With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Most carbon emission reduction studies in transportation focus on the natural use of motor vehicles. However, little attention has been paid to fatal driving violations that lead to the sunk costs of human resources investment and motor vehicle carbon emissions in the manufacturing process of motor vehicles. This article aimed to determine if a driver would be seriously harmful to human capital investment and carbon emissions reduction. 1893 drivers in China were formally surveyed by questionnaire to find out the statistical characteristics. Results indicated that different drivers show significant differences in different driving violations dimensions. Specific drivers showing horrible driving violation (HDV) and dangerous driving violation (DDV) mainly endanger personal security and environmental damage. The identification of the focused drivers would contribute to formulate more refined management policies in carbon emission reduction, improve natural resource utilization, raise human capital benefit, and promote social sustainable development.

Motivation: With the decreased cost of RNA-Seq, an increasing number of non-model organisms have been sequenced. Due to the lack of reference genomes, de novo transcriptome assembly is required. However, there is limited systematic research evaluating the quality of de novo transcriptome assemblies and how the assembly quality influences downstream analysis. Results: We used two authentic RNA-Seq datasets from Arabidopsis thaliana, and produced transcriptome assemblies using eight programs with a series of k-mer sizes (from 25 to 71), including BinPacker, Bridger, IDBA-tran, Oases-Velvet, SOAPdenovo-Trans, SSP, Trans-ABySS and Trinity. We measured the assembly quality in terms of reference genome base and gene coverage, transcriptome assembly base coverage, number of chimeras and number of recovered full-length transcripts. SOAPdenovo-Trans performed best in base coverage, while Trans-ABySS performed best in gene coverage and number of recovered full-length transcripts. In terms of chimeric sequences, BinPacker and Oases-Velvet were the worst, while IDBA-tran, SOAPdenovo-Trans, Trans-ABySS and Trinity produced fewer chimeras across all single k-mer assemblies. In differential gene expression analysis, about 70% of the significantly differentially expressed genes (DEG) were the same using reference genome and de novo assemblies. We further identify four reasons for the differences in significant DEG between reference genome and de novo transcriptome assemblies: incomplete annotation, exon level differences, transcript fragmentation and incorrect gene annotation, which we suggest that de novo assembly is beneficial even when a reference genome is available. Availability and Implementation: Software used in this study are publicly available at the authors' websites. Contact: [email protected] Supplimentary Information: Supplementary data are available at Bioinformatics online.

UNLABELLED: The NCI-60 human tumor cell line panel is an invaluable resource for cancer researchers, providing drug sensitivity, molecular and phenotypic data for a range of cancer types. CellMiner is a web resource that provides tools for the acquisition and analysis of quality-controlled NCI-60 data. CellMiner supports queries of up to 150 drugs or genes, but the output is an Excel file for each drug or gene. This output format makes it difficult for researchers to explore the data from large queries. CellMiner Companion is a web application that facilitates the exploration and visualization of output from CellMiner, further increasing the accessibility of NCI-60 data. AVAILABILITY AND IMPLEMENTATION: The web application is freely accessible at https://pul-bioinformatics.shinyapps.io/CellMinerCompanion The R source code can be downloaded at https://github.com/pepascuzzi/CellMinerCompanion.git CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.