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Pooled CRISPR screen is a promising tool in drug targets or essential genes identification combined with different phenotype readouts. Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects. In this issue, Zhao et al. provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing [Detail] ...
Background: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.
Results: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.
Conclusion: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.
Background: MicroRNAs (miRNAs) play an essential role in various biological processes and signaling pathways through the regulation of gene expression and genome stability. Recent data indicated that the next-generation sequencing (NGS)-based high-throughput quantification of miRNAs from biofluids provided exciting possibilities for discovering biomarkers of various diseases and might help promote the development of the early diagnosis of cancer. However, the complex process of library construction for sequencing always introduces bias, which may twist the actual expression levels of miRNAs and reach misleading conclusions.
Results: We discussed the deviation issue in each step during constructing miRNA sequencing libraries and suggested many strategies to generate high-quality data by avoiding or minimizing bias. For example, improvement of adapter design (a blocking element away from the ligation end, a randomized fragment adjacent to the ligation junction and UMI) and optimization of ligation conditions (a high concentration of PEG 8000, reasonable incubation temperature and time, and the selection of ligase) in adapter ligation, high-quality input RNA samples, removal of adapter dimer (solid phase reverse immobilization (SPRI) magnetic bead, locked nucleic acid (LNA) oligonucleotide, and Phi29 DNA polymerase), PCR (linear amplification, touch-down PCR), and product purification are essential factors for achieving high-quality sequencing data. Moreover, we described several protocols that exhibit significant advantages using combinatorial optimization and commercially available low-input miRNA library preparation kits.
Conclusions: Overall, our work provides the basis for unbiased high-throughput quantification of miRNAs. These data will help achieve optimal design involving miRNA profiling and provide reliable guidance for clinical diagnosis and treatment by significantly increasing the credibility of potential biomarkers.
Background: Single-cell RNA sequencing (scRNA-seq) technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes. Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples. Therefore, benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data. However, recent comparing efforts were constrained in sequencing platforms or analyzing pipelines. There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity, precision, and so on.
Methods: We downloaded public scRNA-seq data that was generated by two distinct sequencers, NovaSeq 6000 and MGISEQ 2000. Then data was processed through the Drop-seq-tools, UMI-tools and Cell Ranger pipeline respectively. We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.
Results: We found that all three pipelines had comparable performance, the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.
Conclusions: Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.
Background: There is an urgent demand of drug or therapy to control the COVID-19. Until July 22, 2021 the worldwide total number of cases reported is more than 192 million and the total number of deaths reported is more than 4.12 million. Several countries have given emergency permission for use of repurposed drugs for the treatment of COVID-19 patients. This report presents a computational analysis on repurposing drugs—tenofovir, bepotastine, epirubicin, epoprostenol, tirazavirin, aprepitant and valrubicin, which can be potential inhibitors of the COVID-19.
Method: Density functional theory (DFT) technique is applied for computation of these repurposed drug. For geometry optimization, functional B3LYP/6-311G (d, p) is selected within DFT framework.
Results: DFT based descriptors—highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap, molecular hardness, softness, electronegativity, electrophilicity index, nucleophilicity index and dipole moment of these species are computed. IR and Raman activities are also analysed and studied. The result shows that the HOMO-LUMO gap of these species varies from 1.061 eV to 5.327 eV. Compound aprepitant with a HOMO-LUMO gap of 1.419 eV shows the maximum intensity of IR (786.176 km mol‒1) and Raman spectra (15036.702 a.u.).
Conclusion: Some potential inhibitors of COVID-19 are studied by using DFT technique. This study shows that epirubicin is the most reactive compound whereas tenofovir is found to be the most stable. Further analysis and clinical trials of these compounds will provide more insight.
Background: Cows actions are important factors of cows health and their well-being. By monitoring the individual cows actions, we prevent cows diseases and realize modern precision cows rearing. However, traditional cows actions monitoring is usually conducted through video recording or direct visual observation, which is time-consuming and laborious, and often lead to misjudgement due to the subjective consciousness or negligence.
Methods: This paper proposes a method of cows actions recognition based on tracked trajectories to automatically recognize and evaluate the actions of cows. First, we construct a dataset including 60 videos to describe the popular actions existing in the daily life of cows, providing the basic data for designing our actions recognition method. Second, eight famous trackers are used to track and obtain temporal and spatial information of targets. Third, after studying and analysing the tracked trajectories of different actions about cows, a rigorous and effective constraint method is designed to realize actions recognition by us.
Results: Many experiments demonstrate that our method of actions recognition performs favourably in detecting the actions of cows, and the proposed dataset basically satisfies the actions evaluation for farmers.
Conclusion: The proposed tracking guided actions recognition provides a feasible way to maintain and promote cows health and welfare.
Background: As one of the leading causes of global disability, major depressive disorder (MDD) places a noticeable burden on individuals and society. Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD, studies in applying functional magnetic resonance imaging (fMRI) are still faced with challenges, including the representational ambiguity, small sample size, low statistical power, relatively high false positive rates, etc. Thus, reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.
Methods: In this systematic review, we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size, adequate control of head motion, and a proper multiple comparison control strategy.
Results: We found consistent evidence regarding the dysfunction within and among the default mode network (DMN), the frontoparietal network (FPN), and other brain regions. However, controversy remains, probably due to the heterogeneity of participants and data processing strategies.
Conclusion: Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements, consider the heterogeneity of MDD patients and other potentially confounding factors, apply surface-based neuroscientific network fMRI approaches, and advance research transparency and open science by applying state-of-the-art pipelines along with open data sharing.
Background: China is a multi-ethnic country. It is of great significance for the skull identification to realize the skull ethnic classification through computers, which can promote the development of forensic anthropology and accelerate the exploration of national development.
Methods: In this paper, the 3D skull model is transformed into 2D auxiliary image including curvature, depth and elevation information, and then the deep learning method of the 2D auxiliary image is used for ethnic classification. We construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image classification. In order to optimize the network, Adam algorithm is adopted to avoid falling into local minimum, and to ensure the stability of the algorithm with regularization terms.
Results: Experiments on 400 skull models have been conducted for ethnic classification by our method. We set different learning rates to compare the performance of the model, the highest accuracy of ethnic classification is 98.75%, which have better performance than other five classical neural network structures.
Conclusions: Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance.
Background: Fear of negative evaluation (FNE), referring to negative expectation and feelings toward other people’s social evaluation, is closely associated with social anxiety that plays an important role in our social life. Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research (e.g., studies on social anxiety).
Methods: To search for potentially relevant biomarkers of FNE in human brain, the current study applied multivariate relevance vector regression, a machine-learning and data-driven approach, on brain morphological features (e.g., cortical thickness) derived from structural imaging data; further, we used these features as indexes to predict self-reported FNE score in each participant.
Results: Our results confirm the predictive power of multiple brain regions, including those engaged in negative emotional experience (e.g., amygdala, insula), regulation and inhibition of emotional feeling (e.g., frontal gyrus, anterior cingulate gyrus), and encoding and retrieval of emotional memory (e.g., posterior cingulate cortex, parahippocampal gyrus).
Conclusions: The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems, from primitive subcortical mechanisms to sophisticated cortical processes.
