Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.     

Mass spectrometric genomic data mining: Novel insights into bioenergetic pathways in Chlamydomonas reinhardtii

A new high-throughput computational strategy was established that improves genomic data mining from MS experiments. The MS/MS data were analyzed by the SEQUEST search algorithm and a combination of de novo amino acid sequencing in conjunction with an error-tolerant database search tool, operating on a 256 processor computer cluster. The error-tolerant search tool, previously established as GenomicPeptideFinder (GPF), enables detection of intron-split and/or alternatively spliced peptides from MS/MS data when deduced from genomic DNA. Isolated thylakoid membranes from the eukaryotic green alga Chlamydomonas reinhardtii were separated by 1-D SDS gel electrophoresis, protein bands were excised from the gel, digested in-gel with trypsin and analyzed by coupling nano-flow LC with MS/MS. The concerted action of SEQUEST and GPF allowed identification of 2622 distinct peptides. In total 448 peptides were identified by GPF analysis alone, including 98 intron-split peptides, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.     

GIST: Genomic island suite of tools for predicting genomic islands in genomic sequences

Genomic Islands (GIs) are genomic regions that are originally from other organisms, through a process known as Horizontal Gene Transfer (HGT). Detection of GIs plays a significant role in biomedical research since such align genomic regions usually contain important features, such as pathogenic genes. We have developed a use friendly graphic user interface, Genomic Island Suite of Tools (GIST), which is a platform for scientific users to predict GIs. This software package includes five commonly used tools, AlienHunter, IslandPath, Colombo SIGI-HMM, INDeGenIUS and Pai-Ida. It also includes an optimization program EGID that ensembles the result of existing tools for more accurate prediction. The tools in GIST can be used either separately or sequentially. GIST also includes a downloadable feature that facilitates collecting the input genomes automatically from the FTP server of the National Center for Biotechnology Information (NCBI). GIST was implemented in Java, and was compiled and executed on Linux/Unix operating systems. AVAILABILITY: The database is available for free at http://www5.esu.edu/cpsc/bioinfo/software/GIST.     

Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

     

Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).     

Identification of genomic functional hotspots with copy number alteration in liver cancer

Copy number alterations (CNAs) can be observed in most of cancer patients. Several oncogenes and tumor suppressor genes with CNAs have been identified in different kinds of tumor. However, the systematic survey of CNA-affected functions is still lack. By employing systems biology approaches, instead of examining individual genes, we directly identified the functional hotspots on human genome. A total of 838 hotspots on human genome with 540 enriched Gene Ontology functions were identified. Seventy-six aCGH array data of hepatocellular carcinoma (HCC) tumors were employed in this study. A total of 150 regions which putatively affected by CNAs and the encoded functions were identified. Our results indicate that two immune related hotspots had copy number alterations in most of patients. In addition, our data implied that these immune-related regions might be involved in HCC oncogenesis. Also, we identified 39 hotspots of which copy number status were associated with patient survival. Our data implied that copy number alterations of the regions may contribute in the dysregulation of the encoded functions. These results further demonstrated that our method enables researchers to survey biological functions of CNAs and to construct regulation hypothesis at pathway and functional levels.     

A genomic data viewer for iPad

The Integrative Genomics Viewer (IGV) for iPad, based on the popular IGV application for desktop and laptop computers, supports researchers who wish to take advantage of the mobility of today’s tablet computers to view genomic data and present findings to colleagues.     

环境微生物不依赖于培养的基因组学研究及应用

微生物在生物圈中分布广泛,并且在地球物质循环中占有重要地位,但是约99﹪的微生物目前还不能通过传统的培养方法得到纯培养物（即未培养微生物）,给这些未培养微生物的研究带来很大的困难。随着分子生物学的快速发展及其在微生物研究中的广泛运用,促进了以环境中未培养微生物为研究对象的新兴学科--环境基因组学的产生和发展。在不进行相关微生物培养分离的情况下,通过从环境样品中直接提取获得所有微小生物的全部遗传物质,并构建环境基因组文库;进一步利用功能基因组学研究策略,从文库中寻找编码产生新的有生物活性产物的基因;通过对系统发育相关锚定位点基因序列分析,从而确定特定生态环境体系中未培养微生物的种类结构组成及进化地位,并最终重建该体系中微生物群体的基本物质循环模式。此外,环境基因组学也可以在对未培养微生物生理生化特性深入了解的基础上,建立发展合适的培养体系,最终获得某些特定微生物的纯培养物。本文对环境基因组的构建及相关分析研究策略的进展进行了综述;同时介绍了其在微生物分类及生态学研究的应用。     

SARS-CoV 中国株基因组全长cDNA的构建及其恢复病毒的生物学性质

严重急性呼吸综合征是SARS-CoV引起的一种重要新发传染病,其致病机制的研究对于防治该病十分必要。为了利用反向遗传学技术研究SARS-CoV的致病机制,将覆盖SARS-CoVBJ01株基因组全长的7个cDNA片段纯化后进行体外连接,构建基因组全长cDNA分子,以其为模板,使用T7RNA聚合酶系统在体外进行转录,获得病毒RNA。用电穿孔转染法将转录体RNA导入VeroE6细胞,可观察到典型的SARS-CoV致细胞病变作用。对收获的恢复病毒采用RT-PCR方法进行鉴定,结果表明获得的恢复病毒与SARS-CoVBJ01株原病毒序列一致。以针对SARS-CoV的抗体对感染细胞作间接免疫荧光反应,证明获得了具有特异感染性的恢复病毒。同时用细胞病变法和空斑试验测定了恢复病毒及其亲本毒株的病毒滴度,结果表明二者在致病性上没有明显差异,恢复病毒具有与原型株相似的生物学特性。SARS-CoVBJ01株基因组全长cDNA的成功构建及对恢复病毒生物学性质的研究将为进一步探索SARS-CoV致病的分子机制及研制新型疫苗奠定良好的基础。     

Integrating cell-signalling pathways with NF-kappaB and IKK function

Nuclear factor (NF)-kappaB and inhibitor of NF-kappaB kinase (IKK) proteins regulate many physiological processes, including the innate- and adaptive-immune responses, cell death and inflammation. Disruption of NF-kappaB or IKK function contributes to many human diseases, including cancer. However, the NF-kappaB and IKK pathways do not exist in isolation and there are many mechanisms that integrate their activity with other cell-signalling networks. This crosstalk constitutes a decision-making process that determines the consequences of NF-kappaB and IKK activation and, ultimately, cell fate.     

Genomic pathways database and biological data management

In this paper, we discuss the properties of biological data and challenges it poses for data management, and argue that, in order to meet the data management requirements for 'digital biology', careful integration of the existing technologies and the development of new data management techniques for biological data are needed. Based on this premise, we present PathCase: Case Pathways Database System. PathCase is an integrated set of software tools for modelling, storing, analysing, visualizing and querying biological pathways data at different levels of genetic, molecular, biochemical and organismal detail. The novel features of the system include: (i) genomic information integrated with other biological data and presented starting from pathways; (ii) design for biologists who are possibly unfamiliar with genomics, but whose research is essential for annotating gene and genome sequences with biological functions; (iii) database design, implementation and graphical tools which enable users to visualize pathways data in multiple abstraction levels and to pose exploratory queries; (iv) a wide range of different types of queries including, 'path' and 'neighbourhood queries' and graphical visualization of query outputs; and (v) an implementation that allows for web (XML)-based dissemination of query outputs (i.e. pathways data in BIOPAX format) to researchers in the community, giving them control on the use of pathways data.     

Restriction pattern analysis of genomic DNA ofFrankia isolates

Summary Total genomic DNAs ofFrankia isolates were subjected to restriction enzyme digestion and subsequent agarose gel electrophoresis. Restriction fragment banding patterns were unique for each isolate and may therefore be used as a method to distinguish between isolates which may be morphologically indistinguishable. This method might be useful for practical purposes such as tracing specificFrankia strains during field studies.     

Genomic and physiological diversity amongst strains of Thiobacillus ferrooxidans,and genomic comparison with Thiobacillus thiooxidans

Twenty-three strains of Thiobacillus ferrooxidans of known pedigree were examined. Thirteen strains survived 65° C for 5 min and 7 of these for 10 min, but sporulation was never observed. All strains grew between 25° C and 35° C and some strains grew at 5° and 40° C. They were genomically diverse, comprising 7 DNA homology groups, and the GC content varied from 55–65 mol %. Correlation between genomic group and growth temperature was noted. All strains grew on ferrous sulfate as energy source, but some failed to utilize elemental sulfur. Acidified thiosulfate supported growth of most of the strains examined but it was judged to be a poor substrate upon which to base taxonomic conclusions because of decomposition of thiosulfate in acid. Six strains of Thiobacillus thiooxidans showed negligible genomic affinity to T. ferrooxidans, and they comprised 2 DNA homology groups and their GC content varied from 52–62 mol%. Anomalies due to contaminants in cultures of T. ferrooxidans were resolved, and the contaminants were identified.     

一株新型裂解K63荚膜型肺炎克雷伯菌的噬菌体分离鉴定和生物学特性研究及全基因组分析

[背景]噬菌体具有特定的杀菌能力,对生态和细菌的进化具有重要影响。近年来由于多重耐药细菌的全球出现,噬菌体疗法逐渐引起了人们的关注。[目的]对一株新型裂解K63荚膜型肺炎克雷伯菌的噬菌体vB_KpnP_IME308进行生物学特性研究、测序和比较基因组学的分析。[方法]以一株从临床分离到的肺炎克雷伯菌为宿主菌分离噬菌体,应用双层平板法进行噬菌体最佳感染复数(optimal multiplicity of infection)、一步生长曲线(one-step growth curve)、温度以及pH敏感性实验测定,纯化噬菌体并通过透射电镜观察噬菌体形态;应用标准的苯酚-氯仿提取方案提取噬菌体全基因组,使用Illumina MiSeq测序平台进行噬菌体全基因组测序,测序后对噬菌体全基因组序列进行组装、注释、进化和比较基因组学分析。[结果]分离到一株新型的肺炎克雷伯菌噬菌体,命名为vB_KpnP_IME308;其最佳感染复数为0.001,一步生长曲线结果显示,其感染宿主菌的潜伏期约为20 min,裂解期约为80 min,平均裂解量330PFU/cell;噬菌体vB_KpnP_IME308在4-50℃和pH 5.0-10.0范围内稳定;电镜观察该噬菌体属于短尾噬菌体科(Podoviridae)。基因组测序结果表明,噬菌体基因组全长为43 091bp,(G+C)mol%含量为53.9%,(A+T)mol%含量为46.1%。BLASTn比对结果表明,该噬菌体与目前已知噬菌体基因组仅84%区域有相似性。噬菌体进化树结果表明该噬菌体属于Autographivirinae亚科的Drulisvirus属的成员。[结论]从医院污水中分离鉴定了一株新型的肺炎克雷伯菌噬菌体,表征并分析了噬菌体全基因组序列,这些结果均表明该噬菌体具有开发为抗肺炎克雷伯菌制剂的潜力,为噬菌体治疗多重耐药细菌感染奠定了基础。     

Identification of dominant signaling pathways from proteomics expression data

The availability of the results of high-throughput analyses coming from ‘omic’ technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a ‘pathway search engine’, where the input is the ‘omic’ data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF_β, stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.