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Abstract Background Previous studies on plant long noncoding RNAs (lncRNAs) lacked consistency and suffered from many factors like heterogeneous data sources and experimental protocols, different plant tissues, inconsistent bioinformatics pipelines, etc. For example, the sequencing of RNAs with poly(A) tails excluded a large portion of lncRNAs without poly(A), and use of regular RNA-sequencing technique did not distinguish transcripts’ direction for lncRNAs. The current study was designed to systematically discover and analyze lncRNAs across eight evolutionarily representative plant species, using strand-specific (directional) and whole transcriptome sequencing (RiboMinus) technique. Results A total of 39,945 lncRNAs (25,350 lincRNAs and 14,595 lncNATs) were identified, which showed molecular features of lncRNAs that are consistent across divergent plant species but different from those of mRNA. Further, transposable elements (TEs) were found to play key roles in the origination of lncRNA, as significantly large number of lncRNAs were found to contain TEs in gene body and promoter region, and transcription of many lncRNAs was driven by TE promoters. The lncRNA sequences were divergent even in closely related species, and most plant lncRNAs were genus/species-specific, amid rapid turnover in evolution. Evaluated with PhastCons scores, plant lncRNAs showed similar conservation level to that of intergenic sequences, suggesting that most lincRNAs were young and with short evolutionary age. INDUCED BY PHOSPHATE STARVATION (IPS) was found so far to be the only plant lncRNA group with conserved motifs, which may play important roles in the adaptation of terrestrial life during migration from aquatic to terrestrial. Most highly and specially expressed lncRNAs formed co-expression network with coding genes, and their functions were believed to be closely related to their co-expression genes. Conclusion The study revealed novel features and complexity of lncRNAs in plants through systematic analysis, providing important insights into the origination and evolution of plant lncRNAs.

Abstract Background RNA-Sequencing (RNA-seq) is now commonly used to reveal quantitative spatiotemporal snapshots of the transcriptome, the structures of transcripts (splice variants and fusions) and landscapes of expressed mutations. However, standard approaches for library construction typically require relatively high amounts of input RNA, are labor intensive, and are time consuming. Methods Here, we report the outcome of a systematic effort to optimize and streamline steps in strand-specific RNA-seq library construction. Results This work has resulted in the identification of an optimized messenger RNA isolation protocol, a potent reverse transcriptase for cDNA synthesis, and an efficient chemistry and a simplified formulation of library construction reagents. We also present an optimization of bead-based purification and size selection designed to maximize the recovery of cDNA fragments. Conclusions These developments have allowed us to assemble a rapid high throughput pipeline that produces high quality data from amounts of total RNA as low as 25 ng. While the focus of this study is on RNA-seq sample preparation, some of these developments are also relevant to other next-generation sequencing library types.

Abstract Background RNA-Seq is now widely used as a research tool. Choices must be made whether to use paired-end (PE) or single-end (SE) sequencing, and whether to use strand-specific or non-specific (NS) library preparation kits. To date there has been no analysis of the effect of these choices on identifying differentially expressed genes (DEGs) between controls and treated samples and on downstream functional analysis. Results We undertook four mammalian transcriptomics experiments to compare the effect of SE and PE protocols on read mapping, feature counting, identification of DEGs and functional analysis. For three of these experiments we also compared a non-stranded (NS) and a strand-specific approach to mapping the paired-end data. SE mapping resulted in a reduced number of reads mapped to features, in all four experiments, and lower read count per gene. Up to 4.3% of genes in the SE data and up to 12.3% of genes in the NS data had read counts which were significantly different compared to the PE data. Comparison of DEGs showed the presence of false positives (average 5%, using voom) and false negatives (average 5%, using voom) using the SE reads. These increased further, by one or two percentage points, with the NS data. Gene ontology functional enrichment (GO) of the DEGs arising from SE or NS approaches, revealed striking differences in the top 20 GO terms, with as little as 40% concordance with PE results. Caution is therefore advised in the interpretation of such results. By comparison, there was overall consistency in gene set enrichment analysis results. Conclusions A strand-specific protocol should be used in library preparation to generate the most reliable and accurate profile of expression. Ideally PE reads are also recommended particularly for transcriptome assembly. Whilst SE reads produce a DEG list with around 5% of false positives and false negatives, this method can substantially reduce sequencing cost and this saving could be used to increase the number of biological replicates thereby increasing the power of the experiment. As SE reads, when used in association with gene set enrichment, can generate accurate biological results, this may be a desirable trade-off.