Single-cell transcriptomics is a powerful approach for characterizing gene transcription at cellular resolution. This approach requires efficient computational pipelines to undertake essential tasks, including clustering, dimensionality reduction, imputation, and denoising. Currently, most such pipelines undertake these computational tasks separately without considering the interdependence among these tasks. Here, we present an advanced pipeline, MUSIC-GCN, by employing a graph convolutional neural (GCN) network and autoencoder to perform multi-task single-cell RNA-sequencing (scRNA-seq) data analysis. The rationale is that multiple related tasks can be carried out simultaneously to enable enhanced learning and more effective representations through the 'sharing of knowledge' regarding individual tasks. Benchmarking experiments using various scRNA-seq datasets show that MUSIC-GCN can achieve a competitive performance on multi-tasks when benchmarked with state-of-the-art approaches.

MOTIVATION: High-throughput sequencing technologies [next-generation sequencing (NGS)] are increasingly used to address diverse biological questions. Despite the rich information in NGS data, particularly with the growing datasets from repositories like the Genome Sequence Archive (GSA) at NGDC, programmatic access to public sequencing data and metadata remains limited. RESULTS: We developed iSeq to enable quick and straightforward retrieval of metadata and NGS data from multiple databases via the command-line interface. iSeq supports simultaneous retrieval from GSA, SRA, ENA, and DDBJ databases. It handles over 25 different accession formats, supports Aspera downloads, parallel downloads, multi-threaded processes, FASTQ file merging, and integrity verification, simplifying data acquisition and enhancing the capacity for reanalyzing NGS data. AVAILABILITY AND IMPLEMENTATION: iSeq is freely available on Bioconda (https://anaconda.org/bioconda/iseq) and GitHub (https://github.com/BioOmics/iSeq).

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Deoxyribonuclease I (DNase I)-hypersensitive site sequencing (DNase-seq) has been widely used to determine chromatin accessibility and its underlying regulatory lexicon. However, exploring DNase-seq data requires sophisticated downstream bioinformatics analyses. In this study, we first review computational methods for all of the major steps in DNase-seq data analysis, including experimental design, quality control, read alignment, peak calling, annotation of cis-regulatory elements, genomic footprinting and visualization. The challenges associated with each step are highlighted. Next, we provide a practical guideline and a computational pipeline for DNase-seq data analysis by integrating some of these tools. We also discuss the competing techniques and the potential applications of this pipeline for the analysis of analogous experimental data. Finally, we discuss the integration of DNase-seq with other functional genomics techniques.

SUMMARY: MyBioNet is a web-based application for biological network analysis, which provides user-friendly web interfaces to visualize, edit and merge biological networks. In addition, MyBioNet integrated KEGG metabolic network data from 1366 organisms and allows users to search and navigate interesting networks. AVAILABILITY AND IMPLEMENTATION: All KEGG metabolic network data are organized and stored in the MySQL database. MyBioNet is implemented in Flex/Actionscript and PHP languages and deployed on an Apache web server. MyBioNet is accessible through all the Flash-embedded browsers at http://bis.zju.edu.cn/mybionet/. CONTACT: [email protected].

High-throughput sequencing (HTS) has opened up a new era for small RNA (sRNA) exploration. Using HTS data for a global survey of sRNAs in 26 angiosperms, elevated GC contents were detected in the monocots, whereas the 5(')-terminal compositions were quite uniform among the angiosperms. Chromosome-wide distribution patterns of sRNAs were investigated by using scrolling-window analysis. We performed de novo natural antisense transcript (NAT) prediction, and found that the overlapping regions of trans-NATs, but not cis-NATs, were hotspots for sRNA generation. One cis-NAT generates phased natural antisense short interfering RNAs (nat-siRNAs) specifically from flowers in Arabidopsis, while one in rice produces phased nat-siRNAs from grains, suggesting their organ-specific regulatory roles.