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Novel scripts for improved annotation and selection of variants from whole exome sequencing in cancer research.

Hansen MC, Nederby L, Roug A, Villesen P, Kjeldsen E, Nyvold CG, Hokland P - MethodsX (2015)

Bottom Line: Sequencing the exome is quickly becoming the preferred method for discovering disease-inducing mutations.It also provides the researcher with the opportunity to extend the analysis by having a full-fledged programming and analysis environment of Mathematica at hand.In brief, post-processing is performed by: •Mapping of germ line and somatic variants to coding regions, and defining variant sets within Mathematica.•Processing of variants in variant effect predictor.•Extended annotation, relevance scoring and defining focus areas through the provided functions.

View Article: PubMed Central - PubMed

Affiliation: Department of Hematology, Aarhus University Hospital, Aarhus, Denmark.

ABSTRACT
Sequencing the exome is quickly becoming the preferred method for discovering disease-inducing mutations. While obtaining data sets is a straightforward procedure, the subsequent analysis and interpretation of the data is a limiting step for clinical applications. Thus, while the initial mutation and variant calling can be performed by a bioinformatician or trained researcher, the output from robust packages such as MuTect and GATK is not directly informative for the general life scientists. In attempt to obviate this problem we have created complementary Wolfram scripts, which enable easy downstream annotation and selection, presented here in the perspective of hematological relevance. It also provides the researcher with the opportunity to extend the analysis by having a full-fledged programming and analysis environment of Mathematica at hand. In brief, post-processing is performed by: •Mapping of germ line and somatic variants to coding regions, and defining variant sets within Mathematica.•Processing of variants in variant effect predictor.•Extended annotation, relevance scoring and defining focus areas through the provided functions.

No MeSH data available.


Pseudo-heatmap of the genes retrieved with result table with normalized data from BioGPS.
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fig0010: Pseudo-heatmap of the genes retrieved with result table with normalized data from BioGPS.

Mentions: Getting a representation of the expected differential gene expression in various types of tissue can be practical when assessing the possible role of the genes. Thus, we provide the function HeatMap which displays a pseudo-heatmap of the genes called with ResultTable (if found in the array data reference). Please note that your data and this map only intersect by having the genes in common, and we do not attempt to provide anything else. From Fig. 2 it can be at least argued that the high ranked mutations are expressed by Hematopoietic progenitor cell antigen (CD34+) presenting cells, and thus may have a biological role, consistent with the literature. We have normalized the expression profiles, but data have been extracted through the BioGPS Dataset Library (from BioGPS.org).


Novel scripts for improved annotation and selection of variants from whole exome sequencing in cancer research.

Hansen MC, Nederby L, Roug A, Villesen P, Kjeldsen E, Nyvold CG, Hokland P - MethodsX (2015)

Pseudo-heatmap of the genes retrieved with result table with normalized data from BioGPS.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487347&req=5

fig0010: Pseudo-heatmap of the genes retrieved with result table with normalized data from BioGPS.
Mentions: Getting a representation of the expected differential gene expression in various types of tissue can be practical when assessing the possible role of the genes. Thus, we provide the function HeatMap which displays a pseudo-heatmap of the genes called with ResultTable (if found in the array data reference). Please note that your data and this map only intersect by having the genes in common, and we do not attempt to provide anything else. From Fig. 2 it can be at least argued that the high ranked mutations are expressed by Hematopoietic progenitor cell antigen (CD34+) presenting cells, and thus may have a biological role, consistent with the literature. We have normalized the expression profiles, but data have been extracted through the BioGPS Dataset Library (from BioGPS.org).

Bottom Line: Sequencing the exome is quickly becoming the preferred method for discovering disease-inducing mutations.It also provides the researcher with the opportunity to extend the analysis by having a full-fledged programming and analysis environment of Mathematica at hand.In brief, post-processing is performed by: •Mapping of germ line and somatic variants to coding regions, and defining variant sets within Mathematica.•Processing of variants in variant effect predictor.•Extended annotation, relevance scoring and defining focus areas through the provided functions.

View Article: PubMed Central - PubMed

Affiliation: Department of Hematology, Aarhus University Hospital, Aarhus, Denmark.

ABSTRACT
Sequencing the exome is quickly becoming the preferred method for discovering disease-inducing mutations. While obtaining data sets is a straightforward procedure, the subsequent analysis and interpretation of the data is a limiting step for clinical applications. Thus, while the initial mutation and variant calling can be performed by a bioinformatician or trained researcher, the output from robust packages such as MuTect and GATK is not directly informative for the general life scientists. In attempt to obviate this problem we have created complementary Wolfram scripts, which enable easy downstream annotation and selection, presented here in the perspective of hematological relevance. It also provides the researcher with the opportunity to extend the analysis by having a full-fledged programming and analysis environment of Mathematica at hand. In brief, post-processing is performed by: •Mapping of germ line and somatic variants to coding regions, and defining variant sets within Mathematica.•Processing of variants in variant effect predictor.•Extended annotation, relevance scoring and defining focus areas through the provided functions.

No MeSH data available.