BanSatDB, a whole-genome-based database of putative and experimentally validated microsatellite markers of three Musa species

Bagchi

et al. 2020

Genes

Trait tagging through molecular markers is an important molecular breeding tool for crop improvement. SSR markers encoded by functionally relevant parts of a genome are well suited for this task because they may be directly related to traits. However, a limited number of these markers are known for Musa spp. Here, we report 35136 novel functionally relevant SSR markers (FRSMs). Among these, 17,561, 15,373 and 16,286 FRSMs were mapped in-silico to the genomes of Musa acuminata, M. balbisiana and M. schizocarpa, respectively. A set of 273 markers was validated using eight accessions of Musa spp., from which 259 markers (95%) produced a PCR product of the expected size and 203 (74%) were polymorphic. In-silico comparative mapping of FRSMs onto Musa and related species indicated sequence-based orthology and synteny relationships among the chromosomes of Musa and other plant species. Fifteen FRSMs were used to estimate the phylogenetic relationships among 50 banana accessions, and the results revealed that all banana accessions group into two major clusters according to their genomic background. Here, we report the first large-scale development and characterization of functionally relevant Musa SSR markers. We demonstrate their utility for germplasm characterization, genetic diversity studies, and comparative mapping in Musa spp. and other monocot species. The sequences for these novel markers are freely available via a searchable web interface called Musa Marker Database.

Section: Resultsmentioning

confidence: 99%

Genome-Wide Novel Genic Microsatellite Marker Resource Development and Validation for Genetic Diversity and Population Structure Analysis of Banana

Bagchi

et al. 2020

Genes

“…SSR genotyping has been extensively used over the last two decades to study the Musa genus, resulting in a huge amount of SSR markers currently available. Recently, genome-scale analyses have been carried out to classify SSR markers according to their usability for breeding programmes (Biswas et al, 2015), and an online database that contains more than 341,000 putative SSR markers from M. acuminata, M. balbisiana and Musa itinerans has been developed (Arora et al, 2018). However, only 580 (.16%) of them have been experimentally validated (Arora et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, genome-scale analyses have been carried out to classify SSR markers according to their usability for breeding programmes (Biswas et al, 2015), and an online database that contains more than 341,000 putative SSR markers from M. acuminata, M. balbisiana and Musa itinerans has been developed (Arora et al, 2018). However, only 580 (.16%) of them have been experimentally validated (Arora et al, 2018). In the present work, a set of 14 SSR markers has been selected from bibliography (Amorim et al, 2012;Miller et al, 2010;Passos et al, 2012) hybrid cultivars (Tables 1 and 2; Figure 1).…”

Section: Discussionmentioning

confidence: 99%

Comparative assessment of microsatellite and retrotransposon‐based markers for genetic characterization of commercial banana cultivars (Musa spp.)

et al. 2021

Banana cultivars of agronomic interest have been genetically characterized using two different molecular markers. On the one hand, a panel of 14 trinucleotide single sequence repeats (SSRs or microsatellites) was optimized for homogeneous PCR conditions. It was tested with 50 individuals from seven cultivars, yielding 76 alleles and 5.4 ± 1.8 alleles per locus, while the presence of cultivar-exclusive alleles allowed the discrimination of all cultivars. On the other hand, a retrotransposon-based marker system named inter-primer binding site (iPBS) was implemented for the first time in the Musa genus. A total of 120 bands were detected in eight different Musa cultivars, from which 65.8% were polymorphic and 23.3% were cultivar exclusive. Both techniques allowed a cut-off identification of all cultivars studied, but overall, iPBS analysis was a more straightforward and economical choice. Despite the fact that we were unable to distinguish local banana varieties belonging to the same cultivar, new cultivar-specific molecular markers have been developed for the Musa genus, which could be used to guide new breeding programmes and maintain high quality of Plátano de Canarias.

“…Most of the molecular marker databases deal with SSR markers as described in an earlier section of this article. Although some of the existing marker databases integrate molecular markers from Musa sp., among them SSRome [23], PMDBase [26], Banana Genome Hub [27], and BanSatDb [28] marker databases are notable. SSRome and PMDBase only integrate SSR marker data from the Musa acuminata genome; BGH and TropGENE store only Musa SNP markers.…”

Section: Introductionmentioning

confidence: 99%

The Musa Marker Database: A Comprehensive Genomic Resource for the Improvement of the Musaceae Family

Biswas,

et al. 2024

Agronomy

Molecular markers, including Simple Sequence Repeat (SSR), Single Nucleotide Polymorphism (SNP), and Intron Length Polymorphism (ILP), are widely utilized in crop improvement and population genetics studies. However, these marker resources remain insufficient for Musa species. In this study, we developed genome-wide SSR, SNP, and ILP markers from Musa and its sister species, creating a comprehensive molecular marker repository for the improvement of Musa species. This database contains 2,115,474 SSR, 63,588 SNP, and 91,547 ILP markers developed from thirteen Musa species and two of its relative species. We found that 77% of the SSR loci are suitable for marker development; 38% of SNP markers originated from the genic region, and transition mutations (C↔T; A↔G) were more frequent than transversion. The database is freely accessible and follows a ‘three-tier architecture,’ organizing marker information in MySQL tables. It has a user-friendly interface, written in JavaScript, PHP, and HTML code. Users can employ flexible search parameters, including marker location in the chromosome, transferability, polymorphism, and functional annotation, among others. These distinctive features distinguish the Musa Marker Database (MMdb) from existing marker databases by offering a novel approach that is tailored to the precise needs of the Musa research community. Despite being an in silico method, searching for markers based on various attributes holds promise for Musa research. These markers serve various purposes, including germplasm characterization, gene discovery, population structure analysis, and QTL mapping.