BowStrap v1.0: Assigning statistical significance to expressed genes using short-read transcriptome data.
Identifieur interne : 002B78 ( Main/Exploration ); précédent : 002B77; suivant : 002B79BowStrap v1.0: Assigning statistical significance to expressed genes using short-read transcriptome data.
Auteurs : Peter E. Larsen [États-Unis] ; Frank R. CollartSource :
- BMC research notes [ 1756-0500 ] ; 2012.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Alignement de séquences (MeSH), Analyse de profil d'expression de gènes (méthodes), Analyse de profil d'expression de gènes (statistiques et données numériques), Analyse de séquence d'ARN (méthodes), Analyse de séquence d'ARN (statistiques et données numériques), Bases de données d'acides nucléiques (MeSH), Expression des gènes (MeSH), Gènes de synthèse (MeSH), Laccaria (génétique), Logiciel (MeSH), Populus (génétique), Séquençage nucléotidique à haut débit (MeSH), Transcriptome (MeSH).
- MESH :
- génétique : Laccaria, Populus.
- méthodes : Analyse de profil d'expression de gènes, Analyse de séquence d'ARN.
- statistiques et données numériques : Analyse de profil d'expression de gènes, Analyse de séquence d'ARN.
- Algorithmes, Alignement de séquences, Bases de données d'acides nucléiques, Expression des gènes, Gènes de synthèse, Logiciel, Séquençage nucléotidique à haut débit, Transcriptome.
English descriptors
- KwdEn :
- Algorithms (MeSH), Databases, Nucleic Acid (MeSH), Gene Expression (MeSH), Gene Expression Profiling (methods), Gene Expression Profiling (statistics & numerical data), Genes, Synthetic (MeSH), High-Throughput Nucleotide Sequencing (MeSH), Laccaria (genetics), Populus (genetics), Sequence Alignment (MeSH), Sequence Analysis, RNA (methods), Sequence Analysis, RNA (statistics & numerical data), Software (MeSH), Transcriptome (MeSH).
- MESH :
- genetics : Laccaria, Populus.
- methods : Gene Expression Profiling, Sequence Analysis, RNA.
- statistics & numerical data : Gene Expression Profiling, Sequence Analysis, RNA.
- Algorithms, Databases, Nucleic Acid, Gene Expression, Genes, Synthetic, High-Throughput Nucleotide Sequencing, Sequence Alignment, Software, Transcriptome.
Abstract
BACKGROUND
Background: Deep RNA sequencing, the application of Next Generation sequencing technology to generate a comprehensive profile of the message RNA present in a set of biological samples, provides unprecedented resolution into the molecular foundations of biological processes. By aligning short read RNA sequence data to a set of gene models, expression patterns for all of the genes and gene variants in a biological sample can be calculated. However, accurate determination of gene model expression from deep RNA sequencing is hindered by the presence of ambiguously aligning short read sequences.
FINDINGS
BowStrap, a program for implementing the sequence alignment tool 'Bowtie' in a bootstrap-style approach, accommodates multiply-aligning short read sequences and reports gene model expression as an averaged aligned reads per Kb of gene model sequence per million aligned deep RNA sequence reads with a confidence interval, suitable for calculating statistical significance of presence/absence of detected gene model expression. BowStrap v1.0 was validated against a simulated metatranscriptome. Results were compared with two alternate 'Bowtie'-based calculations of gene model expression. BowStrap is better at accurately identifying expressed gene models in a dataset and provides a more accurate estimate of gene model expression level than methods that do not incorporate a boot-strap style approach.
CONCLUSIONS
BowStrap v1.0 is superior in ability to detect significant gene model expression and calculate accurate determination of gene model expression levels compared to other alignment-based methods of determining patterns of gene expression. BowStrap v1.0 also can utilize multiple processors as has decreased run time compared to the previous version, BowStrap 0.5. We anticipate that BowStrap will be a highly useful addition to the available set of Next Generation RNA sequence analysis tools.
DOI: 10.1186/1756-0500-5-275
PubMed: 22676709
PubMed Central: PMC3494516
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<term>Analyse de profil d'expression de gènes (statistiques et données numériques)</term>
<term>Analyse de séquence d'ARN (méthodes)</term>
<term>Analyse de séquence d'ARN (statistiques et données numériques)</term>
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<term>Populus (génétique)</term>
<term>Séquençage nucléotidique à haut débit (MeSH)</term>
<term>Transcriptome (MeSH)</term>
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<term>Analyse de séquence d'ARN</term>
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<term>Analyse de séquence d'ARN</term>
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<term>Gènes de synthèse</term>
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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>Background: Deep RNA sequencing, the application of Next Generation sequencing technology to generate a comprehensive profile of the message RNA present in a set of biological samples, provides unprecedented resolution into the molecular foundations of biological processes. By aligning short read RNA sequence data to a set of gene models, expression patterns for all of the genes and gene variants in a biological sample can be calculated. However, accurate determination of gene model expression from deep RNA sequencing is hindered by the presence of ambiguously aligning short read sequences.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>FINDINGS</b>
</p>
<p>BowStrap, a program for implementing the sequence alignment tool 'Bowtie' in a bootstrap-style approach, accommodates multiply-aligning short read sequences and reports gene model expression as an averaged aligned reads per Kb of gene model sequence per million aligned deep RNA sequence reads with a confidence interval, suitable for calculating statistical significance of presence/absence of detected gene model expression. BowStrap v1.0 was validated against a simulated metatranscriptome. Results were compared with two alternate 'Bowtie'-based calculations of gene model expression. BowStrap is better at accurately identifying expressed gene models in a dataset and provides a more accurate estimate of gene model expression level than methods that do not incorporate a boot-strap style approach.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>BowStrap v1.0 is superior in ability to detect significant gene model expression and calculate accurate determination of gene model expression levels compared to other alignment-based methods of determining patterns of gene expression. BowStrap v1.0 also can utilize multiple processors as has decreased run time compared to the previous version, BowStrap 0.5. We anticipate that BowStrap will be a highly useful addition to the available set of Next Generation RNA sequence analysis tools.</p>
</div>
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