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Background The fishery and aquaculture of the widely distributed mangrove crab Scylla serrata is a steadily growing, high-value, global industry. Climate change poses a risk to this industry as temperature elevations are expected to threaten the mangrove crab habitat and the supply of mangrove crab juveniles from the wild. It is therefore important to understand the genomic and molecular basis of how mangrove crab populations from sites with different climate profiles respond to heat stress. Towards this, we performed RNA-seq on the gill tissue of S. serrata individuals sampled from 3 sites (Cagayan, Bicol, and Bataan) in the Philippines, under normal and heat-stressed conditions. To compare the transcriptome expression profiles, we designed a 2-factor generalized linear model containing interaction terms, which allowed us to simultaneously analyze within-site response to heat-stress and across-site differences in the response. Results We present the first ever transcriptome assembly of S. serrata obtained from a data set containing 66 Gbases of cleaned RNA-seq reads. With lowly-expressed and short contigs excluded, the assembly contains roughly 17,000 genes with an N50 length of 2,366 bp. Our assembly contains many almost full-length transcripts – 5229 shrimp and 3049 fruit fly proteins have alignments that cover >80% of their sequence lengths to a contig. Differential expression analysis found population-specific differences in heat-stress response. Within-site analysis of heat-stress response showed 177, 755, and 221 differentially expressed (DE) genes in the Cagayan, Bataan, and Bicol group, respectively. Across-site analysis showed that between Cagayan and Bataan, there were 389 genes associated with 48 signaling and stress-response pathways, for which there was an effect of site in the response to heat; and between Cagayan and Bicol, there were 101 such genes affecting 8 pathways. Conclusion In light of previous work on climate profiling and on population genetics of marine species in the Philippines, our findings suggest that the variation in thermal response among populations might be derived from acclimatory plasticity due to pre-exposure to extreme temperature variations or from population structure shaped by connectivity which leads to adaptive genetic differences among populations.

BackgroundRNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive.ResultsHere we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence.ConclusionsRBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available athttp://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/.

Background The circadian rhythms regulate physiological functions and metabolism. Circadian Time (CT) is a unit to quantify the rhythm of endogenous circadian clock, independent of light influence. To understand the gene expression changes throughout CT, C57BL/6 J mice were maintained under constant darkness (DD) for 6 weeks, and the liver samples were collected starting at 9:00 AM (CT1), and every 4 h in a 24-h cycle (CT5, CT9, CT13, CT17 and CT21). Total RNA was extracted and subjected to RNA-Seq data (deposited as GSE 133342, L-DD). To compare gene oscillation pattern under normal light-dark condition (LD, GSE114400) and short time (2 days) dark-dark condition (S-DD, GSE70497), these data were retried from GEO database, and the trimmed mean of M-values normalization was used to normalize the three RNA-seq data followed by MetaCycle analysis. Results Approximate 12.1% of the genes under L-DD exhibited significant rhythmically expression. The top 5 biological processes enriched in L-DD oscillation genes were mRNA processing, aromatic compound catabolic process, mitochondrion organization, heterocycle catabolic process and cellular nitrogen compound mitotic catabolic process. The endogenous circadian rhythms of clock genes, P450 genes and lipid metabolism genes under L-DD were further compared with LD and S-DD. The oscillation patterns were similar but the period and amplitude of those oscillation genes were slightly altered. RT-qPCR confirmed the selected RNA sequence findings. Conclusions This is the first study to profile oscillation gene expressions under L-DD. Our data indicate that clock genes, P450 genes and lipid metabolism genes expressed rhythmically under L-DD. Light was not the necessary factor for persisting circadian rhythm but influenced the period and amplitude of oscillation genes.