CuAS: a database of annotated transcripts generated by alternative splicing in cucumbers

PlaASDB: a comprehensive database of plant alternative splicing events in response to stress

BACKGROUND

Alternative splicing (AS) is a co-transcriptional regulatory mechanism of plants in response to environmental stress. However, the role of AS in biotic and abiotic stress responses remains largely unknown. To speed up our understanding of plant AS patterns under different stress responses, development of informative and comprehensive plant AS databases is highly demanded.

DESCRIPTION

In this study, we first collected 3,255 RNA-seq data under biotic and abiotic stresses from two important model plants (Arabidopsis and rice). Then, we conducted AS event detection and gene expression analysis, and established a user-friendly plant AS database termed PlaASDB. By using representative samples from this highly integrated database resource, we compared AS patterns between Arabidopsis and rice under abiotic and biotic stresses, and further investigated the corresponding difference between AS and gene expression. Specifically, we found that differentially spliced genes (DSGs) and differentially expressed genes (DEG) share very limited overlapping under all kinds of stresses, suggesting that gene expression regulation and AS seemed to play independent roles in response to stresses. Compared with gene expression, Arabidopsis and rice were more inclined to have conserved AS patterns under stress conditions.

CONCLUSION

PlaASDB is a comprehensive plant-specific AS database that mainly integrates the AS and gene expression data of Arabidopsis and rice in stress response. Through large-scale comparative analyses, the global landscape of AS events in Arabidopsis and rice was observed. We believe that PlaASDB could help researchers understand the regulatory mechanisms of AS in plants under stresses more conveniently. PlaASDB is freely accessible at http://zzdlab.com/PlaASDB/ASDB/index.html .

TeaPVs: a comprehensive genomic variation database for tea plant (Camellia sinensis)

Genome variation not only plays an important role in plant phenotypic modeling and adaptive evolution, but also enhances population genetic diversity and regulates gene expression. The tea tree (Camellia sinensis) has a large genome (~ 3.0 Gb), making the identification of genome-wide variants time-consuming and expensive. With the continuous publication of a large number of different types of population sequencing data, there is a lack of an open platform to integrate these data and identify variants in the tea plant genome.To integrate the genetic variation confidence in the tea plant population genome, 238 whole-genome resequencing, 213 transcriptome sequencing, and 96 hybrid F1 individuals with a total of more than 20 Tb were collected for mutation site identification. Based on these variations information, we constructed the first tea tree variation web service database TeaPVs ( http://47.106.184.91:8025/ and http://liushang.top:8025/ ). It supports users to search all SNP, Indel, SV mutations and SSR/Polymorphic SSR sequences by location or gene ID. Furthermore, the website also provides the functions of gene expression search of different transcriptome, sequence blast, sequence extraction of CDS and mutation loci, etc.The features of the TeaPVs database make it a comprehensive tea plant genetic variation bioinformatics platform for researchers, and will also be helpful for revealing new functional mutations in the tea plant genome and molecular marker-assisted breeding.

DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

Alternative splicing is crucial for a wide range of biological processes. However, limited by the availability of reference genomes, genome-wide patterns of alternative splicing remain unknown in most nonmodel organisms. We present an attention-based convolutional neural network model, DeepASmRNA, for predicting alternative splicing events using only transcriptomic data. DeepASmRNA consists of two parts: identification of alternatively spliced transcripts and classification of alternative splicing events, which outperformed the state-of-the-art method, AStrap, and other deep learning models. Then, we utilize transfer learning to increase the performance in species with limited training data and use an interpretation method to decipher splicing codes. Finally, applying Amborella, DeepASmRNA can identify more AS events than AStrap while maintaining the same level of precision, suggesting that DeepASmRNA has superior sensitivity to identify alternative splicing events. In summary, DeepASmRNA is scalable and interpretable for detecting genome-wide patterns of alternative splicing in species without a reference genome.

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DeepASmRNA uses only the transcriptome to predict alternative splicing events

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DeepASmRNA identifies adjacent HSPs to greatly improve the recall

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DeepASmRNA uses attention-based convolutional neural network to classify AS events

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Transfer learning is used to increase the predictive power of a target species

Serial-Omics and Molecular Function Study Provide Novel Insight into Cucumber Variety Improvement

Cucumbers are rich in vitamins and minerals. The cucumber has recently become one of China’s main vegetable crops. More specifically, the adjustment of the Chinese agricultural industry’s structure and rapid economic development have resulted in increases in the planting area allocated to Chinese cucumber varieties and in the number of Chinese cucumber varieties. After complete sequencing of the “Chinese long” genome, the transcriptome, proteome, and metabolome were obtained. Cucumber has a small genome and short growing cycle, and these traits are conducive to the application of molecular breeding techniques for improving fruit quality. Here, we review the developments and applications of molecular markers and genetic maps for cucumber breeding and introduce the functions of gene families from the perspective of genomics, including fruit development and quality, hormone response, resistance to abiotic stress, epitomizing the development of other omics, and relationships among functions.

Genome-Wide Analysis of Alternative Splicing and Non-Coding RNAs Reveal Complicated Transcriptional Regulation in Cannabis sativa L

It is of significance to mine the structural genes related to the biosynthetic pathway of fatty acid (FA) and cellulose as well as explore the regulatory mechanism of alternative splicing (AS), microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in the biosynthesis of cannabinoids, FA and cellulose, which would enhance the knowledge of gene expression and regulation at post-transcriptional level in Cannabis sativa L. In this study, transcriptome, small RNA and degradome libraries of hemp 'Yunma No.1' were established, and comprehensive analysis was performed. As a result, a total of 154, 32 and 331 transcripts encoding key enzymes involved in the biosynthesis of cannabinoids, FA and cellulose were predicted, respectively, among which AS occurred in 368 transcripts. Moreover, 183 conserved miRNAs, 380 C. sativa-specific miRNAs and 7783 lncRNAs were predicted. Among them, 70 miRNAs and 17 lncRNAs potentially targeted 13 and 17 transcripts, respectively, encoding key enzymes or transporters involved in the biosynthesis of cannabinoids, cellulose or FA. Finally, the crosstalk between AS and miRNAs or lncRNAs involved in cannabinoids and cellulose was also predicted. In summary, all these results provided insights into the complicated network of gene expression and regulation in C. sativa.

MustSeq, an alternative approach for multiplexible strand-specific 3' end sequencing of mRNA transcriptome confers high efficiency and practicality

RNA-seq has been widely used to reveal the molecular mechanism of variants of life process. We have developed an alternative method, MustSeq, which generates multiple second strands along a single 1^st strand cDNA by random-priming initiation, immediately after reverse transcription for each RNA extract using sample-barcoded poly-dT primers, then 3' ends-enriching PCR is applied to construct the library. Unlike the conventional RNA seq, MustSeq avoids procedures such as mRNA isolation, fragmentation and RNA 5'-end capture, enables early pooling of multiple samples, and requires only one twentieth of sequencing reads of full-length sequencing. We demonstrate the power and features of MustSeq comparing with TruSeq and NEBNext RNA-seq, two conventional full-length methods and QuantSeq, an industrial 3' end method. In cancer cell lines, the reads distribution of CDS-exon as well as genes, lncRNAs and GO terms detected by MustSeq are closer than QuantSeq to TruSeq. In mouse hepatocarcinoma and healthy livers, MustSeq enriches the same pathways as by NEBNext, and reveals the molecular profile of carcinogenesis. Overall MustSeq is a robust and accurate RNA-seq method allowing efficient library construction, sequencing and analysis, particularly valuable for analysis of differentially expressed genes with a large number of samples. MustSeq will greatly accelerate the application of bulk RNA-seq on different fields, and potentially applicable for single cell RNA-seq.

TeaAS: a comprehensive database for alternative splicing in tea plants (Camellia sinensis)

Alternative splicing (AS) increases the diversity of transcripts and proteins through the selection of different splice sites and plays an important role in the growth, development and stress tolerance of plants. With the release of the reference genome of the tea plant (Camellia sinensis) and the development of transcriptome sequencing, researchers have reported the existence of AS in tea plants. However, there is a lack of a platform, centered on different RNA-seq datasets, that provides comprehensive information on AS.To facilitate access to information on AS and reveal the molecular function of AS in tea plants, we established the first comprehensive AS database for tea plants (TeaAS, http://www.teaas.cn/index.php ). In this study, 3.96 Tb reads from 66 different RNA-seq datasets were collected to identify AS events. TeaAS supports four methods of retrieval of AS information based on gene ID, gene name, annotation (non-redundant/Kyoto encyclopedia of genes and genomes/gene ontology annotation or chromosomal location) and RNA-seq data. It integrates data pertaining to genome annotation, type of AS event, transcript sequence, and isoforms expression levels from 66 RNA-seq datasets. The AS events resulting from different environmental conditions and that occurring in varied tissue types, and the expression levels of specific transcripts can be clearly identified through this online database. Moreover, it also provides two useful tools, Basic Local Alignment Search Tool and Generic Genome Browser, for sequence alignment and visualization of gene structure.The features of the TeaAS database make it a comprehensive AS bioinformatics platform for researchers, as well as a reference for studying AS events in woody crops. It could also be helpful for revealing the novel biological functions of AS in gene regulation in tea plants.