Review of general algorithmic features for genome assemblers for next generation sequencers

In the realm of bioinformatics and computational biology,the most rudimentary data upon which all the analysis is built is the sequence data of genes,proteins and RNA.The sequence data of the entire genome is the solution to the genome assembly problem.The scope of this contribution is to provide an overview on the art of problem-solving applied within the domain of genome assembly in the nextgeneration sequencing(NGS) platforms.This article discusses the major genome assemblers that were proposed in the literature during the past decade by outlining their basic working principles.It is intended to act as a qualitative,not a quantitative,tutorial to all working on genome assemblers pertaining to the next generation of sequencers.We discuss the theoretical aspects of various genome assemblers,identifying their working schemes.We also discuss briefly the direction in which the area is headed towards along with discussing core issues on software simplicity.     

Agents in bioinformatics, computational and systems biology

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.     

Workflow management systems for gene sequence analysis and evolutionary studies – A Review

Post ‘omic’ era has resulted in the development of many primary, secondary and derived databases. Many analytical and visualization bioinformatics tools have been developed to manage and analyze the data available through large sequencing projects. Availability of heterogeneous databases and tools make it difficult for researchers to access information from varied sources and run different bioinformatics tools to get desired analysis done. Building integrated bioinformatics platforms is one of the most challenging tasks that bioinformatics community is facing. Integration of various databases, tools and algorithm is a challenging problem to deal with. This article describes the bioinformatics analysis workflow management systems that are developed in the area of gene sequence analysis and phylogeny. This article will be useful for biotechnologists, molecular biologists, computer scientists and statisticians engaged in computational biology and bioinformatics research.     

APBioNet: the Asia-Pacific regional consortium for bioinformatics

Bioinformatics and computational biology, along with the related fields of genomics, proteomics, functional genomics and systems biology are new wave scientific disciplines that harness composite computational power across networks to advance biological knowledge at the most basic level and to direct traditional laboratory-based research efforts in the biomedical sciences. 'Fostering the growth of bioinformatics and allied disciplines in the Asia-Pacific region' is the motto of the first regional bioinformatics society, the Asia-Pacific Bioinformatics Network (APBioNet). APBioNet addresses the issues of hardware, software, databases and networks pertaining to bioinformatics, with the additional layer of pertinent education, training and research. Recent milestones achieved include hosting an international bioinformatics symposium in Asia and setting up large-scale regional grid-computing projects.     

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.     

Bioconductor: open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

BioManager: the use of a bioinformatics web application as a teaching tool in undergraduate bioinformatics training

The completion of the human genome project, and other genome sequencing projects, has spearheaded the emergence of the field of bioinformatics. Using computer programs to analyse DNA and protein information has become an important area of life science research and development. While it is not necessary for most life science researchers to develop specialist bioinformatic skills (including software development), basic skills in the application of common bioinformatics software and the effective interpretation of results are increasingly required by all life science researchers. Training in bioinformatics is increasingly occurring within the university system as part of existing undergraduate science and specialist degrees. One difficulty in bioinformatics education is the sheer number of software programs required in order to provide a thorough grounding in the subject to the student. Teaching requires either a well-maintained internal server with all the required software, properly interfacing with student terminals, and with sufficient capacity to handle multiple simultaneous requests, or it requires the individual installation and maintenance of every piece of software on each computer. In both cases, there are difficult issues regarding site maintenance and accessibility. In this article, we discuss the use of BioManager, a web-based bioinformatics application integrating a variety of common bioinformatics tools, for teaching, including its role as the main bioinformatics training tool in some Australian and international universities. We discuss some of the issues with using a bioinformatics resource primarily created for research in an undergraduate teaching environment.     

Molecular archeology of L1 insertions in the human genome

Background  

As the rough draft of the human genome sequence nears a finished product and other genome-sequencing projects accumulate sequence data exponentially, bioinformatics is emerging as an important tool for studies of transposon biology. In particular, L1 elements exhibit a variety of sequence structures after insertion into the human genome that are amenable to computational analysis. We carried out a detailed analysis of the anatomy and distribution of L1 elements in the human genome using a new computer program, TSDfinder, designed to identify transposon boundaries precisely.     

Teaching Bioinformatics in Concert

Can biology students without programming skills solve problems that require computational solutions? They can if they learn to cooperate effectively with computer science students. The goal of the in-concert teaching approach is to introduce biology students to computational thinking by engaging them in collaborative projects structured around the software development process. Our approach emphasizes development of interdisciplinary communication and collaboration skills for both life science and computer science students.     

Modeling and simulation in signal transduction pathways: a systems biology approach

Modeling, the heart of systems biology, of complex processes (example: signal transduction) is a wide scientific discipline where many approaches from different areas are confronted with the aim of better understanding, identifying and modeling of complex data coming from various sources. The purpose of this paper is to introduce the basic steps of systems biology view towards signaling pathways, which mainly deals with the computational tools. The paper emphasizes the modeling and simulation approach in the signal transduction pathways using the topologies of the biochemical reactions with an overview of the different types of software platforms. Finally, we demonstrated the epidermal growth factor receptor signaling pathway model as an example to study the growth factor mediated signaling system with biological experiments. This paper will enables new comers to underline the strengths of the computational approaches towards signal transduction, as well as to highlight the systems biology research directions.     

卡奇霉素产生菌棘孢小单胞菌ATCC 15837的全基因组测序及序列分析

棘孢小单胞菌(Micromonospora echinospora) ATCC 15837是一种高GC含量的革兰氏阳性稀有放线菌,能够合成烯二炔类抗肿瘤抗生素卡奇霉素(calicheamicin, CLM)。目前,还没有相关研究报道棘孢小单胞菌ATCC 15837的全基因组序列,这限制了其代谢产物合成途径和比较基因组学等研究。本研究首次通过高通量测序技术对棘孢小单胞菌ATCC 15837进行全基因组测序,使用相关生物信息学软件对数据进行组装和注释等分析。使用Velvet软件进行组装拼接得到77个Contigs,GC含量为72.36%,基因组大小约为7.69 Mb。序列已提交至美国国立生物技术信息中心(NCBI)的GenBank数据库(登录号为NGNT00000000)。本研究首次报道了一株烯二炔类抗肿瘤抗生素卡奇霉素产生菌棘孢小单胞菌ATCC 15837的全基因组序列,分析了基因组基本特征,预测了该菌株的次级代谢产物生物合成基因簇,为后续的进一步代谢调控与合成生物学提供了理论基础。     

Bio.Phylo: A unified toolkit for processing, analyzing and visualizing phylogenetic trees in biopython

ABSTRACT: BACKGROUND: Ongoing innovation in phylogenetics and evolutionary biology has been accompanied by a proliferation of software tools, data formats, analytical techniques and web servers. This brings with it the challenge of integrating phylogenetic and other related biological data found in a wide variety of formats, and underlines the need for reusable software that can read, manipulate and transform this information into the various forms required to build computational pipelines. RESULTS: We built a Python software library for working with phylogenetic data that is tightly integrated with Biopython, a broad-ranging toolkit for computational biology. Our library, Bio.Phylo, is highly interoperable with existing libraries, tools and standards, and is capable of parsing common file formats for phylogenetic trees, performing basic transformations and manipulations, attaching rich annotations, and visualizing trees. We unified the modules for working with the standard file formats Newick, NEXUS and phyloXML behind a consistent and simple API, providing a common set of functionality independent of the data source. CONCLUSIONS: Bio.Phylo meets a growing need in bioinformatics for working with heterogeneous types of phylogenetic data. By supporting interoperability with multiple file formats and leveraging existing Biopython features, this library simplifies the construction of phylogenetic workflows. We also provide examples of the benefits of building a community around a shared open-source project. Bio.Phylo is included with Biopython, available through the Biopython website, http://biopython.org.     

Comparative Genomics Analysis of Mycobacterium ulcerans for the Identification of Putative Essential Genes and Therapeutic Candidates

Mycobacterium ulcerans, the causative agent of Buruli ulcer, is the third most common mycobacterial disease after tuberculosis and leprosy. The present treatment options are limited and emergence of treatment resistant isolates represents a serious concern and a need for better therapeutics. Conventional drug discovery methods are time consuming and labor-intensive. Unfortunately, the slow growing nature of M. ulcerans in experimental conditions is also a barrier for drug discovery and development. In contrast, recent advancements in complete genome sequencing, in combination with cheminformatics and computational biology, represent an attractive alternative approach for the identification of therapeutic candidates worthy of experimental research. A computational, comparative genomics workflow was defined for the identification of novel therapeutic candidates against M. ulcerans, with the aim that a selected target should be essential to the pathogen, and have no homology in the human host. Initially, a total of 424 genes were predicted as essential from the M. ulcerans genome, via homology searching of essential genome content from 20 different bacteria. Metabolic pathway analysis showed that the most essential genes are associated with carbohydrate and amino acid metabolism. Among these, 236 proteins were identified as non-host and essential, and could serve as potential drug and vaccine candidates. Several drug target prioritization parameters including druggability were also calculated. Enzymes from several pathways are discussed as potential drug targets, including those from cell wall synthesis, thiamine biosynthesis, protein biosynthesis, and histidine biosynthesis. It is expected that our data will facilitate selection of M. ulcerans proteins for successful entry into drug design pipelines.     

ePMV embeds molecular modeling into professional animation software environments

Increasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties, and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology.     

High performance computing in structural determination by electron cryomicroscopy

Computational advances have significantly contributed to the current role of electron cryomicroscopy (cryoEM) in structural biology. The needs for computational power are constantly growing with the increasing complexity of algorithms and the amount of data needed to push the resolution limits. High performance computing (HPC) is becoming paramount in cryoEM to cope with those computational needs. Since the nineties, different HPC strategies have been proposed for some specific problems in cryoEM and, in fact, some of them are already available in common software packages. Nevertheless, the literature is scattered in the areas of computer science and structural biology. In this communication, the HPC approaches devised for the computation-intensive tasks in cryoEM (single particles and tomography) are retrospectively reviewed and the future trends are discussed. Moreover, the HPC capabilities available in the most common cryoEM packages are surveyed, as an evidence of the importance of HPC in addressing the future challenges.     

Bioinformatics

Bioinformatics is an interdisciplinary field that blends computer science and biostatistics with biological and biomedical sciences such as biochemistry, cell biology, developmental biology, genetics, genomics, and physiology. An important goal of bioinformatics is to facilitate the management, analysis, and interpretation of data from biological experiments and observational studies. The goal of this review is to introduce some of the important concepts in bioinformatics that must be considered when planning and executing a modern biological research study. We review database resources as well as data mining software tools.     

Visualizing the knowledge structure and evolution of bioinformatics

Background

Bioinformatics has gained much attention as a fast growing interdisciplinary field. Several attempts have been conducted to explore the field of bioinformatics by bibliometric analysis, however, such works did not elucidate the role of visualization in analysis, nor focus on the relationship between sub-topics of bioinformatics.

Results

First, the hotspot of bioinformatics has moderately shifted from traditional molecular biology to omics research, and the computational method has also shifted from mathematical model to data mining and machine learning. Second, DNA-related topics are bridge topics in bioinformatics research. These topics gradually connect various sub-topics that are relatively independent at first. Third, only a small part of topics we have obtained involves a number of computational methods, and the other topics focus more on biological aspects. Fourth, the proportion of computing-related topics hit a trough in the 1980s. During this period, the use of traditional calculation methods such as mathematical model declined in a large proportion while the new calculation methods such as machine learning have not been applied in a large scale. This proportion began to increase gradually after the 1990s. Fifth, although the proportion of computing-related topics is only slightly higher than the original, the connection between other topics and computing-related topics has become closer, which means the support of computational methods is becoming increasingly important for the research of bioinformatics.

Conclusions

The results of our analysis imply that research on bioinformatics is becoming more diversified and the ranking of computational methods in bioinformatics research is also gradually improving.