Cell-level somatic mutation detection from single-cell RNA sequencing
Author(s) -
Trung Nghia Vu,
Ha-Nam Nguyen,
Stefano Calza,
Krishna R. Kalari,
Liewei Wang,
Yudi Pawitan
Publication year - 2019
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz288
Subject(s) - false positive paradox , computational biology , mutation , dna sequencing , biology , gene , genetics , computer science , artificial intelligence
Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.
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