Comparison of alternative approaches for analysing multi-level RNA-seq data.
RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigoro...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182694&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850230655044026368 |
|---|---|
| author | Irina Mohorianu Amanda Bretman Damian T Smith Emily K Fowler Tamas Dalmay Tracey Chapman |
| author_facet | Irina Mohorianu Amanda Bretman Damian T Smith Emily K Fowler Tamas Dalmay Tracey Chapman |
| author_sort | Irina Mohorianu |
| collection | DOAJ |
| description | RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments. |
| format | Article |
| id | doaj-art-ce214055b1e3460e95da87ea88cd422c |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ce214055b1e3460e95da87ea88cd422c2025-08-20T02:03:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01128e018269410.1371/journal.pone.0182694Comparison of alternative approaches for analysing multi-level RNA-seq data.Irina MohorianuAmanda BretmanDamian T SmithEmily K FowlerTamas DalmayTracey ChapmanRNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182694&type=printable |
| spellingShingle | Irina Mohorianu Amanda Bretman Damian T Smith Emily K Fowler Tamas Dalmay Tracey Chapman Comparison of alternative approaches for analysing multi-level RNA-seq data. PLoS ONE |
| title | Comparison of alternative approaches for analysing multi-level RNA-seq data. |
| title_full | Comparison of alternative approaches for analysing multi-level RNA-seq data. |
| title_fullStr | Comparison of alternative approaches for analysing multi-level RNA-seq data. |
| title_full_unstemmed | Comparison of alternative approaches for analysing multi-level RNA-seq data. |
| title_short | Comparison of alternative approaches for analysing multi-level RNA-seq data. |
| title_sort | comparison of alternative approaches for analysing multi level rna seq data |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182694&type=printable |
| work_keys_str_mv | AT irinamohorianu comparisonofalternativeapproachesforanalysingmultilevelrnaseqdata AT amandabretman comparisonofalternativeapproachesforanalysingmultilevelrnaseqdata AT damiantsmith comparisonofalternativeapproachesforanalysingmultilevelrnaseqdata AT emilykfowler comparisonofalternativeapproachesforanalysingmultilevelrnaseqdata AT tamasdalmay comparisonofalternativeapproachesforanalysingmultilevelrnaseqdata AT traceychapman comparisonofalternativeapproachesforanalysingmultilevelrnaseqdata |