Bandingkan kaedah

	Analisis Kepelbagaian Mikrobiom Sel Tunggal	Pemanggilan varian sel tunggal
Bidang	Bioinformatik	Bioinformatik
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2019-2022	2016 (Monovar; foundational single-cell SNV calling)
Pengasas	Paul Blainey lab and Bhatt lab (pioneered microSPLiT and single-microbe genomics approaches)	Hamim Zafar, Ken Chen, Nicholas Navin and colleagues
Jenis	Computational-experimental omics pipeline	Computational genomics pipeline
Sumber perintis	Kehe, J., Kulesa, A., Ortiz, A., Ackerman, C. M., Thakku, S. G., Sellers, D., Bhatt, S., ... & Blainey, P. C. (2019). Massively parallel screening of synthetic microbial communities. Proceedings of the National Academy of Sciences, 116(26), 12804-12809.	Zafar, H., Wang, Y., Nakhleh, L., Navin, N., & Chen, K. (2016). Monovar: single-nucleotide variant detection in single cells. Nature Methods, 13(6), 505–507.
Alias	sc-microbiome analysis, single-cell microbial profiling, single-bacterium sequencing, microSPLiT analysis	scVariant calling, single-cell SNV calling, scDNA-seq variant detection, single-cell somatic mutation calling
Berkaitan	3	1
Ringkasan	Single-cell microbiome diversity analysis resolves the composition and functional heterogeneity of microbial communities at the level of individual cells or bacteria. By combining single-cell or single-bacterium isolation with high-throughput sequencing, this pipeline overcomes the averaging effect of bulk metagenomics, enabling detection of rare strains, intra-species variation, and cell-to-cell heterogeneity within complex microbiomes such as the gut, oral cavity, or environmental samples.	Single-cell variant calling is a bioinformatics pipeline that identifies DNA sequence variants — single-nucleotide variants (SNVs), small insertions and deletions, and copy-number alterations — within individual cells rather than across a bulk tissue mixture. By resolving the mutational landscape cell by cell, it reveals intra-tumoral heterogeneity, clonal architecture, and somatic mutation patterns that bulk sequencing obscures. The approach is central to cancer genomics, developmental biology, and any study where cell-to-cell genetic diversity is the primary question.