Colorectal cancer refers to the cancer that occurs in the colon and rectum. It has been established as the third most
common cancer and the forth one in causing worldwide mortality. Cancer caused by the mutation of several genes that
usually involved in the regulation of cell proliferation, growth and cell death. The mutation that leads to abnormal
function of genes, either in enabling the genes to gain or loss of function was termed as driver mutation and the genes
with driver mutation ability was termed as driver genes. The identification of driver genes provides insight on mechanistic
process of cancer development where this information can be used to further understand their mode of action for causing
dysregulation in signaling pathways. In this study, two bioinformatic tools, i.e. CGI and iCAGES were used to predict
potential driver genes from the genome of eight colorectal cancer patients with annotated variants datasets. 44 unique
driver genes and 21 pathways have been identified; such as p53 signaling, PI3K-AKT, Endocrine resistance, MAPK and
cell cycle pathways. The identification of these pathways can lead to the identification of potential drugs targeting these
pathways.
Porphyromonas gingivalis is the bacterium responsible for chronic periodontitis, a severe periodontal disease. Virulence
factors produced by this bacterium are secreted by the Type IX Secretion System (T9SS). The specific functions for
each protein component of the T9SS have yet to be characterized thus limiting our understanding of the mechanisms
associated with the translocation and modification processes of the T9SS. This study aims to identify the sequence motifs
for each T9SS component and predict the functions associated with each discovered motif using motif comparisons. We
extracted the sequences of 20 T9SS components from the P. gingivalis proteome that were experimentally identified to
be important for T9SS function and used them for homology searching against fully sequenced bacterial proteomes.
We developed a rigorous pipeline for the identification of seed sequences for each protein family of T9SS components.
We verified that each selected seed sequence are true members of the protein family hence sharing conserved sequence
motifs using profile Hidden Markov Models. The motifs for each T9SS component are identified and compared to motifs
in the Pfam database. The discovered motifs for 11 components with known functions matched the motifs associated
with the reported functions. We also suggested the putative functions for four components. PorM and PorW might form
the putative energy transduction complex. PorP and PorT might be the putative O-deacylases. The identified motifs for
five components matched the motifs associated with functions that related/unrelated to the T9SS.