Nucleic Acids Research Advance Access originally published online on October 14, 2008
Nucleic Acids Research 2009 37(Database issue):D951-D953; doi:10.1093/nar/gkn650
Nucleic Acids Research, 2009, Vol. 37, Database issue D951-D953
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
AutoSNPdb: an annotated single nucleotide polymorphism database for crop plants
Chris Duran1,2,
Nikki Appleby1,2,
Terry Clark1,2,
David Wood1,3,
Michael Imelfort1,2,
Jacqueline Batley1,4 and
David Edwards1,2,*
1Australian Centre for Plant Functional Genomics, Brisbane, School of Land, Crop and Food Sciences, 2The Institute for Molecular Bioscience, 3Queensland Facility for Advanced Bioinformatics, The Institute for Molecular Bioscience, ARC Centre of Excellence in Bioinformatics and 4ARC Centre of Excellence for Integrative Legume Research, University of Queensland, Brisbane, QLD 4072, Australia
*To whom correspondence should be addressed. Tel: +61 (0)7 3346 2615; Fax: +61 (0)7 3346 2101; Email: Dave.Edwards{at}uq.edu.au
Received August 12, 2008. Revised September 15, 2008. Accepted September 18, 2008.
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ABSTRACT
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Single nucleotide polymorphisms (SNPs) may be considered the
ultimate genetic marker as they represent the finest resolution
of a DNA sequence (a single nucleotide), are generally abundant
in populations and have a low mutation rate. Analysis of assembled
EST sequence data provides a cost-effective means to identify
large numbers of SNPs associated with functional genes. We have
developed an integrated SNP discovery pipeline, which identifies
SNPs from assembled EST sequences. The results are maintained
in a custom relational database along with EST source and annotation
information. The current database hosts data for the important
crops rice, barley and Brassica. Users may rapidly identify
polymorphic sequences of interest through BLAST sequence comparison,
keyword searches of annotations derived from UniRef90 and GenBank
comparisons, GO annotations or in genes corresponding to syntenic
regions of reference genomes. In addition, SNPs between specific
varieties may be identified for targeted mapping and association
studies. SNPs are viewed using a user-friendly graphical interface.
The database is freely accessible at
http://autosnpdb.qfab.org.au/.
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INTRODUCTION
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Molecular genetic markers describe genetic variations and provide
a link between observed phenotypes and the underlying genotype.
The development of high-throughput methods for the detection
of single nucleotide polymorphisms (SNPs) and small insertion/deletions
(indels) has led to a revolution in their use as molecular markers.
SNPs may be considered the ultimate genetic marker as they represent
the finest resolution of a DNA sequence, are generally abundant
in populations and have a low mutation rate (
1). However, SNP
markers can be costly to develop, especially where re-sequencing
from multiple individuals is required. The mining of readily
available sequence data significantly reduces the costs associated
with SNP discovery (
2). The principal challenge in SNP discovery
remains the discrimination between true genetic polymorphisms
and the often more abundant sequence errors. Where sequence
trace files are available to filter polymorphisms in traces
of dubious quality, these can be used to differentiate between
true SNPs and sequence error (
3). Where trace files are unavailable,
the identification of sequence errors can be based on two further
methods to determine SNP confidence: redundancy of the polymorphism
in a sequence alignment, and co-segregation of putative SNPs
with haplotype. The frequency of occurrence of a polymorphism
at a particular locus provides a measure of confidence that
the SNP represents a true polymorphism; this is referred to
as the SNP redundancy score. In addition, true SNPs that represent
divergence between homologous genes co-segregate to define a
conserved haplotype. A co-segregation score based on whether
a SNP position contributes to defining a haplotype provides
a second independent measure of SNP confidence. The SNP redundancy
score and co-segregation score together provide an effective
means for estimating confidence in the validity of SNPs independently
of sequence trace files (
4–6).
We have combined SNP discovery software and sequence annotation within the relational database schema of autoSNPdb to enable the efficient identification of SNP and indel polymorphisms related to specific genes or traits. Here, we present the application of autoSNPdb to barley, rice and Brassica species. AutoSNPdb has a flexible interface facilitating a variety of queries. Users may search for SNPs within genes of predicted function, and through sequence identity with known genes. In addition, it is possible to add additional levels of annotation and novel queries specific to areas of interest. In the current version, we include plant cultivar information to allow the identification of SNPs that discriminate between plant cultivars.
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METHODS
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Data processing
Brassica, rice and barley expressed sequences were downloaded
from GenBank release 159. RepeatMasker (
www.repeatmasker.org)
was used to identify and mask repeats prior to assembly using
CAP3 (
7) with the parameters –p 90, –o 50. The resulting
assemblies and singleton sequences were parsed into a MySQL
database. SNP discovery used the autoSNP method (
4) implemented
with custom Perl scripts, and the results were parsed to the
database. Assemblies containing four sequences or more were
examined for polymorphisms, with gaps created during the assembly
process classified as indels. The minimum redundancy score defining
a polymorphism was varied in proportion to the number of sequences
in the assembly at the SNP position. A minimum redundancy score
of 2 was required for up to seven sequences; 3 for between 8
and 11 sequences; 4 for between 12 and 19 sequences, and a minimum
redundancy of 5 was required for predicting SNPs represented
by 20 or more sequence reads. Each SNP was compared to all SNPs
in an assembly to calculate the SNP co-segregation score, with
the weighted co-segregation score calculated according to the
proportion of missing data at that position in the assembly.
Input sequences were annotated with cultivar type, tissue source
and developmental stage where available. Consensus and singleton
sequences were annotated based on sequence alignment using BLAST
(
8) against GenBank and UniRef90 databases. Gene Ontology (GO)
annotations were derived from UniRef90 annotations. Comparative
rice and Arabidopsis genome positions were derived by WU-BLAST
comparison with TIGR rice pseudo-chromosomes (version 5) and
TAIR Arabidopsis pseudo-chromosomes (v01222004), respectively.
Database content, access and interface
Barley, rice and Brassica sequences were downloaded from GenBank and processed through the autpSNPdb pipeline. A custom web interface allows users to query and visualize the SNP and annotation data (Figure 1). The maintenance of these data within a relational database enables numerous query options. Sequence annotations may be searched by gene keyword, sequence ID, GO term or through similarity to defined regions of the rice or Arabidopsis genome. A BLAST interface enables identification by sequence similarity. SNPs may be retrieved that differentiate between cultivars, providing a valuable resource for genetic mapping and association studies. To aid interpretation of the predicted SNP data, SNPs are viewed graphically as vertical bars, where the position of the bar along the x-axis reflects the relative position of the SNP in the consensus sequence; the height of the bar represents the SNP redundancy score; and the bar colour reflects the SNP-weighted co-segregation score. Information about each SNP is displayed by moving the cursor over the bar, while selecting a bar centres the sequence assembly at that position. The sequence assembly may be moved using the scroll bar and can be toggled between the full sequence assembly and a SNP summary. Labels to the left of the sequence may also be toggled between cultivars, GenBank accession numbers, tissue type and development stage for the respective sequences. The interface is documented with help pages and database build information.
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FUTURE DIRECTIONS
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The autoSNPdb system was developed for flexible use and permits
extension to a broad range of annotation and species. We plan
to extend this system for other crops, including wheat and next-generation
Roche 454 sequence data.
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FUNDING
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Funding for open access charge: the Australian Research Council.
Conflict of interest statement. None declared.
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ACKNOWLEDGEMENTS
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Support from The National Computing Infrastructure (NCI) and
the Queensland Facility for Advanced Bioinformatics (QFAB) is
gratefully acknowledged. This research was supported under Australian
Research Council's
Linkage Projects funding scheme.
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REFERENCES
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