Production of useful secondary metabolites in plants: Functional genomics approaches

The paradigm of biological research has been changed by recent developments in genomics, high-throughput biology, and bioinformatics. Conventional biology often was based on empirical, labor-intensive, and time-consuming methods. In the new paradigm, biological research e is driven by a holistic approach on the basis of rational, automatic, and high-throughput methods. New functional compounds can be discovered by using high-throughput screening systems. Secondary metabolite pathways and the genes involved in those pathways are then determined by studying functional genomics in conjunction with the data-mining tools of bioinformatics. In addition, these advances in metabolic engineering enable researchers to confer new secondary metabolic pathways to crops by transferring three to five, or more, heterologous genes taken from various other species. In the future, engineering for the production of useful compounds will be designed by a set of software tools that allows the user to specify a cell’s genes, proteins, and other molecules, as well as their individual interactions.     

Defining characteristics and conservation of poorly annotated genes in Caenorhabditis elegans using WormCat 2.0

Omics tools provide broad datasets for biological discovery. However, the computational tools for identifying important genes or pathways in RNA-seq, proteomics, or GWAS (Genome-Wide Association Study) data depend on Gene Ontogeny annotations and are biased toward well-described pathways. This limits their utility as poorly annotated genes, which could have novel functions, are often passed over. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, which provides an intuitive visualization output. Unlike Gene Ontogeny-based enrichment tools, which exclude genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and can include genes with biological functions identified in published datasets. Poorly annotated genes are often considered to be potentially species-specific and thus, of reduced interest to the biomedical community. Instead, we find that around 3% of the UNASSIGNED genes have human orthologs, including some linked to human diseases. These human orthologs themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that the failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes. This suggests that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows the association of UNASSIGNED genes with specific phenotypes and known pathways. Building these associations in C. elegans, with its robust genetic tools, provides a path to further functional study and insight into these understudied genes.     

Shedding light on black boxes in protein identification

Performing a well thought‐out proteomics data analysis can be a daunting task, especially for newcomers to the field. Even researchers experienced in the proteomics field can find it challenging to follow existing publication guidelines for MS‐based protein identification and characterization in detail. One of the primary goals of bioinformatics is to enable any researcher to interpret the vast amounts of data generated in modern biology, by providing user‐friendly and robust end‐user applications, clear documentation, and corresponding teaching materials. In that spirit, we here present an extensive tutorial for peptide and protein identification, available at http://compomics.com/bioinformatics‐for‐proteomics . The material is completely based on freely available and open‐source tools, and has already been used and refined at numerous international courses over the past 3 years. During this time, it has demonstrated its ability to allow even complete beginners to intuitively conduct advanced bioinformatics workflows, interpret the results, and understand their context. This tutorial is thus aimed at fully empowering users, by removing black boxes in the proteomics informatics pipeline.     

Appearance frequency modulated gene set enrichment testing

Background  

Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, current methods for gene set enrichment testing treat all genes equal. It is well-known that some genes, such as those responsible for housekeeping functions, appear in many pathways, whereas other genes are more specialized and play a unique role in a single pathway. Drawing inspiration from the field of information retrieval, we have developed and present here an approach to incorporate gene appearance frequency (in KEGG pathways) into two current methods, Gene Set Enrichment Analysis (GSEA) and logistic regression-based LRpath framework, to generate more reproducible and biologically meaningful results.     

中国在翻译后修饰的生物信息学研究领域的进展与前瞻

翻译后修饰在调控蛋白质构象变化、活性以及功能方面具有重要作用,并参与了几乎所有细胞通路和过程。蛋白质翻译后修饰的鉴定是阐明细胞内分子机理的基础。相对于劳动密集的、耗费时间的实验工作,利用各种生物信息学方法开展翻译后修饰预测,能够提供准确、简便和快速的研究方案,并产生有价值的信息为进一步实验研究提供参考。文章主要综述了中国生物信息学者在翻译后修饰生物信息学领域所取得的研究进展,包括修饰底物与位点预测的计算方法学设计与完善、在线或本地化工具的设计与维护、修饰相关数据库及数据资源的构建及基于修饰蛋白质组学数据的生物信息学分析。通过比较国内外的同类研究,发现优势和不足,并对未来的研究作出前瞻。     

Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA.

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentially expressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method accounting for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling of predicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysis tools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. To illustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aberrantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using a combinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well as biological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; statistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of predicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatory response to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathways reportedly affected in a particular cancer.     

The origins of bioinformatics

Bioinformatics is often described as being in its infancy, but computers emerged as important tools in molecular biology during the early 1960s. A decade before DNA sequencing became feasible, computational biologists focused on the rapidly accumulating data from protein biochemistry. Without the benefits of super computers or computer networks, these scientists laid important conceptual and technical foundations for bioinformatics today.     

A gentle introduction to SNP analysis: resources and tools

Bioinformatics is the use of informatics tools and techniques in the study of molecular biology, genetic, or clinical data. The field of bioinformatics has expanded tremendously to cope with the large expansion of information generated by the mouse and human genome projects, as newer generations of computers that are much more powerful have emerged in the commercial market. It is now possible to employ the computing hardware and software at hand to generate novel methodologies in order to link data across the different databanks generated by these international projects and derive clinical and biological relevance from all of the information gathered. The ultimate goal would be to develop a computer program that can provide information correlating genes, their single nucleotide polymorphisms (SNPs), and the possible structural and functional effects on the encoded proteins with relation to known information on complex diseases with great ease and speed. Here, the recent developments of available software methods to analyze SNPs in relation to complex diseases are reviewed with emphasis on the type of predictions on protein structure and functions that can be made. The need for further development of comprehensive bioinformatics tools that can cope with information generated by the genomics communities is emphasized.     

Towards a systems level analysis of health and nutrition

Although theoretical systems analysis has been available for over half a century, the recent advent of omic high-throughput analytical platforms along with the integration of individual tools and technologies has given rise to the field of modern systems biology. Coupled with information technology, bioinformatics, knowledge management and powerful mathematical models, systems biology has opened up new vistas in our understanding of complex biological systems. Currently there are two distinct approaches that include the inductively driven computational systems biology (bottom-up approach) and the deductive data-driven top-down analysis. Such approaches offer enormous potential in the elucidation of disease as well as defining key pathways and networks involved in optimal human health and nutrition. The tools and technologies now available in systems biology analyses offer exciting opportunities to develop the emerging areas of personalized medicine and individual nutritional profiling.     

Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence.     

MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.     

Rat mitochondrion-neuron focused microarray (rMNChip) and bioinformatics tools for rapid identification of differential pathways in brain tissues

Mitochondrial function is of particular importance in brain because of its high demand for energy (ATP) and efficient removal of reactive oxygen species (ROS). We developed rat mitochondrion-neuron focused microarray (rMNChip) and integrated bioinformatics tools for rapid identification of differential pathways in brain tissues. rMNChip contains 1,500 genes involved in mitochondrial functions, stress response, circadian rhythms and signal transduction. The bioinformatics tool includes an algorithm for computing of differentially expressed genes, and a database for straightforward and intuitive interpretation for microarray results. Our application of these tools to RNA samples derived from rat frontal cortex (FC), hippocampus (HC) and hypothalamus (HT) led to the identification of differentially-expressed signal-transduction-bioenergenesis and neurotransmitter-synthesis pathways with a dominant number of genes (FC/HC = 55/6; FC/HT = 55/4) having significantly (p<0.05, FDR<10.70%) higher (≥1.25 fold) RNA levels in the frontal cortex than the others, strongly suggesting active generation of ATP and neurotransmitters and efficient removal of ROS. Thus, these tools for rapid and efficient identification of differential pathways in brain regions will greatly facilitate our systems-biological study and understanding of molecular mechanisms underlying complex and multifactorial neurodegenerative diseases.     

Annotating the Plasmodium genome and the enigma of the shikimate pathway

The completion of the Plasmodium falciparum genome sequence heralds a new era in the effort to identify all the parasite's genes along with their cellular functions. A combination of bioinformatics and experimental proof will facilitate this process. Many enzymes in metabolic processes have been identified, but several examples exist of incomplete pathways, such as the shikimate pathway. This review uses the example of the shikimate pathway to examine the application of bioinformatics to lead experimental design in post-genomic biology.     

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.     

MACBenAbim: A Multi-platform Mobile Application for searching keyterms in Computational Biology and Bioinformatics

Computational biology and bioinformatics are gradually gaining grounds in Africa and other developing nations of the world. However, in these countries, some of the challenges of computational biology and bioinformatics education are inadequate infrastructures, and lack of readily-available complementary and motivational tools to support learning as well as research. This has lowered the morale of many promising undergraduates, postgraduates and researchers from aspiring to undertake future study in these fields. In this paper, we developed and described MACBenAbim (Multi-platform Mobile Application for Computational Biology and Bioinformatics), a flexible user-friendly tool to search for, define and describe the meanings of keyterms in computational biology and bioinformatics, thus expanding the frontiers of knowledge of the users. This tool also has the capability of achieving visualization of results on a mobile multi-platform context.

Availability

MACBenAbim is available from the authors for non-commercial purposes.     

GSEA在全基因组表达谱芯片数据分析中的应用

GSEA是一个可下载后免费使用的全基因组表达谱芯片数据分析工具。它根据已有的对基因的定位、性质、功能、生物学意义等知识的基础上,首先构建了一个分子标签数据库,数据库中包含了多个功能基因集。通过分析一组处于两个生物学状态的基因表达谱杂交数据,它们在特定的功能基因集中的表达状况,以及这种表达状况是否存在某种统计学显著性。GSEA是从另一个角度来诠释生物信息,可进一步完善我们对相关生物学事件的认识。     

Energy balance for analysis of complex metabolic networks

Predicting behavior of large-scale biochemical networks represents one of the greatest challenges of bioinformatics and computational biology. Computational tools for predicting fluxes in biochemical networks are applied in the fields of integrated and systems biology, bioinformatics, and genomics, and to aid in drug discovery and identification of potential drug targets. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are promising tools for the analysis of large complex networks. Here we introduce energy balance analysis (EBA)--the theory and methodology for enforcing the laws of thermodynamics in such simulations--making the results more physically realistic and revealing greater insight into the regulatory and control mechanisms operating in complex large-scale systems. We show that EBA eliminates thermodynamically infeasible results associated with FBA.     

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.     

Online analysis of development

The genomics revolution has altered the very nature of research in molecular biology, from how to find genes to how to find out what specific genes do. Given the availability of so many fully (or nearly) sequenced genomes, it is now relatively easy to track down dozens or even hundreds of genes relevant to a particular field of study. Unfortunately, up till now, the tools for determining what these genes actually do in embryos and cells have not kept pace, but the burgeoning field of bioinformatics should help correct this shortcoming and introduce the power of genomics to the study of developmental biology. In this review, some of the bioinformatics resources relevant to developmental biologists are described along with some simple approaches for applying these tools to analyzing early development.