BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks

BiNoM (Biological Network Manager) is a new bioinformatics software that significantly facilitates the usage and the analysis of biological networks in standard systems biology formats (SBML, SBGN, BioPAX). BiNoM implements a full-featured BioPAX editor and a method of 'interfaces' for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as path and cycle analysis, clustering sub-networks, decomposition of network into modules, clipboard operations and others. AVAILABILITY: Last version of BiNoM distributed under the LGPL licence together with documentation, source code and API are available at http://bioinfo.curie.fr/projects/binom     

Biana: a software framework for compiling biological interactions and analyzing networks

Background  

The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.     

BLogo: a tool for visualization of bias in biological sequences

Blogo is a web-based tool that detects and displays statistically significant position-specific sequence bias with reduced background noise. The over-represented and under-represented symbols in a particular position are shown above and below the zero line. When the sequences are in open reading frames, the background frequency of nucleotides could be calculated separately for the three positions of a codon, thus greatly reducing the background noise. The chi(2)-test or Fisher's exact test is used to evaluate the statistical significance of every symbol in every position and only those that are significant are highlighted in the resulting logo. The perl source code of the program is freely available and can be run locally. AVAILABILITY: http://acephpx.cropdb.org/blogo/, http://www.bioinformatics.org/blogo/.     

MathSBML: a package for manipulating SBML-based biological models

MathSBML is a Mathematica package designed for manipulating Systems Biology Markup Language (SBML) models. It converts SBML models into Mathematica data structures and provides a platform for manipulating and evaluating these models. Once a model is read by MathSBML, it is fully compatible with standard Mathematica functions such as NDSolve (a differential-algebraic equations solver). MathSBML also provides an application programming interface for viewing, manipulating, running numerical simulations; exporting SBML models; and converting SBML models in to other formats, such as XPP, HTML and FORTRAN. By accessing the full breadth of Mathematica functionality, MathSBML is fully extensible to SBML models of any size or complexity. AVAILABILITY: Open Source (LGPL) at http://www.sbml.org and http://www.sf.net/projects/sbml     

Pattern matching of biological sequences with limited storage

Existing methods for getting the locally best matched alignmentsbetween a pair of biological sequences require O(N²) computationalsteps and O(N²) storage, where N is the average sequence length.An improved method is presented with which the storage requirementis greatly reduced, while the computational steps remain O(N²).Only a small number of additional steps are required to displayany common sub–sequences with similarity scores greaterthan a given threshold. The aligments found by the algorithmare optimal in the sense that their scores are locally maximal,where each score is a sum of weights given to individual matches/replacements,insertions and deletions involved in the alignment. The algorithmwas implemented in C programming language on a personal computer.Data area of 64 kbytes on random access memory and a few hundredkbytes on a disk is sufficient for comparing two protein ornucleic acid sequences of 2500 residues. The programs are particularlyvaluable when used in combination with fast sequence searchprograms. Received on July 25, 1986; accepted on October 27, 1986     

Optimal alignments of biological sequences on a microcomputer

An algorithm and a program have been developed which enableoptimal alignments of biological sequences on an 8–bitmicrocomputer. The compiled program can process sequences upto 1000 residues on a Commodore 64. Since this program was writtenoriginally in the BASIC language, it may readily be adaptedto other microcomputers with small changes. Received on March 11, 1985; accepted on March 14, 1985     

Clustering metagenomic sequences with interpolated Markov models

Background  

Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.     

PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization

Patikaweb provides a Web interface for retrieving and analyzing biological pathways in the Patika database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats.     

Pseudo-periodic partitions of biological sequences

MOTIVATION: Algorithm development for finding typical patterns in sequences, especially multiple pseudo-repeats (pseudo-periodic regions), is at the core of many problems arising in biological sequence and structure analysis. In fact, one of the most significant features of biological sequences is their high quasi-repetitiveness. Variation in the quasi-repetitiveness of genomic and proteomic texts demonstrates the presence and density of different biologically important information. It is very important to develop sensitive automatic computational methods for the identification of pseudo-periodic regions of sequences through which we can infer, describe and understand biological properties, and seek precise molecular details of biological structures, dynamics, interactions and evolution. RESULTS: We develop a novel, powerful computational tool for partitioning a sequence to pseudo-periodic regions. The pseudo-periodic partition is defined as a partition, which intuitively has the minimal bias to some perfect-periodic partition of the sequence based on the evolutionary distance. We devise a quadratic time and space algorithm for detecting a pseudo-periodic partition for a given sequence, which actually corresponds to the shortest path in the main diagonal of the directed (acyclic) weighted graph constructed by the Smith-Waterman self-alignment of the sequence. We use several typical examples to demonstrate the utilization of our algorithm and software system in detecting functional or structural domains and regions of proteins. A big advantage of our software program is that there is a parameter, the granularity factor, associated with it and we can freely choose a biological sequence family as a training set to determine the best parameter. In general, we choose all repeats (including many pseudo-repeats) in the SWISS-PROT amino acid sequence database as a typical training set. We show that the granularity factor is 0.52 and the average agreement accuracy of pseudo-periodic partitions, detected by our software for all pseudo-repeats in the SWISS-PROT database, is as high as 97.6%.     

'Genomemark': detecting word periodicity in biological sequences

Identifying and predicting the structural characteristics of novel repeats throughout the genome can lend insight into biological function. Specific repeats are believed to have biological significance as a function of their distribution patterns. We have developed 'GenomeMark,' a computer program that detects and statistically analyzes candidate repeats. Specifically, 'GenomeMark' identifies the periodic distribution of unique words, calculating their chi2 and Z-score values. Using 'GenomeMark,' we identified novel sequence words present in tandem throughout genomes. We found that these sequences have remarkable spacer sequence distributions and many were genome specific, validating the genome signature theory. Further analysis confirmed that many of these sequences have a specific biological function. The program is available from the authors upon request and is freely available for non-commercial and academic entities.     

MassSorter: a tool for administrating and analyzing data from mass spectrometry experiments on proteins with known amino acid sequences

Background  

Proteomics is the study of the proteome, and is critical to the understanding of cellular processes. Two central and related tasks of proteomics are protein identification and protein characterization. Many small laboratories are interested in the characterization of a small number of proteins, e.g., how posttranslational modifications change under different conditions.     

Algorithmic specification as a technique for computing with informal biological models

Conceptual biological models can sometimes be usefully expressed in algorithmic form. Models expressed in this way are often capable of capturing more aspects of the complete system than could be captured by other modeling approaches, though in general each aspect is captured in a highly simplified way. Two examples are given. The first involves algorithmic specification of the theory of evolution. The second involves a recently implemented computational model of the brain. Work with such models has, to some extent, the style of experimental work. It often suggests interesting problems or exposes subtle features of the system whose importance has been overlooked.     

DrugViz: a Cytoscape plugin for visualizing and analyzing small molecule drugs in biological networks

SUMMARY: DrugViz is a Cytoscape plugin that is designed to visualize and analyze small molecules within the framework of the interactome. DrugViz can import drug-target network information in an extended SIF file format to Cytoscape and display the two-dimensional (2D) structures of small molecule nodes in a unified visualization environment. It also can identify small molecule nodes by means of three different 2D structure searching methods, namely isomorphism, substructure and fingerprint-based similarity searches. After selections, users can furthermore conduct a two-side clustering analysis on drugs and targets, which allows for a detailed analysis of the active compounds in the network, and elucidate relationships between these drugs and targets. DrugViz represents a new tool for the analysis of data from chemogenomics, metabolomics and systems biology. AVAILABILITY: DrugViz and data set used in Application are freely available for download at http://202.127.30.184:8080/software.html.     

Supervised reconstruction of biological networks with local models

MOTIVATION: Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest. RESULTS: While most previous approaches based on the partial knowledge of the network to be inferred build global models to predict new edges over the network, we introduce here a novel method which predicts whether there is an edge from a newly added vertex to each of the vertices of a known network using local models. This involves learning individually a certain subnetwork associated with each vertex of the known network, then using the discovered classification rule associated with only that vertex to predict the edge to the new vertex. Excellent experimental results are shown in the case of metabolic and protein-protein interaction network reconstruction from a variety of genomic data. AVAILABILITY: An implementation of the proposed algorithm is available upon request from the authors.