Although RNA sequencing (RNA‐seq) has become the most advanced technology for transcriptome analysis, it also confronts various challenges. As we all know, the workflow of RNA‐seq is extremely complicated and it is easy to produce bias. This may damage the quality of RNA‐seq dataset and lead to an incorrect interpretation for sequencing result. Thus, our detailed understanding of the source and nature of these biases is essential for the interpretation of RNA‐seq data, finding methods to improve the quality of RNA‐seq experimental, or development bioinformatics tools to compensate for these biases. Here, we discuss the sources of experimental bias in RNA‐seq. And for each type of bias, we discussed the method for improvement, in order to provide some useful suggestions for researcher in RNA‐seq experimental.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics’ characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

As ammonia nitrogen pollution poses a critical threat to aquaculture sustainability and juvenile cichlid health remains poorly understood, this study investigated the physiological and microbiological responses of juvenile cichlid Pseudotropheus zebra (7.9 ± 0.5 g) to acute ammonia stress. The total ammonia nitrogen concentrations of 0 (control group), 5, and 12.25 mg/L were setup. Then, samples were collected at 0, 12, 24, 48, and 96 h ammonia nitrogen stress. The results showed that the indexes such as respiratory enzyme activity, immune, and antioxidant related enzyme in ammonia nitrogen stress were significantly ( p < 0.05) changed than that of control group. After 96 h of ammonia nitrogen stress, morphology and structure index of gills, liver, and intestines in ammonia nitrogen groups were remarkably ( p < 0.05) changed with compared to control group. Exposure to higher concentrations of ammonia nitrogen results in more severe damage, including intestinal swelling and increased of muscle layer thickness. There were significant ( p < 0.05) differences in intestinal microorganisms between ammonia nitrogen stress and control group. The results of α‐diversity analysis revealed that ammonia nitrogen stress significantly altered the abundance and community structure of intestinal microorganisms. The abundance of beneficial bacteria, such as Firmicutes, was reduced in ammonia nitrogen stress than that of control group. The activity of respiratory enzyme, immune, and antioxidant related enzyme were remarkably ( p < 0.05) correlated with abundance changes of beneficial bacterial communities, including Acinetobacter , Blautia , and Lactobacillus . Overall, these findings provide novel insights into the interactions among nitrogen stress and intestinal microbiota and can guide practices for culture of juvenile cichlid Pseudotropheus zebra .

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