Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing

Abstract

Accurate variant calling and genotyping represent major limiting factors for downstream applications of single-cell genomics. Here, we report Conbase for the identification of somatic mutations in single-cell DNA sequencing data. Conbase leverages phased read data from multiple samples in a dataset to achieve increased confidence in somatic variant calls and genotype predictions. Comparing the performance of Conbase to three other methods, we find that Conbase performs best in terms of false discovery rate and specificity and provides superior robustness on simulated data, in vitro expanded fibroblasts and clonal lymphocyte populations isolated directly from a healthy human donor. Electronic supplementary material The online version of this article (10.1186/s13059-019-1673-8) contains supplementary material, which is available to authorized users.

Parallel RNA and DNA analysis after deep sequencing (PRDD-seq) reveals cell type-specific lineage patterns in human brain

“…To expand the utility of scMH when a matched bulk sample is unavailable, we further designed a "bulk-free" mode that can utilize a "synthetic" bulk WGS dataset, generated by in silico merging of the many WGS datasets of multiple single cells obtained from the same donor. We benchmarked scMH using 45× single-cell WGS of 24 neurons-22 of which were sequenced in previous studies (16,17) -as well as ∼200× bulk WGS of PFC (both from the brain of the same individual, UMB1465, who died at age 17 with no neurological diagnosis) against existing single-cell sSNV callers including Monovar (22), SCcaller (23), LiRA (24), and Conbase (25). Sensitivity and false discovery rate (FDR) were estimated based on experimentally validated mutations and clade annotations identified previously (16).…”

Eleven grand challenges in single-cell data science

“…Furthermore, such models could integrate the structure of cell lineage trees with the structure implicit in haplotypes that link alleles. For haplotype phasing, Satas and Raphael [241] recently proposed an approach based on contiguous stretches of amplification bias (similar to SCcaller, see above), whereas others propose read-backed phasing in two recent studies [242,243]. In addition, the integration with deep bulk sequencing data, as well as with scRNA-seq data, remains unexplored, although it promises to improve the precision of callers without compromising sensitivity.…”

Single-cell sequencing techniques from individual to multiomics analyses

“…Care must be taken in the analysis of such genome sequencing data, especially in the detection of point mutations. Bioinformatics methods such as SCcaller 39 , Monovar 40 , LiRA 41 , and Conbase 42 have been developed to detect single-nucleotide variants (SNVs) considering allelic dropout and amplification artifacts. For automatic library construction, the C1 system supports single-cell whole-genome and whole-exome sequencing.…”