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Exome-assistant: a rapid and easy detection of disease-related genes and genetic variations from exome sequencing.

Liu Q, Shen E, Min Q, Li X, Wang X, Li X, Sun ZS, Wu J - BMC Genomics (2012)

Bottom Line: Protein-coding regions in human genes harbor 85% of the mutations that are associated with disease-related traits.The identified candidate disease-related genetic variations can be annotated according to their sample features.In summary, by exploring exome sequencing data, Exome-assistant can provide researchers with detailed biological insights into genetic variation events and permits the identification of potential genetic causes of human diseases and related traits.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Genomic Medicine, Wenzhou Medical College, Wenzhou 325035, China.

ABSTRACT

Background: Protein-coding regions in human genes harbor 85% of the mutations that are associated with disease-related traits. Compared with whole-genome sequencing of complex samples, exome sequencing serves as an alternative option because of its dramatically reduced cost. In fact, exome sequencing has been successfully applied to identify the cause of several Mendelian disorders, such as Miller and Schinzel-Giedio syndrome. However, there remain great challenges in handling the huge data generated by exome sequencing and in identifying potential disease-related genetic variations.

Results: In this study, Exome-assistant (http://122.228.158.106/exomeassistant), a convenient tool for submitting and annotating single nucleotide polymorphisms (SNPs) and insertion/deletion variations (InDels), was developed to rapidly detect candidate disease-related genetic variations from exome sequencing projects. Versatile filter criteria are provided by Exome-assistant to meet different users' requirements. Exome-assistant consists of four modules: the single case module, the two cases module, the multiple cases module, and the reanalysis module. The two cases and multiple cases modules allow users to identify sample-specific and common variations. The multiple cases module also supports family-based studies and Mendelian filtering. The identified candidate disease-related genetic variations can be annotated according to their sample features.

Conclusions: In summary, by exploring exome sequencing data, Exome-assistant can provide researchers with detailed biological insights into genetic variation events and permits the identification of potential genetic causes of human diseases and related traits.

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The workflow of the Exome-assistant pipeline.
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Figure 1: The workflow of the Exome-assistant pipeline.

Mentions: The basic principle of the single case analysis is to annotate the SNPs/InDels based on the information from public databases (Figure 1), which include: CCDS [27] and dbSNP (v.137) [28] for site/region-specific annotation, KEGG pathway [29] and Gene Ontology for gene-based annotation. In the dbSNP database, the SNPs flagged as ‘clinically associated’ are considered as disease-related SNPs and excluded from the database. The human reference gene definition was downloaded from UCSC (http://hgdownload.cse.ucsc.edu) and integrated into Exome-assistant. Also integrated are the HapMap data, which are a resource of genotype data from ~4 million common SNPs derived from four human populations (African YRI, Japanese JPT, Han Chinese CHB and European CEU). It is used to calculate minor allele frequency (MAF) of SNPs in different populations and estimate whether a significant difference exists between the samples (p < 0.05) using a chi-square test. To better understand if the variations, especially non-synonymous variations, lead to functional alterations to the corresponding genes, the SIFT algorithm [30] was also integrated into Exome-assistant.


Exome-assistant: a rapid and easy detection of disease-related genes and genetic variations from exome sequencing.

Liu Q, Shen E, Min Q, Li X, Wang X, Li X, Sun ZS, Wu J - BMC Genomics (2012)

The workflow of the Exome-assistant pipeline.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3539923&req=5

Figure 1: The workflow of the Exome-assistant pipeline.
Mentions: The basic principle of the single case analysis is to annotate the SNPs/InDels based on the information from public databases (Figure 1), which include: CCDS [27] and dbSNP (v.137) [28] for site/region-specific annotation, KEGG pathway [29] and Gene Ontology for gene-based annotation. In the dbSNP database, the SNPs flagged as ‘clinically associated’ are considered as disease-related SNPs and excluded from the database. The human reference gene definition was downloaded from UCSC (http://hgdownload.cse.ucsc.edu) and integrated into Exome-assistant. Also integrated are the HapMap data, which are a resource of genotype data from ~4 million common SNPs derived from four human populations (African YRI, Japanese JPT, Han Chinese CHB and European CEU). It is used to calculate minor allele frequency (MAF) of SNPs in different populations and estimate whether a significant difference exists between the samples (p < 0.05) using a chi-square test. To better understand if the variations, especially non-synonymous variations, lead to functional alterations to the corresponding genes, the SIFT algorithm [30] was also integrated into Exome-assistant.

Bottom Line: Protein-coding regions in human genes harbor 85% of the mutations that are associated with disease-related traits.The identified candidate disease-related genetic variations can be annotated according to their sample features.In summary, by exploring exome sequencing data, Exome-assistant can provide researchers with detailed biological insights into genetic variation events and permits the identification of potential genetic causes of human diseases and related traits.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Genomic Medicine, Wenzhou Medical College, Wenzhou 325035, China.

ABSTRACT

Background: Protein-coding regions in human genes harbor 85% of the mutations that are associated with disease-related traits. Compared with whole-genome sequencing of complex samples, exome sequencing serves as an alternative option because of its dramatically reduced cost. In fact, exome sequencing has been successfully applied to identify the cause of several Mendelian disorders, such as Miller and Schinzel-Giedio syndrome. However, there remain great challenges in handling the huge data generated by exome sequencing and in identifying potential disease-related genetic variations.

Results: In this study, Exome-assistant (http://122.228.158.106/exomeassistant), a convenient tool for submitting and annotating single nucleotide polymorphisms (SNPs) and insertion/deletion variations (InDels), was developed to rapidly detect candidate disease-related genetic variations from exome sequencing projects. Versatile filter criteria are provided by Exome-assistant to meet different users' requirements. Exome-assistant consists of four modules: the single case module, the two cases module, the multiple cases module, and the reanalysis module. The two cases and multiple cases modules allow users to identify sample-specific and common variations. The multiple cases module also supports family-based studies and Mendelian filtering. The identified candidate disease-related genetic variations can be annotated according to their sample features.

Conclusions: In summary, by exploring exome sequencing data, Exome-assistant can provide researchers with detailed biological insights into genetic variation events and permits the identification of potential genetic causes of human diseases and related traits.

Show MeSH
Related in: MedlinePlus