Physiological changes and transcript identification in Coreopsis tinctoria Nutt. in early stages of salt stress

Coreopsis tinctoria Nutt. (C. tinctoria) is a special tea ingredient that adapts to certain salt stresses and shares the functions of chrysanthemum. With annual expansion of the cultivation area of C. tinctoria in Xinjiang (China), soil salinity may become a constraint for chrysanthemum cultivation. To investigate the response of C. tinctoria to salt stress, physiological and transcriptional changes in C. tinctoria in the early stages of low (50 mM NaCl) and high (200 mM NaCl) salt stress were analyzed and identified. The results showed that the contents of osmotic regulators (free proline, soluble sugar, and soluble protein) and antioxidant enzymes (catalase and peroxidase) under salt stress increased to various extents compared with those of the control (CK) within 72 h, and the increase was higher under 200 mM NaCl treatments. De novo RNA-seq was used to analyze changes in the transcripts under 50 and 200 mM NaCl treatments for up to 48 h. In total, 8,584, 3,760, 7,833, 19,341, 13,233, and 9,224 differentially expressed genes (DEGs) were detected under 12 h, 24 h, and 48 h for 50 and 200 mM NaCl treatments, respectively. Weighted correlation network analysis (WGCNA) was used to analyze the correlations between all DEGs and physiological indexes. We found that the coexpression modules blue2 and Lightskyblue4 highly correlated with osmotic regulators and CAT and identified 20 and 30 hub genes, respectively. The results provide useful data for the further study of salt tolerance in C. tinctoria.

Use of short-read RNA-Seq data to identify transcripts that can translate novel ORFs

Identification of as of yet unannotated or undefined novel open reading frames (nORFs) and exploration of their functions in multiple organisms has revealed that vast regions of the genome have remained unexplored or ‘hidden’. Present within both protein-coding and noncoding regions, these nORFs signify the presence of a much more diverse proteome than previously expected. Given the need to study nORFs further, proper identification strategies must be in place, especially because they cannot be identified using conventional gene signatures. Although Ribo-Seq and proteogenomics are frequently used to identify and investigate nORFs, in this study, we propose a workflow for identifying nORF containing transcripts using our precompiled database of nORFs with translational evidence, using sample transcript information. Further, we discuss the potential uses of this identification, the caveats involved in such a transcript identification and finally present a few representative results from our analysis of naive mouse B and T cells, human post-mortem brain and cichlid fish transcriptome. Our proposed workflow can identify noncoding transcripts that can potentially translate intronic, intergenic and several other classes of nORFs.One-line summaryA systematic workflow to identify nORF containing transcripts using sample transcript information.

Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering

Single-cell transcriptomics has sensitivity limits that restrict low abundance transcript identification, affects clustering and introduce artefact. Here, we describe Constellation DropSeq (C-DropSeq), a molecular transcriptome filter that delivers two orders of magnitude sensitivity gains by maximising read utility while reducing sequencing depth and costs. The simple and powerful method is broadly compatible with library preparation routines and was demonstrated by identifying and characterizing the activation of rare dendritic cell sub-populations.

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

MotivationGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.ResultsHere, we present an extension of the gene prediction tool GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.AvailabilityGeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/[email protected]