The PERSIANN family of global satellite precipitation data: A review and evaluation of products

Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments

Comprehensive analysis of SET domain gene family in foxtail millet identifies the putative role of SiSET14 in abiotic stress tolerance

SET domain-containing genes catalyse histone lysine methylation, which alters chromatin structure and regulates the transcription of genes that are involved in various developmental and physiological processes. The present study identified 53 SET domain-containing genes in C(4) panicoid model, foxtail millet (Setaria italica) and the genes were physically mapped onto nine chromosomes. Phylogenetic and structural analyses classified SiSET proteins into five classes (I–V). RNA-seq derived expression profiling showed that SiSET genes were differentially expressed in four tissues namely, leaf, root, stem and spica. Expression analyses using qRT-PCR was performed for 21 SiSET genes under different abiotic stress and hormonal treatments, which showed differential expression of these genes during late phase of stress and hormonal treatments. Significant upregulation of SiSET gene was observed during cold stress, which has been confirmed by over-expressing a candidate gene, SiSET14 in yeast. Interestingly, hypermethylation was observed in gene body of highly differentially expressed genes, whereas methylation event was completely absent in their transcription start sites. This suggested the occurrence of demethylation events during various abiotic stresses, which enhance the gene expression. Altogether, the present study would serve as a base for further functional characterization of SiSET genes towards understanding their molecular roles in conferring stress tolerance