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Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker , a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae ; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.

Identifying reliable biomarkers in peripheral blood is critical for advancing the diagnosis and management of multiple sclerosis (MS), particularly given the invasive nature of cerebrospinal fluid (CSF) sampling. This review explores the role of B cells and immunoglobulins (Igs), particularly IgG and IgM, as biomarkers for MS. B cell oligoclonal bands (OCBs) in the CSF are well-established diagnostic tools, yet peripheral biomarkers remain underdeveloped. Emerging evidence highlights structural and functional variations in immunoglobulin that may correlate with disease activity and progression. A recent novel discovery of blood IgG aggregates in MS patients that fail to bind Protein A reveals promising diagnostic potential and confirms previous findings of the unique features of immunoglobulin G in MS and the potential link between the superantigen Protein A and MS. These aggregates, enriched in IgG1 and IgG3 subclasses, exhibit unique structural properties, including mutations in the framework region 3 (FR3) of IGHV3 genes, and are associated with complement-dependent neuronal apoptosis. Data based on ELISA have demonstrated that IgG aggregates in plasma can distinguish MS patients from healthy controls and other central nervous system (CNS) disorders with high accuracy and differentiate between disease subtypes. This suggests a role for IgG aggregates as non-invasive biomarkers for MS diagnosis and monitoring.

In multiple sclerosis (MS) pathogenic B cells likely act on both sides of the blood-brain barrier (BBB). However, it is unclear whether antigen-experienced B cells are shared between the CNS and the peripheral blood (PB) compartments. We applied deep repertoire sequencing of IgG heavy chain variable region genes (IgG-VH) in paired cerebrospinal fluid and PB samples from patients with MS and other neurological diseases to identify related B cells that are common to both compartments. For the first time to our knowledge, we found that a restricted pool of clonally related B cells participated in robust bidirectional exchange across the BBB. Some clusters of related IgG-VH appeared to have undergone active diversification primarily in the CNS, while others have undergone active diversification in the periphery or in both compartments in parallel. B cells are strong candidates for autoimmune effector cells in MS, and these findings suggest that CNS-directed autoimmunity may be triggered and supported on both sides of the BBB. These data also provide a powerful approach to identify and monitor B cells in the PB that correspond to clonally amplified populations in the CNS in MS and other inflammatory states.

Large-scale DNA sequencing of immunological repertoires offers an opportunity for the discovery of novel biomarkers for autoimmune disease. Available bioinformatics techniques however, are not adequately suited for elucidating possible biomarker candidates from within large immunosequencing datasets due to unsatisfactory scalability and sensitivity. Here, we present the Haystack Heuristic, an algorithm customized to computationally extract disease-associated motifs from next-generation-sequenced repertoires by contrasting disease and healthy subjects. This technique employs a local-search graph-theory approach to discover novel motifs in patient data. We apply the Haystack Heuristic to nine million B-cell receptor sequences obtained from nearly 100 individuals in order to elucidate a new motif that is significantly associated with multiple sclerosis. Our results demonstrate the effectiveness of the Haystack Heuristic in computing possible biomarker candidates from high throughput sequencing data and could be generalized to other datasets.

This dissertation is concerned with the construction and validation of an organizational framework for processing large protein sequence datasets. The framework relies on the accurate clustering of input sequences into functionally similar families. We demonstrate how the quality of output for existing protein clustering techniques may be improved by running a simple edge weight selection heuristic prior to clustering. Once clustering is completed, we are able to topologically organize the data by treating each cluster as a node in a network and searching for the union of minimum spanning trees that reconnects the clusters to each other. When thusly organized, the topological relationships between neighboring clusters exhibit properties similar to evolutionary relationships computed from phylogenetic models. We demonstrate how these topological relationships may be used to algorithmically identify the functionally significant residues within the sequences in the organized dataset. This predictive capacity of the organizational framework serves as a quantitative metric for validating the framework's biological significance.

Off-Chain transactions allow for the immediate transfer of Cryptocurrency between two parties, without delays or unavoidable transaction fees. Such capabilities are critical for mainstream Cryptocurrency adaption. They allow for the \"Coffee-Coin Criteria\"; under which a customer orders a coffee and pays for that coffee in bitcoins. This is not possible with On-Chain transactions today. Unfortunately, all existing Off-Chain transaction protocols are notoriously unreliable The current generation of third-party facilitators are vulnerable to hacker-based attacks. As Mt. Gox tragically demonstrated, centralized-transaction institutions are easy targets for Cryptocurrency thieves. The slightest security flaw in a third-party system will pounced on by hackers, who will proceed to devour it like ants devouring a crab. Under such circumstances, it no wonder that the Public treats most Cryptocurrency services with a constant shadow of suspicion. For Bitcoin to flourish, its anti-hierarchy principles must be applied to safe Off-Chain transactions. First and foremost, we need a new hacker-proof protocol that can easily be executed by any experienced developer. Preferably, the protocol will be open-sourced for full reliability and transparency. This paper presents one such procedure, which allows for he safe transmission of Bitcoin private key control by way of Cryptocubic transactions.

Motivation: The study of diverse enzyme superfamilies can provide important insight into the relationships between protein sequence, structure and function. It is often challenging, however, to discover these relationships across a large and diverse superfamily. Contemporary similarity network visualization techniques allow researchers to aggregate sequence similarity information into a single global view. Network visualization provides a qualitative estimate of functional diversity within a superfamily, but is unable to quantitate explicit boundaries, when present, between neighboring families in sequence space. This limits the potential of existing sequence-based algorithms to generate functional predictions from superfamily datasets. Results: By building on current network analysis tools, we have developed a new algorithm for elucidating pairs of homologous families within a sequence dataset. Our algorithm is able to filter through a dense similarity network in order to estimate both the boundaries of individual families and also how the families neighbor one another. Globally, these neighboring families define a topology across the entire superfamily. The topology is simple to interpret by visualizing the network output generated by our filtration protocol. We have compared the network topology within the kinase superfamily against available phylogenetic data. Our results suggest that neighbors within the filtered kinase network are more likely to share structural and functional properties than more distant network clusters.

Understanding the connection between mechanics and cell structure requires the exploration of the key molecular constituents responsible for cell shape and motility. One of these molecular bridges is the cytoskeleton, which is involved with intracellular organization and mechanotransduction. In order to examine the structure in cells, we have developed a computational technique that is able to probe the self-assembly of actin filaments through a lattice based Monte Carlo method. We have modeled the polymerization of these filaments based upon the interactions of globular actin through a probabilistic model encompassing both inert and active proteins. The results show similar response to classic ordinary differential equations at low molecular concentrations, but a bi-phasic divergence at realistic concentrations for living mammalian cells. Further, by introducing localized mobility parameters, we are able to simulate molecular gradients that are observed in non-homogeneous protein distributionsin vivo. The method and results have potential applications in cell and molecular biology as well as self-assembly for organic and inorganic systems.