Calibrating genomic and allelic coverage bias in single-cell sequencing
Artifacts introduced in whole-genome amplification (WGA) make it difficult to derive accurate genomic information from single-cell genomes and require different analytical strategies from bulk genome analysis. Here, we describe statistical methods to quantitatively assess the amplification bias resu...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Nature Publishing Group
2017
|
| Online Access: | http://hdl.handle.net/1721.1/111665 https://orcid.org/0000-0003-4555-2485 https://orcid.org/0000-0003-0921-3144 |
| _version_ | 1826214785020067840 |
|---|---|
| author | Zhang, Cheng-Zhong Francis, Joshua Cornils, Hauke Jung, Joonil Maire, Cecile Ligon, Keith L. Meyerson, Matthew Adalsteinsson, Viktor A. Love, John C |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Zhang, Cheng-Zhong Francis, Joshua Cornils, Hauke Jung, Joonil Maire, Cecile Ligon, Keith L. Meyerson, Matthew Adalsteinsson, Viktor A. Love, John C |
| author_sort | Zhang, Cheng-Zhong |
| collection | MIT |
| description | Artifacts introduced in whole-genome amplification (WGA) make it difficult to derive accurate genomic information from single-cell genomes and require different analytical strategies from bulk genome analysis. Here, we describe statistical methods to quantitatively assess the amplification bias resulting from whole-genome amplification of single-cell genomic DNA. Analysis of single-cell DNA libraries generated by different technologies revealed universal features of the genome coverage bias predominantly generated at the amplicon level (1–10 kb). The magnitude of coverage bias can be accurately calibrated from low-pass sequencing (∼0.1 × ) to predict the depth-of-coverage yield of single-cell DNA libraries sequenced at arbitrary depths. We further provide a benchmark comparison of single-cell libraries generated by multi-strand displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC). Finally, we develop statistical models to calibrate allelic bias in single-cell whole-genome amplification and demonstrate a census-based strategy for efficient and accurate variant detection from low-input biopsy samples. |
| first_indexed | 2024-09-23T16:11:06Z |
| format | Article |
| id | mit-1721.1/111665 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:11:06Z |
| publishDate | 2017 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1116652022-09-29T18:49:56Z Calibrating genomic and allelic coverage bias in single-cell sequencing Zhang, Cheng-Zhong Francis, Joshua Cornils, Hauke Jung, Joonil Maire, Cecile Ligon, Keith L. Meyerson, Matthew Adalsteinsson, Viktor A. Love, John C Massachusetts Institute of Technology. Department of Chemical Engineering Koch Institute for Integrative Cancer Research at MIT Love, John C Adalsteinsson, Viktor A. Love, John C Artifacts introduced in whole-genome amplification (WGA) make it difficult to derive accurate genomic information from single-cell genomes and require different analytical strategies from bulk genome analysis. Here, we describe statistical methods to quantitatively assess the amplification bias resulting from whole-genome amplification of single-cell genomic DNA. Analysis of single-cell DNA libraries generated by different technologies revealed universal features of the genome coverage bias predominantly generated at the amplicon level (1–10 kb). The magnitude of coverage bias can be accurately calibrated from low-pass sequencing (∼0.1 × ) to predict the depth-of-coverage yield of single-cell DNA libraries sequenced at arbitrary depths. We further provide a benchmark comparison of single-cell libraries generated by multi-strand displacement amplification (MDA) and multiple annealing and looping-based amplification cycles (MALBAC). Finally, we develop statistical models to calibrate allelic bias in single-cell whole-genome amplification and demonstrate a census-based strategy for efficient and accurate variant detection from low-input biopsy samples. National Cancer Institute (U.S.) (Grant P30-CA14051) 2017-10-02T14:11:45Z 2017-10-02T14:11:45Z 2015-04 2014-08 Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/111665 Zhang, Cheng-Zhong et al. “Calibrating Genomic and Allelic Coverage Bias in Single-Cell Sequencing.” Nature Communications 6 (April 2015): 6822 © 2015 Macmillan Publishers Limited https://orcid.org/0000-0003-4555-2485 https://orcid.org/0000-0003-0921-3144 en_US http://dx.doi.org/10.1038/ncomms7822 Nature Communications Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Nature Publishing Group Prof. Love via Erja Kajosalo |
| spellingShingle | Zhang, Cheng-Zhong Francis, Joshua Cornils, Hauke Jung, Joonil Maire, Cecile Ligon, Keith L. Meyerson, Matthew Adalsteinsson, Viktor A. Love, John C Calibrating genomic and allelic coverage bias in single-cell sequencing |
| title | Calibrating genomic and allelic coverage bias in single-cell sequencing |
| title_full | Calibrating genomic and allelic coverage bias in single-cell sequencing |
| title_fullStr | Calibrating genomic and allelic coverage bias in single-cell sequencing |
| title_full_unstemmed | Calibrating genomic and allelic coverage bias in single-cell sequencing |
| title_short | Calibrating genomic and allelic coverage bias in single-cell sequencing |
| title_sort | calibrating genomic and allelic coverage bias in single cell sequencing |
| url | http://hdl.handle.net/1721.1/111665 https://orcid.org/0000-0003-4555-2485 https://orcid.org/0000-0003-0921-3144 |
| work_keys_str_mv | AT zhangchengzhong calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT francisjoshua calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT cornilshauke calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT jungjoonil calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT mairececile calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT ligonkeithl calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT meyersonmatthew calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT adalsteinssonviktora calibratinggenomicandalleliccoveragebiasinsinglecellsequencing AT lovejohnc calibratinggenomicandalleliccoveragebiasinsinglecellsequencing |