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1.
Bioinformatics ; 25(19): 2581-7, 2009 Oct 01.
Article in English | MEDLINE | ID: mdl-19608707

ABSTRACT

MOTIVATION: Developmental transcriptional networks in plants and animals operate in both space and time. To understand these transcriptional networks it is essential to obtain whole-genome expression data at high spatiotemporal resolution. Substantial amounts of spatial and temporal microarray expression data previously have been obtained for the Arabidopsis root; however, these two dimensions of data have not been integrated thoroughly. Complicating this integration is the fact that these data are heterogeneous and incomplete, with observed expression levels representing complex spatial or temporal mixtures. RESULTS: Given these partial observations, we present a novel method for reconstructing integrated high-resolution spatiotemporal data. Our method is based on a new iterative algorithm for finding approximate roots to systems of bilinear equations. AVAILABILITY: Source code for solving bilinear equations is available at http://math.berkeley.edu/ approximately dustin/bilinear/. Visualizations of reconstructed patterns on a schematic Arabidopsis root are available at http://www.arexdb.org/.


Subject(s)
Computational Biology/methods , Gene Expression , Gene Expression Profiling
2.
BMC Plant Biol ; 8: 12, 2008 Jan 28.
Article in English | MEDLINE | ID: mdl-18226250

ABSTRACT

BACKGROUND: Until recently, only a small number of low- and mid-throughput methods have been used for single nucleotide polymorphism (SNP) discovery and genotyping in grapevine (Vitis vinifera L.). However, following completion of the sequence of the highly heterozygous genome of Pinot Noir, it has been possible to identify millions of electronic SNPs (eSNPs) thus providing a valuable source for high-throughput genotyping methods. RESULTS: Herein we report the first application of the SNPlexgenotyping system in grapevine aiming at the anchoring of an eukaryotic genome. This approach combines robust SNP detection with automated assay readout and data analysis. 813 candidate eSNPs were developed from non-repetitive contigs of the assembled genome of Pinot Noir and tested in 90 progeny of Syrah x Pinot Noir cross. 563 new SNP-based markers were obtained and mapped. The efficiency rate of 69% was enhanced to 80% when multiple displacement amplification (MDA) methods were used for preparation of genomic DNA for the SNPlex assay. CONCLUSION: Unlike other SNP genotyping methods used to investigate thousands of SNPs in a few genotypes, or a few SNPs in around a thousand genotypes, the SNPlex genotyping system represents a good compromise to investigate several hundred SNPs in a hundred or more samples simultaneously. Therefore, the use of the SNPlex assay, coupled with whole genome amplification (WGA), is a good solution for future applications in well-equipped laboratories.


Subject(s)
Genetic Testing/methods , Polymorphism, Single Nucleotide , Vitis/genetics , Base Sequence , DNA, Plant/genetics , Expressed Sequence Tags , Genome, Plant , Genomics/methods , Genotype
3.
PLoS One ; 2(12): e1326, 2007 Dec 19.
Article in English | MEDLINE | ID: mdl-18094749

ABSTRACT

BACKGROUND: Worldwide, grapes and their derived products have a large market. The cultivated grape species Vitis vinifera has potential to become a model for fruit trees genetics. Like many plant species, it is highly heterozygous, which is an additional challenge to modern whole genome shotgun sequencing. In this paper a high quality draft genome sequence of a cultivated clone of V. vinifera Pinot Noir is presented. PRINCIPAL FINDINGS: We estimate the genome size of V. vinifera to be 504.6 Mb. Genomic sequences corresponding to 477.1 Mb were assembled in 2,093 metacontigs and 435.1 Mb were anchored to the 19 linkage groups (LGs). The number of predicted genes is 29,585, of which 96.1% were assigned to LGs. This assembly of the grape genome provides candidate genes implicated in traits relevant to grapevine cultivation, such as those influencing wine quality, via secondary metabolites, and those connected with the extreme susceptibility of grape to pathogens. Single nucleotide polymorphism (SNP) distribution was consistent with a diffuse haplotype structure across the genome. Of around 2,000,000 SNPs, 1,751,176 were mapped to chromosomes and one or more of them were identified in 86.7% of anchored genes. The relative age of grape duplicated genes was estimated and this made possible to reveal a relatively recent Vitis-specific large scale duplication event concerning at least 10 chromosomes (duplication not reported before). CONCLUSIONS: Sanger shotgun sequencing and highly efficient sequencing by synthesis (SBS), together with dedicated assembly programs, resolved a complex heterozygous genome. A consensus sequence of the genome and a set of mapped marker loci were generated. Homologous chromosomes of Pinot Noir differ by 11.2% of their DNA (hemizygous DNA plus chromosomal gaps). SNP markers are offered as a tool with the potential of introducing a new era in the molecular breeding of grape.


Subject(s)
Consensus Sequence , Genome, Plant , Heterozygote , Vitis/genetics , Chromosomes, Plant , DNA, Plant/genetics , Evolution, Molecular , Phenols/metabolism , Plant Diseases/genetics , Polymorphism, Single Nucleotide , Terpenes/metabolism , Transcription Factors/metabolism , Vitis/metabolism
4.
Genetics ; 176(4): 2637-50, 2007 Aug.
Article in English | MEDLINE | ID: mdl-17603124

ABSTRACT

The construction of a dense genetic map for Vitis vinifera and its anchoring to a BAC-based physical map is described: it includes 994 loci mapped onto 19 linkage groups, corresponding to the basic chromosome number of Vitis. Spanning 1245 cM with an average distance of 1.3 cM between adjacent markers, the map was generated from the segregation of 483 single-nucleotide polymorphism (SNP)-based genetic markers, 132 simple sequence repeats (SSRs), and 379 AFLP markers in a mapping population of 94 F(1) individuals derived from a V. vinifera cross of the cultivars Syrah and Pinot Noir. Of these markers, 623 were anchored to 367 contigs that are included in a physical map produced from the same clone of Pinot Noir and covering 352 Mbp. On the basis of contigs containing two or more genetically mapped markers, region-dependent estimations of physical and recombinational distances are presented. The markers used in this study include 118 SSRs common to an integrated map derived from five segregating populations of V. vinifera. The positions of these SSR markers in the two maps are conserved across all Vitis linkage groups. The addition of SNP-based markers introduces polymorphisms that are easy to database, are useful for evolutionary studies, and significantly increase the density of the map. The map provides the most comprehensive view of the Vitis genome reported to date and will be relevant for future studies on structural and functional genomics and genetic improvement.


Subject(s)
Chromosome Mapping , Vitis/genetics , Chromosomes, Artificial, Bacterial/genetics , Contig Mapping , Genetic Markers , Genome, Plant , Polymorphism, Single Nucleotide
5.
Genetics ; 176(4): 2521-7, 2007 Aug.
Article in English | MEDLINE | ID: mdl-17277374

ABSTRACT

Genetic maps are built using the genotypes of many related individuals. Genotyping errors in these data sets can distort genetic maps, especially by inflating the distances. We have extended the traditional likelihood model used for genetic mapping to include the possibility of genotyping errors. Each individual marker is assigned an error rate, which is inferred from the data, just as the genetic distances are. We have developed a software package, called TMAP, which uses this model to find maximum-likelihood maps for phase-known pedigrees. We have tested our methods using a data set in Vitis and on simulated data and confirmed that our method dramatically reduces the inflationary effect caused by increasing the number of markers and leads to more accurate orders.


Subject(s)
Chromosome Mapping/statistics & numerical data , Software , Algorithms , Data Interpretation, Statistical , Genetic Markers , Genotype , Likelihood Functions , Models, Genetic , Monte Carlo Method
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