Analysis for free: comparing programs for sequence analysis

Programs to import, manage and align sequences and to analyse the properties of DNA, RNA and proteins are essential for every biological laboratory. This review describes two different freeware (BioEdit and pDRAW for MS Windows) and a commercial program (Sequencher for MS Windows and Apple MacOS). Bioedit and Sequencher offer functions such as sequence alignment and editing plus reading of sequence trace files. pDRAW is a very comfortable visualisation tool with a variety of analysis functions. While Sequencher impresses with a very user-friendly interface and easy-to-use tools, BioEdit offers the largest and most customisable variety of tools. The strength of pDRAW is drawing and analysis of single sequences for priming and restriction sites and virtual cloning. It has a database function for user-specific oligonucleotides and restriction enzymes.     

Modeling DNA sequence-based cis-regulatory gene networks

Gene network analysis requires computationally based models which represent the functional architecture of regulatory interactions, and which provide directly testable predictions. The type of model that is useful is constrained by the particular features of developmentally active cis-regulatory systems. These systems function by processing diverse regulatory inputs, generating novel regulatory outputs. A computational model which explicitly accommodates this basic concept was developed earlier for the cis-regulatory system of the endo16 gene of the sea urchin. This model represents the genetically mandated logic functions that the system executes, but also shows how time-varying kinetic inputs are processed in different circumstances into particular kinetic outputs. The same basic design features can be utilized to construct models that connect the large number of cis-regulatory elements constituting developmental gene networks. The ultimate aim of the network models discussed here is to represent the regulatory relationships among the genomic control systems of the genes in the network, and to state their functional meaning. The target site sequences of the cis-regulatory elements of these genes constitute the physical basis of the network architecture. Useful models for developmental regulatory networks must represent the genetic logic by which the system operates, but must also be capable of explaining the real time dynamics of cis-regulatory response as kinetic input and output data become available. Most importantly, however, such models must display in a direct and transparent manner fundamental network design features such as intra- and intercellular feedback circuitry; the sources of parallel inputs into each cis-regulatory element; gene battery organization; and use of repressive spatial inputs in specification and boundary formation. Successful network models lead to direct tests of key architectural features by targeted cis-regulatory analysis.     

A computational method for design of connected catalytic networks in proteins

Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side‐chain functional groups which maximize transition‐state stabilization, and then searching for sites in protein scaffolds where the specified side‐chain–transition‐state interactions can be realized. A limitation of this approach is that the interactions between the side chains themselves are not constrained. An extensive connected hydrogen bond network involving the catalytic residues was observed in a designed retroaldolase following directed evolution. Such connected networks could increase catalytic activity by preorganizing active site residues in catalytically competent orientations, and enabling concerted interactions between side chains during catalysis, for example, proton shuffling. We developed a method for designing active sites in which the catalytic side chains, in addition to making interactions with the transition state, are also involved in extensive hydrogen bond networks. Because of the added constraint of hydrogen‐bond connectivity between the catalytic side chains, to find solutions, a wider range of interactions between these side chains and the transition state must be considered. Our new method starts from a ChemDraw‐like two‐dimensional representation of the transition state with hydrogen‐bond donors, acceptors, and covalent interaction sites indicated, and all placements of side‐chain functional groups that make the indicated interactions with the transition state, and are fully connected in a single hydrogen‐bond network are systematically enumerated. The RosettaMatch method can then be used to identify realizations of these fully‐connected active sites in protein scaffolds. The method generates many fully‐connected active site solutions for a set of model reactions that are promising starting points for the design of fully‐preorganized enzyme catalysts.     

The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems

Multiscale computational modeling of drug delivery systems (DDS) is poised to provide predictive capabilities for the rational design of targeted drug delivery systems, including multi-functional nanoparticles. Realistic, mechanistic models can provide a framework for understanding the fundamental physico-chemical interactions between drug, delivery system, and patient. Multiscale computational modeling, however, is in its infancy even for conventional drug delivery. The wide range of emerging nanotechnology systems for targeted delivery further increases the need for reliable in silico predictions. This review will present existing computational approaches at different scales in the design of traditional oral drug delivery systems. Subsequently, a multiscale framework for integrating continuum, stochastic, and computational chemistry models will be proposed and a case study will be presented for conventional DDS. The extension of this framework to emerging nanotechnology delivery systems will be discussed along with future directions. While oral delivery is the focus of the review, the outlined computational approaches can be applied to other drug delivery systems as well.     

The computational challenges of applying comparative-based computational methods to whole genomes

The explosion in genomic sequence available in public databases has resulted in an unprecedented opportunity for computational whole genome analyses. A number of promising comparative-based approaches have been developed for gene finding, regulatory element discovery and other purposes, and it is clear that these tools will play a fundamental role in analysing the enormous amount of new data that is currently being generated. The synthesis of computationally intensive comparative computational approaches with the requirement for whole genome analysis represents both an unprecedented challenge and opportunity for computational scientists. We focus on a few of these challenges, using by way of example the problems of alignment, gene finding and regulatory element discovery, and discuss the issues that have arisen in attempts to solve these problems in the context of whole genome analysis pipelines.     

PipTools: a computational toolkit to annotate and analyze pairwise comparisons of genomic sequences

Sequence conservation between species is useful both for locating coding regions of genes and for identifying functional noncoding segments. Hence interspecies alignment of genomic sequences is an important computational technique. However, its utility is limited without extensive annotation. We describe a suite of software tools, PipTools, and related programs that facilitate the annotation of genes and putative regulatory elements in pairwise alignments. The alignment server PipMaker uses the output of these tools to display detailed information needed to interpret alignments. These programs are provided in a portable format for use on common desktop computers and both the toolkit and the PipMaker server can be found at our Web site (http://bio.cse.psu.edu/). We illustrate the utility of the toolkit using annotation of a pairwise comparison of the mouse MHC class II and class III regions with orthologous human sequences and subsequently identify conserved, noncoding sequences that are DNase I hypersensitive sites in chromatin of mouse cells.     

Sensitivity analysis of Repast computational ecology models with R/Repast

Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in‐silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.     

Dynamical approaches to modeling developmental gene regulatory networks

The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their internal dynamics in a robust and coordinated fashion. The highly dynamic nature of this process lends itself to interpretation and analysis in the language of dynamical models. Modeling provides a means of systematically untangling the complicated structure of GRNs, a framework within which to simulate the behavior of reconstructed systems and, in some cases, suites of analytic tools for exploring that behavior and its implications. This review provides a general background to the idea of treating a regulatory network as a dynamical system, and describes a variety of different approaches that have been taken to the dynamical modeling of GRNs. Birth Defects Research (Part C) 87:131–142, 2009. © 2009 Wiley‐Liss, Inc.     

基因表达数据聚类分析技术及其软件工具

随着DNA芯片技术的广泛应用,基因表达数据分析已成为生命科学的研究热点之一。概述基因表达聚类技术类型、算法分类与特点、结果可视化与注释;阐述一些流行的和新型的算法;介绍17个最新相关软件包和在线web服务工具;并说明软件工具的研究趋向。     

Structural relatedness via flow networks in protein sequence space

A novel approach for evaluation of sequence relatedness via a network over the sequence space is presented. This relatedness is quantified by graph theoretical techniques. The graph is perceived as a flow network, and flow algorithms are applied. The number of independent pathways between nodes in the network is shown to reflect structural similarity of corresponding protein fragments. These results provide an appropriate parameter for quantitative estimation of such relatedness, as well as reliability of the prediction. They also demonstrate a new potential for sequence analysis and comparison by means of the flow network in the sequence space.     

Analysis of the real EADGENE data set: Multivariate approaches and post analysis (Open Access publication)

The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.     

An automated annotation tool for genomic DNA sequences using GeneScan and BLAST

Genomic sequence data are often available well before the annotated sequence is published. We present a method for analysis of genomic DNA to identify coding sequences using the GeneScan algorithm and characterize these resultant sequences by BLAST. The routines are used to develop a system for automated annotation of genome DNA sequences.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Multi-organ whole-genome measurements and reverse engineering to uncover gene networks underlying complex traits

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.     

A computational model for dynamic analysis of the human gait

Biomechanical models are important tools in the study of human motion. This work proposes a computational model to analyse the dynamics of lower limb motion using a kinematic chain to represent the body segments and rotational joints linked by viscoelastic elements. The model uses anthropometric parameters, ground reaction forces and joint Cardan angles from subjects to analyse lower limb motion during the gait. The model allows evaluating these data in each body plane. Six healthy subjects walked on a treadmill to record the kinematic and kinetic data. In addition, anthropometric parameters were recorded to construct the model. The viscoelastic parameter values were fitted for the model joints (hip, knee and ankle). The proposed model demonstrated that manipulating the viscoelastic parameters between the body segments could fit the amplitudes and frequencies of motion. The data collected in this work have viscoelastic parameter values that follow a normal distribution, indicating that these values are directly related to the gait pattern. To validate the model, we used the values of the joint angles to perform a comparison between the model results and previously published data. The model results show a same pattern and range of values found in the literature for the human gait motion.     

Advances in medical decision support systems for diagnosis of acute graft-versus-host disease: molecular and computational intelligence joint approaches

Acute graft-versus-host disease (aGVHD) is a serious systemic complication of allogeneic hematopoietic stem cell transplantation (HSCT) causing considerable morbidity and mortality. Acute GVHD occurs when alloreactive donor-derived T cells recognize host-recipient antigens as foreign. These trigger a complex multiphase process that ultimately results in apoptotic injury in target organs. The early events leading to GVHD seem to occur very soon, presumably within hours from the graft infusion. Therefore, when the first signs of aGVHD clinically manifest, the disease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated. So, it comes as no surprise that progress in treatment based on clinical diagnosis of aGVHD has been limited in the past 30 years. It is likely that a pre-emptive strategy using systemic high-dose corticosteroids as early as possible could improve the outcome of aGVHD. Due to the deleterious effects of such treatment particularly in terms of infection risk posed by systemic steroid administration in a population that is already immune-suppressed, it is critical to identify biomarker signatures for approaching this very complex task. Some research groups have begun addressing this issue through molecular and proteomic analyses, combining these approaches with computational intelligence techniques, with the specific aim of facilitating the identification of diagnostic biomarkers in aGVHD. In this review, we focus on the aGVHD scenario and on the more recent state-of-the-art. We also attempt to give an overview of the classical and novel techniques proposed as medical decision support system for the diagnosis of GVHD.     

Structural genomics: computational methods for structure analysis

The success of structural genomics initiatives requires the development and application of tools for structure analysis, prediction, and annotation. In this paper we review recent developments in these areas; specifically structure alignment, the detection of remote homologs and analogs, homology modeling and the use of structures to predict function. We also discuss various rationales for structural genomics initiatives. These include the structure-based clustering of sequence space and genome-wide function assignment. It is also argued that structural genomics can be integrated into more traditional biological research if specific biological questions are included in target selection strategies.     

Systems analysis of cellular networks under uncertainty

Besides the often-quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems-level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems. As a common denominator, the methods focus on structural, rather than more detailed, kinetic network properties. A deeper understanding, better scaling, and the ability to combine the approaches pose formidable challenges for future theory developments.     

Genome sequence comparisons: hurdles in the fast lane to functional genomics

An important computational technique for extracting the wealth of information hidden in human genomic sequence data is to compare the sequence with that from the corresponding region of the mouse genome, looking for segments that are conserved over evolutionary time. Moreover, the approach generalises to comparison of sequences from any two related species. The underlying rationale (which is abundantly confirmed by observation) is that a random mutation in a functional region is usually deleterious to the organism, and hence unlikely to become fixed in the population, whereas mutations in a non-functional region are free to accumulate over time.The potential value of this approach is so attractive that the public and private projects to sequence the human genome are now turning to sequencing the mouse, and you will soon be able to compare the human and mouse sequences of your favourite genomic region.We are currently witnessing an explosion of computer tools for comparative analysis of two genomic sequences. Here the capabilities of two new network servers for comparing genomic sequences from any pair of closely related species are sketched.The Syntenic Gene Prediction Program SGP-I utilises sequence comparisons to enhance the ability to locate protein coding segments in genomic data. PipMaker attempts to determine all conserved genomic regions, regardless of their function.