Evaluation and comparison of computational tools for RNA-seq isoform quantification
Abstract Background Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2017-08-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-017-4002-1 |
| _version_ | 1818215411035930624 |
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| author | Chi Zhang Baohong Zhang Lih-Ling Lin Shanrong Zhao |
| author_facet | Chi Zhang Baohong Zhang Lih-Ling Lin Shanrong Zhao |
| author_sort | Chi Zhang |
| collection | DOAJ |
| description | Abstract Background Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the transcript level is limited with current RNA-seq technologies because of, for example, limited read length and the cost of deep sequencing. Results A large number of tools have been developed to tackle this problem, and we performed a comprehensive evaluation of these tools using both experimental and simulated RNA-seq datasets. We found that recently developed alignment-free tools are both fast and accurate. The accuracy of all methods was mainly influenced by the complexity of gene structures and caution must be taken when interpreting quantification results for short transcripts. Using TP53 gene simulation, we discovered that both sequencing depth and the relative abundance of different isoforms affect quantification accuracy Conclusions Our comprehensive evaluation helps data analysts to make informed choice when selecting computational tools for isoform quantification. |
| first_indexed | 2024-12-12T06:35:39Z |
| format | Article |
| id | doaj.art-be9fe4f12408405781911b93c4f3b099 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-12T06:35:39Z |
| publishDate | 2017-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-be9fe4f12408405781911b93c4f3b0992022-12-22T00:34:28ZengBMCBMC Genomics1471-21642017-08-0118111110.1186/s12864-017-4002-1Evaluation and comparison of computational tools for RNA-seq isoform quantificationChi Zhang0Baohong Zhang1Lih-Ling Lin2Shanrong Zhao3Early Clinical Development, Pfizer Worldwide R&DEarly Clinical Development, Pfizer Worldwide R&DInflammation and Immunology RU, Pfizer Worldwide R&DEarly Clinical Development, Pfizer Worldwide R&DAbstract Background Alternatively spliced transcript isoforms are commonly observed in higher eukaryotes. The expression levels of these isoforms are key for understanding normal functions in healthy tissues and the progression of disease states. However, accurate quantification of expression at the transcript level is limited with current RNA-seq technologies because of, for example, limited read length and the cost of deep sequencing. Results A large number of tools have been developed to tackle this problem, and we performed a comprehensive evaluation of these tools using both experimental and simulated RNA-seq datasets. We found that recently developed alignment-free tools are both fast and accurate. The accuracy of all methods was mainly influenced by the complexity of gene structures and caution must be taken when interpreting quantification results for short transcripts. Using TP53 gene simulation, we discovered that both sequencing depth and the relative abundance of different isoforms affect quantification accuracy Conclusions Our comprehensive evaluation helps data analysts to make informed choice when selecting computational tools for isoform quantification.http://link.springer.com/article/10.1186/s12864-017-4002-1RNA-seqQuantificationIsoformData analysisRSEMSalmon |
| spellingShingle | Chi Zhang Baohong Zhang Lih-Ling Lin Shanrong Zhao Evaluation and comparison of computational tools for RNA-seq isoform quantification BMC Genomics RNA-seq Quantification Isoform Data analysis RSEM Salmon |
| title | Evaluation and comparison of computational tools for RNA-seq isoform quantification |
| title_full | Evaluation and comparison of computational tools for RNA-seq isoform quantification |
| title_fullStr | Evaluation and comparison of computational tools for RNA-seq isoform quantification |
| title_full_unstemmed | Evaluation and comparison of computational tools for RNA-seq isoform quantification |
| title_short | Evaluation and comparison of computational tools for RNA-seq isoform quantification |
| title_sort | evaluation and comparison of computational tools for rna seq isoform quantification |
| topic | RNA-seq Quantification Isoform Data analysis RSEM Salmon |
| url | http://link.springer.com/article/10.1186/s12864-017-4002-1 |
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