Deep learning has become an essential tool for identifying transcription factor (TF) binding sites, yet conventional approaches often struggle with limited training data for specific TFs. Here, we introduce QTFPred (Quantum-based TF Predictor), a quantum-classical hybrid framework that integrates quantum convolutional layers within neural networks to predict TF binding at base resolution. By leveraging the exponential feature space offered by quantum circuits and training from scratch via GPU simulation, QTFPred achieves robust performance even in data-sparse scenarios. In benchmarks on 49 Encyclopedia of DNA elements ChIP-seq datasets, QTFPred delivered state-of-the-art accuracy in 92% of binary prediction and 96% of signal prediction tasks, outperforming conventional models in precision and stability. Moreover, the method reveals underlying TF motif representations, offering insights into cooperative binding mechanisms. These results highlight the potential of quantum machine learning to overcome the limitations of traditional deep learning in genomics modeling.

BACKGROUND: With the recent development of microarray and high-throughput sequencing (HTS) technologies, a number of studies have revealed catalogs of copy number variants (CNVs) and their association with phenotypes and complex traits. In parallel, a number of approaches to predict CNV regions and genotypes are proposed for both microarray and HTS data. However, only a few approaches focus on haplotyping of CNV loci. RESULTS: We propose a novel approach to infer copy unit alleles and their numbers in each sample simultaneously from population-scale HTS data by variational Bayesian inference on a generative probabilistic model inspired by latent Dirichlet allocation, which is a well studied model for document classification problems. In simulation studies, we evaluated concordance between inferred and true copy unit alleles for lower-, middle-, and higher-copy number dataset, in which precision and recall were ≥ 0.9 for data with mean coverage ≥ 10× per copy unit. We also applied the approach to HTS data of 1123 samples at highly variable salivary amylase gene locus and a pseudogene locus, and confirmed consistency of the estimated alleles within samples belonging to a trio of CEPH/Utah pedigree 1463 with 11 offspring. CONCLUSIONS: Our proposed approach enables detailed analysis of copy number variations, such as association study between copy unit alleles and phenotypes or biological features including human diseases.

MOTIVATION: Variant calling from genome-wide sequencing data is essential for the analysis of disease-causing mutations and elucidation of disease mechanisms. However, variant calling in low coverage regions is difficult due to sequence read errors and mapping errors. Hence, variant calling approaches that are robust to low coverage data are demanded. RESULTS: We propose a new variant calling approach that considers pedigree information and haplotyping based on sequence reads spanning two or more heterozygous positions termed phase informative reads. In our approach, genotyping and haplotyping by the assignment of each read to a haplotype based on phase informative reads are simultaneously performed. Therefore, positions with low evidence for heterozygosity are rescued by phase informative reads, and such rescued positions contribute to haplotyping in a synergistic way. In addition, pedigree information supports more accurate haplotyping as well as genotyping, especially in low coverage regions. Although heterozygous positions are useful for haplotyping, homozygous positions are not informative and weaken the information from heterozygous positions, as majority of positions are homozygous. Thus, we introduce latent variables that determine zygosity at each position to filter out homozygous positions for haplotyping. In performance evaluation with a parent-offspring trio sequencing data, our approach outperforms existing approaches in accuracy on the agreement with single nucleotide polymorphism array genotyping results. Also, performance analysis considering distance between variants showed that the use of phase informative reads is effective for accurate variant calling, and further performance improvement is expected with longer sequencing data. CONTACT: [email protected] .

UNLABELLED: XiP (eXtensible integrative Pipeline) is a flexible, editable and modular environment with a user-friendly interface that does not require previous advanced programming skills to run, construct and edit workflows. XiP allows the construction of workflows by linking components written in both R and Java, the analysis of high-throughput data in grid engine systems and also the development of customized pipelines that can be encapsulated in a package and distributed. XiP already comes with several ready-to-use pipeline flows for the most common genomic and transcriptomic analysis and ∼300 computational components. AVAILABILITY: XiP is open source, freely available under the Lesser General Public License (LGPL) and can be downloaded from http://xip.hgc.jp.

MOTIVATION: Many human genes express multiple transcript isoforms through alternative splicing, which greatly increases diversity of protein function. Although RNA sequencing (RNA-Seq) technologies have been widely used in measuring amounts of transcribed mRNA, accurate estimation of transcript isoform abundances from RNA-Seq data is challenging because reads often map to more than one transcript isoforms or paralogs whose sequences are similar to each other. RESULTS: We propose a statistical method to estimate transcript isoform abundances from RNA-Seq data. Our method can handle gapped alignments of reads against reference sequences so that it allows insertion or deletion errors within reads. The proposed method optimizes the number of transcript isoforms by variational Bayesian inference through an iterative procedure, and its convergence is guaranteed under a stopping criterion. On simulated datasets, our method outperformed the comparable quantification methods in inferring transcript isoform abundances, and at the same time its rate of convergence was faster than that of the expectation maximization algorithm. We also applied our method to RNA-Seq data of human cell line samples, and showed that our prediction result was more consistent among technical replicates than those of other methods. AVAILABILITY: An implementation of our method is available at http://github.com/nariai/tigar CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Protein-protein interactions (PPIs) are mediated through specific regions on proteins. Some proteins have two or more protein interacting regions (IRs) and some IRs are competitively used for interactions with different proteins. IRView currently contains data for 3417 IRs in human and mouse proteins. The data were obtained from different sources and combined with annotated region data from InterPro. Information on non-synonymous single nucleotide polymorphism sites and variable regions owing to alternative mRNA splicing is also included. The IRView web interface displays all IR data, including user-uploaded data, on reference sequences so that the positional relationship between IRs can be easily understood. IRView should be useful for analyzing underlying relationships between the proteins behind the PPI networks. AVAILABILITY: IRView is publicly available on the web at http://ir.hgc.jp/

BACKGROUND: Structural variations (SVs) in genomes are commonly observed even in healthy individuals and play key roles in biological functions. To understand their functional impact or to infer molecular mechanisms of SVs, they have to be characterized with the maximum resolution. However, high-resolution analysis is a difficult task because it requires investigation of the complex structures involved in an enormous number of alignments of next-generation sequencing (NGS) reads and genome sequences that contain errors. RESULTS: We propose a new method called ChopSticks that improves the resolution of SV detection for homozygous deletions even when the depth of coverage is low. Conventional methods based on read pairs use only discordant pairs to localize the positions of deletions, where a discordant pair is a read pair whose alignment has an aberrant strand or distance. In contrast, our method exploits concordant reads as well. We theoretically proved that when the depth of coverage approaches zero or infinity, the expected resolution of our method is asymptotically equal to that of methods based only on discordant pairs under double coverage. To confirm the effectiveness of ChopSticks, we conducted computational experiments against both simulated NGS reads and real NGS sequences. The resolution of deletion calls by other methods was significantly improved, thus demonstrating the usefulness of ChopSticks. CONCLUSIONS: ChopSticks can generate high-resolution deletion calls of homozygous deletions using information independent of other methods, and it is therefore useful to examine the functional impact of SVs or to infer SV generation mechanisms.

We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.

SUMMARY: Manual curation and validation of large-scale biological pathways are required to obtain high-quality pathway databases. In a typical curation process, model validation and model update based on appropriate feedback are repeated and requires considerable cooperation of scientists. We have developed a CSO (Cell System Ontology) validator to reduce the repetition and time during the curation process. This tool assists in quickly obtaining agreement among curators and domain experts and in providing a consistent and accurate pathway database. AVAILABILITY: The tool is available on http://csovalidator.csml.org. CONTACT: [email protected].

UNLABELLED: Model checking is playing an increasingly important role in systems biology as larger and more complex biological pathways are being modeled. In this article we report the release of an efficient model checker MIRACH 1.0, which supports any model written in popular formats such as CSML and SBML. MIRACH is integrated with a Petri-net-based simulation engine, enabling efficient online (on-the-fly) checking. In our experiment, by using Levchenko et al. model, we reveal that timesaving gains by using MIRACH easily surpass 400% compared with its offline-based counterpart. AVAILABILITY AND IMPLEMENTATION: MIRACH 1.0 was developed using Java and thus executable on any platform installed with JDK 6.0 (not JRE 6.0) or later. MIRACH 1.0, along with its source codes, documentation and examples are available at http://sourceforge.net/projects/mirach/ under the LGPLv3 license.

SUMMARY: The Macrophage Pathway Knowledgebase (MACPAK) is a computational system that allows biomedical researchers to query and study the dynamic behaviors of macrophage molecular pathways. It integrates the knowledge of 230 reviews that were carefully checked by specialists for their accuracy and then converted to 230 dynamic mathematical pathway models. MACPAK comprises a total of 24 009 entities and 12 774 processes and is described in the Cell System Markup Language (CSML), an XML format that runs on the Cell Illustrator platform and can be visualized with a customized Cytoscape for further analysis. AVAILABILITY: MACPAK can be accessed via an interactive web site at http://macpak.csml.org. The CSML pathway models are available under the Creative Commons license.

UNLABELLED: SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. AVAILABILITY: SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. CONTACT: [email protected].

BACKGROUND: Modeling in systems biology is vital for understanding the complexity of biological systems across scales and predicting system-level behaviors. To obtain high-quality pathway databases, it is essential to improve the efficiency of model validation and model update based on appropriate feedback. RESULTS: We have developed a new method to guide creating novel high-quality biological pathways, using a rule-based validation. Rules are defined to correct models against biological semantics and improve models for dynamic simulation. In this work, we have defined 40 rules which constrain event-specific participants and the related features and adding missing processes based on biological events. This approach is applied to data in Cell System Ontology which is a comprehensive ontology that represents complex biological pathways with dynamics and visualization. The experimental results show that the relatively simple rules can efficiently detect errors made during curation, such as misassignment and misuse of ontology concepts and terms in curated models. CONCLUSIONS: A new rule-based approach has been developed to facilitate model validation and model complementation. Our rule-based validation embedding biological semantics enables us to provide high-quality curated biological pathways. This approach can serve as a preprocessing step for model integration, exchange and extraction data, and simulation.

BACKGROUND: Structural variations (SVs) change the structure of the genome and are therefore the causes of various diseases. Next-generation sequencing allows us to obtain a multitude of sequence data, some of which can be used to infer the position of SVs. METHODS: We developed a new method and implementation named ClipCrop for detecting SVs with single-base resolution using soft-clipping information. A soft-clipped sequence is an unmatched fragment in a partially mapped read. To assess the performance of ClipCrop with other SV-detecting tools, we generated various patterns of simulation data - SV lengths, read lengths, and the depth of coverage of short reads - with insertions, deletions, tandem duplications, inversions and single nucleotide alterations in a human chromosome. For comparison, we selected BreakDancer, CNVnator and Pindel, each of which adopts a different approach to detect SVs, e.g. discordant pair approach, depth of coverage approach and split read approach, respectively. RESULTS: Our method outperformed BreakDancer and CNVnator in both discovering rate and call accuracy in any type of SV. Pindel offered a similar performance as our method, but our method crucially outperformed for detecting small duplications. From our experiments, ClipCrop infer reliable SVs for the data set with more than 50 bases read lengths and 20x depth of coverage, both of which are reasonable values in current NGS data set. CONCLUSIONS: ClipCrop can detect SVs with higher discovering rate and call accuracy than any other tool in our simulation data set.

Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells.

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures, existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the small network, as well as others that could not be captured during the small network estimation. The results also revealed master-regulator genes that are not in the small network but that control many of the genes in the small network. These analyses were impossible to realize without our proposed algorithm.

We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the sparse probabilistic principal component analysis. A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.

SUMMARY: Data assimilation (DA) is a computational approach that estimates unknown parameters in a pathway model using time-course information. Particle filtering, the underlying method used, is a well-established statistical method that approximates the joint posterior distributions of parameters by using sequentially generated Monte Carlo samples. In this article, we report the release of Java-based software (DA 1.0) with an intuitive and user-friendly interface to allow users to carry out parameters estimation using DA. AVAILABILITY AND IMPLEMENTATION: DA 1.0 was developed using Java and thus would be executable on any platform installed with JDK 6.0 (not JRE 6.0) or later. DA 1.0 is freely available for academic users and can be launched or downloaded from http://da.csml.org.

MOTIVATION: A number of unsupervised gene set screening methods have recently been developed for search of putative functional gene sets based on their expression profiles. Most of the methods statistically evaluate whether the expression profiles of each gene set are fit to assumed models: e.g. co-expression across all samples or a subgroup of samples. However, it is possible that they fail to capture informative gene sets whose expression profiles are not fit to the assumed models. RESULTS: To overcome this limitation, we propose a model-free unsupervised gene set screening method, Matrix Information Enrichment Analysis (MIEA). Without assuming any specific models, MIEA screens gene sets based on information richness of their expression profiles. We extensively compared the performance of MIEA to those of other unsupervised gene set screening methods, using various types of simulated and real data. The benchmark tests demonstrated that MIEA can detect singular expression profiles that the other methods fail to find, and performs broadly well for various types of input data. Taken together, this study introduces MIEA as a broadly applicable gene set screening tool for mining regulatory programs from transcriptome data.

MOTIVATION: Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time series. RESULTS: We propose an integrated approach for inferring multiple gene networks from time series expression data under varying conditions. To the best of our knowledge, our approach is the first reverse-engineering method that is intended for transcriptomic network comparison between varying conditions. Furthermore, we propose a state-of-the-art parameter estimation method, relevance-weighted recursive elastic net, for providing higher precision and recall than existing reverse-engineering methods. We analyze experimental data of MCF-7 human breast cancer cells stimulated by epidermal growth factor or heregulin with several doses and provide novel biological hypotheses through network comparison. AVAILABILITY: The software NETCOMP is available at http://bonsai.ims.u-tokyo.ac.jp/ approximately shima/NETCOMP/.

BACKGROUND: Graph drawing is one of the important techniques for understanding biological regulations in a cell or among cells at the pathway level. Among many available layout algorithms, the spring embedder algorithm is widely used not only for pathway drawing but also for circuit placement and www visualization and so on because of the harmonized appearance of its results. For pathway drawing, location information is essential for its comprehension. However, complex shapes need to be taken into account when torus-shaped location information such as nuclear inner membrane, nuclear outer membrane, and plasma membrane is considered. Unfortunately, the spring embedder algorithm cannot easily handle such information. In addition, crossings between edges and nodes are usually not considered explicitly. RESULTS: We proposed a new grid-layout algorithm based on the spring embedder algorithm that can handle location information and provide layouts with harmonized appearance. In grid-layout algorithms, the mapping of nodes to grid points that minimizes a cost function is searched. By imposing positional constraints on grid points, location information including complex shapes can be easily considered. Our layout algorithm includes the spring embedder cost as a component of the cost function. We further extend the layout algorithm to enable dynamic update of the positions and sizes of compartments at each step. CONCLUSIONS: The new spring embedder-based grid-layout algorithm and a spring embedder algorithm are applied to three biological pathways; endothelial cell model, Fas-induced apoptosis model, and C. elegans cell fate simulation model. From the positional constraints, all the results of our algorithm satisfy location information, and hence, more comprehensible layouts are obtained as compared to the spring embedder algorithm. From the comparison of the number of crossings, the results of the grid-layout-based algorithm tend to contain more crossings than those of the spring embedder algorithm due to the positional constraints. For a fair comparison, we also apply our proposed method without positional constraints. This comparison shows that these results contain less crossings than those of the spring embedder algorithm. We also compared layouts of the proposed algorithm with and without compartment update and verified that latter can reach better local optima.

BACKGROUND: Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. RESULTS: To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. CONCLUSION: Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer.

MOTIVATION: Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations; thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks. RESULTS: We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex. AVAILABILITY: Java implementations of our layout algorithms are available in Cell Illustrator. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Mathematical modeling and simulation, based on biochemical rate equations, provide us a rigorous tool for unraveling complex mechanisms of biological pathways. To proceed to simulation experiments, it is an essential first step to find effective values of model parameters, which are difficult to measure from in vivo and in vitro experiments. Furthermore, once a set of hypothetical models has been created, any statistical criterion is needed to test the ability of the constructed models and to proceed to model revision. RESULTS: The aim of our research is to present a new statistical technology towards data-driven construction of in silico biological pathways. The method starts with a knowledge-based modeling with hybrid functional Petri net. It then proceeds to the Bayesian learning of model parameters for which experimental data are available. This process exploits quantitative measurements of evolving biochemical reactions, e.g. gene expression data. Another important issue that we consider is statistical evaluation and comparison of the constructed hypothetical pathways. For this purpose, we have developed a new Bayesian information-theoretic measure that assesses the predictability and the biological robustness of in silico pathways. AVAILABILITY: The FORTRAN source codes are available at the URL http://daweb.ism.ac.jpyoshidar/GDA/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Some splicing isoform-specific transcriptional regulations are related to disease. Therefore, detection of disease specific splice variations is the first step for finding disease specific transcriptional regulations. Affymetrix Human Exon 1.0 ST Array can measure exon-level expression profiles that are suitable to find differentially expressed exons in genome-wide scale. However, exon array produces massive datasets that are more than we can handle and analyze on personal computer. RESULTS: We have developed ExonMiner that is the first all-in-one web service for analysis of exon array data to detect transcripts that have significantly different splicing patterns in two cells, e.g. normal and cancer cells. ExonMiner can perform the following analyses: (1) data normalization, (2) statistical analysis based on two-way ANOVA, (3) finding transcripts with significantly different splice patterns, (4) efficient visualization based on heatmaps and barplots, and (5) meta-analysis to detect exon level biomarkers. We implemented ExonMiner on a supercomputer system in order to perform genome-wide analysis for more than 300,000 transcripts in exon array data, which has the potential to reveal the aberrant splice variations in cancer cells as exon level biomarkers. CONCLUSION: ExonMiner is well suited for analysis of exon array data and does not require any installation of software except for internet browsers. What all users need to do is to access the ExonMiner URL http://ae.hgc.jp/exonminer. Users can analyze full dataset of exon array data within hours by high-level statistical analysis with sound theoretical basis that finds aberrant splice variants as biomarkers.

Alternative splicing plays a prominent role in eukaryotic gene regulations that allow a single gene to generate the multiple mRNA products. The recent advent of GeneChipregHuman Exon 1.0 ST Array enables us to measure the exon expression profiles of human cells on a genome-wide scale. With this advent, analysis of functional gene regulation could be extended to detect not only differentially expressed genes, but also specific splicing events that occur in target cells, but not in normal controls. We address some statistical issues for the identification of biomarker splice variations with exon expression data. The proposed method involves the following steps: (1) Whole transcript analysis with the nonparametric analysis of variance (ANOVA) to identify potential biomarkers that present specific splice variations. (2) Meta-analysis for discriminating non-specific splice variations that are caused by clinical heterogeneity in the collected samples. In the analysis of human cells, controlling non-specific splicing factors is essential for success in the detection of biomarker splice variations because splice patterns are possibly affected by inter-individual differences in the collected samples. We demonstrate its utility and perform a whole transcript analysis of exon expression profiles of colorectal carcinoma.

BACKGROUND: Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i) the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii) from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii) it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. RESULTS: We propose a new grid layout algorithm. To address problem (i), we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii), we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii), we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates. CONCLUSION: Layouts of the new grid layout algorithm are compared with that of the previous algorithm and human layout in an endothelial cell model, three times as large as the apoptosis model. The total cost of the result from the new grid layout algorithm is similar to that of the human layout. In addition, its convergence time is drastically reduced (40% reduction).

BACKGROUND: Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139-1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing large-scale proteome data. RESULTS: To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report. CONCLUSION: AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact [email protected] if you are interested in the application. The project web page is http://www.csml.org/ayums/.