31,941 research outputs found
SWIM: A computational tool to unveiling crucial nodes in complex biological networks
SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer
Computational methods in cancer gene networking
In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. 

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks
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Chromatin accessibility underlies synthetic lethality of SWI/SNF subunits in ARID1A-mutant cancers.
ARID1A, a subunit of the SWI/SNF chromatin remodeling complex, is frequently mutated in cancer. Deficiency in its homolog ARID1B is synthetically lethal with ARID1A mutation. However, the functional relationship between these homologs has not been explored. Here, we use ATAC-seq, genome-wide histone modification mapping, and expression analysis to examine colorectal cancer cells lacking one or both ARID proteins. We find that ARID1A has a dominant role in maintaining chromatin accessibility at enhancers, while the contribution of ARID1B is evident only in the context of ARID1A mutation. Changes in accessibility are predictive of changes in expression and correlate with loss of H3K4me and H3K27ac marks, nucleosome spacing, and transcription factor binding, particularly at growth pathway genes including MET. We find that ARID1B knockdown in ARID1A mutant ovarian cancer cells causes similar loss of enhancer architecture, suggesting that this is a conserved function underlying the synthetic lethality between ARID1A and ARID1B
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