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AllelicImbalance: an R/bioconductor package for detecting, managing, and visualizing allele expression imbalance data from RNA sequencing.

Gådin JR, van't Hooft FM, Eriksson P, Folkersen L - BMC Bioinformatics (2015)

Bottom Line: This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms.The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing.The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

View Article: PubMed Central - PubMed

Affiliation: Atherosclerosis Research Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Bldg L8:03, S-171 76, Stockholm, Sweden. Jesper.r.gadin@ki.se.

ABSTRACT

Background: One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases.

Results: We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons.

Conclusions: The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

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Related in: MedlinePlus

Short Title: AI consistency using glocationplot. Detailed Legend: On top are the fractions of alleles over APOB for SNPs with a MAF > 0.1. Each bar represents one of eight samples, and the grey lines in the middle show the SNP locations in APOB beneath in yellow. All SNPs shown are close around the black line, denoting 1:1 expression of the alleles. See Additional file 3 for the total allele count for each SNP
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Fig1: Short Title: AI consistency using glocationplot. Detailed Legend: On top are the fractions of alleles over APOB for SNPs with a MAF > 0.1. Each bar represents one of eight samples, and the grey lines in the middle show the SNP locations in APOB beneath in yellow. All SNPs shown are close around the black line, denoting 1:1 expression of the alleles. See Additional file 3 for the total allele count for each SNP

Mentions: The AllelicImbalance package was developed to address these issues, allowing the user to test AI at a single gene or SNP quickly. Nevertheless, the package is suitable for performing any custom global AI analysis, because there is always a counting step and the need to store counts in a smart container, which facilitates access to custom requests from the user. For genes that have more than one heterozygous SNP and at least one sample, there is a function to visualize AI consistency easily over the gene as an internal validation to select SNPs that are suitable for further AI QTL study (Fig. 1). The package is easy to use, comprising an infrastructure that is linked to the Bioconductor environment, and allows the user to pose genetic questions quickly.Fig. 1


AllelicImbalance: an R/bioconductor package for detecting, managing, and visualizing allele expression imbalance data from RNA sequencing.

Gådin JR, van't Hooft FM, Eriksson P, Folkersen L - BMC Bioinformatics (2015)

Short Title: AI consistency using glocationplot. Detailed Legend: On top are the fractions of alleles over APOB for SNPs with a MAF > 0.1. Each bar represents one of eight samples, and the grey lines in the middle show the SNP locations in APOB beneath in yellow. All SNPs shown are close around the black line, denoting 1:1 expression of the alleles. See Additional file 3 for the total allele count for each SNP
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4465016&req=5

Fig1: Short Title: AI consistency using glocationplot. Detailed Legend: On top are the fractions of alleles over APOB for SNPs with a MAF > 0.1. Each bar represents one of eight samples, and the grey lines in the middle show the SNP locations in APOB beneath in yellow. All SNPs shown are close around the black line, denoting 1:1 expression of the alleles. See Additional file 3 for the total allele count for each SNP
Mentions: The AllelicImbalance package was developed to address these issues, allowing the user to test AI at a single gene or SNP quickly. Nevertheless, the package is suitable for performing any custom global AI analysis, because there is always a counting step and the need to store counts in a smart container, which facilitates access to custom requests from the user. For genes that have more than one heterozygous SNP and at least one sample, there is a function to visualize AI consistency easily over the gene as an internal validation to select SNPs that are suitable for further AI QTL study (Fig. 1). The package is easy to use, comprising an infrastructure that is linked to the Bioconductor environment, and allows the user to pose genetic questions quickly.Fig. 1

Bottom Line: This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms.The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing.The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

View Article: PubMed Central - PubMed

Affiliation: Atherosclerosis Research Unit, Karolinska University Hospital Solna, Center for Molecular Medicine, Bldg L8:03, S-171 76, Stockholm, Sweden. Jesper.r.gadin@ki.se.

ABSTRACT

Background: One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases.

Results: We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons.

Conclusions: The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

Show MeSH
Related in: MedlinePlus