Marco Mina

Semantic similarity analysis of protein data: assessment with biological features and issues

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

AlignMCL: Comparative analysis of protein interaction networks through Markov clustering

Evolutionary analysis and comparison of biological networks may result in the identification of conserved mechanism between species as well as conserved modules, such as protein complexes and pathways. Following an holistic philosophy several algorithms, known as network alignment algorithms, have been proposed recently as counterpart of sequence and structure alignment algorithms, to unravel relations between different species at the interactome level. In this work we present AlignMCL, a local alignment algorithm for the identification of conserved subnetworks in different species. As many other existing tools, AlignMCL is based on the idea of merging many protein interaction networks in a single alignment graph and subsequently mining it to identify potentially conserved subnetworks. In order to asses AlignMCL we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset. Finally, to improve the usability of our tool we developed a Cytoscape plugin, AlignMCL, that offers a graphical user interface to an MCL engine.

M-Finder: Uncovering functionally associated proteins from interactome data integrated with GO annotations

BackgroundProtein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.ResultsWe propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.ConclusionsM-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at http://bionet.ecs.baylor.edu/mfinder

Autonomous discovery, localization and recognition of smart objects through WSN and image features

This paper presents a framework that enables the interaction of robotic systems and wireless sensor network technologies for discovering, localizing and recognizing a number of smart objects (SO) placed in an unknown environment. Starting with no a priori knowledge of the environment, the robot will progressively build a virtual reconstruction of the surroundings in three phases: first, it discovers the SOs located in the area by using radio communication; second, it performs a rough localization of the SOs by using a range-only SLAM algorithm based on the RSSI-range measurements; third, it refines the SOs localization by comparing the descriptors extracted from the images acquired by the onboard camera with those transmitted by the motes attached to the SOs. Experimental results show how the combined use of the RSSI data and of the image features allows to discover and localize the SOs located in the environment with a good accuracy.

Discovery, localization and recognition of smart objects by a mobile robot

This paper presents a robotic system that exploits Wireless Sensor Network (WSN) technologies for implementing an ambient intelligence scenario. We address the problems of robot object discovery, localization, and recognition in a fully distributed way. We propose to embed some memory, some computational power, and some communication capability in the objects, by attaching a WSN mote to each object.We called the union of an object and of a mote, a smart object. The robot does not have any information on the number nor on the kind of objects in the environment. The robot discovers the objects through the radio frequency communication provided by the WSN motes. The robot roughly locates the motes by performing a range-only SLAM algorithm based on the RSSI-range measurements. A more precise localization and recognition step is performed by processing images acquired by the camera installed on the robot and matching the descriptors extracted from these images with those transmitted by the motes. Experiments with eight smart objects in a cluttered office environment with many dummy objects are reported. The robot was able to correctly locate the motes, to navigate toward them and to correctly recognize the smart objects.

Towards the assessment of semantic similarity analysis of protein data: main approaches and issues

Bioinformatics approaches to the study of proteins yield to the introduction of different methodologies and related tools for the analysis of different types of data related to proteins, ranging from primary, secondary and tertiary structures to interaction data [1], not to mention functional knowledge. One of the most advanced tools for encoding and representing functional knowledge in a formal way is the Gene Ontology (GO) [2,3]. It is composed of three ontologies, named Biological Process (BP), Molecular Function (MF) and Cellular Component (CC). Each ontology consists of a set of terms (GO terms) representing different functions, biological processes and cellular components within the cell. GO terms are connected each other to form a hierarchical graph. Terms representing similar functions are close to each other within this graph. Biological molecules are associated with GO terms that represent their functions, biological roles and localization. This process, usually referred to as annotation process, can be performed under the supervision of an expert or in a fully automated way. Obviously, computationally inferred annotations, commonly known as Electronically Inferred Annotation (IEA), are not as reliable as experimentally determined annotations. For this reason every annotation is labeled with an Evidence Code (EC) that keeps track of the type of process used to produce the annotation itself. Considering the release of annotations of April, 2010, about the 98% of all the annotations is an IEA annotation [4]. The term annotation corpus is commonly used to identify all the annotations involving a set of proteins or genes, usually referring the whole proteomes and genomes (i.e. the annotation corpus of yeast). For lack of space we do not further describe the Gene Ontology. A comprehensive review has been provided by du Plessis et al. [4] and by Guzzi et al. [5]. The availability of well formalized functional data enabled the use of computational methods to analyse genes and proteins from the functional point of view. For example, a set of algorithms, known as functional enrichment algorithms, have been developed to determine the statistical significance of the presence (or the absence) of a GO Term in a set of gene products. A detailed review of these algorithms can be found in [4]. An interesting problem is how to express quantitatively the relationships between GO terms. Several measures, referred to as (term) semantic similarity (SS) measures, has been introduced in the last decade. Given two or more GO terms, they try to quantify the similarity of the functional aspects represented by the terms within the cell. Exploiting annotation corpora, semantic similarity measures have been further extended to the evaluation of the similarity of genes and proteins on the basis of their annotations. Many different works have focused on the following tasks: (i) the definition of ad-hoc semantic similarity measures tailored to the characteristics of Gene Ontology; (ii) the definition of measures of comparison of genes and proteins; (iii) the introduction of methodologies for the systematic assessment of semantic similarity measures; (iv) the use of semantic similarity measures in many different contexts and applications. Despite its relevance, the application of semantic similarity for the systematic analysis of protein data is still an open research area. There are, in fact, two main questions that have to be addressed: (i) the systematic assessment of SS with respect to other biological features, i.e. how much an high or a low value of SS is biologically meaningful; (ii) how reliable are the SS themselves, i.e. is there any systematic error or bias in the calculation of SS? Both these problems are relevant for the diffusion of SS measures; while in the first case several approaches have been proposed, confronting SS measures with a pletora of different biological features, only few works dealt with the second problem in a systematic way [5,6,7].

Guest Editorial for Special Section on Semantic-Based Approaches for Analysis of Biological Data

The papers in this special section present recent developments in semantic-based approaches for biological data analysis. The systematic integration of biological data with biological knowledge is a recent trend in bioinformatics. Current biological information is spread among multiple sources and encoded in different ontologies. Biological information is associated to biological concepts in a process known as annotation. The annotation of biological data with this additional information enable the use (and the development) of algorithms that use biological ontologies as a framework to mine annotated data based on the use of semantics. The use of such annotations for the analysis of protein data is a relatively novel research area that is becoming more and more important in the field. Indeed, as shown in literature, there is a positive trend in the use of biological information in the analysis of protein data.