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

Data-driven network alignment

Biological network alignment (NA) aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for transfer of functional knowledge between the aligned nodes. However, current NA methods do not end up aligning functionally related nodes. A likely reason is that they assume it is topologically similar nodes that are functionally related. However, we show that this assumption does not hold well. So, a paradigm shift is needed with how the NA problem is approached. We redefine NA as a data-driven framework, TARA (daTA-dRiven network Alignment), which attempts to learn the relationship between topological relatedness and functional relatedness without assuming that topological relatedness corresponds to topological similarity, like traditional NA methods do. TARA trains a classifier to predict whether two nodes from different networks are functionally related based on their network topological patterns. We find that TARA is able to make accurate predictions. TARA then takes each pair of nodes that are predicted as related to be part of an alignment. Like traditional NA methods, TARA uses this alignment for the across-species transfer of functional knowledge. Clearly, TARA as currently implemented uses topological but not protein sequence information for this task. We find that TARA outperforms existing state-of-the-art NA methods that also use topological information, WAVE and SANA, and even outperforms or complements a state-of-the-art NA method that uses both topological and sequence information, PrimAlign. Hence, adding sequence information to TARA, which is our future work, is likely to further improve its performance

Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA

The aim of Network Alignment in Protein-Protein Interaction Networks is discovering functionally similar regions between compared organisms. One major compromise for solving a network alignment problem is the trade-off among multiple similarity objectives while applying an alignment strategy. An alignment may lose its biological relevance while favoring certain objectives upon others due to the actual relevance of unfavored objectives. One possible solution for solving this issue may be blending the stronger aspects of various alignment strategies until achieving mature solutions. This study proposes a parallel approach called PERSONA that allows aligners to share their partial solutions continuously while they progress. All these aligners pursue their particular heuristics as part of a particle swarm that searches for multi-objective solutions of the same alignment problem in a reactive actor environment. The actors use the stronger portion of a solution as a subgraph that they receive from leading or other actors and send their own stronger subgraphs back upon evaluation of those partial solutions. Moreover, the individual heuristics of each actor takes randomized parameter values at each cycle of parallel execution so that the problem search space can thoroughly be investigated. The results achieved with PERSONA are remarkably optimized and balanced for both topological and node similarity objectives

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

Disentangling ecological networks in marine microbes

There is a myriad of microorganisms on Earth contributing to global biogeochemical cycles, and their interactions are considered pivotal for ecosystem function. Previous studies have already determined relationships between a limited number of microorganisms. Yet, we still need to understand a large number of interactions to increase our knowledge of complex microbiomes. This is challenging because of the vast number of possible interactions. Thus, microbial interactions still remain barely known to date. Networks are a great tool to handle the vast number of microorganisms and their connections, explore potential microbial interactions, and elucidate patterns of microbial ecosystems. This thesis locates at the intersection of network inference and network analysis. The presented methodology aims to support and advance marine microbial investigations by reducing noise and elucidating patterns in inferred association networks for subsequent biological down-stream analyses. This thesis’s main contribution to marine microbial interactions studies is the development of the program EnDED (Environmentally-Driven Edge Detection), a computational framework to identify environmentally-driven associations inside microbial association networks, inferred from omics datasets. We applied the methodology to a model marine microbial ecosystem at the Blanes Bay Microbial Observatory (BBMO) in the North-Western Mediterranean Sea (ten years of monthly sampling). We also applied the methodology to a dataset compilation covering six global-ocean regions from the surface (3 m) to the deep ocean (down to 4539 m). Thus, our methodology provided a step towards studying the marine microbial distribution in space via the horizontal (ocean regions) and vertical (water column) axes.Hi ha una infinitat de microorganismes a la Terra que contribueixen als cicles biogeoquímics mundials i les seves interaccions es consideren fonamentals pel funcionament dels ecosistemes. Estudis previs ja han determinat les relacions entre un nombre limitat de microorganismes. Tot i això, encara hem d’entendre un gran nombre d’interaccions per augmentar el nostre coneixement dels microbiomes complexos. Això és un repte a causa del gran nombre d'interaccions possibles. Per això, les interaccions microbianes encara són poc conegudes fins ara. Les xarxes són una gran eina per tractar el gran nombre de microorganismes i les seves connexions, explorar interaccions microbianes potencials i dilucidar patrons d’ecosistemes microbians. Aquesta tesi es situa a la intersecció de la inferència de xarxes i l’anàlisi de la xarxes. La metodologia presentada té com a objectiu donar suport i avançar en investigacions microbianes marines reduint el soroll i dilucidant patrons en xarxes d’associació inferides per a posteriors anàlisis biològiques. La principal contribució d’aquesta tesi als estudis d’interaccions microbianes marines és el desenvolupament del programa EnDED (Environmentally-Driven Edge Detection), un marc computacional per identificar associacions impulsades pel medi ambient dins de xarxes d’associació microbiana, inferides a partir de conjunts de dades òmics. S’ha aplicat la metodologia a un model d’ecosistema microbià marí a l’Observatori Microbià de la Badia de Blanes (BBMO) al mar Mediterrani nord-occidental (deu anys de mostreig mensual). També s’ha la metodologia a una recopilació de dades que cobreix sis regions oceàniques globals des de la superfície (3 m) fins a l'oceà profund (fins a 4539 m).Hay una gran cantidad de microorganismos en la Tierra que contribuyen a los ciclos biogeoquímicos globales, y sus interacciones se consideran fundamentales para la función del ecosistema. Estudios previos ya han determinado relaciones entre un número limitado de microorganismos. Sin embargo, todavía necesitamos comprender una gran cantidad de interacciones para aumentar nuestro conocimiento de los microbiomas más complejos. Esto representa un gran desafío debido a la gran cantidad de posibles interacciones. Por lo tanto, las interacciones microbianas son aun poco conocidas. Las redes representan una gran herramienta para analizar la gran cantidad de microorganismos y sus conexiones, explorar posibles interacciones y dilucidar patrones en ecosistemas microbianos. Esta tesis se ubica en la intersección entre la inferencia de redes y el análisis de redes. La metodología presentada tiene como objetivo avanzar las investigaciones sobre interacciones microbianas marinas mediante la reducción del ruido en las inferencias de redes y elucidar patrones en redes de asociación permitiendo análisis biológicos posteriores. La principal contribución de esta tesis a los estudios de interacciones microbianas marinas es el desarrollo del programa EnDED (Environmentally-Driven Edge Detection), un marco computacional para identificar asociaciones generadas por el medio ambiente en redes de asociaciones microbianas, inferidas a partir de datos ómicos. Aplicamos la metodología a un modelo de ecosistema microbiano marino en el Observatorio Microbiano de la Bahía de Blanes (BBMO) en el Mar Mediterráneo Noroccidental (diez años de muestreo mensual). También, aplicamos la metodología a una compilación de conjuntos de datos que cubren seis regiones oceánicas globales desde la superficie (3 m) hasta las profundidades del océano (hasta 4539 m). Por lo tanto, nuestra metodología significa un paso adelante hacia de los patrones temporales microbianos marinos y el estudio de la distribución microbiana marina en el espacio a través de los ejes horizontal (regiones oceánicas) y vertical (columna de agua). Para llegar a hipótesis de interacción precisas, es importante determinar, cuantificar y eliminar las asociaciones generadas por el medio ambiente en las redes de asociaciones microbianas marinas. Además, nuestros resultados subrayaron la necesidad de estudiar la naturaleza dinámica de las redes, en contraste con el uso de redes estáticas únicas agregadas en el tiempo o el espacio. Nuestras nuevas metodologías pueden ser utilizadas por una amplia gama de investigadores que investigan redes e interacciones en diversos microbiomas.Postprint (published version