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PubMed2005

Identifying active transcription factors and kinases from expression data using pathway queries.

MOTIVATION: Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data. RESULTS: We apply the algorithm to the Rosetta Yeast Compendium dataset, finding that in many cases the results are in concordance with biological knowledge. We were able to confirm that transcription factors and to a lesser degree, kinases identified by our method play an important role in the biological processes affected by the respective knockouts. Furthermore, we show that correlation of inferred activities can provide evidence for a physical interaction or cooperation of transcription factors where correlation of plain expression data fails to do so.

Algorithms↗

PubMed2004

Transcription factor binding element detection using functional clustering of mutant expression data.

As a powerful tool to reveal gene functions, gene mutation has been used extensively in molecular biology studies. With high throughput technologies, such as DNA microarray, genome-wide gene expression changes can be monitored in mutants. Here we present a simple approach to detect the transcription-factor-binding motif using microarray expression data from a mutant in which the relevant transcription factor is deleted. A core part of our approach is clustering of differentially expressed genes based on functional annotations, such as Gene Ontology (GO). We tested our method with eight microarray data sets from the Rosetta Compendium and were able to detect canonical binding motifs for at least four transcription factors. With the support of chromatin IP chip data, we also predict a possible variant of the Swi4 binding motif and recover a core motif for Arg80. Our approach should be readily applicable to microarray experiments using other types of molecular biology techniques, such as conditional knockout/overexpression or RNAi-mediated 'knockdown', to perturb the expression of a transcription factor. Functional clustering included in our approach may also provide new insights into the function of the relevant transcription factor.

Base Sequence↗

PubMedDate not supplied

An ontology for pharmaceutical ligands and its application for in silico screening and library design.

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

Combinatorial Chemistry Techniques↗

PubMed2025

nf-core/pacvar: a pipeline for analyzing long-read PacBio whole genome and repeat expansion sequencing data.

MOTIVATION: Pacific Biosciences (PacBio) single-molecule, long-read sequencing enables whole genome annotation and the characterization of 20 complex repetitive repeat regions, especially relevant to neurodegenerative diseases, through their PureTarget panel. Long-read whole-genome sequencing (WGS) also allows for the detection of structural variants that would be difficult to detect with traditional short-read sequencing. However, the raw unaligned Binary Alignment Map data need to be processed before analysis. There is a need for an intuitive comprehensive bioinformatic pipeline that can analyze these data. RESULTS: We present nf-core/pacvar, a comprehensive pipeline for analyzing both PacBio single-molecule PureTarget and WGS data that demultiplexes and parallelizes pre-processing, variant calling and repeat characterization. nf-core/pacvar is compatible with little configuration and has few dependencies. This pipeline enables rapid end-to-end, parallel processing of PacBio single-molecule whole genome and targeted repeat expansion sequencing. AVAILABILITY AND IMPLEMENTATION: nf-core/pacvar is available on nf-core website (https://nf-co.re/pacvar/) and on github (https://github.com/nf-core/pacvar) under MIT License (DOI: 10.5281/zenodo.14813048).

Software↗

PubMed2026

Segzoo: a turnkey system that summarizes genome annotations.

MOTIVATION: Segmentation and automated genome annotation (SAGA) techniques, such as Segway and ChromHMM, assign labels to every part of the genome, identifying similar patterns across multiple genomic input signals. Inferring biological meaning in these patterns remains challenging. Doing so requires a time-consuming process of manually downloading reference data, running multiple analysis methods, and interpreting many individual results. RESULTS: To simplify these tasks, we developed the turnkey system Segzoo. As input, Segzoo only requires a genome annotation file in browser extensible data (BED) format. It automatically downloads the rest of the data required for comparisons. Segzoo performs analyses using these data and summarizes results in a single visualization. AVAILABILITY AND IMPLEMENTATION: The source code for Python ≥ 3.7 on Linux is freely available for download at https://github.com/hoffmangroup/segzoo under the GNU General Public License (GPL) version 2. Segzoo is also available in the Bioconda package segzoo: https://anaconda.org/bioconda/segzoo. We have deposited in Zenodo the version of the Segzoo source which produced the results in this article (https://doi.org/10.5281/zenodo.10988775), other code and data used to produce the results (https://doi.org/10.5281/zenodo.10477083), and the results (https://doi.org/10.5281/zenodo.10477106).

Software↗

PubMed2005

Evaluating concentration estimation errors in ELISA microarray experiments.

BACKGROUND: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors. RESULTS: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. CONCLUSIONS: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.

Algorithms↗

PubMed2003

Scoring clustering solutions by their biological relevance.

MOTIVATION: A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorithms for gene expression analysis, very few works addressed the systematic comparison and evaluation of clustering results. Typically, different clustering algorithms yield different clustering solutions on the same data, and there is no agreed upon guideline for choosing among them. RESULTS: We developed a novel statistically based method for assessing a clustering solution according to prior biological knowledge. Our method can be used to compare different clustering solutions or to optimize the parameters of a clustering algorithm. The method is based on projecting vectors of biological attributes of the clustered elements onto the real line, such that the ratio of between-groups and within-group variance estimators is maximized. The projected data are then scored using a non-parametric analysis of variance test, and the score's confidence is evaluated. We validate our approach using simulated data and show that our scoring method outperforms several extant methods, including the separation to homogeneity ratio and the silhouette measure. We apply our method to evaluate results of several clustering methods on yeast cell-cycle gene expression data. AVAILABILITY: The software is available from the authors upon request.

Algorithms↗

PubMed2002

SeqHound: biological sequence and structure database as a platform for bioinformatics research.

BACKGROUND: SeqHound has been developed as an integrated biological sequence, taxonomy, annotation and 3-D structure database system. It provides a high-performance server platform for bioinformatics research in a locally-hosted environment. RESULTS: SeqHound is based on the National Center for Biotechnology Information data model and programming tools. It offers daily updated contents of all Entrez sequence databases in addition to 3-D structural data and information about sequence redundancies, sequence neighbours, taxonomy, complete genomes, functional annotation including Gene Ontology terms and literature links to PubMed. SeqHound is accessible via a web server through a Perl, C or C++ remote API or an optimized local API. It provides functionality necessary to retrieve specialized subsets of sequences, structures and structural domains. Sequences may be retrieved in FASTA, GenBank, ASN.1 and XML formats. Structures are available in ASN.1, XML and PDB formats. Emphasis has been placed on complete genomes, taxonomy, domain and functional annotation as well as 3-D structural functionality in the API, while fielded text indexing functionality remains under development. SeqHound also offers a streamlined WWW interface for simple web-user queries. CONCLUSIONS: The system has proven useful in several published bioinformatics projects such as the BIND database and offers a cost-effective infrastructure for research. SeqHound will continue to develop and be provided as a service of the Blueprint Initiative at the Samuel Lunenfeld Research Institute. The source code and examples are available under the terms of the GNU public license at the Sourceforge site http://sourceforge.net/projects/slritools/ in the SLRI Toolkit.

Amino Acid Sequence↗

PubMed2026

Chromosome-level assembly and annotation of the Jaguar (Panthera onca) genome.

OBJECTIVES: The Jaguar (Panthera onca) is a large cat species native to the Americas. Despite being successful predators, jaguar populations have declined due to habitat loss. Genome resources can help in conservation efforts as well as in understanding the interesting biology of these Felids. Beside contiguity, a well annotated reference genome provides contextual information for variants that will benefit the design of appropriate conservation programs. DATA DESCRIPTION: We sequenced material from two individuals using a combination of ONT reads and Illumina PE. The resulting nuclear genome assembly has a larger contig N50 (48.04 Mb) compared with the existing annotated chromosome-level assembly published by the DNA Zoo project. Using public Hi-C data, we obtained an improved chromosome-level assembly of the Jaguar genome (mPanOnc3.5) with larger contigs, 99.85% of the sequence assigned to chromosomes and 25,267 protein coding genes annotated. Overall, this improved assembly provides a better reference to study this threatened species.

Animals↗

PubMed2005

Annotated proteome of a human T-cell lymphoma.

As the reliable identification of proteins by tandem mass spectrometry becomes increasingly common, the full characterization of large data sets of proteins remains a difficult challenge. Our goal was to survey the proteome of a human T-cell lymphoma-derived cell line in a single set of experiments and present an automated method for the annotation of lists of proteins. A downstream application of these data includes the identification of novel pathogenetic and candidate diagnostic markers of T-cell lymphoma. Total protein isolated from cytoplasmic, membrane, and nuclear fractions of the SUDHL-1 T-cell lymphoma cell line was resolved by SDS-PAGE, and the entire gel lanes digested and analyzed by tandem mass spectrometry. Acquired data files were searched against the UniProt protein database using the SEQUEST algorithm. Search results for each subcellular fraction were analyzed using INTERACT and ProteinProphet. All protein identifications with an error rate of less than 10% were directly exported into excel and analyzed using GOMiner (NIH/NCI). The Gene ontology molecular function and cell location data were summarized for the identified proteins and results exported as user-interactive directed acyclic graphs. A total of 1105 unique proteins were identified and fully annotated, including numerous proteins that had not been previously characterized in lymphoma, in functional categories such as cell adhesion, migration, signaling, and stress response. This study demonstrates the utility of currently available bioinformatics tools for the robust identification and annotation of large numbers of proteins in a batchwise fashion.

Algorithms↗

PubMed2005

Local modeling of global interactome networks.

MOTIVATION: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity-purification/mass-spectrometry (AP-MS) technologies measure different protein-protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput 'interactome' data collection. Static graphs currently used to model Y2H and AP-MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function. RESULTS: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available datasets and demonstrate its uses, interpretation and limitations. Specifically, we use this technology to address four major issues pertaining to protein-protein networks. (1) We motivate the need to move from static global interactome graphs to local protein complex models. (2) We formally show that accurate local interactome models require both Y2H and AP-MS data, even in idealized situations. (3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. (4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity and coordination with gene-expression data. AVAILABILITY: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org CONTACT: dscholtens@northwestern.edu SUPPLEMENTARY INFORMATION: http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling

Algorithms↗

PubMed2005

Representation and high-quality annotation of the Physcomitrella patens transcriptome demonstrates a high proportion of proteins involved in metabolism in mosses.

To gain insight into the transcriptome of the well-used plant model system Physcomitrella patens, several EST sequencing projects have been undertaken. We have clustered, assembled, and annotated all publicly available EST and CDS sequences in order to represent the transcriptome of this non-seed plant. Here, we present our fully annotated knowledge resource for the Physcomitrella patens transcriptome, integrating annotation from the production process of the clustered sequences and from a high-quality annotation pipeline developed during this study. Each transcript is represented as an entity containing full annotations and GO term associations. The whole production, filtering, clustering, and annotation process is being modelled and results in seven datasets, representing the annotated Physcomitrella transcriptome from different perspectives. We were able to annotate 63.4 % of the 26 123 virtual transcripts. The transcript archetype, as covered by our clustered data, is compared to a compilation based on all available Physcomitrella full length CDS. The distribution of the gene ontology annotations (GOA) for the virtual transcriptome of Physcomitrella patens demonstrates consistency in the ratios of the core molecular functions among the plant GOA. However, the metabolism subcategory is over-represented in bryophytes as compared to seed plants. This observation can be taken as an indicator for the wealth of alternative metabolic pathways in moss in comparison to spermatophytes. All resources presented in this study have been made available to the scientific community through a suite of user-friendly web interfaces via www.cosmoss.org and form the basis for assembly and annotation of the moss genome, which will be sequenced in 2005.

3' Untranslated Regions↗

PubMed2025

The chromosome-level genome of Stylosanthes guianensis provides insights into genome evolution and environmental adaptation.

Stylosanthes guianensis is a leguminous forage crop of significant economic importance, primarily distributed in tropical and subtropical regions. It exhibits strong adaptability to various stresses, yet the genetic basis underlying this trait remains unclear. In this study, we constructed the first chromosome-scale reference genome of S. guianensis using a combination of Nanopore and Hi-C sequencing technologies. The assembled genome size is 1254 Mb, with 10 pseudochromosomes. Using Nanopore full-length transcriptome data, we generated high-quality transcript-level gene annotations, identifying 36 585 gene models and 110 601 transcripts. The repetitive sequences in S. guianensis account for 79.16% of the genome, with the extensive expansion of Gypsy elements in long terminal repeats contributing to its genome size enlargement. Comparative genomic and transcriptomic analyses revealed that flavonoid metabolism plays a pivotal role in stress adaptation, providing new insights into the genetic basis of stress tolerance. Additionally, we generated whole-genome methylation profiles under cold treatment and control conditions, offering valuable data for future epigenomic research. These findings provide essential molecular resources for understanding stress resilience in S. guianensis and advancing its molecular breeding.

Genome, Plant↗

PubMed2001

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.

Acetates↗

PubMed2002

Full-length messenger RNA sequences greatly improve genome annotation.

BACKGROUND: Annotation of eukaryotic genomes is a complex endeavor that requires the integration of evidence from multiple, often contradictory, sources. With the ever-increasing amount of genome sequence data now available, methods for accurate identification of large numbers of genes have become urgently needed. In an effort to create a set of very high-quality gene models, we used the sequence of 5,000 full-length gene transcripts from Arabidopsis to re-annotate its genome. We have mapped these transcripts to their exact chromosomal locations and, using alignment programs, have created gene models that provide a reference set for this organism. RESULTS: Approximately 35% of the transcripts indicated that previously annotated genes needed modification, and 5% of the transcripts represented newly discovered genes. We also discovered that multiple transcription initiation sites appear to be much more common than previously known, and we report numerous cases of alternative mRNA splicing. We include a comparison of different alignment software and an analysis of how the transcript data improved the previously published annotation. CONCLUSIONS: Our results demonstrate that sequencing of large numbers of full-length transcripts followed by computational mapping greatly improves identification of the complete exon structures of eukaryotic genes. In addition, we are able to find numerous introns in the untranslated regions of the genes.

Alternative Splicing↗

PubMed2000

Gene index analysis of the human genome estimates approximately 120,000 genes.

Although sequencing of the human genome will soon be completed, gene identification and annotation remains a challenge. Early estimates suggested that there might be 60,000-100,000 (ref. 1) human genes, but recent analyses of the available data from EST sequencing projects have estimated as few as 45,000 (ref. 2) or as many as 140, 000 (ref. 3) distinct genes. The Chromosome 22 Sequencing Consortium estimated a minimum of 45,000 genes based on their annotation of the complete chromosome, although their data suggests there may be additional genes. The nearly 2,000,000 human ESTs in dbEST provide an important resource for gene identification and genome annotation, but these single-pass sequences must be carefully analysed to remove contaminating sequences, including those from genomic DNA, spurious transcription, and vector and bacterial sequences. We have developed a highly refined and rigorously tested protocol for cleaning, clustering and assembling EST sequences to produce high-fidelity consensus sequences for the represented genes (F.L. et al., manuscript submitted) and used this to create the TIGR Gene Indices-databases of expressed genes for human, mouse, rat and other species (http://www.tigr.org/tdb/tgi.html). Using highly refined and tested algorithms for EST analysis, we have arrived at two independent estimates indicating the human genome contains approximately 120,000 genes.

Algorithms↗

PubMed2000

PAHdb: a locus-specific knowledgebase.

PAHdb is an online relational locus-specific "mutation database" (http://www.mcgill.ca/pahdb) for the human phenylalanine hydroxylase gene (symbol PAH) and its associated phenotypes (protein, metabolic, clinical). When combined with associated information (population distribution of allele, haplotype association, etc.) PAHdb functions as a knowledgebase. From the outset, and in the absence of raw data (e.g., sequence gels), PAHdb has instead been an annotated repository of information about mutations maintained by a team of curators. It is also disease-oriented, being focused on a variant phenotype (hyperphenylalaninemia (HPA) and its most important form of disease, phenylketonuria (PKU)) resulting from primary dysfunction of the PAH enzyme (EC 1.14.16.1); it is "patient friendly" in that it contains information for those personally involved with HPA/PKU (MIM# 261600). PAHdb also serves its community through direct interaction.

Alleles↗

PubMed2006

Shotgun proteomic analysis of human-induced sputum.

Induced sputum is a readily accessible biological fluid whose composition may alter as a consequence of disease. To date, however, the proteins that routinely populate this biofluid are largely unknown, in part due to the technical difficulties in processing such mucin-rich samples. To provide a catalogue of sputum proteins, we have surveyed the proteome of human-induced sputum (sputome). A combination of 2-D gel analysis and GeLC-MS/MS allowed a total of 191 human proteins to be confidently assigned. In addition to the expected components, several hitherto unreported proteins were found to be present, including three members of the annexin family, kallikreins 1 and 11, and peroxiredoxins 1, 2 and 5. Other sets of proteins identified included four proteins previously annotated as hypothetical or conserved hypothetical. Taken together, these data represent the first extensive survey of the proteome of induced sputum and provide a platform for future identification of biomarkers of lung disease.

Chromatography, Liquid↗