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PubMed2002

Proteomic technologies in modern biomedical science.

This review highlights modern technologies employed in proteomics. Methods of sample preparations are discussed with special emphasis on the requirements for preparation of biological material, which may seriously influence the results of proteomic studies. Methods of solubilization, electrophoresis, chromatographic protein separation, and visualization of protein spots in gels are described. Modern methods of mass spectrometry used in proteomic studies include combination of protein chips with mass spectrometry. The review also describes approaches of functional proteomics, i.e., interactomics, and also bioinformatic resources used in proteomics for image analysis of 2D-gel-electrophoresis and for identification of protein sequences by mass spectra.

Biomedical Research↗

PubMed2004

Biochemical identification of Argonaute 2 as the sole protein required for RNA-induced silencing complex activity.

RNA interference is carried out by the small double-stranded RNA-induced silencing complex (RISC). The RISC-bound small RNA guides the RISC complex to identify and cleave mRNAs with complementary sequences. The proteins that make up the RISC complex and cleave mRNA have not been unequivocally defined. Here, we report the biochemical purification of RISC activity to homogeneity from Drosophila Schnieder 2 cell extracts. Argonaute 2 (Ago-2) is the sole protein component present in the purified, functional RISC. By using a bioinformatics method that combines sequence-profile analysis with predicted protein secondary structure, we found homology between the PIWI domain of Ago-2 and endonuclease V and identified potential active-site amino acid residues within the PIWI domain of Ago-2.

Amino Acid Sequence↗

PubMed1995

PROFILER: a tool for automatic searching of internally maintained databases.

A new application program, the BIOINFORMATICS PROFILER, is described which simplifies the analysis of new genome sequence information appearing on a daily basis. Control tasks are defined through an intuitive graphical user interface and are executed at user defined nightly intervals. Electronic mail is sent to indicate that search results have been found. All task output is presented to users in the form of hypertext (HTML), allowing easy browsing. Currently supported tasks include BLAST and FastA sequence searching, keyword based searching of network news articles and WAIS databases, examination of GenBank sequence entries using regular expressions and boolean operations and protein sequence motif searching.

Amino Acid Sequence↗

PubMed2004

YETI: Yeast Exploration Tool Integrator.

UNLABELLED: Yeast Exploration Tool Integrator (YETI) is a novel bioinformatics tool for the integrated visualization and analysis of functional genomic data sets from the budding yeast Saccharomyces cerevisiae. AVAILABILITY: YETI is freely available for use over the WWW, or download under license, at http://www.bru.ed.ac.uk/~orton/yeti.html

Computer Graphics↗

PubMed2006

Mutation parameters from DNA sequence data using graph theoretic measures on lineage trees.

MOTIVATION: B cells responding to antigenic stimulation can fine-tune their binding properties through a process of affinity maturation composed of somatic hypermutation, affinity-selection and clonal expansion. The mutation rate of the B cell receptor DNA sequence, and the effect of these mutations on affinity and specificity, are of critical importance for understanding immune and autoimmune processes. Unbiased estimates of these properties are currently lacking due to the short time-scales involved and the small numbers of sequences available. RESULTS: We have developed a bioinformatic method based on a maximum likelihood analysis of phylogenetic lineage trees to estimate the parameters of a B cell clonal expansion model, which includes somatic hypermutation with the possibility of lethal mutations. Lineage trees are created from clonally related B cell receptor DNA sequences. Important links between tree shapes and underlying model parameters are identified using mutual information. Parameters are estimated using a likelihood function based on the joint distribution of several tree shapes, without requiring a priori knowledge of the number of generations in the clone (which is not available for rapidly dividing populations in vivo). A systematic validation on synthetic trees produced by a mutating birth-death process simulation shows that our estimates are precise and robust to several underlying assumptions. These methods are applied to experimental data from autoimmune mice to demonstrate the existence of hypermutating B cells in an unexpected location in the spleen.

Algorithms↗

PubMed2004

The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes.

In this paper, we present the Functional Catalogue (FunCat), a hierarchically structured, organism-independent, flexible and scalable controlled classification system enabling the functional description of proteins from any organism. FunCat has been applied for the manual annotation of prokaryotes, fungi, plants and animals. We describe how FunCat is implemented as a highly efficient and robust tool for the manual and automatic annotation of genomic sequences. Owing to its hierarchical architecture, FunCat has also proved to be useful for many subsequent downstream bioinformatic applications. This is illustrated by the analysis of large-scale experiments from various investigations in transcriptomics and proteomics, where FunCat was used to project experimental data into functional units, as 'gold standard' for functional classification methods, and also served to compare the significance of different experimental methods. Over the last decade, the FunCat has been established as a robust and stable annotation scheme that offers both, meaningful and manageable functional classification as well as ease of perception.

Abstracting and Indexing↗

PubMed2005

The MicrobesOnline Web site for comparative genomics.

At present, hundreds of microbial genomes have been sequenced, and hundreds more are currently in the pipeline. The Virtual Institute for Microbial Stress and Survival has developed a publicly available suite of Web-based comparative genomic tools (http://www.microbesonline.org) designed to facilitate multispecies comparison among prokaryotes. Highlights of the MicrobesOnline Web site include operon and regulon predictions, a multispecies genome browser, a multispecies Gene Ontology browser, a comparative KEGG metabolic pathway viewer, a Bioinformatics Workbench for in-depth sequence analysis, and Gene Carts that allow users to save genes of interest for further study while they browse. In addition, we provide an interface for genome annotation, which like all of the tools reported here, is freely available to the scientific community.

Animals↗

PubMed2005

Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis.

Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.

Arabidopsis↗

PubMed2024

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

Shigella dysenteriae↗

PubMed2005

Biomedical informatics methods in pharmacogenomics.

Pharmacogenomics is the study of the genetic basis of individual variation in response to therapeutic agents. Pharmacogenomics may potentially affect on every step of health care and every drug treatment protocol. The optimal approach to pharmacogenomics in hypertension requires the integration of different disciplines, in which biomedical informatics plays an essential role. This chapter describes biomedical informatics methods used in dealing with key issues in pharmacogenomics. These key issues include the association between structure and function, the interaction between gene and drug, and the correlation between genotype and phenotype. Heterogeneous resources, including web sites, databases, and software analysis tools, are selected, organized, and integrated in practical methods to support these studies. Bioinformatics methods described in this chapter include genetic sequence searching, comparison, structural modeling, functional analysis, and systems biology studies, with emphasis on single-nucleotide polymorphism (SNP) analysis. Medical informatics methods such as disease and drug information and clinical terminology are also embraced in this chapter. This combination of both biological and medical informatics provides comprehensive methodologies to resolve complex problems in pharmacogenomics.

Computational Biology↗

PubMed2005

Decoding cis-regulatory systems in ascidians.

Ascidians, or sea squirts, are lower chordates, and share basic gene repertoires and many characteristics, both developmental and physiological, with vertebrates. Therefore, decoding cis-regulatory systems in ascidians will contribute toward elucidating the genetic regulatory systems underlying the developmental and physiological processes of vertebrates. cis-Regulatory DNAs can also be used for tissue-specific genetic manipulation, a powerful tool for studying ascidian development and physiology. Because the ascidian genome is compact compared with vertebrate genomes, both intergenic regions and introns are relatively small in ascidians. Short upstream intergenic regions contain a complete set of cis-regulatory elements for spatially regulated expression of a majority of ascidian genes. These features of the ascidian genome are a great advantage in identifying cis-regulatory sequences and in analyzing their functions. Function of cis-regulatory DNAs has been analyzed for a number of tissue-specific and developmentally regulated genes of ascidians by introducing promoter-reporter fusion constructs into ascidian embryos. The availability of the whole genome sequences of the two Ciona species, Ciona intestinalis and Ciona savignyi, facilitates comparative genomics approaches to identify cis-regulatory DNAs. Recent studies demonstrate that computational methods can help identify cis-regulatory elements in the ascidian genome. This review presents a comprehensive list of ascidian genes whose cis-regulatory regions have been subjected to functional analysis, and highlights the recent advances in bioinformatics and comparative genomics approaches to cis-regulatory systems in ascidians.

Animals↗

PubMed2005

Apolipoprotein l6, a novel proapoptotic Bcl-2 homology 3-only protein, induces mitochondria-mediated apoptosis in cancer cells.

Cancer cells frequently possess defects in the genetic and biochemical pathways of apoptosis. Members of the Bcl-2 family play pivotal roles in regulating apoptosis and possess at least one of four Bcl-2 homology (BH) domains, designated BH1 to BH4. The BH3 domain is the only one conserved in proapoptotic BH3-only proteins and plays an important role in protein-protein interactions in apoptosis by regulating homodimerization and heterodimerization of the Bcl-2 family members. To date, 10 BH3-only proapoptotic proteins have been identified and characterized in the human genome. The completion of the Human Genome Project and the availability of various public databases and sequence analysis algorithms allowed us to use the bioinformatic database-mining approach to identify one novel BH3-only protein, apolipoprotein L6 (ApoL6). The full-length cDNA of ApoL6 was identified, cloned, and functionally expressed in p53-null colorectal cancer cells (DLD-1). We found that overexpression of wild-type ApoL6 induced mitochondria-mediated apoptosis in DLD-1 cells characterized by release of cytochrome c and Smac/DIABLO from mitochondria and activation of caspase-9, whereas ApoL6 BH3 domain deletion allele did not. In addition, overexpression of ApoL6 also induced activation of caspase-8. Furthermore, we showed that adenovirus harboring the full-length cDNA of ApoL6 induced marked apoptosis in a variety of cancer cell types, and ApoL6 recruited and interacted with lipid/fatty acid components during the induction of apoptosis. To our knowledge, this is the first example that intracellular overproduction of an apolipoprotein induces marked apoptosis.

Alleles↗

PubMed2026

Genome-Wide Identification and Expression Pattern of the ANK Gene Family in Sorghum bicolor Under Salt Stress.

The Ankyrin-repeat proteins (ANKs) play a key role in plant development and in response to abiotic stress. This research identified family members of the ANK genes in Sorghum bicolor at the whole-genome level, analyzed their sequence characteristics, evolutionary relationships, and expression patterns, and provided a scientific basis for elucidating the functionality of SbANK genes and for salt-tolerant breeding. Using bioinformatics methods, this study conducted a comprehensive identification of the SbANK gene family, analyzing its physicochemical properties, domain composition, chromosomal distribution, colinearity relationships, promoter cis-acting elements, and conserved protein motifs. Transcriptomic data and qRT-PCR were used to detect changes in their expression under salt stress. A total of 186 ANK family members were identified in the Sorghum bicolor genome, classified into 13 subfamilies and unevenly distributed across 10 chromosomes. Intra-species colinearity analysis revealed 7 pairs of duplicated genes, while inter-species colinearity analysis showed that S. bicolor and Oryza sativa share 88 pairs of orthologs, far exceeding the number found in Arabidopsis thaliana (11 pairs). Promoter analysis indicated that SbANK genes are enriched with cis-acting elements associated with hormone responses (particularly MeJA elements, accounting for 51.7%) and stress responses (particularly anaerobic-inducible elements, accounting for 60.9%). Transcriptomic expression analysis revealed that SbANK genes exhibit distinct tissue specificity, with the ANK-IQ subfamily highly expressed in leaves and the ANK-M subfamily showing the most widespread response under salt stress. Expression levels of the 10 candidate genes showing the most significant responses to salt stress were analyzed using qRT-PCR. The results indicated that SbANK91, SbANK135, and SbANK136 were significantly upregulated under 200 mmol/L NaCl treatment. The SbANK family is distinguished by a large number of member genes and structural diversity, with the ANK-M subfamily being the primary group responding to salt stress. SbANK91, SbANK135, and SbANK136 are identified as putative candidate genes for salt stress responses.

Sorghum↗

PubMed2004

Evolutionary mapping of the SHV beta-lactamase and evidence for two separate IS26-dependent blaSHV mobilization events from the Klebsiella pneumoniae chromosome.

OBJECTIVES: To determine the most likely evolutionary pathway that has led to the development of extended-spectrum SHV derivatives, and to the mobilization of blaSHV. MATERIALS AND METHODS: Evolutionary mapping used a shortest-path analysis of aligned blaSHV variants, and other basic bioinformatic approaches, such as CLUSTAL W and Blast were employed. RESULTS: Two main branches of the blaSHV evolutionary tree were located; both are derived from variant blaSHV-1 alleles. Identical mutations, responsible for extended-spectrum SHV substrate profiles, have been selected independently in each branch. There is evidence for a pool of non-mobile blaSHV framework sequences. Analysis of the genome sequence of Klebsiella pneumoniae confirms the chromosomal origin of blaSHV, whose mobilization has occurred at least twice, once for each of the main evolutionary branches. Both these mobilization events have been catalysed by IS26. Evolution of blaSHV to give common extended-spectrum variants is most likely to have occurred following mobilization. CONCLUSIONS: These data shed new light on the evolution and mobilization of blaSHV, and these observations may be useful in predicting what might happen in future, both for blaSHV, and for other beta-lactamase genes.

Alleles↗

PubMed2025

Bioinformatic analyses and validated experiments reveal an aging hallmark gene set and protective miR of coronary artery disease.

To investigate how aging hallmarks exert roles in the age-related disease of coronary artery disease (CAD). R software and the GEO2R online tool identified differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMis) in CAD microarray datasets from the Gene Expression Omnibus. Genes common to target genes of DEMis, DEGs, and an aging gene list from Human Aging Genomic Resources were then identified and analyzed for protein-protein interactions and functional and pathway enrichment. An miR-mRNA network was constructed using Cytoscape. Receiver operating characteristic curve analysis assessed the diagnostic utility of DEMis in CAD. The expression of two DEMis from a CAD cohort was employed to validate the findings. An aging hallmark gene set, comprising 18 genes, was delineated, with the hub gene TP53 established through protein-protein interaction and microRNA-mRNA networks. Within the microRNA-mRNA network, two DEMis (hsa-miR-423-5p and hsa-miR-564) potentially regulated TP53, rendering them potential CAD biomarkers, as indicated by their area under the curves (AUC) surpassing 0.6. Validation experiments corroborated an AUC of 0.7002 for hsa-miR-423-5p and 0.7261 for hsa-miR-564, highlighting its protective association with CAD. Combining hsa-miR-423-5p, hsa-miR-564, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), white blood cells (WBC) achieved an area under the receiver operating characteristics curve of 0.783. A CAD-associated gene set was identified, with TP53 as the central hub. Hsa-miR-564 emerged as a potential protective factor against CAD.

Humans↗

PubMed2005

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures.

The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/

Algorithms↗

PubMed2026

Clinical and genomic characterization of Influenza A co-infection with SARS-CoV-2 and Influenza B: a respiratory surveillance study in Assam, India.

Influenza and SARS-CoV-2 are the primary contributors to seasonal respiratory infections and frequently co-circulate, creating significant health challenges. The present respiratory surveillance study was conducted in Dibrugarh, Assam, India from January 2025 to August 2025 to investigate the genomic characteristics of circulating viruses and identify potential co-infections. Overall, 4,948 respiratory samples were screened using multiplex real-time PCR, followed by subtyping of Influenza A and Influenza B. Next-generation sequencing (NGS) was performed in selected positives of SARS-CoV-2 and Influenza A. Genomic analysis included mutational profiling, phylogenetic analysis and N-glycosylation site prediction using bioinformatics tools. Two co-infection cases were detected: one involving Influenza A (H3N2) with SARS-CoV-2 (Omicron XFG lineage) and another involving Influenza A (H3N2) with Influenza B (Victoria lineage). Both patients experienced mild illness without hospitalisation. NGS revealed that the Influenza A (H3N2) viruses belonged to clade 3C.2a1b.2a.2a.3a.1 while SARS-CoV-2 sequence was classified under the Omicron XFG lineage. Mutational analysis of the HA gene showed several amino acid differences compared to the reference vaccine strain A/Darwin/6/2021. N-glycosylation analysis predicted conserved sites at positions 79, 181, 262, and 301 in all strains along with an additional predicted site at position 110 in both co-infection cases. Although the co-infection cases presented with mild clinical manifestations, the observed genomic variations indicate a potential role of co-infecting viruses in shaping viral evolution. Given the limited genomic data available from Northeast India, the study underscores the need for sustained large scale follow up and genomic surveillance to monitor emerging mutations and target future vaccine strategies.

Humans↗

PubMed2006

SNP-PHAGE--High throughput SNP discovery pipeline.

BACKGROUND: Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable. RESULTS: We developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/. CONCLUSION: SNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Base Sequence↗