DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data

Effects and mechanisms of decabromodiphenyl ethane on Chlorella sorokiniana: Transcriptomics, proteins and fatty acid production

Decabromodiphenyl ethane is a novel brominated flame retardant, that has always been dissolved in organic solvents to explore its activities on aquatic organisms. In this study, the influences of decabromodiphenyl ethane on the microalga Chlorella sorokiniana (C. sorokiniana) were studied, and three microalgae treatments, including decabromodiphenyl ethane dissolved in dimethyl sulfoxide solvent (DBDPE treatment), dimethyl sulfoxide alone (control II) or untreated (control I) were used in the experiment, respectively. The results showed that the growth of C. sorokiniana was remarkably enhanced in the DBDPE treatment compared with the control I and II groups. Conjoint analysis of transcriptomics and quantitative proteome displayed that the upregulated differentially expressed genes and proteins of DBDPE:control I were enriched in 6 pathways, and downregulated genes/proteins of DBDPE:control I were enriched in 3 pathways. The upregulated differentially expressed genes and proteins of DBDPE:control II were enriched in 4 pathways, and downregulated genes/proteins of DBDPE:control II were enriched in 6 pathways. In addition, decabromodiphenyl ethane changed the fatty acid concentration in C. sorokiniana cells. The activities of superoxide dismutase were enhanced when C. sorokiniana were treated by decabromodiphenyl ethane. The data highlighted that the mRNA and protein expression relating to the fatty acid production, of C. sorokiniana were significantly affected by decabromodiphenyl ethane, and decabromodiphenyl ethane pollution changed the physiological metabolism of microalgae and had harmful effects on natural environments.

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Background

RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis.

Results

The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (P_DEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm.

Conclusions

MBCdeg with DEGES normalization can be used in the identification of DEGs when the P_DEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04438-4.

An improved algorithm for mining media content application patterns based on QPop increasing disk time domain segmentation and upgrading1

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.

The regulatory genome of the malaria vector Anopheles gambiae: integrating chromatin accessibility and gene expression

Anopheles gambiae mosquitoes are primary human malaria vectors, but we know very little about their mechanisms of transcriptional regulation. We profiled chromatin accessibility by the assay for transposase-accessible chromatin by sequencing (ATAC-seq) in laboratory-reared A. gambiae mosquitoes experimentally infected with the human malaria parasite Plasmodium falciparum. By integrating ATAC-seq, RNA-seq and ChIP-seq data, we showed a positive correlation between accessibility at promoters and introns, gene expression and active histone marks. By comparing expression and chromatin structure patterns in different tissues, we were able to infer cis-regulatory elements controlling tissue-specific gene expression and to predict the in vivo binding sites of relevant transcription factors. The ATAC-seq assay also allowed the precise mapping of active regulatory regions, including novel transcription start sites and enhancers that were annotated to mosquito immune-related genes. Not only is this study important for advancing our understanding of mechanisms of transcriptional regulation in the mosquito vector of human malaria, but the information we produced also has great potential for developing new mosquito-control and anti-malaria strategies.

Gene expression concerning fatty acid and amino acid metabolism in Chlorella vulgaris cultured with antibiotics

The green alga Chlorella vulgaris has potential as a source of clean bioenergy with abundant metabolites and a high oil content, and antibiotics are often applied to remove bacteria from culture to obtain axenic algal strains. In this work, ceftazidime and gentamicin sulphate (GS) were added separately and in combination in the aseptic processing of C. vulgaris, and gene expression and metabolites were evaluated. The results showed that ceftazidime and GS effectively inhibited the proliferation of Cyanobacteria and Bacteroidetes, respectively. Overall, the effects of antibiotics on C. vulgaris differed: GS increased the algal concentration, whereas ceftazidime alone and in combination with GS treatment decreased the specific algal growth rate. Based on comparative transcription analysis, 5917 and 5899 differentially expressed genes (DEGs) were respectively upregulated and downregulated by ceftazidime, 963 and 3921 DEGs by GS, and 4532 and 1675 DEGs by the ceftazidime and GS combination. Pathway enrichment analysis showed that the downregulated DEGs in the ceftazidime groups were enriched in the fatty acid biosynthesis pathway but that the upregulated DEGs in the GS group were enriched in the fatty acid degradation pathway. Some pathways related to amino acid metabolism were markably influenced by antibiotic treatment. The results further indicated that antibiotics affected the intracellular concentration of fatty acids and amino acids in C. vulgaris. This study provides a new viewpoint regarding the response of C. vulgaris to antibiotics in the process of obtaining axenic algal strains. KEY POINTS: • Ceftazidime and gentamicin sulphate influenced bacterial proliferation. • Downregulated differentially expressed genes mapped to the fatty acid biosynthesis pathway. • Antibiotics affected intracellular concentrations of fatty acids and amino acids.

MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq

BACKGROUND

Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is a popular sequencing method for studying RNA modifications and, in particular, for N6-methyladenosine (m6A), the most abundant RNA methylation modification found in various species. The detection of enriched regions is a main challenge of MeRIP-Seq analysis, however current tools either require a long time or do not fully utilize features of RNA sequencing such as strand information which could cause ambiguous calling. On the other hand, with more attention on the treatment experiments of MeRIP-Seq, biologists need intuitive evaluation on the treatment effect from comparison. Therefore, efficient and user-friendly software that can solve these tasks must be developed.

RESULTS

We developed a software named "model-based analysis and inference of MeRIP-Seq (MoAIMS)" to detect enriched regions of MeRIP-Seq and infer signal proportion based on a mixture negative-binomial model. MoAIMS is designed for transcriptome immunoprecipitation sequencing experiments; therefore, it is compatible with different RNA sequencing protocols. MoAIMS offers excellent processing speed and competitive performance when compared with other tools. When MoAIMS is applied to studies of m6A, the detected enriched regions contain known biological features of m6A. Furthermore, signal proportion inferred from MoAIMS for m6A treatment datasets (perturbation of m6A methyltransferases) showed a decreasing trend that is consistent with experimental observations, suggesting that the signal proportion can be used as an intuitive indicator of treatment effect.

CONCLUSIONS

MoAIMS is efficient and easy-to-use software implemented in R. MoAIMS can not only detect enriched regions of MeRIP-Seq efficiently but also provide intuitive evaluation on treatment effect for MeRIP-Seq treatment datasets.