Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
| dc.contributor.author | Westoby, Jennifer. | |
| dc.contributor.author | Herrera, Marcela S. | |
| dc.contributor.author | Ferguson Smith, Anne C. | |
| dc.contributor.author | Hemberg, Martin. | |
| dc.date.accessioned | 2019-10-17T18:22:20Z | |
| dc.date.available | 2019-10-17T18:22:20Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-10-14T19:02:05Z | |
| dc.description.abstract | Abstract Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells.Abstract Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells.Abstract Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells.Abstract Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells.Abstract Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells. | |
| dc.fuente.origen | Biomed Central | |
| dc.identifier.citation | Genome Biology. 2018 Nov 07;19(1):191 | |
| dc.identifier.doi | 10.1186/s13059-018-1571-5 | |
| dc.identifier.uri | https://repositorio.uc.cl/handle/11534/26836 | |
| dc.issue.numero | No. 191 | |
| dc.language.iso | en | |
| dc.pagina.final | 14 | |
| dc.pagina.inicio | 1 | |
| dc.revista | Genome Biology | es_ES |
| dc.rights.holder | The Author(s). | |
| dc.subject.ddc | 570 | |
| dc.subject.dewey | Biología | es_ES |
| dc.subject.other | Análisis de secuencia | es_ES |
| dc.subject.other | Secuencia de nucleótidos - Procesamiento de datos. | es_ES |
| dc.title | Simulation-based benchmarking of isoform quantification in single-cell RNA-seq | es_ES |
| dc.type | artículo | |
| dc.volumen | Vol.19 | |
| sipa.codpersvinculados | 1040445 |