Computational pan-genomics: Status, promises and challenges

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different Computational methods and paradigms are needed.We will witness the rapid extension of Computational pan-genomics, a new sub-area of research in Computational biology. In this article, we generalize existing definitions and understand a pangenome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a Computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations

Low Duplicability and Network Fragility of Cancer Genes

We identified genomic and network properties of approximately 600 genes mutated in different cancer types. These genes tend not to duplicate but, unlike most human singletons, they encode central hubs of highly interconnected modules within the protein-protein interaction network (PIN). We find that cancer genes are fragile components of the human gene repertoire, sensitive to dosage modification. Furthermore, other nodes of the human PIN with similar properties are rare and probably enriched in candidate cancer genes

Global repression of cancer gene expression in a zebrafish model of melanoma is linked to epigenetic regulation.

We have established a model of melanoma progression in zebrafish through the generation of transgenic lines specifically expressing oncogenic human HRAS in the melanocytic lineage. In these tumors we have carried out quantitative expression analysis of several putative cancer genes, from known and predicted cancer gene lists. In particular, we analyzed 39 out of 101 putative cancer genes identified with a bioinformatics approach and selected for the low frequency of duplication and the high connectivity in protein networks. Data obtained by real-time polymerase chain reaction analysis from zebrafish melanoma tissue shows that the expression of many cancer genes is downregulated in zebrafish melanomas, whereas only cell cycle genes are upregulated. To understand whether this trend is due to global repression of gene expression associated to a repressive chromatin state, we investigated whether changes of histone methylation were detectable in our melanoma model. We found substantial differences in the levels of H3K9me3, H4K20me2, H3K27me3, H3K4me3, and H3R2me2a immunostaining in melanoma tissue when compared with normal skin. Thus our analysis suggests that in our model, like in human melanoma, important changes occur to the methylation status of histones. Although the outcome of these changes is still unknown, they could be responsible for the global repression of gene expression through epigenetic regulation shown in this study

Muon accelerators

We introduce a new phylogenetic reconstruction algorithm which, unlike most previous rigorous inference techniques, does not rely on assumptions regarding the branch lengths or the depth of the tree. The algorithm returns a forest which is guaranteed to contain all edges that are: 1) sufficiently long and 2) sufficiently close to the leaves. How much of the true tree is recovered depends on the sequence length provided. The algorithm is distance-based and runs in polynomial time