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PubMed2006

Annotation of environmental OMICS data: application to the transcriptomics domain.

Researchers working on environmentally relevant organisms, populations, and communities are increasingly turning to the application of OMICS technologies to answer fundamental questions about the natural world, how it changes over time, and how it is influenced by anthropogenic factors. In doing so, the need to capture meta-data that accurately describes the biological "source" material used in such experiments is growing in importance. Here, we provide an overview of the formation of the "Env" community of environmental OMICS researchers and its efforts at considering the meta-data capture needs of those working in environmental OMICS. Specifically, we discuss the development to date of the Env specification, an informal specification including descriptors related to geographic location, environment, organism relationship, and phenotype. We then describe its application to the description of environmental transcriptomic experiments and how we have used it to extend the Minimum Information About a Microarray Experiment (MIAME) data standard to create a domain-specific extension that we have termed MIAME/Env. Finally, we make an open call to the community for participation in the Env Community and its future activities.

Ecology↗
PubMed2003

Unique and redundant roles for HOG MAPK pathway components as revealed by whole-genome expression analysis.

The Saccharomyces cerevisiae high osmolarity glycerol (HOG) mitogen-activated protein kinase pathway is required for osmoadaptation and contains two branches that activate a mitogen-activated protein kinase (Hog1) via a mitogen-activated protein kinase kinase (Pbs2). We have characterized the roles of common pathway components (Hog1 and Pbs2) and components in the two upstream branches (Ste11, Sho1, and Ssk1) in response to elevated osmolarity by using whole-genome expression profiling. Several new features of the HOG pathway were revealed. First, Hog1 functions during gene induction and repression, cross talk inhibition, and in governing the regulatory period. Second, the phenotypes of pbs2 and hog1 mutants are identical, indicating that the sole role of Pbs2 is to activate Hog1. Third, the existence of genes whose induction is dependent on Hog1 and Pbs2 but not on Ste11 and Ssk1 suggests that there are additional inputs into Pbs2 under our inducing conditions. Fourth, the two upstream pathway branches are not redundant: the Sln1-Ssk1 branch has a much more prominent role than the Sho1-Ste11 branch for activation of Pbs2 by modest osmolarity. Finally, the general stress response pathway and both branches of the HOG pathway all function at high osmolarity. These studies demonstrate that cells respond to increased osmolarity by using different signal transduction machinery under different conditions.

Gene Expression Profiling↗
PubMed2006

Cip1 and Cip2 are novel RNA-recognition-motif proteins that counteract Csx1 function during oxidative stress.

Eukaryotic cells reprogram their global patterns of gene expression in response to stress. Recent studies in Schizosaccharomyces pombe showed that the RNA-binding protein Csx1 plays a central role in controlling gene expression during oxidative stress. It does so by stabilizing atf1(+) mRNA, which encodes a subunit of a bZIP transcription factor required for gene expression during oxidative stress. Here, we describe two related proteins, Cip1 and Cip2, that were identified by multidimensional protein identification technology (MudPIT) as proteins that coprecipitate with Csx1. Cip1 and Cip2 are cytoplasmic proteins that have RNA recognition motifs (RRMs). Neither protein is essential for viability, but a cip1Delta cip2Delta strain grows poorly and has altered cellular morphology. Genetic epistasis studies and whole genome expression profiling show that Cip1 and Cip2 exert posttranscriptional control of gene expression in a manner that is counteracted by Csx1. Notably, the sensitivity of csx1Delta cells to oxidative stress and their inability to induce expression of Atf1-dependent genes are partially rescued by cip1Delta and cip2Delta mutations. This study emphasizes the importance of a modulated mRNA stability in the eukaryotic stress response pathways and adds new information to the role of RNA-binding proteins in the oxidative stress response.

Amino Acid Motifs↗
PubMed2026

engGNN: a dual-graph neural network for omics-based disease classification and feature selection.

Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph neural networks offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated graph, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages both external biological networks and data-driven generated graphs. Specifically, engGNN constructs a biologically informed undirected feature graph from established network databases and complements it with a directed feature graph derived from tree-ensemble models. This dual-graph design produces more comprehensive representations, thereby improving predictive performance and interpretability. Through extensive simulation studies and real-world applications to three independent gene expression datasets, engGNN consistently demonstrates strong classification performance compared with competitive baselines. Beyond classification, engGNN provides feature- and source-level interpretability, enabling biologically meaningful analyses such as pathway enrichment analysis. Taken together, these results highlight engGNN as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts.

Graph Neural Networks↗
PubMed2000

Inferring qualitative relations in genetic networks and metabolic pathways.

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.

Algorithms↗
PubMed2002

Oligodb--interactive design of oligo DNA for transcription profiling of human genes.

MOTIVATION: The increasing use of customized oligo-DNA-microarrays has created a demand for convenient design of oligo nucleotides. The oligo-nucleotides have to serve as specific templates in the hybridization reaction. RESULTS: We have devised a web-based system for interactive design of suitable oligos for transcription profiling experiments with user-defined parameters. Oligodb is based on predicted human gene transcripts from the ENSEMBL project. The web interface allows the user to retrieve oligos for human transcripts in a flexible manner. Criteria for selecting the oligos are low similarity to all other known human genes, the absence of stable secondary structures, the length of the oligo, and the absence of low complexity regions. Furthermore, the user may select oligos corresponding to the desired position in the 5'-3' direction of the transcript, and based on the melting temperature. All data and results are tightly web-linked to the corresponding data entries of ENSEMBL project.

Algorithms↗
PubMed2002

Adaptive quality-based clustering of gene expression profiles.

MOTIVATION: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters; they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks. RESULTS: We propose a heuristic iterative two-step algorithm: First, we find in the high-dimensional representation of the data a sphere where the "density" of expression profiles is locally maximal (based on a preliminary estimate of the radius of the cluster-quality-based approach). In a second step, we derive an optimal radius of the cluster (adaptive approach) so that only the significantly coexpressed genes are included in the cluster. This estimation is achieved by fitting a model to the data using an EM-algorithm. By inferring the radius from the data itself, the biologist is freed from finding an optimal value for this radius by trial-and-error. The computational complexity of this method is approximately linear in the number of gene expression profiles in the data set. Finally, our method is successfully validated using existing data sets. AVAILABILITY: http://www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html

Algorithms↗
PubMed2025

Significance in scale space for Hi-C data.

MOTIVATION: Hi-C technology has been developed to profile genome-wide chromosome conformation. So far Hi-C data have been generated from a large compendium of different cell types and different tissue types. Among different chromatin conformation units, chromatin loops were found to play a key role in gene regulation across different cell types. While many different loop calling algorithms have been developed, most loop callers identified shared loops as opposed to cell-type-specific loops. RESULTS: We propose SSSHiC, a new loop calling algorithm based on significance in scale space, which can be used to understand data at different levels of resolution. By applying SSSHiC to neuronal and glial Hi-C data, we detected more loops that are potentially engaged in cell-type-specific gene regulation. Compared with other loop callers, such as Mustache, these loops were more frequently anchored to gene promoters of cellular marker genes and had better APA scores. Therefore, our results suggest that SSSHiC can effectively capture loops that contain more gene regulatory information. AVAILABILITY AND IMPLEMENTATION: The Hi-C data used in this study can be accessed through the PsychENCODE Knowledge Portal at https://www.synapse.org/#! Synapse: syn21760712. The code utilized for Curvature SSS cited in this study is available at https://github.com/jsmarron/MarronMatlabSoftware/blob/master/Matlab9/Matlab9Combined.zip. All custom code used in this research can be found in the GitHub repository: https://github.com/jerryliu01998/HiC. The code has also been submitted to Code Ocean with the doi: 10.24433/CO.1912913.v1.

Algorithms↗
PubMed2026

EMTscore infers divergent EMT pathways from omics data and enables rapid screening for EMT-associated gene sets.

MOTIVATION: Quantitative analyses of epithelial-mesenchymal transition (EMT) have been widely used in several areas of biomedical sciences due to its importance in development and cancer progression, but its multi-contextual nature requires standardization and implementation of gene set scoring methods beyond capacities of conventional tools. RESULTS: We developed EMTscore, a package that provides an efficient implementation of unbiased scoring methods for multiple EMT pathways using individual single-cell or bulk omics data, and the package allows rapid screening for cellular processes correlated with EMT. AVAILABILITY AND IMPLEMENTATION: EMTscore is available from GitHub https://github.com/wenmm/EMTscore under the GNU General Public License, and is uploaded on Zenodo with a DOI 10.5281/zenodo.19487376.

Epithelial-Mesenchymal Transition↗
PubMed2026

ARISE: RNA-anchored shared-edge topology and hierarchical fusion for spatial multi-omics integration.

MOTIVATION: Spatial multi-omics technologies jointly profile transcriptomes, proteins and chromatin accessibility in situ, enabling integrative analysis of tissue organization across molecular layers. However, most existing graph-based integration methods rely on independently constructed modality-specific k-nearest-neighbor graphs. When auxiliary modalities are sparse or noisy, these graphs can become topologically discordant, propagate spurious edges, weaken cross-modal alignment, and reduce spatial domain resolution. RESULTS: We present Anchored RNA for Integrated Spatial Embedding (ARISE), an RNA expression anchored framework for spatial multi-omics integration. ARISE defines a shared-edge topology by intersecting RNA feature-similarity and spatial-proximity graphs, encodes auxiliary modalities on this common scaffold, and integrates them through inside-out hierarchical fusion. We further show theoretically that graph intersection minimizes false-positive edges within a broad class of k-of-r graph fusion rules, providing a principled basis for topology anchoring. Across various spatial multi-omics benchmarks spanning simulated and real datasets in bi-modal and tri-modal settings, ARISE improves spatial domain identification, cross-modal consistency, and preservation of tissue structure relative to existing methods. Furthermore, the learned representation supports biologically meaningful downstream analyses, including marker-based domain annotation, pathway enrichment, and cis-regulatory inference, indicating that ARISE yields a robust and interpretable framework for spatial multi-omics integration. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XiangxiangWang-code/ARISE. The archived version used in this study is available at https://doi.org/10.6084/m9.figshare.32686137.v2.

Multiomics↗
PubMed2026

damidBind: an R/bioconductor package for differential DamID analysis and data exploration.

SUMMARY: DamID, and its cell-type specific adaptations, including Targeted DamID (TaDa) and Chromatin Accessibility TaDa (CATaDa), are now widely-adopted as techniques for the genome-wide profiling of DNA binding proteins. Despite this popularity, no dedicated software solution exists for identifying differentially bound or accessible loci, or differentially transcribed genes, between cell types using DamID. The R/Bioconductor package damidBind provides these functions, allowing an end-user to move from processed binding profiles to identifying differentially-bound loci in a reproducible, statistically appropriate and straightforward workflow. AVAILABILITY AND IMPLEMENTATION: damidBind is an open-source R/Bioconductor package and freely available from Bioconductor at https://bioconductor.org/packages/damidBind/, and from GitHub at https://github.com/marshall-lab/damidBind. It is released under the GPLv3 licence.

Software↗
PubMed2003

Selection of oligonucleotide probes for protein coding sequences.

MOTIVATION: Large arrays of oligonucleotide probes have become popular tools for analyzing RNA expression. However to date most oligo collections contain poorly validated sequences or are biased toward untranslated regions (UTRs). Here we present a strategy for picking oligos for microarrays that focus on a design universe consisting exclusively of protein coding regions. We describe the constraints in oligo design that are imposed by this strategy, as well as a software tool that allows the strategy to be applied broadly. RESULT: In this work we sequentially apply a variety of simple filters to candidate sequences for oligo probes. The primary filter is a rejection of probes that contain contiguous identity with any other sequence in the sample universe that exceeds a pre-established threshold length. We find that rejection of oligos that contain 15 bases of perfect match with other sequences in the design universe is a feasible strategy for oligo selection for probe arrays designed to interrogate mammalian RNA populations. Filters to remove sequences with low complexity and predicted poor probe accessibility narrow the candidate probe space only slightly. Rejection based on global sequence alignment is performed as a secondary, rather than primary, test, leading to an algorithm that is computationally efficient. Splice isoforms pose unique challenges and we find that isoform prevalence will for the most part have to be determined by analysis of the patterns of hybridization of partially redundant oligonucleotides. AVAILABILITY: The oligo design program OligoPicker and its source code are freely available at our website.

Algorithms↗
PubMed2003

Methods for optimizing antiviral combination therapies.

MOTIVATION: Despite some progress with antiretroviral combination therapies, therapeutic success in the management of HIV-infected patients is limited. The evolution of drug-resistant genetic variants in response to therapy plays a key role in treatment failure and finding a new potent drug combination after therapy failure is considered challenging. RESULTS: To estimate the activity of a drug combination against a particular viral strain, we develop a scoring function whose independent variables describe a set of antiviral agents and viral DNA sequences coding for the molecular targets of the respective drugs. The construction of this activity score involves (1) predicting phenotypic drug resistance from genotypes for each drug individually, (2) probabilistic modeling of predicted resistance values and integration into a score for drug combinations, and (3) searching through the mutational neighborhood of the considered strain in order to estimate activity on nearby mutants. For a clinical data set, we determine the optimal search depth and show that the scoring scheme is predictive of therapeutic outcome. Properties of the activity score and applications are discussed.

Algorithms↗
PubMed2003

Ligand-Info, searching for similar small compounds using index profiles.

MOTIVATION: The Ligand-Info system is based on the assumption that small molecules with similar structure have similar functional (binding) properties. The developed system enables a fast and sensitive index based search for similar compounds in large databases. Index profiles, constructed by averaging indexes of related molecules are used to increase the specificity of the search. The utilization of index profiles helps to focus on frequent, common features of a family of compounds. RESULTS: A Java-based tool for clustering and scanning of small molecules has been created. The tool can interactively cluster sets of molecules and create index profiles on the user side and automatically download similar molecules from a databases of 250 000 compounds. The results of the application of index profiles demonstrate that the profile based search strategy can increase the quality of the selection process. AVAILABILITY: The system is available at http://Ligand.Info. The application requires the Java Runtime Environment 1.4, which can be automatically installed during the first use on desktop systems, which support it. A standalone version of the program is available from the authors upon request.

Binding Sites↗
PubMed2003

GFPE: gene-finding program evaluation.

Gene-finding program evaluation (GFPE) is a set of Java classes for evaluating gene-finding programs. A command-line interface is also provided. Inputs to the program include the sequence data (in FASTA format), annotations of "actual" sequence features, and annotations of "predicted" sequence features. Annotation files are in the General Feature Format promoted by the Sanger center. GFPE calculates a number of metrics of accuracy of predictions at three levels:the coding level, the exon level, and the protein level.

Computational Biology↗
PubMed2003

iMap: a database-driven utility to integrate and access the genetic and physical maps of maize.

MOTIVATION: Because of the unique biological features, a bioinformatic platform for the integrated genetic and physical map of maize is required for storing, integrating, accessing and visualizing the underlying data. RESULTS: The goal of the Maize Mapping Project is to develop a fully integrated genetic and physical map for maize. To display this integrated map, we have developed iMap. iMap has three main components: a relational database (iMapDB), a map graphic browser (iMap Viewer) and a search utility (iMap Search). iMapDB is populated with current genetic and physical map data, describing relationships among genetic loci, molecular markers and bacterial artificial chromosome (BAC) contigs. The database also contains integrated information produced by applying a set of anchoring rules to assign BAC contigs to specific locations on the genetic map. The iMap Viewer and iMap Search functions are combined in the user interface to allow viewing and retrieving many types of genetic and physical map data. The iMap Viewer features side-by-side chromosome-based displays of the genetic map and associated BAC contigs. For each genetic locus, information about marker type or contig can be viewed via pop-up windows that feature links to external data resources. Searches can be conducted for genetic locus, probe or sequence accession number; search results include relevant map positions, anchored BAC contigs and links to the graphical display of relevant chromosomes. iMap can be accessed at http://www.maizemap.org AVAILABILITY: The iMap utility package is available for non-commercial use upon request from the authors.

Chromosome Mapping↗
PubMed2004

Comparison of various algorithms for recognizing short coding sequences of human genes.

MOTIVATION: Since the early 1980s of the twentieth century, there has been great progress in the development of computational gene-finding algorithms. Some problems, however, have not yet been solved currently. Recognizing short genes in prokaryotes and short exons in eukaryotes is one of such problems. The paper is devoted to assessing various algorithms, including those currently available and the new ones proposed here, in order to find the best algorithm to solve the issue. RESULTS: The databases consisting of phase-specific coding and non-coding sequences of human genes with length of 192, 162, 129, 108, 87, 63 and 42 bp, respectively, have been established. Based on the databases and a standard benchmark, 19 algorithms were evaluated, which include the methods of Markov models with orders of 1 through 5, codon usage, hexamer usage, codon preference, amino acid usage, codon prototype, Fourier transform and 8 Z curve methods with various numbers of parameters. Consequently, the Z curve methods with 69 and 189 parameters are the best ones among them, based on the databases constructed here. In addition to the highest recognition accuracy confirmed by 10-fold cross-validation tests, the Z curve methods are much simpler computationally than the second best one, the fifth-order Markov chain model, in which 12 288 parameters are used. We hope that the Z curve methods presented in this paper would be beneficial to the further development of gene-finding algorithms. AVAILABILITY: The programs of various Z curve methods are available on request.

Algorithms↗

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