MOTIVATION: One major challenge of interpreting variants from genome-wide association studies (GWAS) of complex traits or diseases is how to efficiently annotate noncoding variants. These variants influence gene expression by disrupting cis-regulatory elements (CREs), whose spatial and cell-type specificity are not adequately captured by conventional tools like multi-marker analysis of genomic annotation. Current methods either rely on linear proximity to genes or quantitative trait locus (QTL) data yet fail to integrate single-cell epigenomic information for a comprehensive annotation. RESULTS: We present SC-VAR, a novel computational tool designed to enhance the interpretation of disease-associated risks from GWAS using single-cell epigenomic data. SC-VAR leverages single-cell epigenomic data to predict functional outcomes including risk genes, pathways, and cell types for both coding and noncoding disease-associated variants. We demonstrate that SC-VAR outperforms state-of-the-art methods by predicting more validated disease-related genes and pathways for multiple diseases. Additionally, SC-VAR identifies cell types that are susceptible to disease, along with their specific CREs and target genes linked to risk. By capturing a broad range of disease risks across human tissues at distinct developmental stages, SC-VAR could enhance our understanding of disease mechanisms in complex tissues across different life stages.

MOTIVATION: Nucleosome depletion around cis-regulatory elements (CREs) is associated with CRE activity and implies the underlying gene regulatory network. Single-cell micrococcal nuclease sequencing (scMNase-seq) enables the simultaneous measurement of nucleosome positioning and chromatin accessibility at single-cell resolution, thereby capturing cellular heterogeneity in epigenetic regulation. However, there is currently no computational tool specifically designed to decode scMNase-seq data, impeding the generation of more precise and context-dependent insights into chromatin dynamics and gene regulation. RESULTS: Here, we present scNucMap, a tool designed to leverage the unique characteristics of scMNase-seq data to map the landscapes of candidate nucleosome-free regions (NFRs). scNucMap demonstrated superior performance and robustness in cell clustering on scMNase-seq data compared to Signac and chromVAR across diverse sample compositions and data complexities, achieving higher overall accuracy and Kappa coefficients. Additionally, scNucMap identified significant TFs associated with nucleosome depletion at CREs at both single-cell and cell-cluster levels, thereby facilitating cell-type annotation and regulatory network inference. When applied to scATAC-seq data, scNucMap enriched standard analyses with complementary insights into nucleosome architecture, underscoring its cross‑modality versatility. Overall, scNucMap exhibits both high reliability and adaptability, making it an effective tool for analyzing scMNase-seq data and supporting multimodal studies, thereby illuminating the intricate relationship between regulatory networks and nucleosome positioning at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: scNucMap is available at https://github.com/qianming-bioinfo/scNucMap.

BACKGROUND: Next-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed. RESULTS: We conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others. CONCLUSIONS: We developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.

MOTIVATION: A high-quality assembly of reads generated from shotgun sequencing is a substantial step in metagenome projects. Although traditional assemblers have been employed in initial analysis of metagenomes, they cannot surmount the challenges created by the features of metagenomic data. RESULT: We present a de novo assembly approach and its implementation named MAP (metagenomic assembly program). Based on an improved overlap/layout/consensus (OLC) strategy incorporated with several special algorithms, MAP uses the mate pair information, resulting in being more applicable to shotgun DNA reads (recommended as >200 bp) currently widely used in metagenome projects. Results of extensive tests on simulated data show that MAP can be superior to both Celera and Phrap for typical longer reads by Sanger sequencing, as well as has an evident advantage over Celera, Newbler and the newest Genovo, for typical shorter reads by 454 sequencing. AVAILABILITY AND IMPLEMENTATION: The source code of MAP is distributed as open source under the GNU GPL license, the MAP program and all simulated datasets can be freely available at http://bioinfo.ctb.pku.edu.cn/MAP/