Genome assembly of the bearded iris, Iris pallida Lam.

Irises are perennial plants, representing a large genus with hundreds of species. While cultivated extensively for their ornamental value, commercial interest in irises lies in the secondary metabolites present in their rhizomes. The Dalmatian Iris (Iris pallida Lam.) is an ornamental plant that also produces secondary metabolites with potential value to the fragrance and pharmaceutical industries. In addition to providing base notes for the fragrance industry, iris tissues and extracts possess antioxidant, anti-inflammatory and immunomodulatory effects. However, study of these secondary metabolites has been hampered by a lack of genomic information, requiring difficult extraction and analysis techniques. Here, we report the genome sequence of Iris pallida Lam., generated with Pacific Bioscience long-read sequencing, resulting in a 10.04-Gbp assembly with a scaffold N50 of 14.34 Mbp and 91.8% complete BUSCOs. This reference genome will allow researchers to study the biosynthesis of these secondary metabolites in much greater detail, opening new avenues of investigation for drug discovery and fragrance formulations.

SDRS--an algorithm for analyzing large-scale dose-response data.

SUMMARY: Dose-response information is critical to understanding drug effects, yet analytical methods for dose-response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose-response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. AVAILABILITY: Bio::SDRS is freely available from CPAN (www.cpan.org). CONTACTS: ruiruji@gmail.com; bruc@acm.org SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Transcriptional profiling of the dose response: a more powerful approach for characterizing drug activities.

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data.

Spatially constrained minimization of macromolecules.

A structural minimization procedure which converges rapidly and restricts the atomic shifts is outlined. It is implemented by adding a harmonic penalty term for the displacements of atomic positions and resetting the reference coordinates with respect to which the constraints are computed during the minimization. The resetting serves to reduce the constraint energy of the minimized structure to negligible levels.

FDR-FET: an optimizing gene set enrichment analysis method.

Gene set enrichment analysis for analyzing large profiling and screening experiments can reveal unifying biological schemes based on previously accumulated knowledge represented as "gene sets". Most of the existing implementations use a fixed fold-change or P value cutoff to generate regulated gene lists. However, the threshold selection in most cases is arbitrary, and has a significant effect on the test outcome and interpretation of the experiment. We developed a new gene set enrichment analysis method, ie, FDR-FET, which dynamically optimizes the threshold choice and improves the sensitivity and selectivity of gene set enrichment analysis. The procedure translates experimental results into a series of regulated gene lists at multiple false discovery rate (FDR) cutoffs, and computes the P value of the overrepresentation of a gene set using a Fisher's exact test (FET) in each of these gene lists. The lowest P value is retained to represent the significance of the gene set. We also implemented improved methods to define a more relevant global reference set for the FET. We demonstrate the validity of the method using a published microarray study of three protease inhibitors of the human immunodeficiency virus and compare the results with those from other popular gene set enrichment analysis algorithms. Our results show that combining FDR with multiple cutoffs allows us to control the error while retaining genes that increase information content. We conclude that FDR-FET can selectively identify significant affected biological processes. Our method can be used for any user-generated gene list in the area of transcriptome, proteome, and other biological and scientific applications.

Conserved fungal genes as potential targets for broad-spectrum antifungal drug discovery.

The discovery of novel classes of antifungal drugs depends to a certain extent on the identification of new, unexplored targets that are essential for growth of fungal pathogens. Likewise, the broad-spectrum capacity of future antifungals requires the target gene(s) to be conserved among key fungal pathogens. Using a genome comparison (or concordance) tool, we identified 240 conserved genes as candidates for potential antifungal targets in 10 fungal genomes. To facilitate the identification of essential genes in Candida albicans, we developed a repressible C. albicans MET3 (CaMET3) promoter system capable of evaluating gene essentiality on a genome-wide scale. The CaMET3 promoter was found to be highly amenable to controlled gene expression, a prerequisite for use in target-based whole-cell screening. When the expression of the known antifungal target C. albicans ERG1 was reduced via down-regulation of the CaMET3 promoter, the CaERG1 conditional mutant strain became hypersensitive, specifically to its inhibitor, terbinafine. Furthermore, parallel screening against a small compound library using the CaERG1 conditional mutant under normal and repressed conditions uncovered several hypersensitive compound hits. This work therefore demonstrates a streamlined process for proceeding from selection and validation of candidate antifungal targets to screening for specific inhibitors.