Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Apr 23;10(4):e0123569.
doi: 10.1371/journal.pone.0123569. eCollection 2015.

XomAnnotate: Analysis of Heterogeneous and Complex Exome- A Step towards Translational Medicine

Asoke K Talukder  1 , Shashidhar Ravishankar  1 , Krittika Sasmal  1 , Santhosh Gandham  1 , Jyothsna Prabhukumar  1 , Prahalad H Achutharao  1 , Debmalya Barh  2 , Francesco Blasi  3
Affiliations

XomAnnotate: Analysis of Heterogeneous and Complex Exome- A Step towards Translational Medicine

Asoke K Talukder et al. PLoS One. .

Abstract

In translational cancer medicine, implicated pathways and the relevant master genes are of focus. Exome's specificity, processing-time, and cost advantage makes it a compelling tool for this purpose. However, analysis of exome lacks reliable combinatory analysis tools and techniques. In this paper we present XomAnnotate--a meta- and functional-analysis software for exome. We compared UnifiedGenotyper, Freebayes, Delly, and Lumpy algorithms that were designed for whole-genome and combined their strengths in XomAnnotate for exome data through meta-analysis to identify comprehensive mutation profile (SNPs/SNVs, short inserts/deletes, and SVs) of patients. The mutation profile is annotated followed by functional analysis through pathway enrichment and network analysis to identify most critical genes and pathways implicated in the disease genesis. The efficacy of the software is verified through MDS and clustering and tested with available 11 familial non-BRCA1/BRCA2 breast cancer exome data. The results showed that the most significantly affected pathways across all samples are cell communication and antigen processing and presentation. ESCO1, HYAL1, RAF1 and PRKCA emerged as the key genes. Network analysis further showed the purine and propanotate metabolism pathways along with RAF1 and PRKCA genes to be master regulators in these patients. Therefore, XomAnnotate is able to use exome data to identify entire mutation landscape, pathways, and the master genes accurately with wide concordance from earlier microarray and whole-genome studies--making it a suitable biomedical software for using exome in next-generation translational medicine.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: AKT, SR, KS, SG, JP, PHA and DB are employees of InterpretOmics, Pvt. Ltd., whose company funded this study. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials. The XomAnnotate software is available free for noncommercial use at URL: http://www.iomics.in/research/XomAnnotate. Also, all necessary databases (open domain and modified) related to hg19 that were used in this paper are made available at this site for download.

Figures

Fig 1
Fig 1. Schematic diagram of XomAnnotate component of iOMICS exome data analysis platform.
The diagram shows four main stapes of XomAnnotate software: (i) Variant filtering and meta-analysis, (ii) Annotation of variants, (iii) Pathway enrichment, and (iv) Network analysis.
Fig 2
Fig 2. Multidimensional Scaling (MDS) plot of mutation burden of breast cancer and healthy samples.
The MDS plot was constructed using entire variation map (mutation count) of the two groups of 11 BC samples against 13 healthy samples. The graph shows distinct clustering of the breast cancer samples (BCx) and the healthy samples (Hx).
Fig 3
Fig 3. Two-mode and one-mode graph.
(a) A bipartite or two-mode graph of pathways and genes. (b) The graph transformed into a one-mode pathway-pathway graph. (c) The graph transformed into a one-mode gene-gene graph.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Heng HH, Bremer WS, Stevens JB, Ye KJ, Liu G, Ye CJ, et al. Genetic and epigenetic heterogeneity in Cancer: A genome centric perspective. J Cell Physiol. 2009;220(3): 538–547. 10.1002/jcp.21799 - DOI - PubMed
    1. Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumour mutations. Nat Methods. 2013;10(11): 1108–1115. 10.1038/nmeth.2651 - DOI - PMC - PubMed
    1. Albertson DG, Collins C, McCormick F, Gray JW. Chromosome aberrations in solid tumours. Nat Genet. 2003;34(4): 369–376. - PubMed
    1. Shlien A, Malkin D. Copy number variations and cancer. Genome Med. 2009;1(6):62 10.1186/gm62 - DOI - PMC - PubMed
    1. McCormick F. Cancer: Survival pathways meet their end. Nature 2004;428: 267–269. - PubMed

Publication types