Bioinformatics is the information technology to deal with biological data. Recently emerging systems biology has drawn great interest inspired by world-wide efforts for modeling and analyzing biological processes with a systems perspective. Bioinformatics, which has analyzed multi-omics data such as genomics, transcriptomics, and proteomics, and explored novel biological patterns embedded within the data, now has a transition to its application to systems biology, called systems bioinformatics. Systems bioinformatics includes various research areas: system modeling, system structure and dynamics analysis, causality analysis, and multi-omics data fusion. In this review, we introduce bioinformatics for genomics, transcriptomics, proteomics, and systems biology according to the different aspects of biological processes.
Biological Processes ; Computational Biology ; Genomics ; Proteomics ; Systems Biology

Every protein can be characterized by either a distinct motif or a combination of motifs. Nevertheless, little is known about the relationships among (more than two) the motifs. Some of the proteins in the world are share motifs for evolutional or other biological benefits - they can save energy, time and resource for controlling and managing a variety of proteins. In some cases of motifs, the tendency is quite common and they can act the 'hub' motif of a network of the motif associations. The hubs are structurally and functionally important in themselves and also important in disease-related mutations. They will be highly resistant mutation to conserve their functions. But, in case of the a rare mutation, mutations on the position of hub can more easily cause fatal diseases.

Even though it represents 6 13% of human genomic DNA, Alu sequences are rarely found in coding regions. When in exon region, over 80 % of them are found in 3' untranslated region (UTR). Pseudogenes are an important component of human genome. Their functions are not clearly known and the mechanism of how they are generated is still debatable. Both the Alu and Pseudogenes are important research problems in molecular biology. mRNA is thought to be a prime source of pseudogene and active research is going on its molecular mechanism. We report, for the first time, that mRNAs containing Alu repeats at 3' UTR has a significantly high correlation with processed pseudogenes, suggesting a possibility that Alu containing mRNAs have a high tendency to become processed pseudogenes. It is known that about 10% of all human genes have been transposed. Transposed genes at 3' UTR without Alu repeat have about two processed pseudogenes per gene on average while we found with statistical significance that a transposed gene with Alu had over three processed Pseudogenes on average. Therefore, we propose Alu repeats as a new and important factor in the generation of pseudogenes.
3' Untranslated Regions* ; Alu Elements* ; Clinical Coding ; DNA ; Exons ; Genome, Human ; Humans ; Molecular Biology ; Pseudogenes* ; RNA, Messenger*

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.
Gene Expression ; Genes, Neoplasm ; Genome ; Genomics ; Sequence Analysis, DNA ; Small Cell Lung Carcinoma