SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

Vivekananda Sarangi, Alexandre Jourdon, Taejeong Bae, Arijit Panda, Flora Vaccarino, Alexej Abyzov

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA.However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results: We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion: Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

Original languageEnglish (US)
Article number521
JournalBMC bioinformatics
Volume21
Issue number1
DOIs
StatePublished - Dec 2020

Keywords

  • MDA
  • Single cell
  • Whole genome amplification

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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