Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules

Abstract

In this editorial preface, I briefly review cancer bioinformatics and introduce the four articles in this special issue highlighting important applications of the field: detection of chromatin states; detection of SNP-containing motifs and association with transcription factor-binding sites; improvements in functional enrichment modules; and gene association studies on aging and cancer. We expect this issue to provide bioinformatics scientists, cancer biologists, and clinical doctors with a better understanding of how cancer bioinformatics can be used to identify candidate biomarkers and targets and to conduct functional analysis.

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

“…Recent genomic and molecular characterizations of cancer, especially the findings reported by the TCGA project, have shed light on cancer heterogeneity and potential targeted therapeutics, for example by recognizing new subtypes of gastric cancer [ 6 ]. In general, targeted computational methods, which make effective use of the available multimodal biological information, can significantly improve our ability to identify candidate biomarkers and targets and to conduct functional analyses [ 7 ]. For example, reducing the redundancy in enrichment analysis helps to reveal gene ontology modules efficiently and systematically [ 8 ].…”