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ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing
As the most widely-used single cell whole genome amplification (WGA) approach, multiple displacement amplification (MDA) has a superior performance, due to the high-fidelity and processivity of phi29 DNA polymerase. However, chimeric reads, generated in MDA, cause severe disruption in many single-ce...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515389/ https://www.ncbi.nlm.nih.gov/pubmed/31010074 http://dx.doi.org/10.3390/ijms20081953 |
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author | Lu, Na Li, Junji Bi, Changwei Guo, Jing Tao, Yuhan Luan, Kaihao Tu, Jing Lu, Zuhong |
author_facet | Lu, Na Li, Junji Bi, Changwei Guo, Jing Tao, Yuhan Luan, Kaihao Tu, Jing Lu, Zuhong |
author_sort | Lu, Na |
collection | PubMed |
description | As the most widely-used single cell whole genome amplification (WGA) approach, multiple displacement amplification (MDA) has a superior performance, due to the high-fidelity and processivity of phi29 DNA polymerase. However, chimeric reads, generated in MDA, cause severe disruption in many single-cell studies. Herein, we constructed ChimeraMiner, an improved chimeric read detection pipeline for analyzing the sequencing data of MDA and classified the chimeric sequences. Two datasets (MDA1 and MDA2) were used for evaluating and comparing the efficiency of ChimeraMiner and previous pipeline. Under the same hardware condition, ChimeraMiner spent only 43.4% (43.8% for MDA1 and 43.0% for MDA2) processing time. Respectively, 24.4 million (6.31%) read pairs out of 773 million reads, and 17.5 million (6.62%) read pairs out of 528 million reads were accurately classified as chimeras by ChimeraMiner. In addition to finding 83.60% (17,639,371) chimeras, which were detected by previous pipelines, ChimeraMiner screened 6,736,168 novel chimeras, most of which were missed by the previous pipeline. Applying in single-cell datasets, all three types of chimera were discovered in each dataset, which introduced plenty of false positives in structural variation (SV) detection. The identification and filtration of chimeras by ChimeraMiner removed most of the false positive SVs (83.8%). ChimeraMiner revealed improved efficiency in discovering chimeric reads, and is promising to be widely used in single-cell sequencing. |
format | Online Article Text |
id | pubmed-6515389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65153892019-05-30 ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing Lu, Na Li, Junji Bi, Changwei Guo, Jing Tao, Yuhan Luan, Kaihao Tu, Jing Lu, Zuhong Int J Mol Sci Article As the most widely-used single cell whole genome amplification (WGA) approach, multiple displacement amplification (MDA) has a superior performance, due to the high-fidelity and processivity of phi29 DNA polymerase. However, chimeric reads, generated in MDA, cause severe disruption in many single-cell studies. Herein, we constructed ChimeraMiner, an improved chimeric read detection pipeline for analyzing the sequencing data of MDA and classified the chimeric sequences. Two datasets (MDA1 and MDA2) were used for evaluating and comparing the efficiency of ChimeraMiner and previous pipeline. Under the same hardware condition, ChimeraMiner spent only 43.4% (43.8% for MDA1 and 43.0% for MDA2) processing time. Respectively, 24.4 million (6.31%) read pairs out of 773 million reads, and 17.5 million (6.62%) read pairs out of 528 million reads were accurately classified as chimeras by ChimeraMiner. In addition to finding 83.60% (17,639,371) chimeras, which were detected by previous pipelines, ChimeraMiner screened 6,736,168 novel chimeras, most of which were missed by the previous pipeline. Applying in single-cell datasets, all three types of chimera were discovered in each dataset, which introduced plenty of false positives in structural variation (SV) detection. The identification and filtration of chimeras by ChimeraMiner removed most of the false positive SVs (83.8%). ChimeraMiner revealed improved efficiency in discovering chimeric reads, and is promising to be widely used in single-cell sequencing. MDPI 2019-04-21 /pmc/articles/PMC6515389/ /pubmed/31010074 http://dx.doi.org/10.3390/ijms20081953 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lu, Na Li, Junji Bi, Changwei Guo, Jing Tao, Yuhan Luan, Kaihao Tu, Jing Lu, Zuhong ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing |
title | ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing |
title_full | ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing |
title_fullStr | ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing |
title_full_unstemmed | ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing |
title_short | ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing |
title_sort | chimeraminer: an improved chimeric read detection pipeline and its application in single cell sequencing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515389/ https://www.ncbi.nlm.nih.gov/pubmed/31010074 http://dx.doi.org/10.3390/ijms20081953 |
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