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PubMed2004

FLOSYS--a web-accessible workflow system for protocol-driven biomolecular sequence analysis.

FLOSYS is an interactive web-accessible bioinformatics workflow system designed to assist biologists in multi-step data analyses. FLOSYS allows the user to create complex analysis pathways (protocols) graphically, similar to drawing a flowchart: icons representing particular bioinformatics tools are dragged and dropped onto a canvas and lines connecting those icons are drawn to specify the relationships between the tools. In addition, FLOSYS permits to select input-data, execute the protocol and store the results in a personal workspace. The three-tier architecture of FLOSYS has been implemented in Java and uses a relational database system together with new technologies for distributed and web computing such as CORBA, RMI, JSP and JDBC. The prototype of FLOSYS, which is part of the bioinformatics workbench AnaBench, is accessible on-line at http://malawimonas.bcm.umontreal.ca: 8091/anabench. The entire package is available on request to academic groups who wish to have a customized local analysis environment for research or teaching.

Computational Biology↗

PubMed2005

A global representation of the carbohydrate structures: a tool for the analysis of glycan.

Glycan resources have been developed of late, such as carbohydrate databases, analysis tools, and algorithms for analysis of carbohydrate features. With this background, bioinformatics approaches to carbohydrate research have recently begun using a large amount of protein and carbohydrate data. This paper introduces one of these projects that elucidates the range of carbohydrate structures. In this study, the variety of carbohydrate structures have been enumerated in a global tree structure called variation trees, using the KEGG GLYCAN database, which is a public-domain glycan resource for bioinformatics analysis. Additionally, a glycosyltransferase mapping list of glycosyltransferases and their catalyzing glycosidic linkages was constructed. From this, we present the composite structure map (CSM), which is a structural variation map integrating its variation trees and glycosyltransferase map list. CSM is able to display, for example, expression data of glycosyltransferases in a compact manner, illustrating its versatility as a new bioinformatics resource and tool capable of analyzing carbohydrate structures on a global scale. These resources are available at http://www.genome.jp/kegg/glycan/.

Carbohydrate Conformation↗

PubMed2026

miRNA Target Prediction: An Overview of the Past and Current Tools.

MicroRNAs (miRNAs) are among the most studied molecules in recent years, and since their discovery, many miRNAs have been identified across various species. As members of the non-coding RNA family, miRNAs are key players in post-transcriptional gene regulation. These molecules can inhibit translation or promote degradation of messenger RNA (mRNA) by binding to the 3' untranslated region (UTR) of mRNA, thereby influencing almost all biological processes. To identify a miRNA's biological role, it is essential to predict the target sites to which it binds, a goal made possible through bioinformatics tools. This chapter discusses the bioinformatics tools commonly used for this purpose. Also, it analyzes the main factors considered in target prediction, such as seed match, free energy, conservation, site accessibility, multiple binding site contribution, and machine learning and deep learning approaches. Understanding the principles underlying these predictive methodologies is crucial for advancing one's biological research on miRNAs.

MicroRNAs↗

PubMed2025

Unveiling novel antimicrobial peptides from the ruminant gastrointestinal microbiomes: A deep learning-driven approach yields an anti-MRSA candidate.

INTRODUCTION: Antimicrobial peptides (AMPs) present a promising avenue to combat the growing threat of antibiotic resistance. The ruminant gastrointestinal microbiome serves as a unique ecosystem that offers untapped potential for AMP discovery. OBJECTIVES: The aims of this study are to develop an effective methodology for the identification of novel AMPs from ruminant gastrointestinal microbiomes, followed by evaluating their antimicrobial efficacy and elucidating the mechanisms underlying their activity. METHODS: We developed a deep learning-based model to identify AMP candidates from a dataset comprising 120 metagenomes and 10,373 metagenome-assembled genomes derived from the ruminant gastrointestinal tract. Both in vivo and in vitro experiments were performed to examine and validate the antimicrobial activities of the AMP candidates that were selected through bioinformatic analysis and subsequently synthesized chemically. Additionally, molecular dynamics simulations were conducted to explore the action mechanism of the most potent AMP candidate. RESULTS: The deep learning model identified 27,192 potential secretory AMP candidates. Following bioinformatic analysis, 39 candidates were synthesized and tested. Remarkably, all synthesized peptides demonstrated antimicrobial activity against Staphylococcus aureus, with 79.5% showing effectiveness against multiple pathogens. Notably, Peptide 4, which exhibited the highest antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), confirmed this effect in a mouse model with wound infection, exhibiting a low propensity for resistance development and minimal cytotoxicity and hemolysis towards mammalian cells. Molecular dynamics simulations provided insights into the mechanism of Peptide 4, primarily its ability to disrupt bacterial cell membranes, leading to cell death. CONCLUSION: This study highlights the power of combining deep learning with microbiome research to uncover novel therapeutic candidates, paving the way for the development of next-generation antimicrobials like Peptide 4 to combat the growing threat of MRSA would infections. It also underscores the value of utilizing ruminant microbial resources.

Animals↗

PubMed2026

CD44 gene rs9666607 polymorphism is associated with papillary thyroid carcinoma and interacts with CREB3L1.

BACKGROUND: The incidence of papillary thyroid carcinoma (PTC) has been rising. CD44 is involved in cell adhesion and migration, but the role of its genetic variation in PTC remains unclear. METHODS: This study aimed to investigate the association of CD44 gene polymorphisms with PTC and to examine the interaction between CD44 and CREB3L1. This study enrolled 354 patients with PTC, and the genotype distribution of the CD44 polymorphism (rs9666607) was analyzed. Key PTC genes were screened using the Gene Expression Omnibus (GEO) database (GSE33630). Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on these key genes. CD44 expression was validated using TCGA database, ELISA, qRTPCR, Western blot, and IHC in patient tissues, PTC mouse model, and human cell lines. The direct interactive molecules were screened through a bioinformatics method. RESULTS: The GSE33630 dataset identified a total of 124 upregulated and 85 downregulated differentially expressed genes. Enrichment analysis revealed 12 key PTC genes, including CD44. TCGA database validation revealed that CD44 was significantly overexpressed in PTC patients. The rs9666607‑A allele was associated with an increased risk of PTC and lymph node metastasis under a dominant model. CD44 mRNA and protein levels were significantly higher in PTC tissues versus adjacent tissue and further elevated in metastatic cases. Bioinformatic analysis predicted CD44 interaction with the transcription factor CREB3L1, and this was confirmed by molecular docking. CREB3L1 expression was synchronously upregulated with CD44 in PTC. CONCLUSION: CD44 polymorphisms, particularly the rs9666607-A allele, are significantly associated with PTC risk and metastasis in the studied population. CD44 is overexpressed in PTC, and its interaction with CREB3L1 suggests a potential novel interaction in PTC pathogenesis.

Hyaluronan Receptors↗

PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2026

A hybrid and cost-efficient barcoding strategy for full-length 16S rRNA gene nanopore sequencing of environmental samples.

BACKGROUND: Accurate species-level identification of bacteria in complex environmental samples is essential for applications in biotechnology, ecological monitoring, and clinical diagnostics. Short-read platforms such as Illumina frequently truncate the 16S rRNA gene, limiting taxonomic resolution. In this work, we applied Oxford Nanopore Technology (ONT) long-read sequencing to full-length 16S rRNA amplicon in samples from natural soil amended with lignocellulosic biomass and a simplified microbial community derived from cultures grown on selective and differential carboxymethyl cellulose (CMC)-based substrates, with the aim to evaluate the difference in performance between a real, complex community and a less complex system. To reduce consumable costs, we substituted the standard ONT Barcoding kits with an in-house hybrid barcoding workflow. Specifically, PacBio PCR-based barcoding protocol was used for sample indexing, followed by library preparation using the ONT Ligation Sequencing Kit. This simplified approach retained compatibility with MinION and Flongle flow cells and supported accurate downstream demultiplexing while lowering barcode costs substantially. Additionally, a new bioinformatic workflow tailored to ONT data was implemented. RESULTS: Overall, the hybrid protocol significantly reduced per-sample barcoding costs while preserving high sequencing quality and throughput. The sequencing run yielded over 5 Gb of quality-filtered data (Q-score ≥ 10). Furthermore, the new bioinformatic workflow allowed taxonomic assignment at the species level for 49.38% of annotated taxa, compared to just 4.59% using Illumina NovaSeq sequencing of the V3-V4 region. ONT also recovered 2.3 times more genera and 1.3 times more families. Although 16S rRNA gene sequencing often cannot distinguish between closely related species, particularly within taxonomically complex groups, in this work, full-length reads substantially improved both taxonomic resolution and database matching. CONCLUSIONS: These results show that full-length 16S rRNA sequencing with ONT, paired with a low-cost barcoding strategy, enhanced taxonomic resolution compared to short-read workflows. This approach also offers a scalable and cost-effective option for high-resolution microbiome profiling in research and applied settings.

RNA, Ribosomal, 16S↗

PubMed2025

Effect of Chang'an decoction on ulcerative colitis by regulating T helper 17 cells and regulatory T cellsRab27 in the p53/high mobility group box 1 pathway.

OBJECTIVE: To explore the effect of Chang'an decoction (, CAD) of ameliorating the immune imbalances in ulcerative colitis (UC) by regulating Rab27 in the P53/high mobility group box 1 pathway. METHODS: The functions and important signaling pathways of the Rab27- and UC-related genes were analyzed viathe use of microarray data from the gene expression omnibus database, gene ontology database, Kyoto encyclopedia of genes and genomes database and gene set enrichment analysis. Dextran sulfate sodium salt-induced colitis mouse model was used to verify the bioinformatics results. Colon length, body weight, and disease activity index were measured. Hematoxylin and eosin staining was applied to validate the histopathology. Tight junction proteins were detected by immunohistochemistry. The proportions of T helper 17 cells (Th17) and regulatory T cells (Treg) in mesenteric lymph nodes were measured viaflow cytometry. Proinflammatory cytokines like interleukin (IL) 17 (IL-17), IL-21 and IL-22 and anti-inflammatory cytokines like transforming growth factor β and IL-10 in the serum and colon of mice were detected by enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction, respectively. The expression levels of high mobility group box 1 (HMGB1), P53 and phospho- P53 (P-P53) in colonic tissues were detected by immunofluorescence and Western blotting. RESULTS: Bioinformatics analysis revealed that compared with normal tissues, the expression of Rab27 was significantly increased in UC tissues. Receiver operating characteristic curve showed that Rab27 has the potential to be used as a biomarker for the diagnosis of disease activity. Enrichment analysis showed that UC and Rab27 were mainly associated with small molecule transport, nutrient metabolism, transmembrane transport and the downstream pathway of P53. According to animal experiments, the expression of Rab27 was increased in UC tissues, which aggravated the colonic pathological damage, activated the expression of HMGB1, and also leaded to the imbalance of Th17 and Treg cells. After CAD intervention, Rab27 overexpression, weight loss, colon shortening, and pathological damage were substantial reduced, the expression of tight junction proteins, zona occludens 1 and Occludin were increased. The effect of CAD at high-dose was more obvious. In addition, CAD upgraded the number of Treg cells and the production of TGF-β and IL-10, while decreasing the number of Th17 cells and the expression of inflammatory cytokines (IL-17, IL-21, and IL-22). Moreover, colon inflammation was alleviated by CAD, as indicated by the regulation of HMGB1 and P-P53 expression. CONCLUSION: The expression of Rab27, HMGB1 and P-P53 could be decreased by CAD, and the balance of Th17 and Treg cells as well as their related cytokines could be regulated by CAD.

Animals↗

PubMed1999

Disruption of six novel ORFs on the left arm of chromosome XII reveals one gene essential for vegetative growth of Saccharomyces cerevisiae.

Deletion via PCR-mediated gene replacement, together with basic functional and bioinformatic analyses, have been performed on six novel open reading-frames (ORFs) on the left arm of chromosome XII of Saccharomyces cerevisiae(YLL033w, YLL032c, YLL031c, YLL030c, YLL029w and YLL028w). ORF deletion was realized using either a short-flanking homology (SFH) or a long-flanking homology (LFH) replacement cassette in the diploid strain FY1679. Sporulation and tetrad analysis showed that YLL031c is the only essential gene of the six. Microscopic examination of the non-growing spores carrying a disrupted copy of the essential gene showed that most of them were blocked after one or two cell divisions with heterogeneous bud size. The standard EUROFAN growth tests failed to reveal any obvious phenotype resulting from the deletion of each the five non-essential ORFs. Bioinformatic analysis revealed that YLL029w is probably an aminopeptidase for mitochondrial or nuclear protein processing and YLL028w may be involved in drug resistance in S. cerevisiae. Replacement cassettes, comprising the promoter and terminator regions of each of the six ORFs, were cloned into pUG7 and demonstrated to efficiently mediate gene replacement in an alternative diploid strain, W303. All the cognate gene clones were constructed, using either PCR products amplified from genomic DNA, or gap-repair. All clones and strains generated have been deposited in the EUROFAN genetic stock centre (EUROSCARF, Frankfurt).

Alleles↗

PubMed2000

Functional analysis of eight open reading frames on chromosomes XII and XIV of Saccharomyces cerevisiae.

Deletion, together with basic functional and bioinformatic analyses has been carried out on eight novel ORFs discovered during the sequencing of the Saccharomyces cerevisiae genome. Six ORFs (YLL049w, YLL051c, YLL052c, YLL053c, YLL054c and YLL055w) located on the left arm, and one (YLR130c) on the right arm, of chromosome XII, and an eighth ORF (YNL331c) on the left arm of the chromosome XIV, have been investigated. ORFs were deleted by the SFH-PCR gene-replacement strategy. Basic functional analysis revealed no obvious phenotype for any of the eight ORFs. Bioinformatic analysis, however, revealed possible functions for seven of the ORFs on the basis of the amino acid sequence similarity of their predicted protein products to those of proteins with known functions. ORF YLL051c (FRE6) shows similarity to iron transport proteins, such as ferric reductase. YLL052c and YLL053c appear to be aquaporins. The product of YLL054c (Yll054p) is highly similar to the oleate-specific transcriptional activator protein (Pip2p), which is involved in the peroxisomal induction pathway (pip). ORF YLL055w is similar to Dal5p, allantoate permease, and may play role in allantoin transport. YLR130c (ZRT2) is a low-affinity zinc transporter protein. YNL331c is also named AAD14, which is induced by chemicals that induce oxidative stress by depleting the cell of glutathione.

Chromosomes, Fungal↗

PubMed2001

Online analysis of development.

The genomics revolution has altered the very nature of research in molecular biology, from how to find genes to how to find out what specific genes do. Given the availability of so many fully (or nearly) sequenced genomes, it is now relatively easy to track down dozens or even hundreds of genes relevant to a particular field of study. Unfortunately, up till now, the tools for determining what these genes actually do in embryos and cells have not kept pace, but the burgeoning field of bioinformatics should help correct this shortcoming and introduce the power of genomics to the study of developmental biology. In this review, some of the bioinformatics resources relevant to developmental biologists are described along with some simple approaches for applying these tools to analyzing early development.

Anatomy↗

PubMed2006

Protein Circular Dichroism Data Bank (PCDDB): data bank and website design.

The Protein Circular Dichroism Data Bank (PCDDB) is a new deposition data bank for validated circular dichroism spectra of biomacromolecules. Its aim is to be a resource for the structural biology and bioinformatics communities, providing open access and archiving facilities for circular dichroism and synchrotron radiation circular dichroism spectra. It is named in parallel with the Protein Data Bank (PDB), a long-existing valuable reference data bank for protein crystal and NMR structures. In this article, we discuss the design of the data bank structure and the deposition website located at http://pcddb.cryst.bbk.ac.uk. Our aim is to produce a flexible and comprehensive archive, which enables user-friendly spectral deposition and searching. In the case of a protein whose crystal structure and sequence are known, the PCDDB entry will be linked to the appropriate PDB and sequence data bank files, respectively. It is anticipated that the PCDDB will provide a readily accessible biophysical catalogue of information on folded proteins that may be of value in structural genomics programs, for quality control and archiving in industrial and academic labs, as a resource for programs developing spectroscopic structural analysis methods, and in bioinformatics studies.

Circular Dichroism↗

PubMed2004

Cytomics--new technologies: towards a human cytome project.

BACKGROUND: Molecular cell systems research (cytomics) aims at the understanding of the molecular architecture and functionality of cell systems (cytomes) by single-cell analysis in combination with exhaustive bioinformatic knowledge extraction. In this way, loss of information as a consequence of molecular averaging by cell or tissue homogenisation is avoided. PROGRESS: The cytomics concept has been significantly advanced by a multitude of current developments. Amongst them are confocal and laser scanning microscopy, multiphoton fluorescence excitation, spectral imaging, fluorescence resonance energy transfer (FRET), fast imaging in flow, optical stretching in flow, and miniaturised flow and image cytometry within laboratories on a chip or laser microdissection, as well as the use of bead arrays. In addition, biomolecular analysis techniques like tyramide signal amplification, single-cell polymerase chain reaction (PCR), and the labelling of biomolecules by quantum dots, magnetic nanobeads, or aptamers open new horizons of sensitivity and molecular specificity at the single-cell level. Data sieving or data mining of the vast amounts of collected multiparameter data for exhaustive multilevel bioinformatic knowledge extraction avoids the inadvertent loss of information from unknown molecular relations being inaccessible to an a priori hypothesis. CHALLENGE: It seems important to address the challenge of a human cytome project using hypothesis-driven molecular information collection from disease associated cell systems, supplemented by systematic and exhaustive knowledge extraction. This will allow the description of the molecular setup of normal and abnormal cell systems within a relational knowledge system, permitting the standardised discrimination of abnormal cell states in disease. As one of the consequences, individualised predictions of further disease course in patients (predictive medicine by cytomics) by characteristic discriminatory data patterns will permit individualised therapies, identification of new pharmaceutical targets, and establishment of a standardised framework of relevant molecular alterations in disease. This special issue of Cytometry, on new technologies in cytomics, focuses on prominent examples of this presently fast-moving scientific field, and represents one of the preconditions for the formulation of a human cytome project.

Cell Biology↗

PubMed2025

Persistent gaps and errors in reference databases impede ecologically meaningful taxonomy assignments in 18S rRNA studies: a case study of terrestrial and marine nematodes.

In metabarcoding studies, Linnaean taxonomy assignments of Operational Taxonomic Units (OTUs) or Amplicon Sequence Variants (ASVs) underpin many downstream bioinformatics analyses and ecological interpretations of environmental DNA (eDNA) datasets. However, public molecular databases (i.e., SILVA, EUKARYOME, BOLD) for most microbial metazoan phyla (nematodes, tardigrades, kinorhynchs, etc.) are sparsely populated, negatively impacting our ability to assign ecologically meaningful taxonomy to these understudied groups. Additionally, the choice of bioinformatics parameters and computational algorithms can further impact the accuracy of eDNA taxonomy assignments. Here, we use two in-silico datasets to show that taxonomy assignments using the 18S rRNA gene can be dramatically improved by curating Linnaean taxonomy strings associated with each reference sequence and closing phylogenetic gaps by improving taxon sampling. Using free-living nematodes as a case study, we applied two commonly used taxonomy assignment algorithms (BLAST+ and the QIIME2 Naïve Bayes classifier) across six iterations of the SILVA 138 reference database to evaluate the precision and accuracy of taxonomy assignments. The BLAST+ top hit with a 90% sequence similarity cutoff often returned the highest percentage of correctly assigned taxonomy at the genus level, and the QIIME2 Naïve Bayes classifier performed similarly well when paired with a reference database containing corrected taxonomy strings. Our results highlight the urgent need for phylogenetically-informed expansions of public reference databases (encompassing both genomes and common gene markers), focused on poorly sampled lineages which are now robustly recovered via eDNA metabarcoding approaches. Additional taxonomy curation efforts should be applied to popular reference databases such as SILVA, and taxon sampling could be rapidly improved by more frequent incorporation of newly published GenBank sequences linked to genus and/or species level identifications.

18S rRNA metabarcoding↗

PubMed2006

Functional genomics of hepatocellular carcinoma.

The majority of DNA-microarray based gene expression profiling studies on human hepatocellular carcinoma (HCC) has focused on identifying genes associated with clinicopathological features of HCC patients. Although notable success has been achieved, this approach still faces significant challenges due to the heterogeneous nature of HCC (and other cancers) as well as the many confounding factors embedded in gene expression profile data. However, these limitations are being overcome by improved bioinformatics and sophisticated analyses. Also, application of cross comparison of multiple gene expression data sets from human tumors and animal models are facilitating the identification of critical regulatory modules in the expression profiles. The success of this new experimental approach, comparative functional genomics, suggests that integration of independent data sets will enhance our ability to identify key regulatory elements in tumor development. Furthermore, integrating gene expression profiles with data from DNA sequence information in promoters, array-based CGH, and expression of non-coding genes (i.e., microRNAs) will further increase the reliability and significance of the biological and clinical inferences drawn from the data. The pace of current progress in the cancer profiling field, combined with the advances in high-throughput technologies in genomics and proteomics, as well as in bioinformatics, promises to yield unprecedented biological insights from the integrative (or systems) analysis of the combined cancer genomics database. The predicted beneficial impact of this "new integrative biology" on diagnosis, treatment and prevention of liver cancer and indeed cancer in general is enormous.

Animals↗

PubMed2006

Prioritizing regions of candidate genes for efficient mutation screening.

The availability of the complete sequence of the human genome has dramatically facilitated the search for disease-causing sequence variations. In fact, the rate-limiting step has shifted from the discovery and characterization of candidate genes to the actual screening of human populations and the subsequent interpretation of observed variations. In this study we tested the hypothesis that some segments of candidate genes are more likely than others to contain disease-causing variations and that these segments can be predicted bioinformatically. A bioinformatic technique, prioritization of annotated regions (PAR), was developed to predict the likelihood that a specific coding region of a gene will harbor a disease-causing mutation based on conserved protein functional domains and protein secondary structures. This method was evaluated by using it to analyze 710 genes that collectively harbor 4,498 previously identified mutations. Nearly 50% of the genes were recognized as disease-associated after screening only 9% of the complete coding sequence. The PAR technique identified 90% of the genes as containing at least one mutation, with less than 40% of the screening resources that traditional approaches would require. These results suggest that prioritization strategies such as PAR can accelerate disease-gene identification through more efficient use of screening resources.

Computational Biology↗

PubMed2006

Gene expression in the cell cycle of human T-lymphocytes: II. Experimental determination by DNASER technology.

Human lymphocytes gene expression before and after PHA stimulation is monitored by DNASER technology, a novel bioinstrumentation entirely constructed in our laboratories as previously reported. The validity of the DNASER measurements is confirmed by standard fluorescence microscopy equipped with CCD. The human lymphocytes gene expression here experimentally probed using commercially available DNA microarrays such as Human Starter, appears compatible both with independent bioinformatic prediction and with existing experimental data, pointing to MYC as the key gene in the G0-G1 transition induced by PHA in resting lymphocytes. It does not escape our notice that in cell biology and cancer research DNASER technology based on microarray constructed with few leader genes identified from bioinformatics represents a meaningful cost-effective route alternative to massive frequently misleading molecular genomics.

Cell Cycle↗

PubMedDate not supplied

Automated protein identification by tandem mass spectrometry: issues and strategies.

Protein identification by tandem mass spectrometry (MS/MS) is key to most proteomics projects and has been widely explored in bioinformatics research. Obtaining good and trustful identification results has important implications for biological and clinical work. Although well matured, automated software identification of proteins from MS/MS data still faces a number of obstacles due to the complexity of the proteome or procedural issues of mass spectrometry data acquisition. Expected or unexpected modifications of the peptide sequences, polymorphisms, errors in databases, missed or non-specific cleavages, unusual fragmentation patterns, and single MS/MS spectra of multiple peptides of the same m/z are so many pitfalls for identification algorithms. A lot of research work has been carried out in recent years that yielded new strategies to handle a number of these issues. Multiple MS/MS identification algorithms are now available or have been theoretically described. The difficulty resides in choosing the most adapted method for each type of spectra being identified. This review presents an overview of the state-of-the-art bioinformatics approaches to the identification of proteins by MS/MS to help the reader doing the spade work of finding the right tools among the many possibilities offered.

Automation↗