Evaluation of machine learning models on protein level inference from prioritized RNA features

Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners

Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.

CRISPR/Cas9 System: A Potential Tool for Genetic Improvement in Floricultural Crops

Demand of flowers is increasing with time worldwide. Floriculture has become one of the most important commercial trades in agriculture. Although traditional breeding methods like hybridization and mutation breeding have contributed significantly to the development of important flower varieties, flower production and quality of flowers can be significantly improved by employing modern breeding approaches. Novel traits of significance have interest to consumers and producers, such as fragrance, new floral color, change in floral architecture and morphology, vase life, aroma, and resistance to biotic and abiotic stresses, have been introduced by genetic manipulation. The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) system has recently emerged as a powerful genome-editing tool for accurately changing DNA sequences at specific locations. It provides excellent means of genetically improving floricultural crops. CRISPR/Cas system has been utilized in gene editing in horticultural cops. There are few reports on the utilization of the CRISPR/Cas9 system in flowers. The current review summarizes the research work done by employing the CRISPR/Cas9 system in floricultural crops including improvement in flowering traits such as color modification, prolonging the shelf life of flowers, flower initiation, and development, changes in color of ornamental foliage by genome editing. CRISPR/Cas9 gene editing could be useful in developing novel cultivars with higher fragrance and enhanced essential oil and many other useful traits. The present review also highlights the basic mechanism and key components involved in the CRISPR/Cas9 system.