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

<div><p>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.</p><p>Availability</p><p><a href="http://www.iomics.in/research/XomAnnotate" target="_blank">http://www.iomics.in/research/XomAnnotate</a></p></div

Two-mode and one-mode graph.

<p>(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.</p

Schematic diagram of XomAnnotate component of iOMICS exome data analysis platform.

<p>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.</p

A simple model of mergers and innovation

We analyze the impact of a merger on firms’ incentives to innovate. We show that the merging parties always decrease their innovation efforts post-merger while the outsiders to the merger respond by increasing their effort. A merger tends to reduce overall innovation. Consumers are always worse off after a merger. Our model calls into question the applicability of the “inverted-U” relationship between innovation and competition to a merger setting

Additional file 5: Figure S2. of In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Homology distribution of Cp essential proteins aligned against hosts. Dark green: proteins homologous to host; Yellow: Proteins with low identity against hosts (identity < 30 %). Dark red: non-host homologous proteins, proteins with low identity and low coverage alignment against hosts (identity x coverage < = 10 %). Dark blue: non-host homologous proteins, proteins with no alignment hits against O. aires and C. hircus. Light blue: non-host homologous proteins, proteins with no alignment hits against the five hosts. The alignment summary is depicted in Additional file 6. (JPG 318 kb

Additional file 4: of In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

List of 181 essential proteins. The amino acid sequence of hubs proteins was compared against bacterial proteins sequence from Database of Essential Genes (DEG). (TXT 62 kb

Additional file 1: of Whole-genome optical mapping reveals a mis-assembly between two rRNA operons of Corynebacterium pseudotuberculosis strain 1002

C. pseudotuberculosis 1002 sequence placement. This XML file contains the restriction site positions used to the sequence placements by MapSolver™. (XML 88 kb

Compounds/Libraries name, MolDock scores and predicted hydrogen bonds for the selected molecules against NP_938502.1 (bioB, Biotin synthase).

<p>Compounds/Libraries name, MolDock scores and predicted hydrogen bonds for the selected molecules against NP_938502.1 (bioB, Biotin synthase).</p

Prières à l'usage de la communauté des Filles de la Charité

Contient une table des matièresAvec mode text

Additional file 2: of Corynebacterium pseudotuberculosis may be under anagenesis and biovar Equi forms biovar Ovis: a phylogenic inference from sequence and structural analysis

Information about the genes of C. pseudotuberculosis. All four genes were used in this work. These were united in the same alignment for phylogenetic analysis. (PDF 7 kb