Research Progress on Fertility of Two-line Male Sterile Rice Lines

Great achievements have been made in the exploration of male sterile resources, gene mapping and cloning and molecular mechanism revealing, as well as in breeding and application of two-line rice in China. This paper briefly reviewed the discovery, types and cloning of sterile genes in rice and molecular breeding of two-line rice, and summarized the research progress of critical sterility inducing temperature, so as to provide new ideas for the research and breeding of two-line hybrid rice.

Comparative Genomic Analysis of Boron Transport Gene Family in Arabidopsis and Five Crops

Nutrient boron(B)is important for crop development.The absorption and transport of B ions are regulated by the B transport(BOR)gene family.Although some members of the BOR gene family have been heavily researched,to the best of our knowledge,no comprehensive research on the structural,evolutionary and functional relationships among crops has been reported.In this study,comparative genomic research of the BOR gene family was performed in five crops,40 BOR genes were identified,and analyses of phylogenetics,structure,conserved motifs,and transmembrane topology were performed.These genes had highly similar physicochemical properties,structure and motif distribution.Specific elements in the C-terminus and functional differences were also found among the genes.In addition,microsynteny and evolutionary analysis suggested that large-scale replication events and purifying selection played essential roles during the OsBOR gene evolutionary process in rice.Moreover,expression pattern of OsBORs were also analyzed.Our research provides comprehensive and detailed information on BOR genes in five crops and establishes a foundation for further functional exploration of these genes in B metabolic pathways.

DeepRetention:A Deep Learning Approach for Intron Retention Detection

As the least understood mode of alternative splicing,Intron Retention(IR)is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease studies.Existing methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained introns.These metrics are not able to describe the pattern of sequencing depth of intronic reads,which is an intuitive and informative characteristic of retained introns.We hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR detection.Here we present DeepRetention,a novel approach for IR detection by modeling the pattern of sequencing depth of introns.Due to the lack of a gold standard dataset of IR,we first compare DeepRetention with two state-of-the-art methods,i.e.iREAD and IRFinder,on simulated RNA-seq datasets with retained introns.The results show that DeepRetention outperforms these two methods.Next,DeepRetention performs well when it is applied to third-generation long-read RNA-seq data,while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing data.Further,we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer’s Disease(AD)samples.The differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene network.The parent genes of differential IRs are enriched in AD-related functions.In summary,DeepRetention detects IR from a new angle of view,providing a valuable tool for IR analysis.

A Comparison of Computational Approaches for Intron Retention Detection

Intron Retention(IR)is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases.IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases.Continuous efforts have been dedicated to the development of IR detection methods.These methods differ in their metrics to quantify retention propensity,performance to detect IR events,functional enrichment of detected IRs,and computational speed.A systematic experimental comparison would be valuable to the selection and use of existing methods.In this work,we conduct an experimental comparison of existing IR detection methods.Considering the unavailability of a gold standard dataset of intron retention,we compare the IR detection performance on simulation datasets.Then,we compare the IR detection results with real RNA-Seq data.We also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment,which is illustrated on an Alzheimer’s disease RNA-Seq dataset.We discuss key principles and features of existing approaches and outline their differences.This systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.