Protein structure-based evaluation of missense variants: Resources,challenges and future directions

We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.     

Identification and evaluation of reference genes for gene expression analysis in the weevil pest Pagiophloeus tsushimanus using RT-qPCR

Pagiophloeus tsushimanus is a newly and specialist wood-boring beetle of Cinnamomum camphora in China. RT-qPCR is an accurate quantitative method to quantify target genes expression, which relies on suitable reference genes for data normalization. Reference genes must to be stably expressed under specific experimental conditions. No suitable reference genes of P. tsushimanus have been reported so far. Therefore, it is necessary to identify and evaluate suitable reference genes for the study of functional genes of this pest. In this research, the expression stability of eight candidate reference genes (RPS3, 18S rRNA, GAPDH, TBP, RPL10, UBQ, GST, and RPS27A) were systematically evaluated in P. tsushimanus by five algorithms (geNorm, BestKeeper, NormFinder, delta C_q, and RefFinder) under different developmental stages, various tissues, and insects reared on different plants, and validated by the olfactory key gene odorant binding protein 33 (PtsuOBP33). The results showed that three stable reference genes combination were necessary for quantitative analysis of target gene. RPS3, RPL10, and UBQ were the optimal reference genes combination for gene expression analysis of developmental stages, while RPL10, RPS3, and 18S rRNA were recommended for different tissues, and 18S rRNA, TBP, and RPS3 were recommended for insects reared on different plants. The results indicated that suitable reference genes should be screened out for gene expression analysis under different conditions. This paper systematically analyzed and obtained suitable reference genes in P. tsushimanus for the first time, which would contribute to the functional analysis of genes and the in-depth mining of genetic resources in it.     

High resolution retrieval of leaf chlorophyll content over Himalayan pine forest using Visible/IR sensors mounted on UAV and radiative transfer model

Forests play an essential role towards net primary productivity, biological cycles and provide habitat to flora & fauna. To monitor key physiological activities in forest canopies such as photosynthesis, respiration, transpiration, spatially-explicit and precise information of the biochemical (biological) variables such as Leaf Chlorophyll Content (LCC) is required. While lookup-table (LUT)-based Radiative Transfer Model (RTM) inversion against optical remote sensing imagery is regarded as a physically sound and robust approach for retrieving biochemical and biophysical variables, regularization procedures are required to offset the problem of ill-posedness. To optimize the RTM inversion of LCC over a sub-tropical pine forest plantation, in the Western Himalaya, we investigated the role of: (1) cost functions (CFs), (2) added noise, and (3) multiple finest solutions in LUT inversion. Principal CFs were evaluated belonging to three categories: information measures, M-estimates, and minimal contrast approaches. The inversion approaches were applied to a LUT produced by the coupled leaf-canopy model known as PROSAIL RTM and tested in contrast field spectral data obtained from reflectance data derived from UAV (Unmanned Aerial Vehicle) images taken over the canopies of covered pine forests. The Bhattacharyya divergence, an information measure, outperformed all other CFs in LCC inversion, with R² of 0.94, RMSE of 6.20 μg/cm² and NRMSE of 12.27% during the validation. The optimized inversion strategy was subsequently applied to a UAV-acquired multispectral image at an 8.2 cm pixel resolution for detailed landscape forest LCC mapping. The associated residuals as provided by the LUT-based inversion provided insights in the spatial consistency of the LCC map.