PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data

Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and is...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, Li [verfasserIn] Liu, Xuejun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	RNA-seq Base-level bias Gene and isoform expression level Expression of uncertainty

Anmerkung:	© Springer-Verlag Berlin Heidelberg 2016

Übergeordnetes Werk:	Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 8(2016), 4 vom: 02. März, Seite 1247-1258
Übergeordnetes Werk:	volume:8 ; year:2016 ; number:4 ; day:02 ; month:03 ; pages:1247-1258

Links:	Volltext

DOI / URN:	10.1007/s13042-016-0497-z

Katalog-ID:	SPR029603137

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520			\|a Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods.
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publishDate	2016
allfields	10.1007/s13042-016-0497-z doi (DE-627)SPR029603137 (SPR)s13042-016-0497-z-e DE-627 ger DE-627 rakwb eng Zhang, Li verfasserin aut PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. RNA-seq (dpeaa)DE-He213 Base-level bias (dpeaa)DE-He213 Gene and isoform expression level (dpeaa)DE-He213 Expression of uncertainty (dpeaa)DE-He213 Liu, Xuejun aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2016), 4 vom: 02. März, Seite 1247-1258 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258 https://dx.doi.org/10.1007/s13042-016-0497-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2016 4 02 03 1247-1258
spelling	10.1007/s13042-016-0497-z doi (DE-627)SPR029603137 (SPR)s13042-016-0497-z-e DE-627 ger DE-627 rakwb eng Zhang, Li verfasserin aut PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. RNA-seq (dpeaa)DE-He213 Base-level bias (dpeaa)DE-He213 Gene and isoform expression level (dpeaa)DE-He213 Expression of uncertainty (dpeaa)DE-He213 Liu, Xuejun aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2016), 4 vom: 02. März, Seite 1247-1258 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258 https://dx.doi.org/10.1007/s13042-016-0497-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2016 4 02 03 1247-1258
allfields_unstemmed	10.1007/s13042-016-0497-z doi (DE-627)SPR029603137 (SPR)s13042-016-0497-z-e DE-627 ger DE-627 rakwb eng Zhang, Li verfasserin aut PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. RNA-seq (dpeaa)DE-He213 Base-level bias (dpeaa)DE-He213 Gene and isoform expression level (dpeaa)DE-He213 Expression of uncertainty (dpeaa)DE-He213 Liu, Xuejun aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2016), 4 vom: 02. März, Seite 1247-1258 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258 https://dx.doi.org/10.1007/s13042-016-0497-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2016 4 02 03 1247-1258
allfieldsGer	10.1007/s13042-016-0497-z doi (DE-627)SPR029603137 (SPR)s13042-016-0497-z-e DE-627 ger DE-627 rakwb eng Zhang, Li verfasserin aut PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. RNA-seq (dpeaa)DE-He213 Base-level bias (dpeaa)DE-He213 Gene and isoform expression level (dpeaa)DE-He213 Expression of uncertainty (dpeaa)DE-He213 Liu, Xuejun aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2016), 4 vom: 02. März, Seite 1247-1258 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258 https://dx.doi.org/10.1007/s13042-016-0497-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2016 4 02 03 1247-1258
allfieldsSound	10.1007/s13042-016-0497-z doi (DE-627)SPR029603137 (SPR)s13042-016-0497-z-e DE-627 ger DE-627 rakwb eng Zhang, Li verfasserin aut PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2016 Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. RNA-seq (dpeaa)DE-He213 Base-level bias (dpeaa)DE-He213 Gene and isoform expression level (dpeaa)DE-He213 Expression of uncertainty (dpeaa)DE-He213 Liu, Xuejun aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 8(2016), 4 vom: 02. März, Seite 1247-1258 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258 https://dx.doi.org/10.1007/s13042-016-0497-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2016 4 02 03 1247-1258
language	English
source	Enthalten in International journal of machine learning and cybernetics 8(2016), 4 vom: 02. März, Seite 1247-1258 volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258
sourceStr	Enthalten in International journal of machine learning and cybernetics 8(2016), 4 vom: 02. März, Seite 1247-1258 volume:8 year:2016 number:4 day:02 month:03 pages:1247-1258
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	RNA-seq Base-level bias Gene and isoform expression level Expression of uncertainty
isfreeaccess_bool	false
container_title	International journal of machine learning and cybernetics
authorswithroles_txt_mv	Zhang, Li @@aut@@ Liu, Xuejun @@aut@@
publishDateDaySort_date	2016-03-02T00:00:00Z
hierarchy_top_id	635135132
id	SPR029603137
language_de	englisch
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author	Zhang, Li
spellingShingle	Zhang, Li misc RNA-seq misc Base-level bias misc Gene and isoform expression level misc Expression of uncertainty PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data
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topic_title	PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data RNA-seq (dpeaa)DE-He213 Base-level bias (dpeaa)DE-He213 Gene and isoform expression level (dpeaa)DE-He213 Expression of uncertainty (dpeaa)DE-He213
topic	misc RNA-seq misc Base-level bias misc Gene and isoform expression level misc Expression of uncertainty
topic_unstemmed	misc RNA-seq misc Base-level bias misc Gene and isoform expression level misc Expression of uncertainty
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title	PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data
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title_full	PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data
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title_sort	pbseq: modeling base-level bias to estimate gene and isoform expression for rna-seq data
title_auth	PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data
abstract	Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. © Springer-Verlag Berlin Heidelberg 2016
abstractGer	Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. © Springer-Verlag Berlin Heidelberg 2016
abstract_unstemmed	Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. Results show that PBseq can accurately estimate gene and isoform expression levels and is computationally efficient compared with other state-of-art methods. © Springer-Verlag Berlin Heidelberg 2016
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title_short	PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data
url	https://dx.doi.org/10.1007/s13042-016-0497-z
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up_date	2024-07-04T01:39:34.517Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR029603137</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331104707.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13042-016-0497-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR029603137</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13042-016-0497-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Li</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Due to its unprecedented high-throughput and high-resolution, RNA-seq rapidly becomes a revolutionary and powerful technology for transcriptome analysis. However, RNA-seq library preparation results in non-uniformity of read distribution in the represented genes. When estimating gene and isoform expression level, the non-uniformity needs to be accounted and corrected to improve the estimation accuracy. In this paper, we propose PBSeq, a Poisson model utilizing a base-level bias correction strategy to estimate gene and isoform expression. The base-level bias correction strategy simultaneously considers the positional and sequence-specific biases at starting position of reads mapped to the genes of interest. The PBSeq not only provides the expression values but also estimates the uncertainty associated with expression estimation, which represents the variation across replicates and is useful for downstream analysis. We utilize a simulated dataset and three real RNA-seq datasets to validate the PBSeq model. 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PBSeq: Modeling base-level bias to estimate gene and isoform expression for RNA-seq data

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