RESUMO
BACKGROUND: Reliable annotation linking oligonucleotide probes to target genes is essential for functional biological analysis of microarray experiments. We used the IMAD, OligoRAP and sigReannot pipelines to update the annotation for the ARK-Genomics Chicken 20 K array as part of a joined EADGENE/SABRE workshop. In this manuscript we compare their annotation strategies and results. Furthermore, we analyse the effect of differences in updated annotation on functional analysis for an experiment involving Eimeria infected chickens and finally we propose guidelines for optimal annotation strategies. RESULTS: IMAD, OligoRAP and sigReannot update both annotation and estimated target specificity. The 3 pipelines can assign oligos to target specificity categories although with varying degrees of resolution. Target specificity is judged based on the amount and type of oligo versus target-gene alignments (hits), which are determined by filter thresholds that users can adjust based on their experimental conditions. Linking oligos to annotation on the other hand is based on rigid rules, which differ between pipelines.For 52.7% of the oligos from a subset selected for in depth comparison all pipelines linked to one or more Ensembl genes with consensus on 44.0%. In 31.0% of the cases none of the pipelines could assign an Ensembl gene to an oligo and for the remaining 16.3% the coverage differed between pipelines. Differences in updated annotation were mainly due to different thresholds for hybridisation potential filtering of oligo versus target-gene alignments and different policies for expanding annotation using indirect links. The differences in updated annotation packages had a significant effect on GO term enrichment analysis with consensus on only 67.2% of the enriched terms. CONCLUSION: In addition to flexible thresholds to determine target specificity, annotation tools should provide metadata describing the relationships between oligos and the annotation assigned to them. These relationships can then be used to judge the varying degrees of reliability allowing users to fine-tune the balance between reliability and coverage. This is important as it can have a significant effect on functional microarray analysis as exemplified by the lack of consensus on almost one third of the terms found with GO term enrichment analysis based on updated IMAD, OligoRAP or sigReannot annotation.
RESUMO
BACKGROUND: The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. RESULTS: Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. CONCLUSION: It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.
RESUMO
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/.
