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

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

PROPER: global protein interaction network alignment through percolation matching

Background The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. Results In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. Conclusions We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch

Novel multispecies microbial consortia involved in lignocellulose and 5-hydroxymethylfurfural bioconversion

To develop a targeted metagenomics approach for the analysis of novel multispecies microbial consortia involved in the bioconversion of lignocellulose and furanic compounds, we applied replicated sequential batch aerobic enrichment cultures with either pretreated or untreated wheat straw as the sources of carbon and energy. After each transfer, exponential growth of bacteria was detected using microscopic cell counts, indicating that the substrate was being utilized. In batch, the final bacterial abundances increased from an estimated 5 to 8.7-9.5 log 16S rRNA gene copy numbers/ml. The abundances of fungal propagules showed greater variation, i.e., between 5.4 and 8.0 log ITS1 copies/ml. Denaturing gradient gel electrophoresis analyses showed that the bacterial consortia in both treatments reached approximate structural stability after six transfers. Moreover, the structures of the fungal communities were strongly influenced by substrate treatment. A total of 124 bacterial strains were isolated from the two types of enrichment cultures. The most abundant strains were affiliated with the genera Raoultella/Klebsiella, Kluyvera, Citrobacter, Enterobacter, Pseudomonas, Acinetobacter, Flavobacterium and Arthrobacter. Totals of 43 and 11 strains obtained from the untreated and pretreated substrates, respectively, showed (hemi)cellulolytic activity (CMC-ase and xylanase), whereas 96 strains were capable of growth in 7.5 mM 5-hydroxymethylfurfural. About 50 % of the latter showed extracellular oxidoreductase activity as detected by a novel iodide oxidation method. Also, (hemi)cellulolytic fungal strains related to Coniochaeta, Plectosphaerella and Penicillium were isolated. One Trichosporon strain was isolated from pretreated wheat straw. The two novel bacterial-fungal consortia are starting points for lignocellulose degradation applications