Dozens of normalization methods for correcting experimental variation and bias in high-throughput expression data have been developed during the last two decades. Up to 23 methods among them consider the skewness of expression data between sample states, which are even more than the conventional methods, such as loess and quantile. From the perspective of reference selection, we classified the normalization methods for skewed expression data into three categories, data-driven reference, foreign reference, and entire gene set. We separately introduced and summarized these normalization methods designed for gene expression data with global shift between compared conditions, including both microarray and RNA-seq, based on the reference selection strategies. To our best knowledge, this is the most comprehensive review of available preprocessing algorithms for the unbalanced transcriptome data. The anatomy and summarization of these methods shed light on the understanding and appropriate application of preprocessing methods.

The CRISPR-Cas9 system has become the most promising and versatile tool for genetic manipulation applications. Albeit the technology has been broadly adopted by both academic and pharmaceutic societies, the activity (on-target) and specificity (off-target) of CRISPR-Cas9 are decisive factors for any application of the technology. Several in silico gRNA activity and specificity predicting models and web tools have been developed, making it much more convenient and precise for conducting CRISPR gene editing studies. In this review, we present an overview and comparative analysis of machine and deep learning (MDL)-based algorithms, which are believed to be the most effective and reliable methods for the prediction of CRISPR gRNA on- and off-target activities. As an increasing number of sequence features and characteristics are discovered and are incorporated into the MDL models, the prediction outcome is getting closer to experimental observations. We also introduced the basic principle of CRISPR activity and specificity and summarized the challenges they faced, aiming to facilitate the CRISPR communities to develop more accurate models for applying.

Endothelial barrier (EB) breaching is a frequent event during inflammation, and it is followed by the rapid recovery of microvascular integrity. The molecular mechanisms of EB recovery are poorly understood. Triggering of MHC molecules by migrating T-cells is a minimal signal capable of inducing endothelial contraction and transient microvascular leakage. Using this model, we show that EB recovery requires a CD31 receptor-induced, robust glycolytic response sustaining junction re-annealing. Mechanistically, this response involves src-homology phosphatase activation leading to Akt-mediated nuclear exclusion of FoxO1 and concomitant β-catenin translocation to the nucleus, collectively leading tocMyctranscription. CD31 signals also sustain mitochondrial respiration, however this pathway does not contribute to junction remodeling. We further show that pathologic microvascular leakage in CD31-deficient mice can be corrected by enhancing the glycolytic flux via pharmacological Akt or AMPK activation, thus providing a molecular platform for the therapeutic control of EB response.

The gut microbiota is associated with cardiovascular diseases, including atherosclerosis. However, the composition, functional capacity, and metabolites of the gut microbiome about atherosclerosis have not been comprehensively studied. Here, we reanalyzed 25 metagenomic stool samples from Sweden and 385 metagenomic stool samples from China using HUMAnN2, PanPhlAn, and MelonnPan to obtain more sufficient information. We found that the samples from atherosclerotic patients in both cohorts were depleted in Bacteroides xylanisolvens , Odoribacter splanchnicus , Eubacterium eligens , Roseburia inulinivorans, and Roseburia intestinalis . At the functional level, healthy metagenomes were both enriched in pathways of starch degradation V, glycolysis III (from glucose), CDP‐diacylglycerol biosynthesis, and folate transformations. R inulinivorans and R intestinalis are major contributors to starch degradation V, while E eligens greatly contribute to the pathway CDP‐diacylglycerol biosynthesis, and B xylanisolvens and B uniformis contribute to folate transformations II. The 11 marker species selected from the Chinese cohort distinguish patients from controls with an area under the receiver operating characteristics curve (AUC) of 0.86. Strain‐level microbial analysis revealed a geographically associated adaptation of the strains from E eligens , B uniformis , and E coli . Two gut microbial metabolites, nicotinic acid and hydrocinnamic acid, had significantly higher predicted abundance in the control samples compared to the patients in the Chinese cohort, and interestinglynicotinic acid is already an effective lipid‐lowering drug to reducing cardiovascular risk. Our results indicate intestinal bacteria such as B xylanisolvens , E eligens, and R inulinivorans could be promising probiotics and potential therapeutic target for atherosclerosis.

Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions.

RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.