SUMMARY: The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. AVAILABILITY AND IMPLEMENTATION: COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ensemble methods that aggregate predictions from a set of diverse base learners consistently outperform individual classifiers. Many such popular strategies have been developed in a supervised setting, where the sample labels have been provided to the ensemble algorithm. However, with the rising interest in unsupervised algorithms for machine learning and growing amounts of uncurated data, the reliance on labeled data precludes the application of ensemble algorithms to many real world problems. To this end we develop a new theoretical framework for ensemble learning, the Strategy for Unsupervised Multiple Method Aggregation (SUMMA), that estimates the performances of base classifiers and uses these estimates to form an ensemble classifier. SUMMA also generates an ensemble ranking of samples based on the confidence score it assigns to each sample. We illustrate the performance of SUMMA using a synthetic example as well as two real world problems.

BACKGROUND: Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg). RESULTS: We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine. CONCLUSIONS: Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.

MOTIVATION: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs. RESULTS: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and 'translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

MOTIVATION: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. RESULTS: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. AVAILABILITY: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

MOTIVATION: Analyses and algorithmic predictions based on high-throughput data are essential for the success of systems biology in academic and industrial settings. Organizations, such as companies and academic consortia, conduct large multi-year scientific studies that entail the collection and analysis of thousands of individual experiments, often over many physical sites and with internal and outsourced components. To extract maximum value, the interested parties need to verify the accuracy and reproducibility of data and methods before the initiation of such large multi-year studies. However, systematic and well-established verification procedures do not exist for automated collection and analysis workflows in systems biology which could lead to inaccurate conclusions. RESULTS: We present here, a review of the current state of systems biology verification and a detailed methodology to address its shortcomings. This methodology named 'Industrial Methodology for Process Verification in Research' or IMPROVER, consists on evaluating a research program by dividing a workflow into smaller building blocks that are individually verified. The verification of each building block can be done internally by members of the research program or externally by 'crowd-sourcing' to an interested community. www.sbvimprover.com IMPLEMENTATION: This methodology could become the preferred choice to verify systems biology research workflows that are becoming increasingly complex and sophisticated in industrial and academic settings.

MOTIVATION: Mass spectrometry data are subjected to considerable noise. Good noise models are required for proper detection and quantification of peptides. We have characterized noise in both quadrupole time-of-flight (Q-TOF) and ion trap data, and have constructed models for the noise. RESULTS: We find that the noise in Q-TOF data from Applied Biosystems QSTAR fits well to a combination of multinomial and Poisson model with detector dead-time correction. In comparison, ion trap noise from Agilent MSD-Trap-SL is larger than the Q-TOF noise and is proportional to Poisson noise. We then demonstrate that the noise model can be used to improve deisotoping for peptide detection, by estimating appropriate cutoffs of the goodness of fit parameter at prescribed error rates. The noise models also have implications in noise reduction, retention time alignment and significance testing for biomarker discovery.

Negative feedback loops are a common cellular motif underlying many important gene regulation pathways, and therefore a clear understanding of their dynamics is important for the study of gene regulation. Here we analyze the linear stability of negative feedback loops with and without explicit time delays. When the degradation rates of each loop component are identical, we derive the analytical solution of the condition of marginal stability. In the most general case, when the degradation rates of each component differ, we derive a novel expansion of the condition of marginal stability in terms of the geometric mean and variance of the degradation rates. We use these results to demonstrate how the mathematical representation of a negative feedback loop can be simplified such that the stability characteristics are maintained. Finally we apply these results to study the stability structure of the p53-Mdm2 feedback loop. By simplifying a model involving nuclear and cytoplasmatic molecular species of mdm2 mRNA and Mdm2 protein to a model involving only nuclear components and a time delay (which summarizes transcriptional, nuclear import and export, and translational time scales), we demonstrate how our methods allow for an elucidation of the dynamic instabilities recently observed experimentally in the p53-Mdm2 system as the transcription of p53 changes from low to middle to high rates.

MOTIVATION: Many classifications of protein function such as Gene Ontology (GO) are organized in directed acyclic graph (DAG) structures. In these classifications, the proteins are terminal leaf nodes; the categories 'above' them are functional annotations at various levels of specialization and the computation of a numerical measure of relatedness between two arbitrary proteins is an important proteomics problem. Moreover, analogous problems are important in other contexts in large-scale information organization--e.g. the Wikipedia online encyclopedia and the Yahoo and DMOZ web page classification schemes. RESULTS: Here we develop a simple probabilistic approach for computing this relatedness quantity, which we call the total ancestry method. Our measure is based on counting the number of leaf nodes that share exactly the same set of 'higher up' category nodes in comparison to the total number of classified pairs (i.e. the chance for the same total ancestry). We show such a measure is associated with a power-law distribution, allowing for the quick assessment of the statistical significance of shared functional annotations. We formally compare it with other quantitative functional similarity measures (such as, shortest path within a DAG, lowest common ancestor shared and Azuaje's information-theoretic similarity) and provide concrete metrics to assess differences. Finally, we provide a practical implementation for our total ancestry measure for GO and the MIPS functional catalog and give two applications of it in specific functional genomics contexts. AVAILABILITY: The implementations and results are available through our supplementary website at: http://gersteinlab.org/proj/funcsim. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChIP-on-chip technology provides a genome-scale view of transcription factor (TF)/target interactions and a systems-level window into transcriptional regulatory networks. However, while many studies have used ChIP-on-chip data to effectively discover new TF targets, statistical methods have fallen short of developing an accurate model to disassociate signals caused by experimental noise from those caused by true biological variation, thus leveraging the technology to provide high confidence predictions of the full range of interactions. This paper presents a novel method to accurately model the significance of binding events measured by ChIP-on-chip data. For each arrayed probe representing a genomic segment, a ChIP-on-chip microarray measures intensity levels for the IP channel, which is enriched in genomic fragments bound by an immunoprecipitated TF, and the WCE channel, which represents random genomic fragments. Statistical significance is inferred by computing the conditional probability, p ( M | A ), where M = log 2 ( I P W C E ) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGnbqtcqGH9aqpcyGGSbaBcqGGVbWBcqGGNbWzcqaIYaGmdaqadaqaamaalaaabaGaemysaKKaemiuaafabaGaem4vaCLaem4qamKaemyraueaaaGaayjkaiaawMcaaaaa@3B1B@ and A = log 2 ( I P ) + log 2 ( W C E ) 2 MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacqWGbbqqcqGH9aqpdaWcaaqaaiGbcYgaSjabc+gaVjabcEgaNjabikdaYiabcIcaOiabdMeajjabdcfaqjabcMcaPiabgUcaRiGbcYgaSjabc+gaVjabcEgaNjabikdaYiabcIcaOiabdEfaxjabdoeadjabdweafjabcMcaPaqaaiabikdaYaaaaaa@43C2@ (Fig. 1 ). A kernel density estimation procedure is used to calculate the joint probability, p ( M , A ), and for each average intensity value, the mean of the null distribution (i.e. distribution for unbound probes) is inferred as M ^ A = arg max M p ( M | A ) MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacuWGnbqtgaqcamaaBaaaleaacqWGbbqqaeqaaOGaeyypa0ZaaCbeaeaacyGGHbqycqGGYbGCcqGGNbWzcyGGTbqBcqGGHbqycqGG4baEaSqaaiabd2eanbqabaGccqWGWbaCcqGGOaakcqWGnbqtcqGG8baFcqWGbbqqcqGGPaqkaaa@4089@ . The distribution of p ( M | A ), for M < M ^ A MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacuWGnbqtgaqcamaaBaaaleaacqWGbbqqaeqaaaaa@2F16@ , is then projected across M ^ A MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaacuWGnbqtgaqcamaaBaaaleaacqWGbbqqaeqaaaaa@2F16@ to yield the inferred null distribution, which is used to assign statistical significance scores. Probes for replicate experiments and probes with genomic locations within the fragmentation length (~500 bp) are integrated to produce a single significance score for each genomic region. (Left) Magnitude versus amplitude (MA) plot of a ChIP-on-chip hybridization. The x-axis represents the average log2 intensity of the IP and WCE channels, and the y-axis represents the log2 ratio of IP/WCE. The black line represents the mean of the inferred null distribution, and the colored lines represent confidence intervals of .1, .01, and .001 probability. The model reveals an intensity dependent mean and variance of the null distribution, and a large number of probes are significantly enriched in the IP channel. (Right) The axes are the same as in the left panel, and colors represent the -log10 p-value of the null distribution. The method is tested on six different ChIP-on-chip arrays representing replicate experiments for three different TFs (NOTCH1, MYC and HES1). For each experiment, this analysis reveals an order of magnitude more genomic binding events than detected by traditional methods, predicting several thousand interactions for each TF and suggesting previously unappreciated complexity of transcriptional regulatory networks. Several independent experiments are used to provide evidence about the validity of these predictions. First, biochemical validation of more than 20 predicted targets by gene specific ChIP and qPCR confirm the accuracy of false discovery rate statistics computed by the method. Second, binding site enrichment analysis indicates that the strength of binding site signals are maintained over several thousand promoters. Finally, gene expression analysis reveals a coordinated downregulation of gene expression for the entire range of predicted NOTCH1 bound genes upon NOTCH1 inhibition experiments in cell lines, indicating that a large percentage of bound genes are also functionally regulated by NOTCH1.

BACKGROUND: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. RESULTS: We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors. CONCLUSION: ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

MOTIVATION: One of the present challenges in biological research is the organization of the data originating from high-throughput technologies. One way in which this information can be organized is in the form of networks of influences, physical or statistical, between cellular components. We propose an experimental method for probing biological networks, analyzing the resulting data and reconstructing the network architecture. METHODS: We use networks of known topology consisting of nodes (genes), directed edges (gene-gene interactions) and a dynamics for the genes' mRNA concentrations in terms of the gene-gene interactions. We proposed a network reconstruction algorithm based on the conditional correlation of the mRNA equilibrium concentration between two genes given that one of them was knocked down. Using simulated gene expression data on networks of known connectivity, we investigated how the reconstruction error is affected by noise, network topology, size, sparseness and dynamic parameters. RESULTS: Errors arise from correlation between nodes connected through intermediate nodes (false positives) and when the correlation between two directly connected nodes is obscured by noise, non-linearity or multiple inputs to the target node (false negatives). Two critical components of the method are as follows: (1) the choice of an optimal correlation threshold for predicting connections and (2) the reduction of errors arising from indirect connections (for which a novel algorithm is proposed). With these improvements, we can reconstruct networks with the topology of the transcriptional regulatory network in Escherichia coli with a reasonably low error rate.

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).

PROSITE is a method for protein classification which relies on a database of biologically significant sites and patterns in protein sequences. Most patterns in PROSITE have been gathered by a a labor intensive combination of experimental characterization of functional residues and sequence alignment. In this paper we present a new and efficient supervised learning procedure, based on the Splash deterministic pattern discovery algorithm and on a framework to assess the statistical significance of patterns. We demonstrate its application to the fully automatic discovery of patterns in 974 PROSITE families. For these families, Splash generates patterns with better specificity and/or sensitivity in 28%, identical statistics in 48%, and worse statistics in 15% of the cases; for the remaining families, patterns exhibited mixed behavior. Second, we have characterized the amount of overlap, on the sequences, between newly discovered patterns and those in PROSITE. In about 75% of the cases, Splash patterns identify sequence sites that overlap more than 50% with those reported in PROSITE. Of the 272 patterns which perform strictly better than the corresponding PROSITE pattern, 178 show more than 70% overlap with the PROSITE pattern. Third, our results suggest that the statistical significance of discovered patterns correlates well with their biological significance. Finally, we use the trypsin subfamily of serine proteases to illustrate the use of this method to exhaustively discover all motifs in a family that are statistically and biologically significant. The complete analysis is sufficiently rapid, taking less than a day for all PROSITE families, to enable the use this methodology for routine curation of existing motif and profile databases.

Article Sequence homology detection through large scale pattern discovery Share on Authors: Aris Floratos Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NYView Profile , Isidore Rigoutsos Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NYView Profile , Laxmi Parida Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY and Courant Institute of Mathematical Sciences, New York University, New York, NYView Profile , Gustova Stolovitzky Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NYView Profile , Yuan Gao Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NY Computational Biology Center, IBM Research Division, T. J. Watson Research Center, P. O. Box 218, Yorktown Heights, NYView Profile Authors Info & Claims RECOMB '99: Proceedings of the third annual international conference on Computational molecular biologyApril 1999 Pages 164–173https://doi.org/10.1145/299432.299477Online:01 April 1999Publication History 6citation379DownloadsMetricsTotal Citations6Total Downloads379Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access