Aerobic photosynthetic organisms such as algae produce reactive oxygen species (ROS) as by-products of metabolism. ROS damage biomolecules such as proteins and lipids in cells, but also act as signaling molecules. The mechanisms that maintain the metabolic balance in aerobic photosynthetic organisms and how the cells specifically respond to different levels of ROS are unclear. Glutathione peroxidase (GPX) enzymes detoxify hydrogen peroxide or organic hydroperoxides, and thus are important components of the antioxidant system. In this study, we employed a Chlamydomonas reinhardtii glutathione peroxidase knockout (gpx5) mutant to identify the genetic response to singlet oxygen (¹O₂) generated by the photosensitizer rose bengal (RB). To this end, we compared the transcriptomes of the parental strain CC4348 and the gpx5 mutant sampled before, and 1 h after, the addition of RB. Functional annotation of differentially expressed genes showed that genes encoding proteins related to ROS detoxification, stress-response-related molecular chaperones, and ubiquitin–proteasome pathway genes were upregulated in CC4338. When GPX5 was mutated, higher oxidative stress specifically induced the TCA cycle and enhanced mitochondrial electron transport. Transcription of selenoproteins and flagellar-associated proteins was depressed in CC4348 and the gpx5 mutant. In addition, we found iron homeostasis played an important role in maintaining redox homeostasis, and we uncovered the relationship between ¹O₂ stress and iron assimilation, as well as selenoproteins. Based on the observed expression profiles in response to different levels of oxidative stress, we propose a model for dose-dependent responses to different ROS levels in Chlamydomonas.

High-throughput RNA sequencing unveiled the complexity of transcriptome and significantly increased the records of long noncoding RNAs (lncRNAs), which were reported to participate in a variety of biological processes. Identification of lncRNAs is a key step in lncRNA analysis, and a bunch of bioinformatics tools have been developed for this purpose in recent years. While these tools allow us to identify lncRNA more efficiently and accurately, they may produce inconsistent results, making selection a confusing issue. We compared the performance of 41 analysis models based on 14 software packages and different data sets, including high-quality data and low-quality data from 33 species. In addition, computational efficiency, robustness, and joint prediction of the models were explored. As a practical guidance, key points for lncRNA identification under different situations were summarized. In this investigation, no one of these models could be superior to others under all test conditions. The performance of a model relied to a great extent on the source of transcripts and the quality of assemblies. As general references, FEELnc_all_cl, CPC, and CPAT_mouse work well in most species while COME, CNCI, and lncScore are good choices for model organisms. Since these tools are sensitive to different factors such as the species involved and the quality of assembly, researchers must carefully select the appropriate tool based on the actual data. Alternatively, our test suggests that joint prediction could behave better than any single model if proper models were chosen. All scripts/data used in this research can be accessed at http://bioinfo.ihb.ac.cn/elit.

Background: Grass carp (Ctenopharyngodon idellus) are important species in Asian aquaculture. A draft genome for grass carp has already been published in 2015. However, there is still a requirement for a suitable genetic linkage map to arrange scaffolds on chromosomal frameworks. QTL analysis is a powerful tool to detect key locations for quantitative traits, especially in aquaculture. There no growth related QTLs of grass carp have been published yet. Even the growth trait is one of the focuses in grass carp culture.Results: In this study, a pair of distantly related parent grass carps and their 100 six-month-old full-sib offspring were used to construct a high-density genetic map with 6429 single nucleotide polymorphisms (SNPs) by 2b-RAD technology. The total length of the consensus map is 5553.43 cM with the average marker interval of 1.92 cM. The map has a good collinearity with both the grass carp draft genome and the zebrafish genome, and it assembled 89.91% of the draft genome to a chromosomal level. Additionally, according to the growth-related traits of progenies, 30 quantitative trait loci (QTLs), including 7 for body weight, 9 for body length, 5 for body height and 9 for total length, were identified in 16 locations on 5 linkage groups. The phenotypic variance explained for these QTLs varies from 13.4 to 21.6%. Finally, 17 genes located in these regions were considered to be growth-related because they either had functional mutations predicted from the resequencing data of the parents.Conclusion: A high density genetic linkage map of grass carp was built and it assembled the draft genome to a chromosomal level. Thirty growth related QTLs were detected. After the cross analysis of Parents resequencing data, 17 candidate genes were obtained for further researches.

Grass carp (Ctenopharyngodon idella) is the most productive freshwater aquaculture fish in worldwide. However, the molecular mechanism of its growth traits has not been fully elucidated. Whole transcriptome analysis of the brain and hepatopancreas of 29 six-month-old grass carp with different growth rates was performed. Weighted gene co-expression network analysis (WGCNA) was used to construct a weighted gene co-expression network of mRNA, miRNA and lncRNA separately. A total of 35 hub mRNAs, 47 hub lncRNAs and 33 hub miRNAs were identified from the brain, 37 hub mRNAs, 110 hub lncRNAs and 36 hub miRNAs were identified from the hepatopancreas. The ceRNA networks in the brain and hepatopancreas were involved in brain development and nutrient metabolism, respectively. Overall, this is the first investigation of the growth-related transcriptomic characteristics in the brain and hepatopancreas of grass carp, thus will help us to further explore the molecular mechanism of grass carp growth rate.

With the continuous development of high-throughput sequencing technology, bioinformatic analysis of omics data plays an increasingly important role in life science research. Many R packages are widely used for omics analysis, such as DESeq2, clusterProfiler and STRINGdb. And some online tools based on them have been developed to free bench scientists from programming with these R packages. However, the charts generated by these tools are usually in a fixed, non-editable format and often fail to clearly demonstrate the details the researchers intend to express. To address these issues, we have created Visual Omics, an online tool for omics data analysis and scientific chart editing. Visual Omics integrates multiple omics analyses which include differential expression analysis, enrichment analysis, protein domain prediction and protein–protein interaction analysis with extensive graph presentations. It can also independently plot and customize basic charts that are involved in omics analysis, such as various PCA/PCoA plots, bar plots, box plots, heat maps, set intersection diagrams, bubble charts and volcano plots. A distinguishing feature of Visual Omics is that it allows users to perform one-stop omics data analyses without programming, iteratively explore the form and layout of graphs online and fine-tune parameters to generate charts that meet publication requirements. Visual Omics can be used at http://bioinfo.ihb.ac.cn/visomics. Source code can be downloaded at http://bioinfo.ihb.ac.cn/software/visomics/visomics-1.1.tar.gz. Supplementary data are available at Bioinformatics online.