SUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. RESULTS: We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independence models of interaction to evaluate drug combination effects. Because the two models imply two extreme limits of drug interaction (mutually exclusive and mutually non-exclusive), a response envelope defined by them provides a quantitatively stringent additivity model for identifying combination effects without knowing the inhibition mechanism. As a demonstration, we apply REA to representative published data from large screens of anticancer and antibiotic combinations. We show that REA is more accurate than existing methods and provides more consistent results in the context of cross-experiment evaluation. AVAILABILITY AND IMPLEMENTATION: The open-source software package associated with REA is available at: https://github.com/4dsoftware/rea. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The investigation of intracellular metabolism is the mainstay in the biotechnology and physiology settings. Intracellular metabolic rates are commonly evaluated using labeling pattern of the identified metabolites obtained from stable isotope labeling experiments. The labeling pattern or mass distribution vector describes the fractional abundances of all isotopologs with different masses as a result of isotopic labeling, which are typically resolved using mass spectrometry. Because naturally occurring isotopes and isotopic impurity also contribute to measured signals, the measured patterns must be corrected to obtain the labeling patterns. Since contaminant isotopologs with the same nominal mass can be resolved using modern mass spectrometers with high mass resolution, the correction process should be resolution dependent. RESULTS: Here we present a software tool, ElemCor, to perform correction of such data in a resolution-dependent manner. The tool is based on mass difference theory (MDT) and information from unlabeled samples (ULS) to account for resolution effects. MDT is a mathematical theory and only requires chemical formulae to perform correction. ULS is semi-empirical and requires additional measurement of isotopologs from unlabeled samples. We validate both methods and show their improvement in accuracy and comprehensiveness over existing methods using simulated data and experimental data from Saccharomyces cerevisiae. The tool is available at https://github.com/4dsoftware/elemcor . CONCLUSIONS: We present a software tool based on two methods, MDT and ULS, to correct LC-MS data from isotopic labeling experiments for natural abundance and isotopic impurity. We recommend MDT for low-mass compounds for cost efficiency in experiments, and ULS for high-mass compounds with relatively large spectral inaccuracy that can be tracked by unlabeled standards.

Motivation: Complex bioinformatic data analysis workflows involving multiple scripts in different languages can be difficult to consolidate, share and reproduce. An environment that streamlines the entire processes of data collection, analysis, visualization and reporting of such multi-language analyses is currently lacking. Results: We developed Script of Scripts (SoS) Notebook, a web-based notebook environment that allows the use of multiple scripting language in a single notebook, with data flowing freely within and across languages. SoS Notebook enables researchers to perform sophisticated bioinformatic analysis using the most suitable tools for different parts of the workflow, without the limitations of a particular language or complications of cross-language communications. Availability and implementation: SoS Notebook is hosted at http://vatlab.github.io/SoS/ and is distributed under a BSD license.

UNLABELLED: PathwaysWeb is a resource-based, well-documented web system that provides publicly available information on genes, biological pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the directionality of interactions) and links to key-related PubMed documents. The PathwaysWeb API simplifies the construction of applications that need to retrieve and interrelate information across multiple, pathway-related data types from a variety of original data sources. PathwaysBrowser is a companion website that enables users to explore the same integrated pathway data. The PathwaysWeb system facilitates reproducible analyses by providing access to all versions of the integrated datasets. Although its GO subsystem includes data for mouse, PathwaysWeb currently focuses on human data. However, pathways for mouse and many other species can be inferred with a high success rate from human pathways. AVAILABILITY AND IMPLEMENTATION: PathwaysWeb can be accessed via the Internet at http://bioinformatics.mdanderson.org/main/PathwaysWeb:Overview. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: High-throughput reverse-phase protein array (RPPA) technology allows for the parallel measurement of protein expression levels in approximately 1000 samples. However, the many steps required in the complex protocol (sample lysate preparation, slide printing, hybridization, washing and amplified detection) may create substantial variability in data quality. We are not aware of any other quality control algorithm that is tuned to the special characteristics of RPPAs. RESULTS: We have developed a novel classifier for quality control of RPPA experiments using a generalized linear model and logistic function. The outcome of the classifier, ranging from 0 to 1, is defined as the probability that a slide is of good quality. After training, we tested the classifier using two independent validation datasets. We conclude that the classifier can distinguish RPPA slides of good quality from those of poor quality sufficiently well such that normalization schemes, protein expression patterns and advanced biological analyses will not be drastically impacted by erroneous measurements or systematic variations. AVAILABILITY AND IMPLEMENTATION: The classifier, implemented in the "SuperCurve" R package, can be freely downloaded at http://bioinformatics.mdanderson.org/main/OOMPA:Overview or http://r-forge.r-project.org/projects/supercurve/. The data used to develop and validate the classifier are available at http://bioinformatics.mdanderson.org/MOAR.

SUMMARY: Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/prada/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: We developed a new algorithmic method, VirusSeq, for detecting known viruses and their integration sites in the human genome using next-generation sequencing data. We evaluated VirusSeq on whole-transcriptome sequencing (RNA-Seq) data of 256 human cancer samples from The Cancer Genome Atlas. Using these data, we showed that VirusSeq accurately detects the known viruses and their integration sites with high sensitivity and specificity. VirusSeq can also perform this function using whole-genome sequencing data of human tissue. AVAILABILITY: VirusSeq has been implemented in PERL and is available at http://odin.mdacc.tmc.edu/∼xsu1/VirusSeq.html. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: Despite recent progress, computational tools that identify gene fusions from next-generation whole transcriptome sequencing data are often limited in accuracy and scalability. Here, we present a software package, BreakFusion that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity and computational efficiency. Availability: http://bioinformatics.mdanderson.org/main/BreakFusion Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

SUMMARY: SpliceSeq is a resource for RNA-Seq data that provides a clear view of alternative splicing and identifies potential functional changes that result from splice variation. It displays intuitive visualizations and prioritized lists of results that highlight splicing events and their biological consequences. SpliceSeq unambiguously aligns reads to gene splice graphs, facilitating accurate analysis of large, complex transcript variants that cannot be adequately represented in other formats. AVAILABILITY AND IMPLEMENTATION: SpliceSeq is freely available at http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview. The application is a Java program that can be launched via a browser or installed locally. Local installation requires MySQL and Bowtie. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: We developed a novel algorithm, PurityEst, to infer the tumor purity level from the allelic differential representation of heterozygous loci with somatic mutations in a human tumor sample with a matched normal tissue using next-generation sequencing data. We applied our tool to a whole cancer genome sequencing datasets and demonstrated the accuracy of PurityEst compared with DNA copy number-based estimation. AVAILABILITY: PurityEst has been implemented in PERL and is available at http://odin.mdacc.tmc.edu/~xsu1/PurityEst.html.

BACKGROUND: The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation. RESULTS: We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories. CONCLUSIONS: RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.

MOTIVATION: The receiver operator characteristic (ROC) curves are commonly used in biomedical applications to judge the performance of a discriminant across varying decision thresholds. The estimated ROC curve depends on the true positive rate (TPR) and false positive rate (FPR), with the key metric being the area under the curve (AUC). With small samples these rates need to be estimated from the training data, so a natural question arises: How well do the estimates of the AUC, TPR and FPR compare with the true metrics? RESULTS: Through a simulation study using data models and analysis of real microarray data, we show that (i) for small samples the root mean square differences of the estimated and true metrics are considerable; (ii) even for large samples, there is only weak correlation between the true and estimated metrics; and (iii) generally, there is weak regression of the true metric on the estimated metric. For classification rules, we consider linear discriminant analysis, linear support vector machine (SVM) and radial basis function SVM. For error estimation, we consider resubstitution, three kinds of cross-validation and bootstrap. Using resampling, we show the unreliability of some published ROC results. AVAILABILITY: Companion web site at http://compbio.tgen.org/paper_supp/ROC/roc.html CONTACT: [email protected].

MOTIVATION: Splice variation plays important roles in evolution and cancer. Different splice variants of a gene may be characteristic of particular cellular processes, subcellular locations or organs. Although several genomic projects have identified splice variants, there have been no large-scale computational studies of the relationship between number of splice variants and biological function. The Gene Ontology (GO) and tools for leveraging GO, such as GoMiner, now make such a study feasible. RESULTS: We partitioned genes into two groups: those with numbers of splice variants b (b=1,..., 10). Then we used GoMiner to determine whether any GO categories are enriched in genes with particular numbers of splice variants. Since there was no a priori 'appropriate' partition boundary, we studied those 'robust' categories whose enrichment did not depend on the selection of a particular partition boundary. Furthermore, because the distribution of splice variant number was a snapshot taken at a particular point in time, we confirmed that those observations were stable across successive builds of GenBank. A small number of categories were found for genes in the lower partitions. A larger number of categories were found for genes in the higher partitions. Those categories were largely associated with cell death and signal transduction. Apoptotic genes tended to have a large repertoire of splice variants, and genes with splice variants exhibited a distinctive 'apoptotic island' in clustered image maps (CIMs). AVAILABILITY: Supplementary tables and figures are available at URL http://discover.nci.nih.gov/OG/supplementaryMaterials.html. The Safari browser appears to perform better than Firefox for these particular items.

BACKGROUND: Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation. RESULTS: We present an alternative visualization approach, area-proportional Euler diagrams, showing set relationships with semi-quantitative size information in a single diagram to support biological hypothesis formulation. The cardinalities of sets and intersection sets are represented by area-proportional Euler diagrams and their corresponding graphical (circular or polygonal) intersection areas. Optimally proportional representations are obtained using swarm and evolutionary optimization algorithms. CONCLUSION: VennMaster's area-proportional Euler diagrams effectively structure and visualize the results of a GO analysis by indicating to what extent flagged genes are shared by different categories. In addition to reducing the complexity of the output, the visualizations facilitate generation of novel hypotheses from the analysis of seemingly unrelated categories that share differentially expressed genes.

BACKGROUND: Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant. RESULTS: A number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript. CONCLUSION: SpliceCenter http://discover.nci.nih.gov/splicecenter provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists.

MOTIVATION: Affymetrix microarrays are widely used to measure global expression of mRNA transcripts. That technology is based on the concept of a probe set. Individual probes within a probe set were originally designated by Affymetrix to hybridize with the same unique mRNA transcript. Because of increasing accuracy in knowledge of genomic sequences, however, a substantial number of the manufacturer's original probe groupings and mappings are now known to be inaccurate and must be corrected. Otherwise, analysis and interpretation of an Affymetrix microarray experiment will be in error. RESULTS: AffyProbeMiner is a computationally efficient platform-independent tool that uses all RefSeq mature RNA protein coding transcripts and validated complete coding sequences in GenBank to (1) regroup the individual probes into consistent probe sets and (2) remap the probe sets to the correct sets of mRNA transcripts. The individual probes are grouped into probe sets that are 'transcript-consistent' in that they hybridize to the same mRNA transcript (or transcripts) and, therefore, measure the same entity (or entities). About 65.6% of the probe sets on the HG-U133A chip were affected by the remapping. Pre-computed regrouped and remapped probe sets for many Affymetrix microarrays are made freely available at the AffyProbeMiner web site. Alternatively, we provide a web service that enables the user to perform the remapping for any type of short-oligo commercial or custom array that has an Affymetrix-format Chip Definition File (CDF). Important features that differentiate AffyProbeMiner from other approaches are flexibility in the handling of splice variants, computational efficiency, extensibility, customizability and user-friendliness of the interface. AVAILABILITY: The web interface and software (GPL open source license), are publicly-accessible at http://discover.nci.nih.gov/affyprobeminer.

BACKGROUND: There are many fewer genes in the human genome than there are expressed transcripts. Alternative splicing is the reason. Alternatively spliced transcripts are often specific to tissue type, developmental stage, environmental condition, or disease state. Accurate analysis of microarray expression data and design of new arrays for alternative splicing require assessment of probes at the sequence and exon levels. DESCRIPTION: SpliceMiner is a web interface for querying Evidence Viewer Database (EVDB). EVDB is a comprehensive, non-redundant compendium of splice variant data for human genes. We constructed EVDB as a queryable implementation of the NCBI Evidence Viewer (EV). EVDB is based on data obtained from NCBI Entrez Gene and EV. The automated EVDB build process uses only complete coding sequences, which may or may not include partial or complete 5' and 3' UTRs, and filters redundant splice variants. Unlike EV, which supports only one-at-a-time queries, SpliceMiner supports high-throughput batch queries and provides results in an easily parsable format. SpliceMiner maps probes to splice variants, effectively delineating the variants identified by a probe. CONCLUSION: EVDB can be queried by gene symbol, genomic coordinates, or probe sequence via a user-friendly web-based tool we call SpliceMiner (http://discover.nci.nih.gov/spliceminer). The EVDB/SpliceMiner combination provides an interface with human splice variant information and, going beyond the very valuable NCBI Evidence Viewer, supports fluent, high-throughput analysis. Integration of EVDB information into microarray analysis and design pipelines has the potential to improve the analysis and bioinformatic interpretation of gene expression data, for both batch and interactive processing. For example, whenever a gene expression value is recognized as important or appears anomalous in a microarray experiment, the interactive mode of SpliceMiner can be used quickly and easily to check for possible splice variant issues.

BACKGROUND: Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java. DESCRIPTION: AbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin. CONCLUSION: AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at http://discover.nci.nih.gov/abminer.

BACKGROUND: Quality-control is an important issue in the analysis of gene expression microarrays. One type of problem is regional bias, in which one region of a chip shows artifactually high or low intensities (or ratios in a two-channel array) relative to the majority of the chip. Current practice in quality assessment for microarrays does not address regional biases. RESULTS: We present methods implemented in R for visualizing regional biases and other spatial artifacts on spotted microarrays and Affymetrix chips. We also propose a statistical index to quantify regional bias and investigate its typical distribution on spotted and Affymetrix arrays. We demonstrate that notable regional biases occur on both Affymetrix and spotted arrays and that they can make a significant difference in the case of spotted microarray results. Although strong biases are also seen at the level of individual probes on Affymetrix chips, the gene expression measures are less affected, especially when the RMA method is used to summarize intensities for the probe sets. A web application program for visualization and quantitation of regional bias is provided at http://www.discover.nci.nih.gov/affytools. CONCLUSION: Researchers should visualize and measure the regional biases and should estimate their impact on gene expression measurements obtained. Here, we (i) introduce pictorial visualizations of the spatial biases; (ii) present for Affymetrix chips a useful resolution of the biases into two components, one related to background, the other to intensity scale factor; (iii) introduce a single parameter to reflect the global bias present across an array. We also examine the pattern distribution of such biases and conclude that algorithms based on smoothing are unlikely to compensate adequately for them.

BACKGROUND: We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. RESULTS: We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. CONCLUSION: High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound.

BACKGROUND: When processing microarray data sets, we recently noticed that some gene names were being changed inadvertently to non-gene names. RESULTS: A little detective work traced the problem to default date format conversions and floating-point format conversions in the very useful Excel program package. The date conversions affect at least 30 gene names; the floating-point conversions affect at least 2,000 if Riken identifiers are included. These conversions are irreversible; the original gene names cannot be recovered. CONCLUSIONS: Users of Excel for analyses involving gene names should be aware of this problem, which can cause genes, including medically important ones, to be lost from view and which has contaminated even carefully curated public databases. We provide work-arounds and scripts for circumventing the problem.

This paper addresses the problem of inferring topological features of gene regulation networks from data that are likely to be available from current experimental methods, such as DNA microarrays. The proposed method uses neural networks to predict the topology class from histograms of perturbation propagation data. The preliminary results with simulated data are encouraging. The trained neural network is able to classify the network topology as random (exponential) or scale-free with 90% accuracy. Compare to the previous network connectivity inference methods that are often problematic with current noisy data, this method is expected to be more robust because it uses global characteristics of dynamic networks.