RadishDB:Approach

From RadishDB

Contents 1 Sequencing goals 2 Target species and subspecies 3 Analysis goals 3.1 SSR from 5’ UTR 3.2 SSR from translated regions (EST-SSR), plus SNPs, CAPs, and dCAPs 3.3 Intron-spanning markers 4 References

Sequencing goals

We propose to produce full-length cDNA sequence libraries from the two named species of radish, the crop radish R. sativus and the native and weedy radish, R. raphanistrum. We will sequence, from both 5’ and 3’ ends, 25,000 clones from each of eight cDNA libraries, four from each species. Thus, a total of 200,000 clones will be sequenced from both ends, for a total of 400,000 reads. This should generate at least 60,000 unique cDNA sequences.

For more details, see the sequencing strategy section.

Target species and subspecies

• We are sequencing seven different strains of radish from two species:

Strain	Scientific name	Description
AR	Raphanus sativus convar. oleifera	Arena oil type. Michigan State University, East Lansing
RS	Raphanus sativus	Early Scarlet Globe, NK Lawn & Garden
RT	Raphanus sativus	Rat-Tail Radish #3870, John Scheepers Kitchen Garden Seeds
CS	Raphanus raphanistrum maritimus	Cesar Gomez-Campo, Escuela T.S. de Ing. Agronomos (UPM). Madrid Spain, collected from beach of Ris, Noja, Santander Province, in northern Spain ID 5843-81.
MS	Raphanus raphanistrum raphanistrum	Cesar Gomez-Campo, Escuela T.S. de Ing Agronomos (UPM), Madrid Spain, collected in Colmenar Viejo, just north of Madrid, from border of cereal field, ID 2240-73.
NY	Raphanus raphanistrum raphanistrum	1989 Field, collected by Jeff Conner
PB	Raphanus raphanistrum landra	Janine Vitou, CSIRO-Europe, France

Analysis goals

The resulting sequence data will be a valuable resource for researchers studying Brassicaceae species as well as for the comparative genomics community in general. In addition to marker identification, we will also initiate comparative analysis with several other plant genomes to generate insights on the evolution of plant genomes. Specifically, we will construct transcript assemblies (TAs), identify potential orthologous sequences from reference genomes including Arabidopsis, Brassica, poplar, and rice, determine the gene gain and loss patterns in various gene families, and examine the nature of selection on plant genes.

These sequences will also be mined to generate a variety of gene-based codominant markers and marker candidates including 5’UTR-SSRs, EST-SSRs, SNPs, CAPs, and dCAPs. We will sequence from both ends to maximize the numbers of full-length cDNAs recovered, as well as to maximize the numbers of highly polymorphic markers discovered. This work will generate or enable the generation of three general classes of markers, listed below in decreasing order of level of polymorphism and increasing level of transferability across species:

SSR from 5’ UTR

The 5’ UTR has been shown to be by far the richest source of SSR markers in Arabidopsis, with almost 2400 SSRs found per MB, compared with less than 1000/MB in introns, 3’UTR, and genomic DNA (1). Because these regions are untranslated, they should be at least as highly variable as SSR derived from genomic DNA, but they also should show lower transferability across species. Thus, these markers will be used for studies within radish, including within-population studies of the biologically important traits described above.

For within-population mapping of outbred species, the most highly polymorphic markers are necessary, which means SSRs are the markers of choice. SSRs derived from genomic DNA are notoriously difficult to transfer between even closely related species, especially in plants (2). Indeed, Conner’s lab screened 450 publicly available microsatellites from Brassica and found only about 25 that amplified well and were interpretable in radish. Of these, only 12 were informative in one outbred cross. Therefore, sequencing of radish directly is necessary to produce many informative SSRs. Besides serving as highly polymorphic markers for mapping and other studies in radish ecology and evolution, some of the SSRs we uncover may provide functional information as well, because recent research has shown that SSRs function in development and gene regulation (Karlin, 1996; Meloni, 1998; Fondon, 2004; Li, 2004).

SSR from translated regions (EST-SSR), plus SNPs, CAPs, and dCAPs

Based on studies from five cereal species (3) and six species and subspecies of Medicago (4), SSR markers located in coding regions (EST-SSR) should be both polymorphic (>70 were polymorphic in Medicago; 4), although not as polymorphic as SSR from the 5’UTR, and more transferable among closely related species than SSR from genomic DNA or UTR. Our sequencing should also uncover a large number of SNPs, many of which can be converted to CAPs and dCAPs markers (5). These should also be transferable among closely related species. Thus, these markers will be most useful for comparative mapping between radish and Brassica.

Intron-spanning markers

For comparative mapping with the more distantly related species in Arabidopsis and Capsella, primers located within exons but that span introns that vary in length across species (6) would be most useful. We will predict the position of radish introns by aligning radish sequences with a corresponding genomic sequence of Arabidopsis, and primers will be designed to anneal in exon sequences and to amplify across intron regions, which will likely harbor ample length variation across species. These primer sequences will be provided to the community for screening.

Thus, we will produce a range of markers useful for a range of studies, spanning within-population, among-population, and among closely and more distantly related species.

For more details, see the analysis section.

References

1. Lawson, M.J. and L. Zhang, Distinct patterns of SSR distribution in the Arabidopsis thaliana and rice genomes. Genome Biology, 2006. 7(2): p. R14.
2. Whitton, J., L.H. Rieseberg, and M.C. Ungerer, Microsatellite loci are not conserved across the Asteraceae. Molecular Biology and Evolution, 1997. 14(2): p. 204-209.
3. Kantety, R.V., et al., Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. Plant Molecular Biology, 2002. 48(5): p. 501-510.
4. Eujayl, I., et al., Medicago truncatula EST-SSRs reveal cross-species genetic markers for Medicago spp. Theoretical and Applied Genetics, 2004. 108(3): p. 414-422.
5. Michaels, S.D. and R.M. Amasino, A robust method for detecting single-nucleotide changes as polymorphic markers by PCR. Plant Journal, 1998. 14(3): p. 381-385.
6. Choi, H.K., et al., A sequence-based genetic map of Medicago truncatula and comparison of marker colinearity with M-sativa. Genetics, 2004. 166(3): p. 1463-1502.