XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation
Abstract Background RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternat...
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| Format: | Article |
| Language: | English |
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BMC
2017-10-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1803-9 |
| _version_ | 1811306488388386816 |
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| author | Yuanhang Liu Ping Wu Jingqi Zhou Teresa L. Johnson-Pais Zhao Lai Wasim H. Chowdhury Ronald Rodriguez Yidong Chen |
| author_facet | Yuanhang Liu Ping Wu Jingqi Zhou Teresa L. Johnson-Pais Zhao Lai Wasim H. Chowdhury Ronald Rodriguez Yidong Chen |
| author_sort | Yuanhang Liu |
| collection | DOAJ |
| description | Abstract Background RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data. Results We present several major updates in XBSeq2, including alternative statistical testing and parameter estimation method for detecting DEGs, capacity to directly process alignment files and methods for testing differential APA usage. We evaluated the performance of XBSeq2 against several other methods by using simulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power. We also benchmarked different methods concerning execution time and computational memory consumed. Finally, we demonstrated the functionality of XBSeq2 by using a set of in-house generated clear cell renal carcinoma (ccRCC) samples. Conclusions We present several major updates to XBSeq. By using simulated datasets, we demonstrated that, overall, XBSeq2 performs equally well as XBSeq in terms of several statistical metrics and both perform better than DESeq2 and edgeR. In addition, XBSeq2 is faster in speed and consumes much less computational memory compared to XBSeq, allowing users to evaluate differential expression and APA events in parallel. XBSeq2 is available from Bioconductor: http://bioconductor.org/packages/XBSeq/ |
| first_indexed | 2024-04-13T08:45:25Z |
| format | Article |
| id | doaj.art-5545a8ada51a4183b9395e2e6a598aab |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-13T08:45:25Z |
| publishDate | 2017-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-5545a8ada51a4183b9395e2e6a598aab2022-12-22T02:53:40ZengBMCBMC Bioinformatics1471-21052017-10-0118S11576510.1186/s12859-017-1803-9XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylationYuanhang Liu0Ping Wu1Jingqi Zhou2Teresa L. Johnson-Pais3Zhao Lai4Wasim H. Chowdhury5Ronald Rodriguez6Yidong Chen7Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San AntonioDepartment of Urology, University of Texas Health Science Center at San AntonioGreehey Children’s Cancer Research Institute, University of Texas Health Science Center at San AntonioDepartment of Urology, University of Texas Health Science Center at San AntonioGreehey Children’s Cancer Research Institute, University of Texas Health Science Center at San AntonioDepartment of Urology, University of Texas Health Science Center at San AntonioDepartment of Urology, University of Texas Health Science Center at San AntonioGreehey Children’s Cancer Research Institute, University of Texas Health Science Center at San AntonioAbstract Background RNA sequencing (RNA-seq) is a high throughput technology that profiles gene expression in a genome-wide manner. RNA-seq has been mainly used for testing differential expression (DE) of transcripts between two conditions and has recently been used for testing differential alternative polyadenylation (APA). In the past, many algorithms have been developed for detecting differentially expressed genes (DEGs) from RNA-seq experiments, including the one we developed, XBSeq, which paid special attention to the context-specific background noise that is ignored in conventional gene expression quantification and DE analysis of RNA-seq data. Results We present several major updates in XBSeq2, including alternative statistical testing and parameter estimation method for detecting DEGs, capacity to directly process alignment files and methods for testing differential APA usage. We evaluated the performance of XBSeq2 against several other methods by using simulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statistical power. We also benchmarked different methods concerning execution time and computational memory consumed. Finally, we demonstrated the functionality of XBSeq2 by using a set of in-house generated clear cell renal carcinoma (ccRCC) samples. Conclusions We present several major updates to XBSeq. By using simulated datasets, we demonstrated that, overall, XBSeq2 performs equally well as XBSeq in terms of several statistical metrics and both perform better than DESeq2 and edgeR. In addition, XBSeq2 is faster in speed and consumes much less computational memory compared to XBSeq, allowing users to evaluate differential expression and APA events in parallel. XBSeq2 is available from Bioconductor: http://bioconductor.org/packages/XBSeq/http://link.springer.com/article/10.1186/s12859-017-1803-9Differential expression analysisXBSeqXBSeq2Alternative polyadenylationRNA-seq |
| spellingShingle | Yuanhang Liu Ping Wu Jingqi Zhou Teresa L. Johnson-Pais Zhao Lai Wasim H. Chowdhury Ronald Rodriguez Yidong Chen XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation BMC Bioinformatics Differential expression analysis XBSeq XBSeq2 Alternative polyadenylation RNA-seq |
| title | XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation |
| title_full | XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation |
| title_fullStr | XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation |
| title_full_unstemmed | XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation |
| title_short | XBSeq2: a fast and accurate quantification of differential expression and differential polyadenylation |
| title_sort | xbseq2 a fast and accurate quantification of differential expression and differential polyadenylation |
| topic | Differential expression analysis XBSeq XBSeq2 Alternative polyadenylation RNA-seq |
| url | http://link.springer.com/article/10.1186/s12859-017-1803-9 |
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