Predicting polymorphic EST-SSRs in silico

Comparative genomic study on the complete plastomes of four officinal Ardisia species in China

Ardisia Sw. (Primulaceae) is naturally distributed in tropical and subtropical areas. Most of them possess edible and medicinal values and are popular in clinical and daily use in China. However, ambiguous species delineation and genetic information limit the development and utilization of this genus. In this study, the chloroplast genomes of four Ardisia species, namely A. gigantifolia Stapf, A. crenata Sims, A. villosa Roxb. and A. mamillata Hance, were sequenced, annotated, and analyzed comparatively. All the four chloroplast genomes possess a typical quadripartite structure, and each of the genomes is about 156 Kb in size. The structure and gene content of the Ardisia plastomes were conservative and showed low sequence divergence. Furthermore, we identified five mutation hotspots as candidate DNA barcodes for Ardisia, namely, trnT-psbD, ndhF-rpl32, rpl32-ccsA, ccsA-ndhD and ycf1. Phylogenetic analysis based on the whole-chloroplast genomes data showed that Ardisia was sister to Tapeinosperma Hook. f. In addition, the results revealed a great topological profile of Ardisia's with strong support values, which matches their geographical distribution patterns. Summarily, our results provide useful information for investigations on taxonomic differences, molecular identification, and phylogenetic relationships of Ardisia plants.

SSRome: an integrated database and pipelines for exploring microsatellites in all organisms

Over the past decade, many databases focusing on microsatellite mining on a genomic scale were released online with at least one of the following major deficiencies: (i) lacking the classification of microsatellites as genic or non-genic, (ii) not comparing microsatellite motifs at both genic and non-genic levels in order to identify unique motifs for each class or (iii) missing SSR marker development. In this study, we have developed ‘SSRome’ as a web-based, user-friendly, comprehensive and dynamic database with pipelines for exploring microsatellites in 6533 organisms. In the SSRome database, 158 million microsatellite motifs are identified across all taxa, in addition to all the mitochondrial and chloroplast genomes and expressed sequence tags available from NCBI. Moreover, 45.1 million microsatellite markers were developed and classified as genic or non-genic. All the stored motif and marker datasets can be downloaded freely. In addition, SSRome provides three user-friendly tools to identify, classify and compare motifs on either a genome- or transcriptome-wide scale. With the implementation of PHP, HTML and JavaScript, users can upload their data for analysis via a user-friendly GUI. SSRome represents a powerful database and mega-tool that will assist researchers in developing and dissecting microsatellite markers on a high-throughput scale.

IDSSR: An Efficient Pipeline for Identifying Polymorphic Microsatellites from a Single Genome Sequence

Simple sequence repeats (SSRs) are known as microsatellites, and consist of tandem 1–6-base motifs. They have become one of the most popular molecular markers, and are widely used in molecular ecology, conservation biology, molecular breeding, and many other fields. Previously reported methods identify monomorphic and polymorphic SSRs and determine the polymorphic SSRs via experimental validation, which is potentially time-consuming and costly. Herein, we present a new strategy named insertion/deletion (INDEL) SSR (IDSSR) to identify polymorphic SSRs by integrating SSRs with nucleotide insertions/deletions (INDEL) solely based on a single genome sequence and the sequenced pair-end reads. These INDEL indexes and polymorphic SSRs were identified, as well as the number of repeats, repeat motifs, chromosome location, annealing temperature, and primer sequences, enabling future experimental approaches to determine the correctness and polymorphism. Experimental validation with the giant panda demonstrated that our method has high reliability and stability. The efficient SSR pipeline would help researchers obtain high-quality genetic markers for plants and animals of interest, save labor, and reduce costly marker-screening experiments. IDSSR is freely available at https://github.com/Allsummerking/IDSSR.

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

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.

Functional marker detection and analysis on a comprehensive transcriptome of large yellow croaker by next generation sequencing

Large yellow croaker (Larimichthys crocea) is an important economic fish in China and Eastern Asia. Because of the exhaustive fishing and overdense aquaculture, the wild population and the mariculture of the species are facing serious challenges on germplasm degeneration and susceptibility to infectious disease agents. However, a comprehensive transcriptome from multi-tissues of the species has not been reported and functional molecular markers have not yet been detected and analyzed. In this work, we applied RNA-seq with the Illumina Hiseq2000 platform for a multi-tissue sample of large yellow croaker and assembled the transcriptome into 88,103 transcripts. Of them, 52,782 transcripts have been successfully annotated by nt/nr, InterPro, GO and KEGG database. Comparing with public fish proteins, we have found that 34,576 protein coding transcripts are shared in large yellow croaker with zebrafish, medaka, pufferfish, and stickleback. For functional markers, we have discovered 1,276 polymorphic SSRs and 261, 000 SNPs. The functional impact analysis of SNPs showed that the majority (~75%) of small variants cause synonymous mutations in proteins, followed by variations in 3' UTR region. The functional enrichment analysis illuminated that transcripts involved in DNA bindings, enzyme activities, and signal pathways prominently exhibit less single-nucleotide variants but genes for the constituent of the muscular tissue, the cytoskeleton, and the immunity system contain more frequent SNP mutations, which may reflect the structural and functional selections of the translated proteins. This is the first work for the high-throughput detection and analysis of functional polymorphic SSR and SNP markers in a comprehensive transcriptome of large yellow croaker. Our study provides valuable transcript sequence and functional marker resources for the quantitative trait locus (QTL) identification and molecular selection of the species in the research community.

Bioinformatics: identification of markers from next-generation sequence data

With the advent of sequencing technology, next-generation sequencing (NGS) technology has dramatically revolutionized plant genomics. NGS technology combined with new software tools enables the discovery, validation, and assessment of genetic markers on a large scale. Among different markers systems, simple sequence repeats (SSRs) and Single nucleotide polymorphisms (SNPs) are the markers of choice for genetics and plant breeding. SSR markers have been a choice for large-scale characterization of germplasm collections, construction of genetic maps, and QTL identification. Similarly, SNPs are the most abundant genetic variations with higher frequencies throughout the genome of plant species. This chapter discusses various tools available for genome assembly and widely focuses on SSR and SNP marker discovery.

CandiSSR: An Efficient Pipeline used for Identifying Candidate Polymorphic SSRs Based on Multiple Assembled Sequences

Simple sequence repeats (SSRs), also known as microsatellites, are ubiquitous short tandem duplications commonly found in genomes and/or transcriptomes of diverse organisms. They represent one of the most powerful molecular markers for genetic analysis and breeding programs because of their high mutation rate and neutral evolution. However, traditionally experimental screening of the SSR polymorphic status and their subsequent applicability to genetic studies are extremely labor-intensive and time-consuming. Thankfully, the recently decreased costs of next generation sequencing and increasing availability of large genome and/or transcriptome sequences have provided an excellent opportunity and sources for large-scale mining this type of molecular markers. However, current tools are limited. Thus we here developed a new pipeline, CandiSSR, to identify candidate polymorphic SSRs (PolySSRs) based on the multiple assembled sequences. The pipeline allows users to identify putative PolySSRs not only from the transcriptome datasets but also from multiple assembled genome sequences. In addition, two confidence metrics including standard deviation and missing rate of the SSR repetitions are provided to systematically assess the feasibility of the detected PolySSRs for subsequent application to genetic characterization. Meanwhile, primer pairs for each identified PolySSR are also automatically designed and further evaluated by the global sequence similarities of the primer-binding region, ensuring the successful rate of the marker development. Screening rice genomes with CandiSSR and subsequent experimental validation showed an accuracy rate of over 90%. Besides, the application of CandiSSR has successfully identified a large number of PolySSRs in the Arabidopsis genomes and Camellia transcriptomes. CandiSSR and the PolySSR marker sources are publicly available at: http://www.plantkingdomgdb.com/CandiSSR/index.html.

Rediscovering medicinal plants' potential with OMICS: microsatellite survey in expressed sequence tags of eleven traditional plants with potent antidiabetic properties

Herbal medicines and traditionally used medicinal plants present an untapped potential for novel molecular target discovery using systems science and OMICS biotechnology driven strategies. Since up to 40% of the world's poor people have no access to government health services, traditional and folk medicines are often the only therapeutics available to them. In this vein, North East (NE) India is recognized for its rich bioresources. As part of the Indo-Burma hotspot, it is regarded as an epicenter of biodiversity for several plants having myriad traditional uses, including medicinal use. However, the improvement of these valuable bioresources through molecular breeding strategies, for example, using genic microsatellites or Simple Sequence Repeats (SSRs) or Expressed Sequence Tags (ESTs)-derived SSRs has not been fully utilized in large scale to date. In this study, we identified a total of 47,700 microsatellites from 109,609 ESTs of 11 medicinal plants (pineapple, papaya, noyontara, bitter orange, bermuda brass, ratalu, barbados nut, mango, mulberry, lotus, and guduchi) having proven antidiabetic properties. A total of 58,159 primer pairs were designed for the non-redundant 8060 SSR-positive ESTs and putative functions were assigned to 4483 unique contigs. Among the identified microsatellites, excluding mononucleotide repeats, di-/trinucleotides are predominant, among which repeat motifs of AG/CT and AAG/CTT were most abundant. Similarity search of SSR containing ESTs and antidiabetic gene sequences revealed 11 microsatellites linked to antidiabetic genes in five plants. GO term enrichment analysis revealed a total of 80 enriched GO terms widely distributed in 53 biological processes, 17 molecular functions, and 10 cellular components associated with the 11 markers. The present study therefore provides concrete insights into the frequency and distribution of SSRs in important medicinal resources. The microsatellite markers reported here markedly add to the genetic stock for cross transferability in these plants and the literature on biomarkers and novel drug discovery for common chronic diseases such as diabetes.

Challenges in analysis and interpretation of microsatellite data for population genetic studies

Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (F ST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.

Next generation characterisation of cereal genomes for marker discovery

Cereal crops form the bulk of the world’s food sources, and thus their importance cannot be understated. Crop breeding programs increasingly rely on high-resolution molecular genetic markers to accelerate the breeding process. The development of these markers is hampered by the complexity of some of the major cereal crop genomes, as well as the time and cost required. In this review, we address current and future methods available for the characterisation of cereal genomes, with an emphasis on faster and more cost effective approaches for genome sequencing and the development of markers for trait association and marker assisted selection (MAS) in crop breeding programs.