An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

A Fast Counting Method for 6-motifs with Low Connectivity

A $k$-motif (or graphlet) is a subgraph on $k$ nodes in a graph or network. Counting of motifs in complex networks has been a well-studied problem in network analysis of various real-word graphs arising from the study of social networks and bioinformatics. In particular, the triangle counting problem has received much attention due to its significance in understanding the behavior of social networks. Similarly, subgraphs with more than 3 nodes have received much attention recently. While there have been successful methods developed on this problem, most of the existing algorithms are not scalable to large networks with millions of nodes and edges. The main contribution of this paper is a preliminary study that genaralizes the exact counting algorithm provided by Pinar, Seshadhri and Vishal to a collection of 6-motifs. This method uses the counts of motifs with smaller size to obtain the counts of 6-motifs with low connecivity, that is, containing a cut-vertex or a cut-edge. Therefore, it circumvents the combinatorial explosion that naturally arises when counting subgraphs in large networks

The occurrence of germline BRCA1 and BRCA2 sequence alterations in Slovenian population

<p>Abstract</p> <p>Background</p> <p>The <it>BRCA1 </it>and <it>BRCA2 </it>mutation spectrum and mutation detection rates according to different family histories were investigated in 521 subjects from 322 unrelated Slovenian cancer families with breast and/or ovarian cancer.</p> <p>Methods</p> <p>The <it>BRCA1 </it>and <it>BRCA2 </it>genes were screened using DGGE, PTT, HRM, MLPA and direct sequencing.</p> <p>Results</p> <p>Eighteen different mutations were found in <it>BRCA1 </it>and 13 in <it>BRCA2 </it>gene. Mutations in one or other gene were found in 96 unrelated families. The mutation detection rates were the highest in the families with at least one breast and at least one ovarian cancer - 42% for <it>BRCA1 </it>and 8% for <it>BRCA2</it>. The mutation detection rate observed in the families with at least two breast cancers with disease onset before the age of 50 years and no ovarian cancer was 23% for <it>BRCA1 </it>and 13% for <it>BRCA2</it>. The mutation detection rate in the families with at least two breast cancers and only one with the disease onset before the age of 50 years was 11% for <it>BRCA1 </it>and 8% for <it>BRCA2</it>. In the families with at least two breast cancers, all of them with disease onset over the age of 50 years, the detection rate was 5% for <it>BRCA2 </it>and 0% for <it>BRCA1</it>.</p> <p>Conclusion</p> <p>Among the mutations detected in Slovenian population, 5 mutations in <it>BRCA1 </it>and 4 mutations in <it>BRCA2 </it>have not been described in other populations until now. The most frequent mutations in our population were c.181T > G, c.1687C > T, c.5266dupC and c.844_850dupTCATTAC in <it>BRCA1 </it>gene and c.7806-2A > G, c.5291C > G and c.3978insTGCT in <it>BRCA2 </it>gene (detected in 69% of <it>BRCA1 </it>and <it>BRCA2 </it>positive families).</p

Genome-wide meta-analysis identifies five new susceptibility loci for cutaneous malignant melanoma.

Thirteen common susceptibility loci have been reproducibly associated with cutaneous malignant melanoma (CMM). We report the results of an international 2-stage meta-analysis of CMM genome-wide association studies (GWAS). This meta-analysis combines 11 GWAS (5 previously unpublished) and a further three stage 2 data sets, totaling 15,990 CMM cases and 26,409 controls. Five loci not previously associated with CMM risk reached genome-wide significance (P < 5 × 10(-8)), as did 2 previously reported but unreplicated loci and all 13 established loci. Newly associated SNPs fall within putative melanocyte regulatory elements, and bioinformatic and expression quantitative trait locus (eQTL) data highlight candidate genes in the associated regions, including one involved in telomere biology.[Please see the Supplementary Note for acknowledgments.]This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ng.337

An introductory guide to aligning networks using SANA, the Simulated Annealing Network Aligner

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological {\em networks} holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology -- the "structure" of the network -- is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment -- which is an essentially solved problem -- network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used. Here we introduce SANA, the {\it Simulated Annealing Network Aligner}. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between 2 or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks. Availability: https://github.com/waynebhayes/SAN

Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways.

The total number of acquired melanocytic nevi on the skin is strongly correlated with melanoma risk. Here we report a meta-analysis of 11 nevus GWAS from Australia, Netherlands, UK, and USA comprising 52,506 individuals. We confirm known loci including MTAP, PLA2G6, and IRF4, and detect novel SNPs in KITLG and a region of 9q32. In a bivariate analysis combining the nevus results with a recent melanoma GWAS meta-analysis (12,874 cases, 23,203 controls), SNPs near GPRC5A, CYP1B1, PPARGC1B, HDAC4, FAM208B, DOCK8, and SYNE2 reached global significance, and other loci, including MIR146A and OBFC1, reached a suggestive level. Overall, we conclude that most nevus genes affect melanoma risk (KITLG an exception), while many melanoma risk loci do not alter nevus count. For example, variants in TERC and OBFC1 affect both traits, but other telomere length maintenance genes seem to affect melanoma risk only. Our findings implicate multiple pathways in nevogenesis