ALBERT

Description: Background Single nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common type of polymorphisms and are frequently used for molecular marker development. Such markers have become very popular for all kinds of genetic analysis, including haplotype reconstruction. Haplotypes can be reconstructed for whole chromosomes but also for specific genes, based on the SNPs present. Haplotypes in the latter context represent the different alleles of a gene. The computational approach to SNP mining is becoming increasingly popular because of the continuously increasing number of sequences deposited in databases, which allows a more accurate identification of SNPs. Several software packages have been developed for SNP mining from databases. From these, QualitySNP is the only tool that combines SNP detection with the reconstruction of alleles, which results in a lower number of false positive SNPs and also works much faster than other programs. We have build a web-based SNP discovery and allele detection tool (HaploSNPer) based on QualitySNP. Results HaploSNPer is a flexible web-based tool for detecting SNPs and alleles in user-specified input sequences from both diploid and polyploid species. It includes BLAST for finding homologous sequences in public EST databases, CAP3 or PHRAP for aligning them, and QualitySNP for discovering reliable allelic sequences and SNPs. All possible and reliable alleles are detected by a mathematical algorithm using potential SNP information. Reliable SNPs are then identified based on the reconstructed alleles and on sequence redundancy. Conclusion Thorough testing of HaploSNPer (and the underlying QualitySNP algorithm) has shown that EST information alone is sufficient for the identification of alleles and that reliable SNPs can be found efficiently. Furthermore, HaploSNPer supplies a user friendly interface for visualization of SNP and alleles. HaploSNPer is available from http://www.bioinformatics.nl/tools/haplosnper/.

Description: Background Transcriptional profiling after herbivore attack reveals, at the molecular level, how plants respond to this type of biotic stress. Comparing herbivore-induced transcriptional responses of plants with different phenotypes provides insight into plant defense mechanisms. Here, we compare the global gene expression patterns induced by Pieris rapae caterpillar attack in two white cabbage (Brassica oleracea var. capitata) cultivars. The two cultivars are shown to differ in their level of direct defense against caterpillar feeding. Because Brassica full genome microarrays are not yet available, 70-mer oligonucleotide microarrays based on the Arabidopsis thaliana genome were used for this non-model plant. Results The transcriptional responses of the two cultivars differed in timing as characterized by changes in their expression pattern after 24, 48 and 72 hours of caterpillar feeding. In addition, they also differed qualitatively. Surprisingly, of all genes induced at any time point, only one third was induced in both cultivars. Analyses of transcriptional responses after jasmonate treatment revealed that the difference in timing did not hold for the response to this phytohormone. Additionally, comparisons between Pieris rapae- and jasmonate-induced transcriptional responses showed that Pieris rapae induced more jasmonate-independent than jasmonate-dependent genes. Conclusion The present study clearly shows that global transcriptional responses in two cultivars of the same plant species in response to insect feeding can differ dramatically. Several of these differences involve genes that are known to have an impact on Pieris rapae performance and probably underlie different mechanisms of direct defense, present in the cultivars.

Description: Background Single nucleotide polymorphisms (SNPs) are important tools in studying complex genetic traits and genome evolution. Computational strategies for SNP discovery make use of the large number of sequences present in public databases (in most cases as expressed sequence tags (ESTs)) and are considered to be faster and more cost-effective than experimental procedures. A major challenge in computational SNP discovery is distinguishing allelic variation from sequence variation between paralogous sequences, in addition to recognizing sequencing errors. For the majority of the public EST sequences, trace or quality files are lacking which makes detection of reliable SNPs even more difficult because it has to rely on sequence comparisons only. Results We have developed a new algorithm to detect reliable SNPs and insertions/deletions (indels) in EST data, both with and without quality files. Implemented in a pipeline called QualitySNP, it uses three filters for the identification of reliable SNPs. Filter 1 screens for all potential SNPs and identifies variation between or within genotypes. Filter 2 is the core filter that uses a haplotype-based strategy to detect reliable SNPs. Clusters with potential paralogs as well as false SNPs caused by sequencing errors are identified. Filter 3 screens SNPs by calculating a confidence score, based upon sequence redundancy and quality. Non-synonymous SNPs are subsequently identified by detecting open reading frames of consensus sequences (contigs) with SNPs. The pipeline includes a data storage and retrieval system for haplotypes, SNPs and alignments. QualitySNP's versatility is demonstrated by the identification of SNPs in EST datasets from potato, chicken and humans. Conclusion QualitySNP is an efficient tool for SNP detection, storage and retrieval in diploid as well as polyploid species. It is available for running on Linux or UNIX systems. The program, test data, and user manual are available at http://www.bioinformatics.nl/tools/snpweb/ and as Additional files.

Description: Background Simple Sequence Repeat (SSR) or microsatellite markers are valuable for genetic research. Experimental methods to develop SSR markers are laborious, time consuming and expensive. In silico approaches have become a practicable and relatively inexpensive alternative during the last decade, although testing putative SSR markers still is time consuming and expensive. In many species only a relatively small percentage of SSR markers turn out to be polymorphic. This is particularly true for markers derived from expressed sequence tags (ESTs). In EST databases a large redundancy of sequences is present, which may contain information on length-polymorphisms in the SSR they contain, and whether they have been derived from heterozygotes or from different genotypes. Up to now, although a number of programs have been developed to identify SSRs in EST sequences, no software can detect putatively polymorphic SSRs. Results We have developed PolySSR, a new pipeline to identify polymorphic SSRs rather than just SSRs. Sequence information is obtained from public EST databases derived from heterozygous individuals and/or at least two different genotypes. The pipeline includes PCR-primer design for the putatively polymorphic SSR markers, taking into account Single Nucleotide Polymorphisms (SNPs) in the flanking regions, thereby improving the success rate of the potential markers. A large number of polymorphic SSRs were identified using publicly available EST sequences of potato, tomato, rice, Arabidopsis, Brassica and chicken. The SSRs obtained were divided into long and short based on the number of times the motif was repeated. Surprisingly, the frequency of polymorphic SSRs was much higher in the short SSRs. Conclusion PolySSR is a very effective tool to identify polymorphic SSRs. Using PolySSR, several hundred putative markers were developed and stored in a searchable database. Validation experiments showed that almost all markers that were indicated as putatively polymorphic by polySSR were indeed polymorphic. This greatly improves the efficiency of marker development, especially in species where there are low levels of polymorphism, like tomato. When combined with the new sequencing technologies PolySSR will have a big impact on the development of polymorphic SSRs in any species. PolySSR and the polymorphic SSR marker database are available from http://www.bioinformatics.nl/tools/polyssr/.