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KSHV is the etiological agent of Kaposi's sarcoma (KS), primary effusion lymphoma (PEL), and a subset of multicentricCastleman's disease (MCD). The fact that KSHV-encoded miRNAs are readily detectable in all KSHV-associated tumors suggests a potential role in viral pathogenesis and tumorigenesis. MiRNA-mediated regulation of gene expression is a complex network with each miRNA having many potential targets, and to date only few KSHV miRNA targets have been experimentally determined. A detailed understanding of KSHV miRNA functions requires high-through putribonomics to globally analyze putative miRNA targets in a cell type-specific manner. We performed Ago HITS-CLIP to identify viral and cellular miRNAs and their cognate targets in two latently KSHV-infected PEL cell lines. Ago HITS-CLIP recovered 1170 and 950 cellular KSHV miRNA targets from BCBL-1 and BC-3, respectively. Importantly, enriched clusters contained KSHV miRNA seed matches in the 3'UTRs of numerous well characterized targets, among them THBS1, BACH1, and C/EBPβ. KSHV miRNA targets were strongly enriched for genes involved in multiple pathways central for KSHV biology, such as apoptosis, cell cycle regulation, lymphocyte proliferation, and immune evasion, thus further supporting a role in KSHV pathogenesis and potentially tumorigenesis. A limited number of viral transcripts were also enriched by HITS-CLIP including vIL-6 expressed only in a subset of PEL cells during latency. Interestingly, Ago HITS-CLIP revealed extremely high levels of Ago-associated KSHV miRNAs especially in BC-3 cells where more than 70% of all miRNAs are of viral origin. This suggests that in addition to seed match-specific targeting of cellular genes, KSHV miRNAs may also function by hijacking RISCs, thereby contributing to a global de-repression of cellular gene expression due to the loss of regulation by human miRNAs. In summary, we provide an extensive list of cellular and viral miRNA targets representing an important resource to decipher KSHV miRNA function.

From the seminal paper by Meeuwissen et al. 2001 to the GSA Journals Series launch in 2015, the field of Genomic Prediction continues to gain momentum. The field is increasingly dynamic, with new technology increasing the scale and scope of the data available. Significant challenges exist in building computational models. Questions of how to appropriately account for different types of data, and which data improve predictions are interwoven. What is the best path forward? What methods will improve predictions? Authors can submit their rejoinder or start a new discussion on one of the many important topics in the field by submitting a Dialogue and Debate article for peer review at G3.

High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500–2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO₂]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses. SQANTI3 offers a flexible tool for quality control, curation and annotation of long-read RNA sequencing data.

In omics experiments, variable selection involves a large number of metabolites/ genes and a small number of samples (the n < p problem). The ultimate goal is often the identification of one, or a few features that are different among conditions- a biomarker. Complicating biomarker identification, the p variables often contain a correlation structure due to the biology of the experiment making identifying causal compounds from correlated compounds difficult. Additionally, there may be elements in the experimental design (blocks, batches) that introduce structure in the data. While this problem has been discussed in the literature and various strategies proposed, the over fitting problems concomitant with such approaches are rarely acknowledged. Instead of viewing a single omics experiment as a definitive test for a biomarker, an unrealistic analytical goal, we propose to view such studies as screening studies where the goal of the study is to reduce the number of features present in the second round of testing, and to limit the Type II error. Using this perspective, the performance of LASSO, ridge regression and Elastic Net was compared with the performance of an ANOVA via a simulation study and two real data comparisons. Interestingly, a dramatic increase in the number of features had no effect on Type I error for the ANOVA approach. ANOVA, even without multiple test correction, has a low false positive rates in the scenarios tested. The Elastic Net has an inflated Type I error (from 10 to 50%) for small numbers of features which increases with sample size. The Type II error rate for the ANOVA is comparable or lower than that for the Elastic Net leading us to conclude that an ANOVA is an effective analytical tool for the initial screening of features in omics experiments.