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At least 1,441 records · Page 80Linked to original sources

PubMed2006

Mitogen-activated protein kinase pathway was significantly activated in human bronchial epithelial cells by nicotine.

Nicotine is potentially associated with the onset of chronic obstructive pulmonary disease (COPD) and lung cancer. To gain insights into the molecular mechanism underlying such nicotine-induced conditions, microarray- bioinformatics analysis was carried out in the present study to explore the gene expression profiles in human bronchial epithelial cells (HBECs) treated with 5 microM nicotine for 4, 8, and 10 h. Of 1,800 assessed genes overall, 260 (14.4%) were upregulated and 17 (0.9%) down regulated significantly. Gene ontology analysis demonstrated that most of the differentially expressed genes belonged to the category of molecular function, especially to the subcategories of enzyme activity. The integration of obtained information with bioinformatics tools in DAVID and KEGG databases indicated that the greatest number of overexpressed genes was involved in mitogen-activated protein kinase (MAPK) pathway. Membrane array analysis subsequently suggested that both extracellular signal-regulated kinase (ERK) 1/2 and c-Jun-NH(2)-terminal kinase (JNK) signalings but not p38 MAPK signaling were activated in response to nicotine. Pretreatment of HBECs with specific inhibitors against ERK 1/2 and JNK but not p38 could significantly inhibit nicotine-induced interleukin- 8 production. These results suggest that MAPK pathway may mediate the effect of nicotine through ERK 1/2 and JNK but not p38 in HBECs treated with nicotine.

Base Sequence↗

PubMed2002

Regulation of alternative pre-mRNA splicing.

The first wave of bioinformatic studies that followed genome and complementary DNA sequencing projects revealed that alternative splicing of messenger RNA precursors (pre-mRNAs) contributes substantially to transcriptome complexity in higher eukaryotes. Together with the realisation of the impact of the process on cell differentiation, development and disease, these studies portray alternative splicing as a fundamental component of gene regulation. Both detailed mechanistic studies and genome-wide analyses will be necessary to unravel the molecular basis for cell type-specific splice site selection. This paper will highlight some recent progress and future challenges for functional genomics and bioinformatics in this rapidly developing area.

Alternative Splicing↗

PubMed2000

Information resources for the bioinformatician.

The collaborative computing project in biosequence and structure analysis (CCPII) was established to foster bioinformatics in the broad community and the UK research community in particular. A World-Wide Web site called 'The Bioinformatics Resource' has been created containing a comprehensive set of information resources of use to the bioinformatician. The activities of CCPII are complementary to other providers of molecular biology information such as the BIOSCI electronic communication forum, which was established to facilitate communication between professionals in the biological sciences.

Computational Biology↗

PubMed2001

Ontologies of developmental anatomy: their current and future roles.

A central problem in current biology is elucidating the molecular networks that drive developmental change and physiological function. Such knowledge is needed partly to understand these networks, partly to be able to manipulate them, and partly to understand and help treat those human congenital abnormalities that arise as a result of mutation. Thus far, bioinformatics technology has been of limited use in this enterprise, mainly because its core focus has been on sequence technology and data archiving. For bioinformatics to be of use in this next tier of investigations, genetic and protein data need to be both archived and searchable by tissue since this is the level at which these networks operate. The resulting databases in turn require ontologies of developmental anatomy that can provide the formal infrastructure for handling gene expression, microarray and other tissue-based data. Here, the progress in making such ontologies, particularly for the developing mouse, is reported and the uses to which they are and will be put, together with the resources and tools currently available for investigating molecular networks and the genetic basis of congenital abnormalities, are considered.

Anatomy↗

PubMed2003

In silico tools for signal transduction research.

Signal transduction is a fundamental process that takes place in all living organisms and understanding how this event occurs at the cellular level is of vital importance to virtually all fields of biomedicine. There are several major steps involved in deciphering the signalling pathways: (a) Which molecules are involved in signalling? (b) Who talks to whom?, ie making sense of the molecular interactions in a context-dependent way. (c) Where are the signalling events taking place?, eg when a resting cell becomes activated. The challenge lies in reconstructing signalling modules and networks evoked in a particular response to a single input as well as correlating the signalling response to different cellular inputs. There is also the need for interpretation of cross-talk between signalling modules in response to single and multiple inputs. To follow up these questions there are many good databases that provide an information system on regulatory networks. This review aims to find some of the bioinformatics tools and websites available to conduct signal transduction research and to discuss the representation of databases available for the processes of signalling. The databases considered here can provide a well-structured overview on the subject and a basis for advanced bioinformatics analysis to interpret the function of genomic sequences or to analyse signalling networks within a cell. However, the knowledge of most signalling pathways is incomplete and for this reason the existing databases will provide insight, but very rarely a more complete picture.

Computational Biology↗

PubMed2004

Globally distributed object identification for biological knowledgebases.

The World-Wide Web provides a globally distributed communication framework that is essential for almost all scientific collaboration, including bioinformatics. However, several limits and inadequacies have become apparent, one of which is the inability to programmatically identify locally named objects that may be widely distributed over the network. This shortcoming limits our ability to integrate multiple knowledgebases, each of which gives partial information of a shared domain, as is commonly seen in bioinformatics. The Life Science Identifier (LSID) and LSID Resolution System (LSRS) provide simple and elegant solutions to this problem, based on the extension of existing internet technologies. LSID and LSRS are consistent with next-generation semantic web and semantic grid approaches. This article describes the syntax, operations, infrastructure compatibility considerations, use cases and potential future applications of LSID and LSRS. We see the adoption of these methods as important steps toward simpler, more elegant and more reliable integration of the world's biological knowledgebases, and as facilitating stronger global collaboration in biology.

Animals↗

PubMed2004

Jemboss reloaded.

Bioinformatics tools are freely available from websites all over the world. Often they are presented as web services, although there are many tools for download and use on a local machine. This tutorial section looks at Jemboss, a Java-based graphical user interface (GUI) for the EMBOSS bioinformatics suite, which combines the advantages of both web service and downloaded software.

Computational Biology↗

PubMed2004

Biological applications of support vector machines.

One of the major tasks in bioinformatics is the classification and prediction of biological data. With the rapid increase in size of the biological databanks, it is essential to use computer programs to automate the classification process. At present, the computer programs that give the best prediction performance are support vector machines (SVMs). This is because SVMs are designed to maximise the margin to separate two classes so that the trained model generalises well on unseen data. Most other computer programs implement a classifier through the minimisation of error occurred in training, which leads to poorer generalisation. Because of this, SVMs have been widely applied to many areas of bioinformatics including protein function prediction, protease functional site recognition, transcription initiation site prediction and gene expression data classification. This paper will discuss the principles of SVMs and the applications of SVMs to the analysis of biological data, mainly protein and DNA sequences.

Algorithms↗

PubMed2006

The Life Sciences Semantic Web is full of creeps!

The Semantic Web for the Life Sciences (SWLS), when realized, will dramatically improve our ability to conduct bioinformatics analyses using the vast and growing stores of web-accessible resources. This ability will be achieved through the widespread acceptance and application of standards for naming, representing, describing and accessing biological information. The W3C-led Semantic Web initiative has established most, if not all, of the standards and technologies needed to achieve a unified, global SWLS. Unfortunately, the bioinformatics community has, thus far, appeared reluctant to fully adopt them. Rather, we are seeing what could be described as 'semantic creep'-timid, piecemeal and ad hoc adoption of parts of standards by groups that should be stridently taking a leadership role for the community. We suggest that, at this point, the primary hindrances to the creation of the SWLS may be social rather than technological in nature, and that, like the original Web, the establishment of the SWLS will depend primarily on the will and participation of its consumers.

Computational Biology↗

PubMed2006

Methods and protocols for prediction of immunogenic epitopes.

T-cell recognition of peptide/major histocompatibility complex (MHC) is a prerequisite for cellular immunity. Recently, there has been an influx of bioinformatics tools to facilitate the identification of T-cell epitopes to specific MHC alleles. This article examines existing computational strategies for the study of peptide/MHC interactions. The most important bioinformatics tools and methods with relevance to the study of peptide/MHC interactions have been reviewed. We have also provided guidelines for predicting antigenic peptides based on the availability of existing experimental data.

Algorithms↗

PubMed2000

Bioinorganic motifs: towards functional classification of metalloproteins.

UNLABELLED: The habitat of bioinorganic motifs (BIMs) is at the interface of biological inorganic chemistry and bioinformatics. BIM is defined as a common structural feature shared by functionally related, but not necessarily homologous, proteins, and consisting of the metal atom(s) and first coordination shell ligands. BIMs appear to be suitable for classification of metal centres at any level, from groups of unrelated proteins with similar function to different functional states of the same protein, and for description of possible evolutionary relationships of metalloproteins. However, they have not attracted wide attention from the bioinformatics community. Although their presence is appreciated, they are difficult to predict-therefore the current 'high-throughput' initiatives are likely to miss or ignore them altogether. The protein sequence databases do not distinguish between proteins containing different prosthetic groups (unless they have different sequences) or between apo- and holoprotein. On the other hand, the protein structure databases include data on 'hetero compounds' of various origin but these data are often inconsistent. A number of specialized databases dealing with BIMs and attempts to classify them are reviewed. SUPPLEMENTARY INFORMATION: The additional bibliography and list of Internet resources on bioinorganic chemistry are available at http://www.ebi.ac.uk/ approximately kirill/biometal/

Amino Acid Motifs↗

PubMed2000

Conceptual modelling of genomic information.

MOTIVATION: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

Computational Biology↗

PubMed2001

Functional and structural genomics using PEDANT.

MOTIVATION: Enormous demand for fast and accurate analysis of biological sequences is fuelled by the pace of genome analysis efforts. There is also an acute need in reliable up-to-date genomic databases integrating both functional and structural information. Here we describe the current status of the PEDANT software system for high-throughput analysis of large biological sequence sets and the genome analysis server associated with it. RESULTS: The principal features of PEDANT are: (i) completely automatic processing of data using a wide range of bioinformatics methods, (ii) manual refinement of annotation, (iii) automatic and manual assignment of gene products to a number of functional and structural categories, (iv) extensive hyperlinked protein reports, and (v) advanced DNA and protein viewers. The system is easily extensible and allows to include custom methods, databases, and categories with minimal or no programming effort. PEDANT is actively used as a collaborative environment to support several on-going genome sequencing projects. The main purpose of the PEDANT genome database is to quickly disseminate well-organized information on completely sequenced and unfinished genomes. It currently includes 80 genomic sequences and in many cases serves as the only source of exhaustive information on a given genome. The database also acts as a vehicle for a number of research projects in bioinformatics. Using SQL queries, it is possible to correlate a large variety of pre-computed properties of gene products encoded in complete genomes with each other and compare them with data sets of special scientific interest. In particular, the availability of structural predictions for over 300 000 genomic proteins makes PEDANT the most extensive structural genomics resource available on the web.

Arabidopsis↗

PubMed2002

Multi-class cancer classification via partial least squares with gene expression profiles.

MOTIVATION: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously. RESULTS: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50). CONTACT: dnguyen@stat.tamu.edu; dmrocke@ucdavis.edu

Algorithms↗

PubMed2003

Whole-proteome interaction mining.

MOTIVATION: A major post-genomic scientific and technological pursuit is to describe the functions performed by the proteins encoded by the genome. One strategy is to first identify the protein-protein interactions in a proteome, then determine pathways and overall structure relating these interactions, and finally to statistically infer functional roles of individual proteins. Although huge amounts of genomic data are at hand, current experimental protein interaction assays must overcome technical problems to scale-up for high-throughput analysis. In the meantime, bioinformatics approaches may help bridge the information gap required for inference of protein function. In this paper, a previously described data mining approach to prediction of protein-protein interactions (Bock and Gough, 2001, Bioinformatics, 17, 455-460) is extended to interaction mining on a proteome-wide scale. An algorithm (the phylogenetic bootstrap) is introduced, which suggests traversal of a phenogram, interleaving rounds of computation and experiment, to develop a knowledge base of protein interactions in genetically-similar organisms. RESULTS: The interaction mining approach was demonstrated by building a learning system based on 1,039 experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. An estimate of the generalization performance of the classifier was derived from 10-fold cross-validation, which indicated expected upper bounds on precision of 80% and sensitivity of 69% when applied to related organisms. One such organism is the enteric pathogen Campylobacter jejuni, in which comprehensive machine learning prediction of all possible pairwise protein-protein interactions was performed. The resulting network of interactions shares an average protein connectivity characteristic in common with previous investigations reported in the literature, offering strong evidence supporting the biological feasibility of the hypothesized map. For inferences about complete proteomes in which the number of pairwise non-interactions is expected to be much larger than the number of actual interactions, we anticipate that the sensitivity will remain the same but precision may decrease. We present specific biological examples of two subnetworks of protein-protein interactions in C. jejuni resulting from the application of this approach, including elements of a two-component signal transduction systems for thermoregulation, and a ferritin uptake network.

Algorithms↗

PubMed2025

Misdetection of frameshifts in SARS-CoV-2 genomes: need for additional harmonisation and efficient monitoring of data workflows.

Five years after the outbreak of the SARS-CoV-2 pandemic in 2020, diagnostic laboratories have moved from massive sequencing of thousands of samples to routine surveillance of SARS-CoV-2 cases, as with all other respiratory viruses. Surveillance remains of paramount importance to prevent a further SARS-CoV-2 surge, as the virus has been shown to mutate rapidly and can render available drugs and vaccines ineffective. During the pandemic, several bioinformatics pipelines and workflows have been developed to streamline analysis, shorten turnaround time and ensure reproducibility. As the number of samples decreases, laboratories are moving towards more flexible sequencing strategies and optimizing the cost per sample. However, workflow redesigns, even if individual steps have proven successful time and time again, can lead to challenges when changes in a bioinformatics pipeline are introduced (e.g. version updates, implementation of new features, etc.), a new combination of viral mutations emerge or a change in wet-lab procedures leads to unpredictable results. Here, we present a report of misidentified frameshift mutations in the consensus sequence of SARS-CoV-2, which led to an incorrect assumption of mutations in the spike and nucleocapsid viral proteins with the potential to affect PCR detection or even antigen testing. This investigation exemplifies the need for better awareness of the challenges that can occur even when using routinely applied protocols and analytical workflows and highlights the need for cooperation between experts of NGS, bioinformaticians and decision-makers towards more harmonized data workflows.

SARS-CoV-2↗

PubMed2025

qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics.

SUMMARY: Clonal hematopoiesis (CH) is a molecular biomarker associated with various adverse outcomes in both healthy individuals and those with underlying conditions, including cancer. Detecting CH usually involves genomic sequencing of individual blood samples followed by robust bioinformatics data filtering. We report an R package, qcCHIP, a bioinformatics pipeline that implements permutation-based parameter optimization to guide quality control filtering and cohort-specific CH identification. We benchmark qcCHIP under various data settings, including different sequencing depths, ranges of cohort sizes, with and without normal-tumor paired samples, and across different cancer types. We show that qcCHIP allows users to customize analysis needs to generate CH calls based on cohort-specific data characteristics. AVAILABILITY AND IMPLEMENTATION: qcCHIP R package is freely accessible at GitHub https://github.com/tenglab/qcCHIP and DOI: 10.5281/zenodo.16421861.

Humans↗

PubMed2026

VIJB: a companion of the JBROWSE genome browser for the visually impaired people.

MOTIVATION: The availability of touch-sensitive and haptic devices has been a keystone development for the inclusion of visually impaired people (VIPs) in modern, highly digitized work environments. Braille displays have proven efficient and versatile enough to parse large and complex text files, making bioinformatics and text-heavy programming accessible to VIPs. However, the complex graphical objects -combining numerous datasets- typically generated during data integration remain challenging, even with the aid of descriptive AI. This is particularly true in functional genomics. Here, we present VIJB, a simple application that displays the multilayered output of the JBROWSE genome browser on a Braille reader, enabling VIPs to fully participate in data integration in functional genomics. AVAILABILITY AND IMPLEMENTATION: VIJB is programmed in Python and relies on the scientific library NumPy, the braillegraph and pyBigWig libraries, and the TABIX software. The architecture is summarized in Supplementary Material 1, available as supplementary data at Bioinformatics online. VIJB is available for download at the GitHub repository https://GitHub.com/NiBuMNHN/VIJB and is licenced under the GPL 3.0.

Persons with Visual Disabilities↗