In single-cell transcriptomics, the accurate identification of cell-type-specific marker genes is fundamental for understanding cellular heterogeneity. However, existing approaches often rely on arbitrary thresholds and clustering heuristics, making them sensitive to noise, annotation bias, and prone to capturing highly expressed rather than truly specific genes. Here, we present scMarkerGene, an interpretable neural network framework for marker gene discovery. scMarkerGene constructs a Contribution Score (CS) matrix that quantitatively measures each gene's influence on cell-type discrimination, transforming neural network predictions into gene-level importance signals. Through a downstream specificity filtering process, scMarkerGene robustly captures cell-type-distinguishing features and demonstrates strong robustness to dropout noise, diverse cell population sizes, varying annotation resolutions, and heterogeneous sequencing technologies. We comprehensively validated scMarkerGene across scRNA-seq datasets from multiple species, as well as on spatial transcriptomics and discretized pseudotime data, where it effectively identified dynamic and spatially resolved marker genes. Collectively, scMarkerGene provides an efficient, accurate, and interpretable framework for single-cell transcriptomic analysis, with broad potential for extension to multi-omics data integration and interpretation. The source code is available at https://scmarkergene.zhaopage.com/.

Abstract Summary: Pan-genome analyses have shed light on the dynamics and evolution of bacterial genome from the point of population. The explosive growth of bacterial genome sequence also brought an extremely big challenge to pan-genome profile analysis. We developed a tool, named PanGP, to complete pan-genome profile analysis for large-scale strains efficiently. PanGP has integrated two sampling algorithms, totally random (TR) and distance guide (DG). The DG algorithm drew sample strain combinations on the basis of genome diversity of bacterial population. The performance of these two algorithms have been evaluated on four bacteria populations with strain numbers varying from 30 to 200, and the DG algorithm exhibited overwhelming advantage on accuracy and stability than the TR algorithm. Availability: PanGP was developed with a user-friendly graphic interface and it was available at http://PanGP.big.ac.cn. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: With the rapid development of DNA sequencing technology, increasing bacteria genome data enable the biologists to dig the evolutionary and genetic information of prokaryotic species from pan-genome sight. Therefore, the high-efficiency pipelines for pan-genome analysis are mostly needed. We have developed a new pan-genome analysis pipeline (PGAP), which can perform five analytic functions with only one command, including cluster analysis of functional genes, pan-genome profile analysis, genetic variation analysis of functional genes, species evolution analysis and function enrichment analysis of gene clusters. PGAP's performance has been evaluated on 11 Streptococcus pyogenes strains. AVAILABILITY: PGAP is developed with Perl script on the Linux Platform and the package is freely available from http://pgap.sf.net. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.