Transcript assembly and annotations: Bias and adjustment.
Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to signi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011734 |
| _version_ | 1797311527235092480 |
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| author | Qimin Zhang Mingfu Shao |
| author_facet | Qimin Zhang Mingfu Shao |
| author_sort | Qimin Zhang |
| collection | DOAJ |
| description | Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. We investigate the impact of annotations on transcript assembly. Surprisingly, we observe that opposite conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios. |
| first_indexed | 2024-03-08T01:59:59Z |
| format | Article |
| id | doaj.art-9affb20fcd454252a08f86d85045e38f |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-08T01:59:59Z |
| publishDate | 2023-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-9affb20fcd454252a08f86d85045e38f2024-02-14T05:31:22ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-12-011912e101173410.1371/journal.pcbi.1011734Transcript assembly and annotations: Bias and adjustment.Qimin ZhangMingfu ShaoTranscript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. We investigate the impact of annotations on transcript assembly. Surprisingly, we observe that opposite conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.https://doi.org/10.1371/journal.pcbi.1011734 |
| spellingShingle | Qimin Zhang Mingfu Shao Transcript assembly and annotations: Bias and adjustment. PLoS Computational Biology |
| title | Transcript assembly and annotations: Bias and adjustment. |
| title_full | Transcript assembly and annotations: Bias and adjustment. |
| title_fullStr | Transcript assembly and annotations: Bias and adjustment. |
| title_full_unstemmed | Transcript assembly and annotations: Bias and adjustment. |
| title_short | Transcript assembly and annotations: Bias and adjustment. |
| title_sort | transcript assembly and annotations bias and adjustment |
| url | https://doi.org/10.1371/journal.pcbi.1011734 |
| work_keys_str_mv | AT qiminzhang transcriptassemblyandannotationsbiasandadjustment AT mingfushao transcriptassemblyandannotationsbiasandadjustment |