As rainfall undergoes a series of complex microphysical processes in the atmosphere, studying its vertical profiles is crucial for understanding the mechanisms of rainfall evolution. While previous research has focused on vertical profiles of rainfall intensity and drop size distribution (DSD) parameters, there remains a gap in the study of rainfall energy. This study uses the dual-frequency precipitation radar (DPR) in global precipitation measurement (GPM) to analyze the vertical profile characteristics of rainfall energy (KEt) for the first time. Using DPR data collected from 2015 to 2022 across the U.K., rainfall energy calculations reveal a strong correlation (over 0.99) between the rainfall energy of adjacent 125-m-height bins, with stratiform rain showing higher correlation than convective rain. Specifically, below 1500 m, the correlation coefficient for KEt in stratiform rain is 0.9973, while for convective rain, it is 0.9957, showing higher KEt variability in convective rain. The study also introduces the change ratio (R) to characterize the degree of change from the lower to upper height bins, finding that rainfall energy variability has a larger standard deviation compared to DSD parameters, with standard deviations for R mean values of KEt reaching up to 28.37% for convective rain and 12.08% for stratiform rain within 1500 m. In addition, the profiles of rainfall energy exhibit significant seasonal variations, with these variations increasing with height. KEt is consistently highest in summer and lowest in winter at all same altitudes. This study enhances the understanding of the vertical pattern of rainfall evolution, contributes to providing more accurate surface rainfall energy estimates, analyzing influencing factors and the uncertainty of vertical rainfall variability.

Current radar techniques focus on rainfall observations, leaving a research gap in rainfall energy (E) involving the interaction of raindrops and land surface processes. E is defined as the accumulated kinetic energy per unit of rainfall and is a key parameter in the understanding process of the rainfall impact on the land surface. Utilizing the capability of dual-polarization radar to detect the rainfall microphysics characteristics, this study proposes the first computational model for estimating E from radar signals. The model investigates the mechanistic correlation between the radar dual-polarization parameters and E and finds that specific differential phase ($K_{\text {DP}}$) and horizontal reflectivity ($Z_{\text {H}}$) have the strongest correlation with E. Therefore, the study develops radar-based empirical regression and random forest (RF) models for E estimation, where RF models consider whether the sensitive$K_{\text {DP}}$is available. The results show that the RF models improve the accuracy of estimating E and have a Pearson coefficient greater than or equal to 0.97 with station-measured E, and the spatially extensive capability of the models is further validated. In addition, both the traditional regression model (TRM) and RF-based radar data have underestimated daily E estimates compared to the disdrometer observations, with smaller BIAS and root mean square error (RMSE) and higher Pearson correlations for RF. This study contributes to enhancing the understanding of rainfall processes in the context of climate change and has great potential for applications in hydrological modeling, flood forecasting, and agricultural planning.

The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays) to sequence features residing in gene promoters (as derived from DNA motif data) and transcription factor binding to gene promoters (as derived from tiling microarrays). We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.

MOTIVATION: In the genomics setting, an increasingly common data configuration consists of a small set of sequences possessing a targeted property (positive instances) amongst a large set of sequences for which class membership is unknown (unlabeled instances). Traditional two-class classification methods do not effectively handle such data. RESULTS: Here, we develop a novel method, likely positive-iterative classification (LP-IC) for this problem, and contrast its performance with the few existing methods, most of which were devised and utilized in the text classification context. LP-IC employs an iterative classification scheme and introduces a class dispersion measure, adopted from unsupervised clustering approaches, to monitor the model selection process. Using two case studies--prediction of HLA binding, and alternative splicing conservation between human and mouse--we show that LP-IC provides superior performance to existing methodologies in terms of: (i) combined accuracy and precision in positive identification from the unlabeled set; and (ii) predictive performance of the resultant classifiers on independent test data.

MOTIVATION: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. RESULTS: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses resampling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The resampling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap samples from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. AVAILABILITY: R functions are available upon request from the authors.

Abstract Motivation: A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. Results: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study. Availability: The approach is implemented in an R package called DEDS, which is available for download from the Bioconductor website (http://www.bioconductor.org/). Contact: [email protected]

Splicing is an important process for regulation of gene expression in eukaryotes, and it has important functional links to other steps of gene expression. Two examples of these linkages include Ceg1, a component of the mRNA capping enzyme, and the chromatin elongation factors Spt4-5, both of which have recently been shown to play a role in the normal splicing of several genes in the yeast Saccharomyces cerevisiae. Using a genomic approach to characterize the roles of Spt4-5 in splicing, we used splicing-sensitive DNA microarrays to identify specific sets of genes that are mis-spliced in ceg1, spt4, and spt5 mutants. In the context of a complex, nested, experimental design featuring 22 dye-swap array hybridizations, comprising both biological and technical replicates, we applied five appropriate statistical models for assessing differential expression between wild-type and the mutants. To refine selection of differential expression genes, we then used a robust model-synthesizing approach, Differential Expression via Distance Synthesis, to integrate all five models. The resultant list of differentially expressed genes was then further analyzed with regard to select attributes: we found that highly transcribed genes with long introns were most sensitive to spt mutations. QPCR confirmation of differential expression was established for the limited number of genes evaluated. In this paper, we showcase splicing array technology, as well as powerful, yet general, statistical methodology for assessing differential expression, in the context of a real, complex experimental design. Our results suggest that the Spt4-Spt5 complex may help coordinate splicing with transcription under conditions that present kinetic challenges to spliceosome assembly or function.