymorphism screening and marker transferability, and enabled simultaneously display SSR markers with other genome features. GMATA applies novel strategies for SSR analysis and primer design in large genomes, which allows GMATA to perform faster calculation and provides more accurate results than existing tools. Our package is also capable of processing DNA sequences of any size on a standard computer. GMATA is user friendly, only requires mouse clicks or types inputs on the command line, and is executable in multiple computing platforms. We demonstrated the application of GMATA in plants genomes and reveal a novel distribution pattern of SSRs in 15 grass genomes. The most abundant motifs are dimer GA/TC, the A/T monomer and the GCG/CGC trimer, rather than the rich G/C content in DNA sequence. We also revealed that SSR count is a linear to the chromosome length in fully assembled grass genomes. GMATA represents a powerful application tool that facilitates genomic sequence analyses. GAMTA is freely available at http://sourceforge.net/projects/gmata/?source=navbar.

Development of high transferability cpSSR markers for individual identification and genetic investigation in Cupressaceae species

“…The high quality, versatility, and applicability of our primers demonstrate the viability of the implemented method for developing SSR loci. We first identified and characterized SSRs along with gene features in each given genome/sequence (Wang & Wang, ). In the following step, simulated marker mapping (e‐mapping) was performed across all genomes/sequences using a forward e‐PCR algorithm, allowing evaluating the transferability of the cpSSRs and calculating the potential intergenomic/intersequence polymorphism of each developed SSR locus (Schuler, ).…”

“…The GMATA2.1 (Wang & Wang, ) program for SSR mining and marker development was employed to identify SSR loci in the plastomes of six species Cupressus gigantean (GenBank ID: ) (Li, Guo, & Zheng, ), Cupressus sempervirens (GenBank ID: ), Juniperus monosperma (GenBank ID: ), Juniperus bermudiana (GenBank ID: ), Juniperus scopulorum (GenBank ID: ), and Juniperus virgiana (GenBank ID: ) (Guo et al., ) using constraints of more than five repeats and a motif length between 2 and 10 bp. Electronic PCR (e‐PCR) refers to the process of recovering unique sequence‐tagged sites in DNA sequences by searching for subsequence that closely match the PCR primers and have the correct order, orientation, and spacing that they could plausibly prime the amplification of a PCR product of the correct molecular weight (Schuler, ).…”

Exceptional subgenome stability and functional divergence in allotetraploid teff, the primary cereal crop in Ethiopia

“…We first identified and masked the simple sequence repeats in the teff genome with GMATA 57 , and then conducted structure-based full-length transposable element (TE) identification using the following bioinformatic tools: LTR_FINDER 58 and LTRharvest 59 to find LTR-RTs, LTR_retriever 60 to acquire high-confidence full LTR retrotransposons, SINE-Finder 61 to identify SINEs, MGEscan-nonLTR (V2) 62 to identify LINEs, MITE-Hunter 63 and MITE Tracker 64 to identify TIRs, and HelitronScanner 65 to identify Helitrons . All TEs were classified and manually checked according to the nomenclature system of transposons as described previously 66 and against Repbase to validate their annotation 67 .…”

An illustrated comparison of processing methods for phase MRI and QSM: removal of background field contributions from sources outside the region of interest

“…This method is referred to as the Laplacian Boundary Value (LBV) method. A MATLAB implementation of LBV is available as part of the Cornell QSM software package [34]. …”