SSRMMD: A Rapid and Accurate Algorithm for Mining SSR Feature Loci and Candidate Polymorphic SSRs Based on Assembled Sequences.
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ABSTRACT:
SUBMITTER: Gou X
PROVIDER: S-EPMC7398111 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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.
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Project description:Angelica dahurica is a widely grown plant species with multiple uses, especially in the medical field. However, the frequent introduction of A. dahurica to new areas has made it difficult to distinguish between varieties. Simple sequence repeats (SSRs) detected based on transcriptome analyses are very useful for constructing genetic maps and analyzing genetic diversity. They are also relevant for the molecular marker-assisted breeding of A. dahurica. We identified 33,724 genic SSR loci based on transcriptome sequencing data. A total of 114 primer pairs were designed for the SSR loci and were tested for their specificity and diversity. Ten SSR loci in untranslated regions were ultimately selected. Subsequently, 56 A. dahurica ecotypes collected from different regions were analyzed. The SSR loci comprised 2-8 alleles, with a mean of 5.2 alleles per locus. The polymorphic information content value and Shannon's information index were 0.6274-0.2702 (average of 0.4091) and 1.3040-0.5618 (average of 0.8475), respectively. Thus, the 10 novel SSRs identified in this study were almost in accordance with Harvey-Weinberg equilibrium and will be useful for analyzing A. dahurica genetic relationships. The results of this study confirm the potential value of transcriptome databases for the development of new SSR markers.
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Project description:Traditional methods for developing polymorphic microsatellite loci without reference sequences are time-consuming and labor-intensive, and the polymorphisms of simple sequence repeat (SSR) loci developed from expressed sequence tag (EST) databases are generally poor. To address this issue, in this study, we developed a new software (PSSRdt) and established an effective method for directly obtaining polymorphism details of SSR loci by analyzing diverse transcriptome data. The new method includes three steps, raw data processing, PSSRdt application, and loci extraction and verification. To test the practicality of the method, we successfully obtained 1940 potential polymorphic SSRs from the transcript dataset combined with 44 pea aphid transcriptomes. Fifty-two SSR loci obtained by the new method were selected for validating the polymorphic characteristics by genotyping in pea aphid individuals. The results showed that over 92% of SSR loci were polymorphic and 73.1% of loci were highly polymorphic. Our new software and method provide an innovative approach to microsatellite development based on RNA-seq data, and open a new path for the rapid mining of numerous loci with polymorphism to add to the body of research on microsatellites.
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Project description:One of the main challenges with bioinformatics software is that the size and complexity of datasets necessitate trading speed for accuracy, or completeness. To combat this problem of computational complexity, a plethora of heuristic algorithms have arisen that report a 'good enough' solution to biological questions. However, in instances such as Simple Sequence Repeats (SSRs), a 'good enough' solution may not accurately portray results in population genetics, phylogenetics and forensics, which require accurate SSRs to calculate intra- and inter-species interactions.We present Kmer-SSR, which finds all SSRs faster than most heuristic SSR identification algorithms in a parallelized, easy-to-use manner. The exhaustive Kmer-SSR option has 100% precision and 100% recall and accurately identifies every SSR of any specified length. To identify more biologically pertinent SSRs, we also developed several filters that allow users to easily view a subset of SSRs based on user input. Kmer-SSR, coupled with the filter options, accurately and intuitively identifies SSRs quickly and in a more user-friendly manner than any other SSR identification algorithm.The source code is freely available on GitHub at https://github.com/ridgelab/Kmer-SSR.perry.ridge@byu.edu.
| S-EPMC5860095 | biostudies-literature
Project description:Simple sequence repeats (SSR) - also known as microsatellites - have been used extensively in genetic analysis, fine mapping, quantitative trait locus (QTL) mapping, as well as marker-assisted selection (MAS) breeding and other techniques. Despite a plethora of studies reporting that perfect SSRs with stable motifs and flanking sequences are more efficient for genetic research, the lack of a high throughput technology for SSR genotyping has limited their use as genetic targets in many crops. In this study, we developed a technology called Target SSR-seq that combined the multiplexed amplification of perfect SSRs with high throughput sequencing. This method can genotype plenty of SSR loci in hundreds of samples with highly accurate results, due to the substantial coverage afforded by high throughput sequencing. We also detected 844 perfect SSRs based on 182 resequencing datasets in cucumber, of which 91 SSRs were selected for Target SSR-seq. Finally, 122 SSRs, including 31 SSRs for varieties identification, were used to genotype 382 key cucumber varieties readily available in Chinese markets using our Target SSR-seq method. Libraries of PCR products were constructed and then sequenced on the Illumina HiSeq X Ten platform. Bioinformatics analysis revealed that 111 filtered SSRs were accurately genotyped with an average coverage of 1289× at an extremely low cost; furthermore, 398 alleles were observed in 382 cucumber cultivars. Genetic analysis identified four populations: northern China type, southern China type, European type, and Xishuangbanna type. Moreover, we acquired a set of 16 core SSRs for the identification of 382 cucumber varieties, of which 42 were isolated as backbone cucumber varieties. This study demonstrated that Target SSR-seq is a novel and efficient method for genetic research.
| S-EPMC6492046 | biostudies-literature
Project description:Salt stress adversely affects the global wheat production and productivity. To improve salinity tolerance of crops, identification of robust molecular markers is highly imperative for development of salt-tolerant cultivars to mimic yield losses under saline conditions. In this study, we mined 171 salt-responsive genes (including 10 miRNAs) from bread wheat genome using the sequence information of functionally validated salt-responsive rice genes. Salt-stress, tissue and developmental stage-specific expression analysis of RNA-seq datasets revealed the constitutive as well as the inductive response of salt-responsive genes in different tissues of wheat. Fifty-four genotypes were phenotyped for salt stress tolerance. The stress tolerance index of the genotypes ranged from 0.30 to 3.18. In order to understand the genetic diversity, candidate gene based SSRs (cg-SSRs) and MIR gene based SSRs (miR-SSRs) were mined from 171 members of salt-responsive genes of wheat and validated among the contrasting panels of 54 tolerant as well as susceptible wheat genotypes. Among 53 SSR markers screened, 10 cg-SSRs and 8 miR-SSRs were found to be polymorphic. Polymorphic information content between the wheat genotypes ranged from 0.07 to 0.67, indicating the extant of wide genetic variation among the salt tolerant and susceptible genotypes at the DNA level. The genetic diversity analysis based on the allelic data grouped the wheat genotypes into three separate clusters of which single group encompassing most of the salt susceptible genotypes and two of them containing salt tolerance and moderately salt tolerance wheat genotypes were in congruence with penotypic data. Our study showed that both salt-responsive genes and miRNAs based SSRs were more diverse and can be effectively used for diversity analysis. This study reports the first extensive survey on genome-wide analysis, identification, development and validation of salt-responsive cg-SSRs and miR-SSRs in wheat. The information generated in the present study on genetic divergence among genotypes having a differential response to salt will help in the selection of suitable lines as parents for developing salt tolerant cultivars in wheat.
| S-EPMC7838269 | biostudies-literature
Project description:BackgroundGenes of the Major Histocompatibility Complex (MHC) are very popular genetic markers among evolutionary biologists because of their potential role in pathogen confrontation and sexual selection. However, MHC genotyping still remains challenging and time-consuming in spite of substantial methodological advances. Although computational haplotype inference has brought into focus interesting alternatives, high heterozygosity, extensive genetic variation and population admixture are known to cause inaccuracies. We have investigated the role of sample size, genetic polymorphism and genetic structuring on the performance of the popular Bayesian PHASE algorithm. To cover this aim, we took advantage of a large database of known genotypes (using traditional laboratory-based techniques) at single MHC class I (N = 56 individuals and 50 alleles) and MHC class II B (N = 103 individuals and 62 alleles) loci in the lesser kestrel Falco naumanni.FindingsAnalyses carried out over real MHC genotypes showed that the accuracy of gametic phase reconstruction improved with sample size as a result of the reduction in the allele to individual ratio. We then simulated different data sets introducing variations in this parameter to define an optimal ratio.ConclusionsOur results demonstrate a critical influence of the allele to individual ratio on PHASE performance. We found that a minimum allele to individual ratio (1:2) yielded 100% accuracy for both MHC loci. Sampling effort is therefore a crucial step to obtain reliable MHC haplotype reconstructions and must be accomplished accordingly to the degree of MHC polymorphism. We expect our findings provide a foothold into the design of straightforward and cost-effective genotyping strategies of those MHC loci from which locus-specific primers are available.
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Project description:Simple Sequence Repeats (SSRs) developed from Expressed Sequence Tags (ESTs), known as EST-SSRs are most widely used and potentially valuable source of gene based markers for their high levels of crosstaxon portability, rapid and less expensive development. The EST sequence information in the publicly available databases is increasing in a faster rate. The emerging computational approach provides a better alternative process of development of SSR markers from the ESTs than the conventional methods. In the present study, 12,851 EST sequences of Camellia sinensis, downloaded from National Center for Biotechnology Information (NCBI) were mined for the development of Microsatellites. 6148 (4779 singletons and 1369 contigs) non redundant EST sequences were found after preprocessing and assembly of these sequences using various computational tools. Out of total 3822.68 kb sequence examined, 1636 (26.61%) EST sequences containing 2371 SSRs were detected with a density of 1 SSR/1.61 kb leading to development of 245 primer pairs. These mined EST-SSR markers will help further in the study of variability, mapping, evolutionary relationship in Camellia sinensis. In addition, these developed SSRs can also be applied for various studies across species.
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Project description:Tibetan annual wild barley is rich in genetic variation. This study was aimed at the exploitation of new SSRs for the genetic diversity and phylogenetic analysis of wild barley by data mining. We developed 49 novel EST-SSRs and confirmed 20 genomic SSRs for 80 Tibetan annual wild barley and 16 cultivated barley accessions. A total of 213 alleles were generated from 69 loci with an average of 3.14 alleles per locus. The trimeric repeats were the most abundant motifs (40.82%) among the EST-SSRs, while the majority of the genomic SSRs were di-nuleotide repeats. The polymorphic information content (PIC) ranged from 0.08 to 0.75 with a mean of 0.46. Besides this, the expected heterozygosity (He) ranged from 0.0854 to 0.7842 with an average of 0.5279. Overall, the polymorphism of genomic SSRs was higher than that of EST-SSRs. Furthermore, the number of alleles and the PIC of wild barley were both higher than that of cultivated barley, being 3.12 vs 2.59 and 0.44 vs 0.37. Indicating more polymorphism existed in the Tibetan wild barley than in cultivated barley. The 96 accessions were divided into eight subpopulations based on 69 SSR markers, and the cultivated genotypes can be clearly separated from wild barleys. A total of 47 SSR-containing EST unigenes showed significant similarities to the known genes. These EST-SSR markers have potential for application in germplasm appraisal, genetic diversity and population structure analysis, facilitating marker-assisted breeding and crop improvement in barley.
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Project description:A variety of artificial spinning methods have been applied to produce regenerated silk fibers; however, how to spin regenerated silk fibers that retain the advantages of natural silks in terms of structural hierarchy and mechanical properties remains challenging. Here, we show a bioinspired approach to spin regenerated silk fibers. First, we develop a nematic silk microfibril solution, highly viscous and stable, by partially dissolving silk fibers into microfibrils. This solution maintains the hierarchical structures in natural silks and serves as spinning dope. It is then spun into regenerated silk fibers by direct extrusion in the air, offering a useful route to generate polymorphic and hierarchical regenerated silk fibers with physical properties beyond natural fiber construction. The materials maintain the structural hierarchy and mechanical properties of natural silks, including a modulus of 11?±?4?GPa, even higher than natural spider silk. It can further be functionalized with a conductive silk/carbon nanotube coating, responsive to changes in humidity and temperature.
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