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A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model
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| Zeitschriftentitel: | Biology Open |
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| Personen und Körperschaften: | , , , , , |
| In: | Biology Open, 2017 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
The Company of Biologists
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| Schlagwörter: |
| author_facet |
Orgeur, Mickael Martens, Marvin Börno, Stefan T. Timmermann, Bernd Duprez, Delphine Stricker, Sigmar Orgeur, Mickael Martens, Marvin Börno, Stefan T. Timmermann, Bernd Duprez, Delphine Stricker, Sigmar |
|---|---|
| author |
Orgeur, Mickael Martens, Marvin Börno, Stefan T. Timmermann, Bernd Duprez, Delphine Stricker, Sigmar |
| spellingShingle |
Orgeur, Mickael Martens, Marvin Börno, Stefan T. Timmermann, Bernd Duprez, Delphine Stricker, Sigmar Biology Open A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model General Agricultural and Biological Sciences General Biochemistry, Genetics and Molecular Biology |
| author_sort |
orgeur, mickael |
| spelling |
Orgeur, Mickael Martens, Marvin Börno, Stefan T. Timmermann, Bernd Duprez, Delphine Stricker, Sigmar 2046-6390 The Company of Biologists General Agricultural and Biological Sciences General Biochemistry, Genetics and Molecular Biology http://dx.doi.org/10.1242/bio.028498 <jats:p>The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.</jats:p> A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model Biology Open |
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| title |
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_unstemmed |
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_full |
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_fullStr |
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_full_unstemmed |
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_short |
A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_sort |
a dual transcript-discovery approach to improve the delimitation of gene features from rna-seq data in the chicken model |
| topic |
General Agricultural and Biological Sciences General Biochemistry, Genetics and Molecular Biology |
| url |
http://dx.doi.org/10.1242/bio.028498 |
| publishDate |
2017 |
| physical |
|
| description |
<jats:p>The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.</jats:p> |
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| author | Orgeur, Mickael, Martens, Marvin, Börno, Stefan T., Timmermann, Bernd, Duprez, Delphine, Stricker, Sigmar |
| author_facet | Orgeur, Mickael, Martens, Marvin, Börno, Stefan T., Timmermann, Bernd, Duprez, Delphine, Stricker, Sigmar, Orgeur, Mickael, Martens, Marvin, Börno, Stefan T., Timmermann, Bernd, Duprez, Delphine, Stricker, Sigmar |
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| description | <jats:p>The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.</jats:p> |
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| spelling | Orgeur, Mickael Martens, Marvin Börno, Stefan T. Timmermann, Bernd Duprez, Delphine Stricker, Sigmar 2046-6390 The Company of Biologists General Agricultural and Biological Sciences General Biochemistry, Genetics and Molecular Biology http://dx.doi.org/10.1242/bio.028498 <jats:p>The sequence of the chicken genome, like several other draft genome sequences, is presently not fully covered. Gaps, contigs assigned with low confidence and uncharacterized chromosomes result in gene fragmentation and imprecise gene annotation. Transcript abundance estimation from RNA sequencing (RNA-seq) data relies on read quality, library complexity and expression normalization. In addition, the quality of the genome sequence used to map sequencing reads and the gene annotation that defines gene features must also be taken into account. Partially covered genome sequence causes the loss of sequencing reads from the mapping step, while an inaccurate definition of gene features induces imprecise read counts from the assignment step. Both steps can significantly bias interpretation of RNA-seq data. Here, we describe a dual transcript-discovery approach combining a genome-guided gene prediction and a de novo transcriptome assembly. This dual approach enabled us to increase the assignment rate of RNA-seq data by nearly 20% as compared to when using only the chicken reference annotation, contributing therefore to a more accurate estimation of transcript abundance. More generally, this strategy could be applied to any organism with partial genome sequence and/or lacking a manually-curated reference annotation in order to improve the accuracy of gene expression studies.</jats:p> A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model Biology Open |
| spellingShingle | Orgeur, Mickael, Martens, Marvin, Börno, Stefan T., Timmermann, Bernd, Duprez, Delphine, Stricker, Sigmar, Biology Open, A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology |
| title | A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_full | A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_fullStr | A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_full_unstemmed | A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_short | A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| title_sort | a dual transcript-discovery approach to improve the delimitation of gene features from rna-seq data in the chicken model |
| title_unstemmed | A dual transcript-discovery approach to improve the delimitation of gene features from RNA-seq data in the chicken model |
| topic | General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology |
| url | http://dx.doi.org/10.1242/bio.028498 |