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PubMed2026

BRIDGE: an interactive application for multi-omics data analysis, visualization and integration.

SUMMARY: BRIDGE is a Shiny-based application that provides an accessible, modular platform for individual and integrative multi-omics analysis. Using an independent SQLite database backend, it offers a local, private, and user-friendly environment that requires no prior computational expertise. The application supports proteomics, phospho-proteomics, and RNA-seq analyses through a comprehensive suite of visualization and analytical modules, together with an integrated multi-omics analysis pipeline. Built-in caching and asynchronous processing improve responsiveness, enabling efficient exploration, analysis, and visualization of multi-omics datasets on moderate hardware. AVAILABILITY AND IMPLEMENTATION: BRIDGE is implemented in R using Shiny and is freely available as a Docker container at https://ghcr.io/paulilab/bridge. A public demonstration server with example datasets is available at https://bridge.imp.ac.at. Code and datasets are also available at https://github.com/paulilab/BRIDGE and under DOI: https://doi.org/10.5281/zenodo.20215824.

Multiomics↗
PubMed2026

Modtector: ultra-fast modification signal mining on mapped sequencing reads.

SUMMARY: Existing tools for RNA epitranscriptomic modification and structural signal analysis are often fragmented, inefficiency, and limited to single signal types. We developed Modtector, an unified tool for extracting mutation and reverse-transcription stop signals from aligned sequencing reads. By using a "count-then-correct" strategy, Modtector reduces computational complexity and enables efficient dual-signal analysis. It achieves multi-fold speedups on large-genome and high-coverage datasets, including completing HEK293 22G data analysis in 5 minutes, and show strong scalability on single-cell datasets with speedups exceeding 50-fold. AVAILABILITY: The source code is available at GitHub (https://github.com/TongZhou2017/modtector) and Crates.io (https://crates.io/crates/modtector). The archived source-code snapshot used in this study is available at Zenodo (DOI: 10.5281/zenodo.20967747), corresponding to GitHub commit 7c60e9d. Workflow examples, datasets, and analysis scripts are available at Zenodo (DOI: 10.5281/zenodo.17316476 and 10.5281/zenodo.18523297).

Humans↗
PubMed2026

dcHiChIP: a comprehensive Nextflow-based pipeline for multiscale analysis of chromatin architecture from HiChIP data.

MOTIVATION: Despite the growing use of HiChIP to investigate protein-directed chromatin architecture, a comprehensive and reproducible pipeline for analysing these datasets-from raw reads to multiscale 3D genome features-remains lacking. Existing tools often focus on isolated components, such as loop calling or matrix generation, but fall short in integrating structural annotation, functional enrichment, and spatial modeling within a unified framework. To address this gap, we developed dcHiChIP, a modular, scalable Nextflow-based workflow that streamlines the analysis of HiChIP data, enabling both routine processing and in-depth exploration of chromatin organization and regulatory interactions. RESULTS: dcHiChIP enables robust and reproducible analysis of HiChIP datasets across multiple scales of chromatin architecture. It accepts raw sequencing data as input and generates high-quality loop calls, domain annotations, and 3D genome models. It also performs functional annotation and motif enrichment analyses. Applied to benchmark CTCF HiChIP datasets, dcHiChIP identifies major chromatin architectural features such as TADs/CCDs, A/B compartments, and chromatin stripes, and offers efficient, end-to-end execution with support for batch processing and workflow resumability. AVAILABILITY: dcHiChIP is publicly available on GitHub at https://github.com/SFGLab/dcHiChIP, with documentation at https://sfglab.github.io/dcHiChIP/. The software version used in this study is archived at Zenodo: https://doi.org/10.5281/zenodo.22030542.

Chromatin↗
PubMed2004

ESPD: a pattern detection model underlying gene expression profiles.

MOTIVATION: DNA arrays permit rapid, large-scale screening for patterns of gene expression and simultaneously yield the expression levels of thousands of genes for samples. The number of samples is usually limited, and such datasets are very sparse in high-dimensional gene space. Furthermore, most of the genes collected may not necessarily be of interest and uncertainty about which genes are relevant makes it difficult to construct an informative gene space. Unsupervised empirical sample pattern discovery and informative genes identification of such sparse high-dimensional datasets present interesting but challenging problems. RESULTS: A new model called empirical sample pattern detection (ESPD) is proposed to delineate pattern quality with informative genes. By integrating statistical metrics, data mining and machine learning techniques, this model dynamically measures and manipulates the relationship between samples and genes while conducting an iterative detection of informative space and the empirical pattern. The performance of the proposed method with various array datasets is illustrated.

Algorithms↗
PubMed2004

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays.

MOTIVATION: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. RESULTS: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.

Algorithms↗
PubMed2004

Mapping Gene Ontology to proteins based on protein-protein interaction data.

MOTIVATION: Gene Ontology (GO) consortium provides structural description of protein function that is used as a common language for gene annotation in many organisms. Large-scale techniques have generated many valuable protein-protein interaction datasets that are useful for the study of protein function. Combining both GO and protein-protein interaction data allows the prediction of function for unknown proteins. RESULT: We apply a Markov random field method to the prediction of yeast protein function based on multiple protein-protein interaction datasets. We assign function to unknown proteins with a probability representing the confidence of this prediction. The functions are based on three general categories of cellular component, molecular function and biological process defined in GO. The yeast proteins are defined in the Saccharomyces Genome Database (SGD). The protein-protein interaction datasets are obtained from the Munich Information Center for Protein Sequences (MIPS), including physical interactions and genetic interactions. The efficiency of our prediction is measured by applying the leave-one-out validation procedure to a functional path matching scheme, which compares the prediction with the GO description of a protein's function from the abstract level to the detailed level along the GO structure. For biological process, the leave-one-out validation procedure shows 52% precision and recall of our method, much better than that of the simple guilty-by-association methods.

Chromosome Mapping↗
PubMed2004

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

MOTIVATION: Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. RESULTS: We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. AVAILABILITY: Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

Algorithms↗
PubMed2004

Why neural networks should not be used for HIV-1 protease cleavage site prediction.

UNLABELLED: Several papers have been published where nonlinear machine learning algorithms, e.g. artificial neural networks, support vector machines and decision trees, have been used to model the specificity of the HIV-1 protease and extract specificity rules. We show that the dataset used in these studies is linearly separable and that it is a misuse of nonlinear classifiers to apply them to this problem. The best solution on this dataset is achieved using a linear classifier like the simple perceptron or the linear support vector machine, and it is straightforward to extract rules from these linear models. We identify key residues in peptides that are efficiently cleaved by the HIV-1 protease and list the most prominent rules, relating them to experimental results for the HIV-1 protease. MOTIVATION: Understanding HIV-1 protease specificity is important when designing HIV inhibitors and several different machine learning algorithms have been applied to the problem. However, little progress has been made in understanding the specificity because nonlinear and overly complex models have been used. RESULTS: We show that the problem is much easier than what has previously been reported and that linear classifiers like the simple perceptron or linear support vector machines are at least as good predictors as nonlinear algorithms. We also show how sets of specificity rules can be generated from the resulting linear classifiers. AVAILABILITY: The datasets used are available at http://www.hh.se/staff/bioinf/

Algorithms↗
PubMed2004

Constructing an enzyme-centric view of metabolism.

MOTIVATION: The current paradigm for viewing metabolism, such as the Boehringer Chart or KEGG, takes a metabolite-centric view that is not ideal for genomics analysis because the same enzyme can appear in multiple places. Therefore an enzyme-centric view is also required. RESULTS: We have eliminated synonymous compound names taken from the ENZYME database ensuring that it is computationally parseable at all levels. Based on these results, we have written a software to create enzyme-centric graphs from reaction data, and we have created a second dataset with hub molecules removed, allowing a greater depth of information to be extracted from these graphs. We also present a detailed analysis of the various stages of the reconditioning process and the characteristics of the subgraphs resulting from the application of our software to the revised datasets. AVAILABILITY: Complete datasets and supplementary material may be downloaded from http://helix.ex.ac.uk/metabolism. The software for the creation of enzyme-centric graphs from reaction data is available on request from the authors.

Computer Simulation↗
PubMed2004

BAPS 2: enhanced possibilities for the analysis of genetic population structure.

UNLABELLED: Bayesian statistical methods based on simulation techniques have recently been shown to provide powerful tools for the analysis of genetic population structure. We have previously developed a Markov chain Monte Carlo (MCMC) algorithm for characterizing genetically divergent groups based on molecular markers and geographical sampling design of the dataset. However, for large-scale datasets such algorithms may get stuck to local maxima in the parameter space. Therefore, we have modified our earlier algorithm to support multiple parallel MCMC chains, with enhanced features that enable considerably faster and more reliable estimation compared to the earlier version of the algorithm. We consider also a hierarchical tree representation, from which a Bayesian model-averaged structure estimate can be extracted. The algorithm is implemented in a computer program that features a user-friendly interface and built-in graphics. The enhanced features are illustrated by analyses of simulated data and an extensive human molecular dataset. AVAILABILITY: Freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.

Algorithms↗
PubMed2004

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

MOTIVATION: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include 'probe sets' of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported 'probe set algorithms' that differ in their interpretation of a probe set to derive a single normalized 'signal' representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms. RESULTS: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = -3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance. AVAILABILITY: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/ Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp http://pepr.cnmcresearch.org Chen et al., 2004).

Cluster Analysis↗
PubMed2004

A comparative method for identification of gene structures and alternatively spliced variants.

MOTIVATION: Alternative splicing (AS) serves as a mechanism to create diversity among functional proteins. Increasing evidence indicates that a large portion of genes have AS forms. Hence AS variants should be considered while analyzing gene structures. RESULTS: A new cross-species gene identification and AS analysis system, PSEP, has been developed. The system is based on expressed sequence tag (EST)-to-genome and genome-to-genome comparisons and is implemented in two steps: sequence alignment and a series of post-alignment processes, including progressive signal extraction and patching. For gene identification, these post-alignment processes serve as noise filters and enable PSEP to eliminate approximately 88% of potential overprediction. The overall accuracy of PSEP is better than or comparable to that of other well-known cross-species gene prediction programs, including the ROSETTA program, TWINSCAN, SGP-1/-2 and SLAM, when tested on three benchmark datasets (the ELN gene region, the HoxA cluster and the ROSETTA set). In addition, 76.2 and 76.0% of multiple-exon genes in the ROSETTA dataset and human chromosome 20, respectively, are found to have AS forms. Approximately 23% of the 210 elementary alternatives identified in the ROSETTA dataset are not conserved between the human and mouse genomes, and none of the 210 transcripts is found in the RefSeq annotation. With its dual functions in cross-species conserved sequence analysis and AS analysis, PSEP is highly suitable for studying the evolution of AS patterns and for finding unidentified gene expression features.

Algorithms↗
PubMed2004

Accuracy improvement for identifying translation initiation sites in microbial genomes.

MOTIVATION: At present the computational gene identification methods in microbial genomes have a high prediction accuracy of verified translation termination site (3' end), but a much lower accuracy of the translation initiation site (TIS, 5' end). The latter is important to the analysis and the understanding of the putative protein of a gene and the regulatory machinery of the translation. Improving the accuracy of prediction of TIS is one of the remaining open problems. RESULTS: In this paper, we develop a four-component statistical model to describe the TIS of prokaryotic genes. The model incorporates several features with biological meanings, including the correlation between translation termination site and TIS of genes, the sequence content around the start codon; the sequence content of the consensus signal related to ribosomal binding sites (RBSs), and the correlation between TIS and the upstream consensus signal. An entirely non-supervised training system is constructed, which takes as input a set of annotated coding open reading frames (ORFs) by any gene finder, and gives as output a set of organism-specific parameters (without any prior knowledge or empirical constants and formulas). The novel algorithm is tested on a set of reliable datasets of genes from Escherichia coli and Bacillus subtillis. MED-Start may correctly predict 95.4% of the start sites of 195 experimentally confirmed E.coli genes, 96.6% of 58 reliable B.subtillis genes. Moreover, the test results indicate that the algorithm gives higher accuracy for more reliable datasets, and is robust to the variation of gene length. MED-Start may be used as a postprocessor for a gene finder. After processing by our program, the improvement of gene start prediction of gene finder system is remarkable, e.g. the accuracy of TIS predicted by MED 1.0 increases from 61.7 to 91.5% for 854 E.coli verified genes, while that by GLIMMER 2.02 increases from 63.2 to 92.0% for the same dataset. These results show that our algorithm is one of the most accurate methods to identify TIS of prokaryotic genomes. AVAILABILITY: The program MED-Start can be accessed through the website of CTB at Peking University: http://ctb.pku.edu.cn/main/SheGroup/MED_Start.htm.

Algorithms↗
PubMed2004

Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer.

MOTIVATION: It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for 'reference versus normal tissue only' and 'reference versus tumor tissue only' hybridizations (n=n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset. RESULTS: We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic. CONTACT: bskim@yonsei.ac.kr.

Adult↗
PubMed2005

Reversal distance for partially ordered genomes.

MOTIVATION: The total order of the genes or markers on a chromosome inherent in its representation as a signed per-mutation must often be weakened to a partial order in the case of real data. This is due to lack of resolution (where several genes are mapped to the same chromosomal position) to missing data from some of the datasets used to compile a gene order, and to conflicts between these datasets. The available genome rearrangement algorithms, however, require total orders as input. A more general approach is needed to handle rearrangements of gene partial orders. RESULTS: We formalize the uncertainty in gene order data by representing a chromosome from each genome as a partial order, summarized by a directed acyclic graph (DAG). The rearrangement problem is then to infer a minimal sequence of reversals for transforming any topological sort of one DAG to any one of the other DAG. Each topological sort represents a possible linearization compatible with all the datasets on the chromosome. The set of all possible topological sorts is embedded in each DAG by appropriately augmenting the edge set, so that it becomes a general directed graph (DG). The DGs representing chromosomes of two genomes are combined to produce a bicoloured graph from which we extract a maximal decomposition into alternating coloured cycles, and from which, in turn, an optimal sequence of reversals can usually be identified. We test this approach on simulated incomplete comparative maps and on cereal chromosomal maps drawn from the Gramene browser.

Algorithms↗
PubMed2005

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity.

MOTIVATION: Small molecules play a fundamental role in organic chemistry and biology. They can be used to probe biological systems and to discover new drugs and other useful compounds. As increasing numbers of large datasets of small molecules become available, it is necessary to develop computational methods that can deal with molecules of variable size and structure and predict their physical, chemical and biological properties. RESULTS: Here we develop several new classes of kernels for small molecules using their 1D, 2D and 3D representations. In 1D, we consider string kernels based on SMILES strings. In 2D, we introduce several similarity kernels based on conventional or generalized fingerprints. Generalized fingerprints are derived by counting in different ways subpaths contained in the graph of bonds, using depth-first searches. In 3D, we consider similarity measures between histograms of pairwise distances between atom classes. These kernels can be computed efficiently and are applied to problems of classification and prediction of mutagenicity, toxicity and anti-cancer activity on three publicly available datasets. The results derived using cross-validation methods are state-of-the-art. Tradeoffs between various kernels are briefly discussed. AVAILABILITY: Datasets available from http://www.igb.uci.edu/servers/servers.html

Animals↗
PubMed2005

A gamma mixture model better accounts for among site rate heterogeneity.

MOTIVATION: Variation of substitution rates across nucleotide and amino acid sites has long been recognized as a characteristic of molecular sequence evolution. Evolutionary models that account for this rate heterogeneity usually use a gamma density function to model the rate distribution across sites. This density function, however, may not fit real datasets, especially when there is a multimodal distribution of rates. Here, we present a novel evolutionary model based on a mixture of gamma density functions. This model better describes the among-site rate variation characteristic of molecular sequence evolution. The use of this model may improve the accuracy of various phylogenetic methods, such as reconstructing phylogenetic trees, dating divergence events, inferring ancestral sequences and detecting conserved sites in proteins. RESULTS: Using diverse sets of protein sequences we show that the gamma mixture model better describes the stochastic process underlying protein evolution. We show that the proposed gamma mixture model fits protein datasets significantly better than the single-gamma model in 9 out of 10 datasets tested. We further show that using the gamma mixture model improves the accuracy of model-based prediction of conserved residues in proteins. AVAILABILITY: C++ source codes are available from the authors upon request.

Chromosome Mapping↗
PubMed2005

Transcription factor binding site identification using the self-organizing map.

MOTIVATION: The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. In this paper, we propose a self-organizing map of position weight matrices as an alternative method for motif discovery. The advantage of this approach is that it can be used to simultaneously characterize every feature present in the dataset, thus lessening the chance that weaker signals will be missed. Features identified are ranked in terms of over-representation relative to a background model. RESULTS: We present an implementation of this approach, named SOMBRERO (self-organizing map for biological regulatory element recognition and ordering), which is capable of discovering multiple distinct motifs present in a single dataset. Demonstrated here are the advantages of our approach on various datasets and SOMBRERO's improved performance over two popular motif-finding programs, MEME and AlignACE. AVAILABILITY: SOMBRERO is available free of charge from http://bioinf.nuigalway.ie/sombrero SUPPLEMENTARY INFORMATION: http://bioinf.nuigalway.ie/sombrero/additional.

Algorithms↗