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PubMed2002

Emerging bioinformatics for the metabolome.

Metabolic profiling applied to functional genomics (metabolomics) is in an early stage of development. Here, the technologies used for metabolite profiling are briefly covered, illustrated by a few pioneering studies. Issues related to bioinformatics, namely data analysis, visualisation and archival, are the main focus of this review. Arguably there is already a need for databases containing metabolite profiles specific for a single organism, and a generic repository containing all metabolite profiling results, regardless of species. Data analyses and visualisations that combine the biological context with chemistry details are suggested as being the most promising.

Animals↗
PubMed2025

polars-bio-fast, scalable, and out-of-core operations on large genomic interval datasets.

MOTIVATION: Genomic studies very often rely on computationally intensive analyses of relationships between features, which are typically represented as intervals along a 1D coordinate system (such as positions on a chromosome). In this context, the Python programming language is extensively used for manipulating and analyzing data stored in a tabular form of rows and columns, called a DataFrame. Pandas is the most widely used Python DataFrame package and has been criticized for inefficiencies and scalability issues, which its modern alternative-Polars-aims to address with a native backend written in the Rust programming language. RESULTS: polars-bio is a Python library that enables fast, parallel and out-of-core operations on large genomic interval datasets. Its main components are implemented in Rust, using the Apache DataFusion query engine and Apache Arrow for efficient data representation. It is compatible with Polars and Pandas DataFrame formats. In a real-world comparison (107 versus 1.2×106 intervals), our library runs overlap queries 6.5×, nearest queries 15.5×, count_overlaps queries 38×, and coverage queries 15× faster than Bioframe. On equally sized synthetic sets (107 versus 107), the corresponding speedups are 1.6×, 5.5×, 6×, and 6×. In streaming mode, on real and synthetic interval pairs, our implementation uses 90× and 15× less memory for overlap, 4.5× and 6.5× less for nearest, 60× and 12× less for count_overlaps, and 34× and 7× less for coverage than Bioframe. Multi-threaded benchmarks show good scalability characteristics. To the best of our knowledge, polars-bio is the most efficient single-node library for genomic interval DataFrames in Python. AVAILABILITY AND IMPLEMENTATION: polars-bio is an open-source Python package distributed under the Apache License available for major platforms, including Linux, macOS, and Windows in the PyPI registry. The online documentation is https://biodatageeks.org/polars-bio/ and the source code is available on GitHub: https://github.com/biodatageeks/polars-bio and Zenodo: https://doi.org/10.5281/zenodo.16374290. are available at Bioinformatics online.

Software↗
PubMed2005

VIS-O-BAC: exploratory visualization of functional genome studies from bacteria.

UNLABELLED: The visualization-aided exploration of complex datasets will allow the research community to formulate novel functional hypotheses leading to a better understanding of biological processes at all levels. Therefore, we have developed a web resource termed VIS-O-BAC designed for the functional investigation of expression data for model systems, such as bacterial pathogens based on a graphical display. Genome-scale datasets derived from typical 'omic' approaches can directly be explored with respect to three biologically relevant aspects, the genome structure (operon organization), the organization of genes in pathways (KEGG) and the gene function with Gene Ontology (GO) terms. The integrated viewers can be used in parallel and combine expression data and functional annotations from different external data repositories. The graphical visualizations evidently accelerate both the validation of regulatory information and the detection of affected biological processes. AVAILABILITY: http://leger2.gbf.de/cgi-bin/vis-o-bac.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Chromosome Mapping↗
PubMed2006

Predicting methylation status of CpG islands in the human brain.

MOTIVATION: Over 50% of human genes contain CpG islands in their 5'-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands. RESULTS: Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database. AVAILABILITY: An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html CONTACT: mzhang@cshl.edu SUPPLEMENTARY INFORMATION: Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.

Algorithms↗
PubMed2005

Ontology driven dynamic linking of biology resources.

Biologists were early adopters of the Web and continue to use it as the primary means of delivering data, tools and knowledge to their community. The Web is made by the links between pages, yet these links have many limitations: they are static and maintained by hand; they can only link one lexical item to another single resource; ownership is necessary for the placement of link anchors and the link mechanism is essentially inflexible. Dynamic linking services, supported by ontologies, offer a mechanism to overcome such restrictions. The Conceptual Open Hypermedia Service (COHSE) system enhances web resources through the dynamic addition of hypertext links. These links are derived through the use of an ontology and associated lexicon along with a mapping from concepts to possible link targets. We describe an application of COHSE to Bioinformatics, using the Gene Ontology (GO) as an ontology and associated keyword mappings and GO associations as link targets. The resulting demonstrator (referred to here as GOHSE) provides both glossary functionality and the possibility of building knowledge based hypertext structures linking bioinformatics resources.

Animals↗
PubMed2004

Structural bioinformatics and its impact to biomedical science.

During the last two decades, the number of sequence-known proteins has increased rapidly. In contrast, the corresponding increment for structure-known proteins is much slower. The unbalanced situation has critically limited our ability to understand the molecular mechanism of proteins and conduct structure-based drug design by timely using the updated information of newly found sequences. Therefore, it is highly desired to develop an automated method for fast deriving the 3D (3-dimensional) structure of a protein from its sequence. Under such a circumstance, the structural bioinformatics was emerging naturally as the time required. In this review, three main strategies developed in structural bioinformatics, i.e., pure energetic approach, heuristic approach, and homology modeling approach, as well as their underlying principles, are briefly introduced. Meanwhile, a series of demonstrations are presented to show how the structural bioinformatics has been applied to timely derive the 3D structures of some functionally important proteins, helping to understand their action mechanisms and stimulating the course of drug discovery. Also, the limitation of these approaches and the future challenges of structural bioinformatics are briefly addressed.

Amino Acid Sequence↗
PubMed2026

Network pharmacology-based prediction and experimental validation of the anti-hyperuricemic effects of oolong tea polyphenols.

OBJECTIVE: This study aimed to identify candidate therapeutic targets of oolong tea polyphenols (TP) against hyperuricemia (HUA) using network pharmacology and bioinformatics, and to validate the predicted molecular mechanism through in vivo experimentation. METHODS: Drug and disease targets were retrieved from public databases, and overlapping targets were identified by Venn diagram analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the shared targets, and a protein-protein interaction (PPI) network was constructed to identify hub genes. For in vivo validation, an HUA mouse model was established by 15 days of oral potassium oxonate (PO) administration. Model mice then received TP by gavage at low (0.5 g⋅kg-1⋅d-1), medium (1 g⋅kg-1⋅d-1), or high (2 g⋅kg-1⋅d-1) doses for an additional 15 days. Serum biochemical markers, histopathological changes, and pathway-related protein expression were assessed by enzyme-linked immunosorbent assay (ELISA), hematoxylin and eosin (HE) staining, and western blot analysis, respectively. RESULTS: Network pharmacology analysis identified 59 overlapping targets between TP and HUA; GO and KEGG enrichment analyses revealed that these targets were primarily associated with hormone metabolism and the PI3K-AKT signaling pathway. In the animal experiment, TP dose-dependently reduced serum uric acid (SUA) levels in hyperuricemic mice. At the molecular level, low and medium doses of TP suppressed phosphorylation of phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR), whereas the high dose paradoxically activated this pathway and concomitantly elevated interleukin-1β levels. These findings indicate that TP modulates uric acid metabolism through a non-monotonic, dose-dependent mechanism. CONCLUSION: By combining network pharmacology with animal experiments, this study identified the PI3K/AKT/mTOR signaling pathway as a likely mediator of the anti-hyperuricemic action of oolong tea polyphenols (TP). A medium dose of TP achieved the most balanced outcome, attenuating inflammation and preserving hepatic and renal architecture; the high dose, by contrast, paradoxically elevated interleukin-1β (IL-1β) and overactivated PI3K/AKT/mTOR signaling, underscoring the importance of dose calibration. These data suggest that a medium dose of TP may represent a feasible dietary strategy against hyperuricemia. Further work-including monomer identification, direct target validation, and clinical evaluation-is warranted to confirm and extend these preclinical findings.

PI3K/Akt/mTOR signaling pathway↗
PubMed2001

Enterococcus faecalis multi-drug resistance transporters: application for antibiotic discovery.

Using bioinformatics approaches, 34 potential multidrug resistance (MDR) transporter sequences representing 4 different transporter families were identified in the unannotated Enterococcus faecalis database (TIGR). A functional genomics campaign generating single-gene insertional disruptions revealed several genes whose absence confers significant hypersensitivities to known antimicrobials. We constructed specific strains, disrupted in a variety of previously unpublished, putative MDR transporter genes, as tools to improve the success of whole-cell antimicrobial screening and discovery. Each of the potential transporters was inactivated at the gene level and then phenotypically characterized, both with single disruption mutants and with 2-gene mutants built upon a delta norA deleted strain background.

ATP-Binding Cassette Transporters↗
PubMed2003

A 3D model of SARS_CoV 3CL proteinase and its inhibitors design by virtual screening.

AIM: To constructed a three-dimensional (3D) model for the 3C like (3CL) proteinase of SARS coronavirus (SARS-CoV), and to design inhibitors of the 3CL proteinase based on the 3D model. METHODS: Bioinformatics analyses were performed to search the homologous proteins of the SARS-CoV 3CL proteinase from the GenBank and PDB database. A 3D model of the proteinase was constructed by using homology modeling technique. Targeting to the 3D model and its X-ray crystal structure of the main proteinase (Mpro) of transmissible gastroenteritis virus (TGEV), virtual screening was performed employing molecular docking method to identify possible 3CL proteinase inhibitors from small molecular databases. RESULTS: Sequence alignment indicated that the SARS-CoV 3CL proteinase was extremely homologous to TGEV Mpro, especially the substrate-binding pocket (active site). Accordingly, a 3D model for the SARS-CoV 3CL proteinase was constructed based on the crystal structure of TGEV Mpro. The 3D model adopts a similar fold of the TGEV Mpro, its structure and binding pocket feature are almost as same as that of TGEV Mpro. The tested virtual screening indicated that 73 available proteinase inhibitors in the MDDR database might dock into both the binding pockets of the TGEV Mpro and the SARS-CoV 3CL proteinase. CONCLUSIONS: Either the 3D model of the SARS-CoV 3CL proteinase or the X-ray crystal structure of the TGEV Mpro may be used as a starting point for design anti-SARS drugs. Screening the known proteinase inhibitors may be an appreciated shortcut to discover anti-SARS drugs.

Computational Biology↗
PubMed2006

[Screening and analysis of coding SNPs of HLA-DQA1 gene involved in susceptibility for cervical cancer].

BACKGROUND & OBJECTIVE: Polymorphisms of human leukocyte antigen (HLA) gene play an important role in the development of cervical cancer. This study was to screen single nucleotide polymorphisms (SNPs) of HLA-DQA1 gene involved in susceptibility of cervical cancer by a bioinformatics approach, and analyze their correlations to abnormal gene functions. METHODS: SNPs of HLA-DQA1 were screened from a public database dbSNP by SNPper software, and relevant FASTA subsequences were also obtained from dbSNP. PARSESNP software was used to analyze cSNPs. RESULTS: Two SNPs, rs9272693 and rs9272703, which may induce mis-sense mutation, were identified in codon region of HLA-DQA1 gene. A PSSM difference>10 was used to predict deleterious mutation. CONCLUSIONS: SNPper software in combination with PARSESNP software could be used to analyze SNPs of HLA-DQA1 gene and select the variants in a conserved region, and it provides an evaluation criterion. But the results need to be verified in cervical cancer patients and control populations.

Databases as Topic↗
PubMed2006

Comparison of transcripts in Phalaenopsis bellina and Phalaenopsis equestris (Orchidaceae) flowers to deduce monoterpene biosynthesis pathway.

BACKGROUND: Floral scent is one of the important strategies for ensuring fertilization and for determining seed or fruit set. Research on plant scents has hampered mainly by the invisibility of this character, its dynamic nature, and complex mixtures of components that are present in very small quantities. Most progress in scent research, as in other areas of plant biology, has come from the use of molecular and biochemical techniques. Although volatile components have been identified in several orchid species, the biosynthetic pathways of orchid flower fragrance are far from understood. We investigated how flower fragrance was generated in certain Phalaenopsis orchids by determining the chemical components of the floral scent, identifying floral expressed-sequence-tags (ESTs), and deducing the pathways of floral scent biosynthesis in Phalaneopsis bellina by bioinformatics analysis. RESULTS: The main chemical components in the P. bellina flower were shown by gas chromatography-mass spectrometry to be monoterpenoids, benzenoids and phenylpropanoids. The set of floral scent producing enzymes in the biosynthetic pathway from glyceraldehyde-3-phosphate (G3P) to geraniol and linalool were recognized through data mining of the P. bellina floral EST database (dbEST). Transcripts preferentially expressed in P. bellina were distinguished by comparing the scent floral dbEST to that of a scentless species, P. equestris, and included those encoding lipoxygenase, epimerase, diacylglycerol kinase and geranyl diphosphate synthase. In addition, EST filtering results showed that transcripts encoding signal transduction and Myb transcription factors and methyltransferase, in addition to those for scent biosynthesis, were detected by in silico hybridization of the P. bellina unigene database against those of the scentless species, rice and Arabidopsis. Altogether, we pinpointed 66% of the biosynthetic steps from G3P to geraniol, linalool and their derivatives. CONCLUSION: This systems biology program combined chemical analysis, genomics and bioinformatics to elucidate the scent biosynthesis pathway and identify the relevant genes. It integrates the forward and reverse genetic approaches to knowledge discovery by which researchers can study non-model plants.

Acyclic Monoterpenes↗
PubMedDate not supplied

Assessing the impact of alternative splicing on domain interactions in the human proteome.

We have constructed a database of alternatively spliced protein forms (ASP), consisting of 13,384 protein isoform sequences of 4422 human genes (www.bioinformatics.ucla.edu/ASP). We identified fifty protein domain types that were selectively removed by alternative splicing at much higher frequencies than average (p-value < 0.01). These include many well-known protein-interaction domains (e.g., KRAB; ankyrin repeats; Kelch) including some that have been previously shown to be regulated functionally by alternative splicing (e.g., collagen domain). We present a number of novel examples (Kruppel transcription factors; Pbx2; Enc1) from the ASP database, illustrating how this pattern of alternative splicing changes the structure of a biological pathway, by redirecting protein interaction networks at key switch points. Our bioinformatics analysis indicates that a major impact of alternative splicing is removal of protein-protein interaction domains that mediate key linkages in protein interaction networks. ASP expands the available dataset of human alternatively spliced protein forms from 1989 human genes (SwissProt release 42) to 5413 (nonredundant set, ASP + SwissProt), a nearly 3-fold increase. ASP will enhance the existing pool of protein sequences that are searched by mass spectroscopy software during the identification of peptide fragments.

Alternative Splicing↗
PubMed2003

[Disease-causing mutations versus neutral polymorphism: use of bioinformatics and DNA diagnosis].

Molecular genetic diagnostics is available for increasing number of genetically determined diseases. A wide spectrum of mutations can be detected by laboratory methods. A mutation can be defined as a change in a specific DNA sequence when compared with the reference sequence published in the gene database. However, in some cases it is difficult to distinguish if the detected sequence variant is a causal mutation or a neutral (polymorphic) variation without any effect on phenotype. The interpretation of rare sequence variants of unknown significance detected in disease-causing genes becomes an increasingly important problem. Further analysis on DNA and on protein levels with the use of bioinformatics are needed to reveal the effect of rare sequence variants. Inherited complex disorders, for example rare hereditary forms of cancer diseases, represent a challenge to molecular geneticists. The identification of exact causal mutation directly responsible for the development of the disease and for the assessment of disease risk resulting from this genetic variation has further implications. Predictive genetic diagnostics allows identify relatives at high risk of genetically determined disease and use of targeted preventive and therapeutic approaches. In severe cases it allows also prenatal or pre-implantation diagnostics.

DNA Mutational Analysis↗
PubMed2006

The comprehensive updated regulatory network of Escherichia coli K-12.

BACKGROUND: Escherichia coli is the model organism for which our knowledge of its regulatory network is the most extensive. Over the last few years, our project has been collecting and curating the literature concerning E. coli transcription initiation and operons, providing in both the RegulonDB and EcoCyc databases the largest electronically encoded network available. A paper published recently by Ma et al. (2004) showed several differences in the versions of the network present in these two databases. Discrepancies have been corrected, annotations from this and other groups (Shen-Orr et al., 2002) have been added, making the RegulonDB and EcoCyc databases the largest comprehensive and constantly curated regulatory network of E. coli K-12. RESULTS: Several groups have been using these curated data as part of their bioinformatics and systems biology projects, in combination with external data obtained from other sources, thus enlarging the dataset initially obtained from either RegulonDB or EcoCyc of the E. coli K12 regulatory network. We kindly obtained from the groups of Uri Alon and Hong-Wu Ma the interactions they have added to enrich their public versions of the E. coli regulatory network. These were used to search for original references and curate them with the same standards we use regularly, adding in several cases the original references (instead of reviews or missing references), as well as adding the corresponding experimental evidence codes. We also corrected all discrepancies in the two databases available as explained below. CONCLUSION: One hundred and fifty new interactions have been added to our databases as a result of this specific curation effort, in addition to those added as a result of our continuous curation work. RegulonDB gene names are now based on those of EcoCyc to avoid confusion due to gene names and synonyms, and the public releases of RegulonDB and EcoCyc are henceforth synchronized to avoid confusion due to different versions. Public flat files are available providing direct access to the regulatory network interactions thus avoiding errors due to differences in database modelling and representation. The regulatory network available in RegulonDB and EcoCyc is the most comprehensive and regularly updated electronically-encoded regulatory network of E. coli K-12.

Computer Simulation↗
PubMed2003

Metric-space indexes as a basis for scalable biological databases.

Biochemical databases will be best served by the development of new specialized database management systems whose storage managers are based on metric-space indexing techniques and the development a database query languages that embody semantics derived from biochemical models of similarity and evolution. Important biochemical data types cannot be effectively mapped to low dimensional coordinate systems on which O(log n) indexing methods rely. It is clear from an abundance of bioinformatic discoveries that biochemical data is not random and exhibits interesting structure with respect to clustering. Metric-space indexing exploits a data set's intrinsic clustering to speed the execution of similarity queries, even when the data cannot be mapped to a coordinate system. Database management systems that seamlessly integrate semantically rich query languages with a metric-storage and retrieval mechanism will allow biologists to simply and concisely develop informatic studies that have traditionally been large and labor intensive.

Computational Biology↗
PubMed2005

BioMOBY successfully integrates distributed heterogeneous bioinformatics Web Services. The PlaNet exemplar case.

The burden of non-interoperability between on-line genomic resources is increasingly the rate-limiting step in large-scale genomic analysis. BioMOBY is a biological Web Service interoperability initiative that began as a retreat of representatives from the model organism database community in September, 2001. Its long-term goal is to provide a simple, extensible platform through which the myriad of on-line biological databases and analytical tools can offer their information and analytical services in a fully automated and interoperable way. Of the two branches of the larger BioMOBY project, the Web Services branch (MOBY-S) has now been deployed over several dozen data sources worldwide, revealing some significant observations about the nature of the integrative biology problem; in particular, that Web Service interoperability in the domain of bioinformatics is, unexpectedly, largely a syntactic rather than a semantic problem. That is to say, interoperability between bioinformatics Web Services can be largely achieved simply by specifying the data structures being passed between the services (syntax) even without rich specification of what those data structures mean (semantics). Thus, one barrier of the integrative problem has been overcome with a surprisingly simple solution. Here, we present a non-technical overview of the critical components that give rise to the interoperable behaviors seen in MOBY-S and discuss an exemplar case, the PlaNet consortium, where MOBY-S has been deployed to integrate the on-line plant genome databases and analytical services provided by a European consortium of databases and data service providers.

Computational Biology↗
PubMed2026

Pan-cancer analysis identifies GPRIN1 as a prognostic biomarker and promoter of cell proliferation in pancreatic cancer.

BACKGROUND: G protein-regulated inducer of neurite outgrowth 1 (GPRIN1), an emerging modulator of GPCR signaling, has been implicated in oncogenesis. However, its comprehensive role across human cancers, particularly in reshaping the tumor microenvironment (TME), remains poorly characterized. We aimed to elucidate the pan-cancer significance of GPRIN1 in TME modulation and its therapeutic implications. METHODS: We analyzed multi-omics data from TCGA and other public databases, performing a systematic analysis of GPRIN1 regarding expression, prognosis, immune infiltration, and genomic instability across 33 cancer types. To validate these bioinformatic findings, we performed lentiviral shRNA-mediated knockdown in pancreatic (PANC-1) and hepatic (HepG2) cancer cells to assess proliferation and migration. Crucially, the clinical relevance of GPRIN1 was further validated in an independent cohort of pancreatic cancer patients (N&#xa0;=&#xa0;17) using immunohistochemistry (IHC). RESULTS: The analysis identified a lineage-dependent expression pattern. Epithelial tumors exhibited upregulation, whereas glioblastoma samples displayed downregulation. GPRIN1 expression consistently correlated with immune subtypes and CD8+ T cell abundance. In vitro assays demonstrated that GPRIN1 depletion significantly inhibited cell proliferation and migration (P&#xa0;<&#xa0;0.0001). In the clinical validation cohort, multivariate Cox regression analysis identified high GPRIN1 protein levels as an independent predictor of post-operative recurrence. These patients also showed a trend toward extended overall survival. CONCLUSIONS: These findings define GPRIN1 as a context-dependent regulator of the TME. By integrating computational and experimental data, this study supports GPRIN1 as a potential biomarker for risk assessment in pancreatic cancer.

Humans↗