SingleScan: a comprehensive resource for single-cell sequencing data processing and mining.

Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data.

BACKGROUND: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference. RESULTS: In the present study, an algorithm called "ATACAmp" that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells. CONCLUSIONS: ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp . Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

A linear and circular dual-conformation noncoding RNA involved in oxidative stress tolerance in Bacillus altitudinis.

Circular RNAs have been extensively studied in eukaryotes, but their presence and/or biological functionality in bacteria are unclear. Here, we show that a regulatory noncoding RNA (DucS) exists in both linear and circular conformation in Bacillus altitudinis. The linear forms promote B. altitudinis tolerance to H2O2 stress, partly through increased translation of a stress-responsive gene, htrA. The 3' end sequences of the linear forms are crucial for RNA circularization, and formation of circular forms can decrease the levels of the regulatory linear cognates. Bioinformatic analysis of available RNA-seq datasets from 30 bacterial species revealed multiple circular RNA candidates, distinct from DucS, for all the examined species. Experiments testing for the presence of selected circular RNA candidates in four species successfully validated 7 out of 9 candidates from B. altitudinis and 4 out of 5 candidates from Bacillus paralicheniformis; However, none of the candidates tested for Bacillus subtilis and Escherichia coli were detected. Our work identifies a dual-conformation regulatory RNA in B. altitutidinis, and indicates that circular RNAs exist in diverse bacteria. However, circularization of specific RNAs does not seem to be conserved across species, and the circularization mechanisms and biological functionality of the circular forms remain unclear.

Widespread Dissemination of Plasmid-Mediated Tigecycline Resistance Gene tet(X4) in Enterobacterales of Porcine Origin.

The emergence of the plasmid-mediated high levels of the tigecycline resistance gene has drawn worldwide attention and has posed a major threat to public health. In this study, we investigated the prevalence of the tet(X4)-positive Enterobacterales isolates collected from a pig slaughterhouse and farms. A total of 101 tigecycline resistance strains were isolated from 353 samples via a medium with tigecycline, of which 33 carried tet(X4) (9.35%, 33/353) and 2 carried tet(X6) (0.57%, 2/353). These strains belong to seven different species, with Escherichia coli being the main host bacteria. Importantly, this report is the first one to demonstrate that tet(X4) was observed in Morganella morganii. Whole-genome sequencing results revealed that tet(X4)-positive bacteria can coexist with other resistance genes, such as blaNDM-1 and cfr. Additionally, we were the first to report that tet(X4) and blaNDM-1 coexist in a Klebsiella quasipneumoniae strain. The phylogenetic tree of 533 tet(X4)-positive E. coli strains was constructed using 509 strains from the NCBI genome assembly database and 24 strains from this study, which arose from 8 sources and belonged to 135 sequence types (STs) worldwide. We used Nanopore sequencing to interpret the selected 21 nonclonal and representative strains and observed that 19 tet(X4)-harboring plasmids were classified into 8 replicon types, and 2 tet(X6) genes were located on integrating conjugative elements. A total of 68.42% of plasmids carrying tet(X4) were transferred successfully with a conjugation frequency of 10-2 to 10-7. These findings highlight that diverse plasmids drive the widespread dissemination of the tigecycline resistance gene tet(X4) in Enterobacterales of porcine origin. IMPORTANCE Tigecycline is considered to be the last resort of defense against diseases caused by broad-spectrum resistant Gram-negative bacteria. In this study, we systematically analyzed the prevalence and genetic environments of the resistance gene tet(X4) in a pig slaughterhouse and farms and the evolutionary relationship of 533 tet(X4)-positive Escherichia coli strains, including 509 tet(X4)-positive E. coli strains selected from the 27,802 assembled genomes of E. coli from the NCBI between 2002 and 2022. The drug resistance of tigecycline is widely prevalent in pig farms where tetracycline is used as a veterinary drug. This prevalence suggests that pigs are a large reservoir of tet(X4) and that tet(X4) can spread horizontally through the food chain via mobile genetic elements. Furthermore, tetracycline resistance may drive tigecycline resistance through some mechanisms. Therefore, it is important to monitor tigecycline resistance, develop effective control measures, and focus on tetracycline use in the pig farms.