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1.  Deep resequencing reveals allelic variation in Sesamum indicum 
BMC Plant Biology  2014;14:225.
Characterization of genome-wide patterns of allelic variation and linkage disequilibrium can be used to detect reliable phenotype–genotype associations and signatures of molecular selection. However, the use of Sesamum indicum germplasm for breeding is limited by the lack of polymorphism data.
Here we describe the massively parallel resequencing of 29 sesame strains from 12 countries at a depth of ≥ 13-fold coverage for each of the samples tested. We detected an average of 127,347 SNPs, 17,961 small InDels, and 9,266 structural variants per sample. The population SNP rate, population diversity (π) and Watterson’s estimator of segregating sites (θw) were estimated at 8.6 × 10-3, 2.5 × 10-3 and 3.0 × 10-3 bp-1, respectively. Of these SNPs, 23.2% were located within coding regions. Polymorphism patterns were nonrandom among gene families, with genes mediating interactions with the biotic or abiotic environment exhibiting high levels of polymorphism. The linkage disequilibrium (LD) decay distance was estimated at 150 kb, with no distinct structure observed in the population. Phylogenetic relationships between each of the 29 sesame strains were consistent with the hypothesis of sesame originating on the Indian subcontinent. In addition, we proposed novel roles for adenylate isopentenyltransferase (ITP) genes in determining the number of flowers per leaf axil of sesame by mediating zeatin biosynthesis.
This study represents the first report of genome-wide patterns of genetic variation in sesame. The high LD distance and abundant polymorphisms described here increase our understanding of the forces shaping population-wide sequence variation in sesame and will be a valuable resource for future gene–phenotype and genome-wide association studies (GWAS).
Electronic supplementary material
The online version of this article (doi:10.1186/s12870-014-0225-3) contains supplementary material, which is available to authorized users.
PMCID: PMC4148021  PMID: 25138716
Sesamum indicum; Resequencing; Variation; Linkage disequilibrium
2.  Genome sequencing of the high oil crop sesame provides insight into oil biosynthesis 
Genome Biology  2014;15(2):R39.
Sesame, Sesamum indicum L., is considered the queen of oilseeds for its high oil content and quality, and is grown widely in tropical and subtropical areas as an important source of oil and protein. However, the molecular biology of sesame is largely unexplored.
Here, we report a high-quality genome sequence of sesame assembled de novo with a contig N50 of 52.2 kb and a scaffold N50 of 2.1 Mb, containing an estimated 27,148 genes. The results reveal novel, independent whole genome duplication and the absence of the Toll/interleukin-1 receptor domain in resistance genes. Candidate genes and oil biosynthetic pathways contributing to high oil content were discovered by comparative genomic and transcriptomic analyses. These revealed the expansion of type 1 lipid transfer genes by tandem duplication, the contraction of lipid degradation genes, and the differential expression of essential genes in the triacylglycerol biosynthesis pathway, particularly in the early stage of seed development. Resequencing data in 29 sesame accessions from 12 countries suggested that the high genetic diversity of lipid-related genes might be associated with the wide variation in oil content. Additionally, the results shed light on the pivotal stage of seed development, oil accumulation and potential key genes for sesamin production, an important pharmacological constituent of sesame.
As an important species from the order Lamiales and a high oil crop, the sesame genome will facilitate future research on the evolution of eudicots, as well as the study of lipid biosynthesis and potential genetic improvement of sesame.
PMCID: PMC4053841  PMID: 24576357
3.  The effectiveness of a combined exercise intervention on physical fitness factors related to falls in community-dwelling older adults 
This study aimed to evaluate the effectiveness of an innovative exercise program on muscle strength, balance, and gait kinematics in elderly community-dwellers. The exercise program included strength and balance training and the 8-form Tai Chi Chuan. The measurements were carried out at baseline and 12 weeks, and consisted of four physical performance tests, joint isokinetic strength tests, and three-dimensional gait analysis. Fifty-six community-dwelling older adults aged 60–80 years old were randomly assigned to an intervention or control group. After 12 weeks, the intervention group showed a 17.6% improvement in the timed up and go test, accompanied by a 54.7% increase in the 30-second chair stand test score. Significant increases in the score of star excursion balance tests, and the strength of the extensor and flexor muscles at knee and ankle joints were also observed. In addition, the intervention group walked at a faster speed with a longer step length, shorter support phase, and a greater sagittal plane range of motion at the hip and ankle joints. No statistical improvements were seen in the control group. This study provided an effective, evidence-based falls prevention program that can be implemented in community settings to improve physical fitness and reduce fall risks among community-dwelling older adults. The star excursion balance test could be a sensitive measure of physical performance for fall risk assessment in older people.
PMCID: PMC3894141  PMID: 24453483
Tai Chi Chuan; resistance training; balance; fall prevention; fall-related risk factors
4.  The Application of Computer Musculoskeletal Modeling and Simulation to Investigate Compressive Tibiofemoral Force and Muscle Functions in Obese Children 
This study aimed to utilize musculoskeletal modelling and simulation to investigate the compressive tibiofemoral force and individual muscle function in obese children. We generated a 3D muscle-driven simulation of eight obese and eight normal-weight boys walking at their self-selected speed. The compressive tibiofemoral force and individual muscle contribution to the support and progression accelerations of center of mass (COM) were computed for each participant based on the subject-specific model. The simulated results were verified by comparing them to the experimental kinematics and EMG data. We found a linear relationship between the average self-selected speed and the normalized peak compressive tibiofemoral force (R2 = 0.611). The activity of the quadriceps contributed the most to the peak compressive tibiofemoral force during the stance phase. Obese children and nonobese children use similar muscles to support and accelerate the body COM, but nonobese children had significantly greater contributions of individual muscles. The obese children may therefore adopt a compensation strategy to avoid increasing joint loads and muscle requirements during walking. The absolute compressive tibiofemoral force and muscle forces were still greater in obese children. The long-term biomechanical adaptations of the musculoskeletal system to accommodate the excess body weight during walking are a concern.
PMCID: PMC3833069  PMID: 24288573
5.  Construction of a high-density genetic map for sesame based on large scale marker development by specific length amplified fragment (SLAF) sequencing 
BMC Plant Biology  2013;13:141.
The genetics and molecular biology of sesame has only recently begun to be studied even though sesame is an important oil seed crop. A high-density genetic map for sesame has not been published yet due to a lack of sufficient molecular markers. Specific length amplified fragment sequencing (SLAF-seq) is a recently developed high-resolution strategy for large-scale de novo SNP discovery and genotyping. SLAF-seq was employed in this study to obtain sufficient markers to construct a high-density genetic map for sesame.
In total, 28.21 Gb of data containing 201,488,285 pair-end reads was obtained after sequencing. The average coverage for each SLAF marker was 23.48-fold in the male parent, 23.38-fold in the female parent, and 14.46-fold average in each F2 individual. In total, 71,793 high-quality SLAFs were detected of which 3,673 SLAFs were polymorphic and 1,272 of the polymorphic markers met the requirements for use in the construction of a genetic map. The final map included 1,233 markers on the 15 linkage groups (LGs) and was 1,474.87 cM in length with an average distance of 1.20 cM between adjacent markers. To our knowledge, this map is the densest genetic linkage map to date for sesame. 'SNP_only’ markers accounted for 87.51% of the markers on the map. A total of 205 markers on the map showed significant (P < 0.05) segregation distortion.
We report here the first high-density genetic map for sesame. The map was constructed using an F2 population and the SLAF-seq approach, which allowed the efficient development of a large number of polymorphic markers in a short time. Results of this study will not only provide a platform for gene/QTL fine mapping, map-based gene isolation, and molecular breeding for sesame, but will also serve as a reference for positioning sequence scaffolds on a physical map, to assist in the process of assembling the sesame genome sequence.
PMCID: PMC3852768  PMID: 24060091
Sesamum indicum L; High-density; Genetic map; Linkage analysis; Sequencing
6.  Genetic diversity assessment of sesame core collection in China by phenotype and molecular markers and extraction of a mini-core collection 
BMC Genetics  2012;13:102.
Sesame (Sesamum indicum L.) is one of the four major oil crops in China. A sesame core collection (CC) was established in China in 2000, but no complete study on its genetic diversity has been carried out at either the phenotypic or molecular level. To provide technical guidance, a theoretical basis for further collection, effective protection, reasonable application, and a complete analysis of sesame genetic resources, a genetic diversity assessment of the sesame CC in China was conducted using phenotypic and molecular data and by extracting a sesame mini-core collection (MC).
Results from a genetic diversity assessment of sesame CC in China were significantly inconsistent at the phenotypic and molecular levels. A Mantel test revealed the insignificant correlation between phenotype and molecular marker information (r = 0.0043, t = 0.1320, P = 0.5525). The Shannon-Weaver diversity index (I) and Nei genetic diversity index (h) were higher (I = 0.9537, h = 0.5490) when calculated using phenotypic data from the CC than when using molecular data (I = 0.3467, h = 0.2218). A mini-core collection (MC) containing 184 accessions was extracted based on both phenotypic and molecular data, with a low mean difference percentage (MD, 1.64%), low variance difference percentage (VD, 22.58%), large variable rate of coefficient of variance (VR, 114.86%), and large coincidence rate of range (CR, 95.76%). For molecular data, the diversity indices and the polymorphism information content (PIC) for the MC were significantly higher than for the CC. Compared to an alternative random sampling strategy, the advantages of capturing genetic diversity and validation by extracting a MC using an advanced maximization strategy were proven.
This study provides a comprehensive characterization of the phenotypic and molecular genetic diversities of the sesame CC in China. A MC was extracted using both phenotypic and molecular data. Low MD% and VD%, and large VR% and CR% suggested that the MC provides a good representation of the genetic diversity of the original CC. The MC was more genetically diverse with higher diversity indices and a higher PIC value than the CC. A MC may aid in reasonably and efficiently selecting materials for sesame breeding and for genotypic biological studies, and may also be used as a population for association mapping in sesame.
PMCID: PMC3574832  PMID: 23153260
Genetic diversity; Assessment; Sesame; Core collection; Mini-core collection
7.  Characterization of the sesame (Sesamum indicum L.) global transcriptome using Illumina paired-end sequencing and development of EST-SSR markers 
BMC Genomics  2011;12:451.
Sesame is an important oil crop, but limited transcriptomic and genomic data are currently available. This information is essential to clarify the fatty acid and lignan biosynthesis molecular mechanism. In addition, a shortage of sesame molecular markers limits the efficiency and accuracy of genetic breeding. High-throughput transcriptomic sequencing is essential to generate a large transcriptome sequence dataset for gene discovery and molecular marker development.
Sesame transcriptomes from five tissues were sequenced using Illumina paired-end sequencing technology. The cleaned raw reads were assembled into a total of 86,222 unigenes with an average length of 629 bp. Of the unigenes, 46,584 (54.03%) had significant similarity with proteins in the NCBI nonredundant protein database and Swiss-Prot database (E-value < 10-5). Of these annotated unigenes, 10,805 and 27,588 unigenes were assigned to gene ontology categories and clusters of orthologous groups, respectively. In total, 22,003 (25.52%) unigenes were mapped onto 119 pathways using the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG). Furthermore, 44,750 unigenes showed homology to 15,460 Arabidopsis genes based on BLASTx analysis against The Arabidopsis Information Resource (TAIR, Version 10) and revealed relatively high gene coverage. In total, 7,702 unigenes were converted into SSR markers (EST-SSR). Dinucleotide SSRs were the dominant repeat motif (67.07%, 5,166), followed by trinucleotide (24.89%, 1,917), tetranucleotide (4.31%, 332), hexanucleotide (2.62%, 202), and pentanucleotide (1.10%, 85) SSRs. AG/CT (46.29%) was the dominant repeat motif, followed by AC/GT (16.07%), AT/AT (10.53%), AAG/CTT (6.23%), and AGG/CCT (3.39%). Fifty EST-SSRs were randomly selected to validate amplification and to determine the degree of polymorphism in the genomic DNA pools. Forty primer pairs successfully amplified DNA fragments and detected significant amounts of polymorphism among 24 sesame accessions.
This study demonstrates that Illumina paired-end sequencing is a fast and cost-effective approach to gene discovery and molecular marker development in non-model organisms. Our results provide a comprehensive sequence resource for sesame research.
PMCID: PMC3184296  PMID: 21929789
8.  An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions 
Bioinformatics  2009;25(19):2478-2485.
Motivation: In both genome-wide association studies (GWAS) and pathway analysis, the modest sample size relative to the number of genetic markers presents formidable computational, statistical and methodological challenges for accurately identifying markers/interactions and for building phenotype-predictive models.
Results: We address these objectives via maximum entropy conditional probability modeling (MECPM), coupled with a novel model structure search. Unlike neural networks and support vector machines (SVMs), MECPM makes explicit and is determined by the interactions that confer phenotype-predictive power. Our method identifies both a marker subset and the multiple k-way interactions between these markers. Additional key aspects are: (i) evaluation of a select subset of up to five-way interactions while retaining relatively low complexity; (ii) flexible single nucleotide polymorphism (SNP) coding (dominant, recessive) within each interaction; (iii) no mathematical interaction form assumed; (iv) model structure and order selection based on the Bayesian Information Criterion, which fairly compares interactions at different orders and automatically sets the experiment-wide significance level; (v) MECPM directly yields a phenotype-predictive model. MECPM was compared with a panel of methods on datasets with up to 1000 SNPs and up to eight embedded penetrance function (i.e. ground-truth) interactions, including a five-way, involving less than 20 SNPs. MECPM achieved improved sensitivity and specificity for detecting both ground-truth markers and interactions, compared with previous methods.
Supplementary information:Supplementary data are available at Bioinformatics online.
PMCID: PMC3140808  PMID: 19608708

Results 1-8 (8)