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PubMed2006

Molecular evolution of serine/arginine splicing factors family (SR) by positive selection.

The serine-rich (SR) protein family is involved in the pre-mRNA splicing process and the DNA sequences of the corresponding genes are highly conserved in the metazoan organisms. The mammalian SR proteins consist of one or two characteristic RNA binding domains (RBD), containing the signature sequences RDAEDA and SWQDLKD and a RS (arginine/serine-rich) domain. We used the amino acid and nucleotide sequences deposited in GenBank and Swiss-Prot databases to perform a phylogenetic analysis using bioinformatics tools. The results of the phylogenetic trees suggest that this family has evolved by several gene duplication events as a result of a positive selection mechanism.

Amino Acid Sequence↗

PubMed2004

Development of public toxicogenomics software for microarray data management and analysis.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.

Databases, Genetic↗

PubMed2006

Analysis and management of HIV peptide microarray experiments.

OBJECTIVES: To develop a tool for then easy and user-friendly management of peptide microarray experiments and for the use of the results of these experiments for the study the immune response against HIV virus infection in clinical samples. METHODS: Applying bioinformatics and statistics for the analysis of data coming from microarray experiments as well as implementing a MIAME (Minimum Information About a Microarray Experiment) compliant database for managing and annotating experiments, results and samples. RESULTS: We present a new tool for managing not only nucleic acid microarray experiments but also protein microarray experiments. From the analysis of experimental data, we can detect different profiles in the reactivity of the sera with different genotypes. CONCLUSIONS: We have developed a new tool for managing microarray data including clinical annotations for the samples as well as the capability of annotating other microarray formats different to those based on nucleic acids. The use of peptide microarrays and bioinformatics analysis opens a new scope for the characterization of the immune response, and analyzing and identifying the humoral response of viruses with different genotypes.

Computational Biology↗

PubMed2005

Clinical bioinformatics.

Clinical bioinformatics provides biological and medical information to allow for individualized healthcare. In this review, we describe the uses of clinical bioinformatics. After the analysis of the complete human genome sequences, clinical bioinformatics enables researchers to search online biological databases and use the biological information in their medical practices. The data obtained from using microarray is extremely complicated. In clinical bioinformatics, selecting appropriate software to analyze the microarray data for medical decision making is crucial. Proteomics strategy tools usually focus on similarity searches, structure prediction, and protein modeling. In clinical bioinformatics, the proteomic data only have meaning if they are integrated with clinical data. In pharmacogenomics, clinical bioinformatics includes elaborate studies of bioinformatics tools and various facets of proteomics related to drug target identification and clinical validation. Using clinical bioinformatics, researchers apply computational and high-throughput experimental techniques to cancer research and systems biology. Meanwhile, researchers of bioinformatics and medical information have incorporated clinical bioinformatics to improve health care, using biological and medical information. Using the high volume of biological information from clinical bioinformatics will contribute to changes in practice standards in the healthcare system. We believe that clinical bioinformatics provides benefits of improving healthcare, disease prevention and health maintenance as we move toward the era of personalized medicine.

Computational Biology↗

PubMed2003

Severe acute respiratory syndrome-associated coronavirus genotype and its characterization.

OBJECTIVE: To study the severe acute respiratory syndrome (SARS)-associated coronavirus genotype and its characteristics. METHODS: A SARS-associated coronavirus isolate named ZJ01 was obtained from throat swab samples taken from a patient in Hangzhou, Zhejing province. The complete genome sequence of ZJ01 consisted of 29,715 bp (GenBank accession: AY297028, version: gi: 30910859). Seventeen SARS-associated coronavirus genome sequences in GenBank were compared to analyze the common sequence variations and the probability of co-occurrence of multiple polymorphisms or mutations. Phylogenetic analysis of those sequences was done. RESULTS: By bioinformatics processing and analysis, the 5 loci nucleotides at ZJ01 genome were found being T, T, G, T and T, respectively. Compared with other SARS-associated coronavirus genomes in the GenBank database, an A/G mutation was detected besides the other 4 mutation loci (C:G:C:C/T:T:T:T) involved in this genetic signature. Therefore a new definition was put forward according to the 5 mutation loci. SARS-associated coronavirus strains would be grouped into two genotypes (C:G:A:C:C/T:T:G:T:T), and abbreviated as SARS coronavirus C genotype and T genotype. On the basis of this new definition, the ZJ01 isolate belongs to SARS-associated coronavirus T genotype, first discovered and reported in mainland China. Phylogenetic analysis of the spike protein gene fragments of these SARS-associated coronavirus strains showed that the GZ01 isolate was phylogenetically distinct from other isolates, and compared with groups F1 and F2 of the T genotype, the isolates of BJ01 and CUHK-W1 were more closely related to the GZ01 isolate. It was interesting to find that two (A/G and C/T) of the five mutation loci occurred in the spike protein gene, which caused changes of Asp to Gly and Thr to Ile in the protein, respectively. CONCLUSION: Attention should be paid to whether these genotype and mutation patterns are related to the virus's biological activities,epidemic characteristics and host clinical symptoms.

Genotype↗

PubMed2004

Characterization of genomic organization of the adenosine A2A receptor gene by molecular and bioinformatics analyses.

The adenosine A(2A) receptor (A(2A)R) is abundantly expressed in brain and emerging as an important therapeutic target for Parkinson's disease and potentially other neuropsychiatric disorders. To understand the molecular mechanisms of A(2A)R gene expression, we have characterized the genomic organization of the mouse and human A(2A)R genes by molecular and bioinformatic analyses. Three new exons (m1A, m1B and m1C) encoding the 5' untranslated regions (5'-UTRs) of mouse A(2A)R mRNA were identified by rapid amplification of 5' cDNA end (5' RACE), RT-PCR analysis and genome sequence analyses. Similar bioinformatics analysis also suggested six variants of the non-coding "exon 1" (h1A, h1B, h1C, h1D, h1E and h1F) in the human A(2A)R gene, which were confirmed by RT-PCR analysis, while three of the human exon 1 variants (h1D, h1E and h1F) were likewise verified by 5' oligonucleotide capping analysis suggesting multiple transcription start sites. Importantly, RT-PCR and quantitative PCR analysis demonstrated that the A(2A)R transcripts with different exon 1 variants displayed tissue-specific expression patterns. For instance, the mouse exon m1A mRNA was detected only in brain (specifically striatum) and the human exon h1D mRNA in lymphoreticular system. Furthermore, the determination of the three new transcription start sites of human A(2A)R gene by 5' oligonucleotide capping and bioinformatics analyses led to the identification of three corresponding promoter regions which contain several important cis elements, providing additional target for further molecular dissection of A(2A)R gene expression. Finally, our analysis indicates that A(2A)R mRNA and a novel transcript partially overlapping with the 3' exon h3, but in opposite orientation to the A(2A)R gene, could conceivably form duplexes to mutually regulate transcript expression. Thus, combined molecular and bioinformatics analyses revealed a new A(2A)R genomic structure, with conserved coding exons 2 and 3 and divergent, tissue-specific exon 1 variants encoding for 5'-UTR. This raises the possibility of generating multiple tissue-specific A(2A)R mRNA species by alternative promoters with varying regulatory susceptibility.

Animals↗

PubMed2003

Bioinformatics meets proteomics--bridging the gap between mass spectrometry data analysis and cell biology.

Proteomics research programs typically comprise the identification of protein content of any given cell, their isoforms, splice variants, post-translational modifications, interacting partners and higher-order complexes under different conditions. These studies present significant analytical challenges owing to the high proteome complexity and the low abundance of the corresponding proteins, which often requires highly sensitive and resolving techniques. Mass spectrometry plays an important role in proteomics and has become an indispensable tool for molecular and cellular biology. However, the analysis of mass spectrometry data can be a daunting task in view of the complexity of the information to decipher, the accuracy and dynamic range of quantitative analysis, the availability of appropriate bioinformatics software and the overwhelming size of data files. The past ten years have witnessed significant technological advances in mass spectrometry-based proteomics and synergy with bioinformatics is vital to fulfill the expectations of biological discovery programs. We present here the technological capabilities of mass spectrometry and bioinformatics for mining the cellular proteome in the context of discovery programs aimed at trace-level protein identification and expression from microgram amounts of protein extracts from human tissues.

Computational Biology↗

PubMed2002

The pathology of brucellosis reflects the outcome of the battle between the host genome and the Brucella genome.

The successful co-existence of each Brucella spp. with its preferred host is the outcome of ancient co-evolutionary relationships and selection pressures that often result in a stalemate where the pathogen has evolved to survive within the biological systems of the host, and the host has evolved innate and acquired immune systems which allow controlled survival of infection by the pathogen, ultimately supporting the survival of the host-pathogen system. In general, Brucella spp. have evolved a similar fundamental pathogenesis of facultative intracellular parasitism though the predominant route of natural exposure varies from oropharynx to genital tract, as does the preferred tissue and cellular tropism, e.g. non-professional placental trophoblasts, fetal lung, professional macrophages of reticulendothelial system, and the male and female reproductive tracts. The morphogenesis of the pyogranulomatous lesions stimulated by Brucella reflects the nature of the persistent parasitism, i.e. genome versus genome. The question is, how can this perplexing array of survival mechanisms be unraveled? Fortunately, the integration of real-time image analysis, cell biology, genome-wide analysis, proteomics and bioinformatics holds the most promise ever for the global analysis of the Brucella infectious process and the host:pathogen interface leading to a clearer understanding of the interactions of these biological systems. These discoveries will be expected to provide a frameshift in rationales for interrupting and/or controlling brucellosis at host and/or pathogen levels.

Animals↗

PubMed2006

Analysis of structure and function of tenascin-C.

Tenascin-C is a multidomain large extracellular matrix glycoprotein composed of six monomers. The size of tenascin-C monomers (180-250 kDa) varies as a result of an alternative splicing of the fibronectin repeats at the pre-mRNA level. For the first time we applied bioinformatic and molecular modeling procedures, for detailed analysis of the organization of tenascin-C and we performed bioinformatic analysis of tenascin-C gene. We detected the presence of heat shock protein 33 in the tenascin-C N-terminal domain that may suggest its role in the protein-protein interactions and stress response. The number of fibronectin type III-like repeats and epidermal growth factor-like repeats were corrected to 15 and 14, respectively. Using polyactylamide gel electophoresis, RT/PCR analysis and microarrays data, we showed the higher level of tenascin-C in the human tumor tissues: brain, intestine and breast. These results suggested a new role of tenascin-C as the potential tumor marker and drug target.

Alternative Splicing↗

PubMed2025

Investigating the molecular mechanisms, drug prediction, and validation of CCNA2 and MAD2L1 in esophageal squamous cell carcinoma based on bioinformatics.

OBJECTIVE: Aims to comprehensively investigate the expression patterns of CCNA2 and MAD2L1 in esophageal squamous cell carcinoma using bioinformatics methods. METHODS: Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression and ESCC-related genes in public databases, were employed for investigating potential biomarkers for prognosis of esophageal squamous cell carcinoma(ESCC).Finally,. performing qRT-PCR and immunohistochemistry to validate. RESULTS: Ultimately identified 4 hub genes: CDK1, CCNA2,TOP2A and MAD2L1. Bioinformatics analysis showed high expression of these four genes in ESCC (P < 0.05). CCNA2 and MAD2L1 were selected for subsequent analysis based on literature.3.Single gene enrichment analysis revealed significant enrichment of CCNA2 and MAD2L1 in pathways related to splicing, bladder cancer, non-homologous end joining and homologous recombination, glycosaminoglycan biosynthesis chondroitin sulfate, progesterone-mediated oocyte maturation and mismatch repair. PASTAA database indicated the involvement of transcription factors such as Roralpha1, Pou6f1, Roralpha2, Atf-1, Pax-3, C/ebpalpha, Nkx2-1 in the regulation of CCNA2, while no transcription factors were predicted for MAD2L1..Immune infiltration analysis revealed a close association between ESCC and plasma cells, CD8 + T cells, monocytes, M0 macrophages, M1 macrophages, dendritic cells, and resting mast cells.Drug prediction for CCNA2 included 7 drugs such as ETHINYL ESTRADIOL, Seliciclib and TAMOXIFEN, while no drugs were predicted for MAD2L1.qRT-PCR and immunohistochemistry demonstrated high expression of CCNA2 in ESCC, while MAD2L1 showed no significant difference between ESCC and normal esophageal squamous epithelial tissues. CONCLUSION: CCNA2 and MAD2L1 may be potential biomarkers for ESCC, providing a novel basis for understanding the molecular mechanisms underlying ESCC pathogenesis.Additionally, the potential drugs predicted for CCNA2 may emerge as a new hope for ESCC patients in the future.

Humans↗

PubMed2002

Structural criteria of biologically active RGD-sites for analysis of protein cellular function - a bioinformatics study.

BACKGROUND: Cell adhesion involves interactions of integrins and extracellular proteins, often facilitated by the RGD motif. Only presence of the RGD in a sequence of a protein may be not sufficient for the biological activity (binding to an integrin) and additional biochemical and/or structural studies are essential. MATERIAL/METHODS: Structural criteria that would allow identification biologically active RGD-sites on the base of a spatial structure may assist analysis of function of a protein in the cell. For the first time, computational analysis of RGD-sites in a large non-redundant set of protein structures was done. RESULTS: Out of 3819 protein chains sequences of about 100 contained RGDs. Analysis of the structures of the RGD-'native' proteins has allowed establishing main determinants of the biologically active conformations of the RGD sites: surface accessibility of the whole RGD-sequence and the secondary structure. The criteria, applied to the remaining proteins of the set, identify 23 proteins ( approximately 25%) with potentially active RGD-sites. The results strongly suggest that RGD has a high propensity for being involved in protein-protein interactions and this may explain occurrence of RGDs in intracellular proteins. Results of the analysis suggest (in some cases, confirm) novel integrin-related activities for 7 membrane/extracellular proteins, as well as confirm RGD-facilitated cell attachment for 5 viral proteins. CONCLUSIONS: Only presence of RGD in a sequence is not sufficient to propose biological activity of this site. The results also suggest that the method can be used on large scale: for example, for identifying potential integrin-interacting proteins in an animal genome.

Amino Acid Sequence↗

PubMed2006

Analyzing proteomes and protein function using graphical comparative analysis of tandem mass spectrometry results.

Although generating large amounts of proteomic data using tandem mass spectrometry has become routine, there is currently no single set of comprehensive tools for the rigorous analysis of tandem mass spectrometry results given the large variety of possible experimental aims. Currently available applications are typically designed for displaying proteins and posttranslational modifications from the point of view of the mass spectrometrist and are not versatile enough to allow investigators to develop biological models of protein function, protein structure, or cell state. In addition, storage and dissemination of mass spectrometry-based proteomic data are problems facing the scientific community. To address these issues, we have developed a relational database model that efficiently stores and manages large amounts of tandem mass spectrometry results. We have developed an integrated suite of multifunctional analysis software for interpreting, comparing, and displaying these results. Our system, Bioinformatic Graphical Comparative Analysis Tools (BIGCAT), allows sophisticated analysis of tandem mass spectrometry results in a biologically intuitive format and provides a solution to many data storage and dissemination issues.

Amino Acid Sequence↗

PubMed2001

SeWeR: a customizable and integrated dynamic HTML interface to bioinformatics services.

SUMMARY: Sequence analysis using Web Resources (SeWeR) is an integrated, Dynamic HTML (DHTML) interface to commonly used bioinformatics services available on the World Wide Web. It is highly customizable, extendable, platform neutral, completely server-independent and can be hosted as a web page as well as being used as stand-alone software running within a web browser.

Computational Biology↗

PubMed2005

Understanding platelets. Lessons from proteomics, genomics and promises from network analysis.

New large-scale analysis techniques such as bioinformatics, mass spectrometry and SAGE data analysis will allow a new framework for understanding platelets. This review analyses some important options and tasks for these tools and examines an outline of the new, refined picture of the platelet outlined by these new techniques. Looking at the platelet-specific building blocks of genome, (active) transcriptome and proteome (notably secretome and phospho-proteome), we summarize current bioinformatical and biochemical approaches, tasks as well as their limitations. Understanding the surprisingly complex platelet regarding compartmentalization, key cascades, and pathways including clinical implications will remain an exciting and hopefully fruitful challenge for the future.

Animals↗

PubMedDate not supplied

A bioinformatics perspective on proteomics: data storage, analysis, and integration.

The field of proteomics is advancing rapidly as a result of powerful new technologies and proteomics experiments yield a vast and increasing amount of information. Data regarding protein occurrence, abundance, identity, sequence, structure, properties, and interactions need to be stored. Currently, a common standard has not yet been established and open access to results is needed for further development of robust analysis algorithms. Databases for proteomics will evolve from pure storage into knowledge resources, providing a repository for information (meta-data) which is mainly not stored in simple flat files. This review will shed light on recent steps towards the generation of a common standard in proteomics data storage and integration, but is not meant to be a comprehensive overview of all available databases and tools in the proteomics community.

Computational Biology↗

PubMed2004

Toward large-scale modeling of the microbial cell for computer simulation.

In the post-genomic era, the large-scale, systematic, and functional analysis of all cellular components using transcriptomics, proteomics, and metabolomics, together with bioinformatics for the analysis of the massive amount of data generated by these "omics" methods are the focus of intensive research activities. As a consequence of these developments, systems biology, whose goal is to comprehend the organism as a complex system arising from interactions between its multiple elements, becomes a more tangible objective. Mathematical modeling of microorganisms and subsequent computer simulations are effective tools for systems biology, which will lead to a better understanding of the microbial cell and will have immense ramifications for biological, medical, environmental sciences, and the pharmaceutical industry. In this review, we describe various types of mathematical models (structured, unstructured, static, dynamic, etc.), of microorganisms that have been in use for a while, and others that are emerging. Several biochemical/cellular simulation platforms to manipulate such models are summarized and the E-Cell system developed in our laboratory is introduced. Finally, our strategy for building a "whole cell metabolism model", including the experimental approach, is presented.

Biotechnology↗

PubMed2005

Expressed sequence tags from the midgut and an epithelial cell line of Chironomus tentans: annotation, bioinformatic classification of unknown transcripts and analysis of expression levels.

Expressed sequence tags (ESTs) were generated from two Chironomus tentans cDNA libraries, constructed from an embryo epithelial cell line and from larva midgut tissue. 8584 5'-end ESTs were generated and assembled into 3110 tentative unique transcripts, providing the largest contribution of C. tentans sequences to public databases to date. Annotation using Blast gave 1975 (63.5%) transcripts with a significant match in the major gene/protein databases, 1170 with a best match to Anopheles gambiae and 480 to Drosophila melanogaster. 1091 transcripts (35.1%) had no match to any database. Studies of open reading frames suggest that at least 323 of these contain a coding sequence, indicating that a large proportion of the genes in C. tentans belong to previously unknown gene families.

Animals↗

PubMed2003

Bioinformatics Resources for In Silico Proteome Analysis.

In the growing field of proteomics, tools for the in silico analysis of proteins and even of whole proteomes are of crucial importance to make best use of the accumulating amount of data. To utilise this data for healthcare and drug development, first the characteristics of proteomes of entire species-mainly the human-have to be understood, before secondly differentiation between individuals can be surveyed. Specialised databases about nucleic acid sequences, protein sequences, protein tertiary structure, genome analysis, and proteome analysis represent useful resources for analysis, characterisation, and classification of protein sequences. Different from most proteomics tools focusing on similarity searches, structure analysis and prediction, detection of specific regions, alignments, data mining, 2D PAGE analysis, or protein modelling, respectively, comprehensive databases like the proteome analysis database benefit from the information stored in different databases and make use of different protein analysis tools to provide computational analysis of whole proteomes.

Journal Article↗