ABSTRACT
Bacterial pustule (BP), caused by Xanthomonas citri pv. glycines, is an important disease that, under favorable conditions, can drastically affect soybean production. We performed a genome-wide association study (GWAS) with a panel containing Brazilian and American cultivars, which were screened qualitatively and quantitatively against two Brazilian X. citri isolates (IBS 333 and IBS 327). The panel was genotyped using a genotyping by sequencing (GBS) approach, and we identified two main new regions in soybeans associated with X. citri resistance on chromosomes 6 (IBS 333) and 18 (IBS 327), different from the traditional rxp gene located on chromosome 17. The region on chromosome 6 was also detected by QTL mapping using a biparental cross between Williams 82 (R) and PI 416937 (S), showing that Williams 82 has another recessive resistance gene besides rxp, which was also detected in nine BP-resistant ancestors of the Brazilian cultivars (including CNS, S-100), based on haplotype analysis. Furthermore, we identified additional SNPs in strong LD (0.8) with peak SNPs by exploring variation available in WGS (whole genome sequencing) data among 31 soybean accessions. In these regions in strong LD, two candidate resistance genes were identified (Glyma.06g311000 and Glyma.18g025100) for chromosomes 6 and 18, respectively. Therefore, our results allowed the identification of new chromosomal regions in soybeans associated with BP disease, which could be useful for marker-assisted selection and will enable a reduction in time and cost for the development of resistant cultivars.
ABSTRACT
BACKGROUND: Computational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. RESULTS: Small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Inspired in these results, we obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. These estimates are within 0.1% of the maximal values obtained with any feature set (SELECT, Section "Results and discussion") while it is 34 times faster to compute. Even compared to another feature set (FS2, see Section "Results and discussion"), which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34 times faster to compute. The results obtained by the tools used as references in the experiments carried out showed that five out of these six tools have lower sensitivity or specificity. CONCLUSION: In miRNA discovery the number of putative miRNA loci is in the order of millions. Analysis of putative pre-miRNAs using a computationally expensive feature set would be wasteful or even unfeasible for large genomes. In this work, we propose a relatively inexpensive feature set and explore most of the learning aspects implemented in current ab-initio pre-miRNA prediction tools, which may lead to the development of efficient ab-initio pre-miRNA discovery tools.The material to reproduce the main results from this paper can be downloaded from http://bioinformatics.rutgers.edu/Static/Software/discriminant.tar.gz.