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Year of Publication
1.  nuScore: a web-interface for nucleosome positioning predictions 
Bioinformatics (Oxford, England)  2008;24(12):1456-1458.
Summary
Sequence-directed mapping of nucleosome positions is of major biological interest. Here, we present a web-interface for estimation of the affinity of the histone core to DNA and prediction of nucleosome arrangement on a given sequence. Our approach is based on assessment of the energy cost of imposing the deformations required to wrap DNA around the histone surface. The interface allows the user to specify a number of options such as selecting from several structural templates for threading calculations and adding random sequences to the analysis.
doi:10.1093/bioinformatics/btn212
PMCID: PMC3807124  PMID: 18445607
2.  An efficient method to identify differentially expressed genes in microarray experiments 
Bioinformatics (Oxford, England)  2008;24(14):1583-1589.
Motivation
Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.
Results
We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.
Availability
The C++ code to implement the proposed method is available upon request for academic use.
doi:10.1093/bioinformatics/btn215
PMCID: PMC3607310  PMID: 18453554
3.  A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking 
Bioinformatics (Oxford, England)  2010;26(9):1169-1175.
Motivation
Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of Molecular Docking, which is in turn an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterise the complex, which also include parameters fitted to experimental or simulation data, and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions.
Results
We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score’s performance was shown to improve dramatically with training set size and hence the future availability of more high quality structural and interaction data is expected to lead to improved versions of RF-Score.
doi:10.1093/bioinformatics/btq112
PMCID: PMC3524828  PMID: 20236947
4.  Post-hoc power estimation in large-scale multiple testing problems 
Bioinformatics (Oxford, England)  2010;26(8):1050-1056.
Background
The statistical power or multiple Type II error rate in large scale multiple testing problems as, for example, in gene expression microarray experiments, depends on typically unknown parameters and is therefore difficult to assess a priori. However, it has been suggested to estimate the multiple Type II error rate post-hoc, based on the observed data.
Methods
We consider a class of post-hoc estimators that are functions of the estimated proportion of true null hypotheses among all hypotheses. Numerous estimators for this proportion have been proposed and we investigate the statistical properties of the derived multiple Type II error rate estimators in an extensive simulation study.
Results
The performance of the estimators in terms of the mean squared error depends sensitively on the distributional scenario. Estimators based on empirical distributions of the null hypotheses are superior in the presence of strongly correlated test statistics.
doi:10.1093/bioinformatics/btq085
PMCID: PMC3500624  PMID: 20189938
5.  Improved breast cancer prognosis through the combination of clinical and genetic markers 
Motivation
Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary.
Methods
A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches.
Results
The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant meta-stases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI: 6.5–68.3), far higher than either genetic or clinical markers alone.
doi:10.1093/bioinformatics/btl543
PMCID: PMC3431620  PMID: 17130137
6.  TMpro web server and web service: transmembrane helix prediction through amino acid property analysis 
Bioinformatics  2007;23(20):2795-2796.
Summary
TMpro is a transmembrane (TM) helix prediction algorithm that uses language processing methodology for TM segment identification. It is primarily based on the analysis of statistical distributions of properties of amino acids in transmembrane segments. This article describes the availability of TMpro on the internet via a web interface. The key features of the interface are: (i) output is generated in multiple formats including a user-interactive graphical chart which allows comparison of TMpro predicted segment locations with other labeled segments input by the user, such as predictions from other methods. (ii) Up to 5000 sequences can be submitted at a time for prediction. (iii) TMpro is available as a web server and is published as a web service so that the method can be accessed by users as well as other services depending on the need for data integration.
doi:10.1093/bioinformatics/btm398
PMCID: PMC3263380  PMID: 17724062
7.  A variable selection method for genome-wide association studies 
Bioinformatics  2010;27(1):1-8.
Motivation: Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs).
Results: We introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data.
Availability: The software implementing GWASelect is available at http://www.bios.unc.edu/~lin.
Access to WTCCC data: http://www.wtccc.org.uk/
Contact: lin@bios.unc.edu
Supplementary information: Supplementary data are available at Bioinformatics Online.
doi:10.1093/bioinformatics/btq600
PMCID: PMC3025714  PMID: 21036813
8.  A variable selection method for genome-wide association studies 
Biometrics  2011;27(1):1-8.
Motivation
Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs).
Results
We introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false-positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium (LD) patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data.
doi:10.1093/bioinformatics/btq600
PMCID: PMC3025714  PMID: 21036813
9.  A novel non-overlapping bi-clustering algorithm for network generation using living cell array data 
Bioinformatics (Oxford, England)  2007;23(17):2306-2313.
Motivation
The living cell array quantifies the contribution of activated transcription factors upon the expression levels of their target genes. The direct manipulation of the regulatory mechanisms offers enormous possibilities for deciphering the machinery that activates and controls gene expression. We propose a novel bi-clustering algorithm for generating non-overlapping clusters of reporter genes and conditions and demonstrate how this information can be interpreted in order to assist in the construction of transcription factor interaction networks.
doi:10.1093/bioinformatics/btm335
PMCID: PMC3208260  PMID: 17827207
10.  Sliding MinPD: building evolutionary networks of serial samples via an automated recombination detection approach 
Bioinformatics (Oxford, England)  2007;23(22):2993-3000.
Motivation
Traditional phylogenetic methods assume tree-like evolutionary models and are likely to perform poorly when provided with sequence data from fast-evolving, recombining viruses. Furthermore, these methods assume that all the sequence data are from contemporaneous taxa, which is not valid for serially-sampled data. A more general approach is proposed here, referred to as the Sliding MinPD method, that reconstructs evolutionary networks for serially-sampled sequences in the presence of recombination.
Results
Sliding MinPD combines distance-based phylogenetic methods with automated recombination detection based on the best-known sliding window approaches to reconstruct serial evolutionary networks. Its performance was evaluated through comprehensive simulation studies and was also applied to a set of serially-sampled HIV sequences from a single patient. The resulting network organizations reveal unique patterns of viral evolution and may help explain the emergence of disease-associated mutants and drug-resistant strains with implications for patient prognosis and treatment strategies.
doi:10.1093/bioinformatics/btm413
PMCID: PMC3187926  PMID: 17717035
11.  Estimating dynamic models for gene regulation networks 
Bioinformatics (Oxford, England)  2008;24(14):1619-1624.
Motivation
Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data.
Results
In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks.
doi:10.1093/bioinformatics/btn246
PMCID: PMC3168542  PMID: 18505754
12.  Advanced significance analysis of microarray data based on weighted resampling: a comparative study and application to gene deletions in Mycobacterium bovis 
Bioinformatics (Oxford, England)  2004;20(3):357-363.
Motivation
When analyzing microarray data, non-biological variation introduces uncertainty in the analysis and interpretation. In this paper we focus on the validation of significant differences in gene expression levels, or normalized channel intensity levels with respect to different experimental conditions and with replicated measurements. A myriad of methods have been proposed to study differences in gene expression levels and to assign significance values as a measure of confidence. In this paper we compare several methods, including SAM, regularized t-test, mixture modeling, Wilk’s lambda score and variance stabilization. From this comparison we developed a weighted resampling approach and applied it to gene deletions in Mycobacterium bovis.
Results
We discuss the assumptions, model structure, computational complexity and applicability to microarray data. The results of our study justified the theoretical basis of the weighted resampling approach, which clearly outperforms the others.
Availability
Algorithms were implemented using the statistical programming language R and available on the author’s web-page.
doi:10.1093/bioinformatics/btg417
PMCID: PMC3128991  PMID: 14960462
13.  Evoker: a visualization tool for genotype intensity data 
Bioinformatics (Oxford, England)  2010;26(14):1786-1787.
Summary
Genome-wide association studies, which produce huge volumes of data, are now being carried out by many groups around the world, creating a need for user friendly tools for data quality control and analysis. One critical aspect of GWAS quality control is evaluating genotype cluster plots to verify sensible genotype calling in putatively associated SNPs. Evoker is a tool for visualizing genotype cluster plots, and provides a solution to the computational and storage problems related to working with such large datasets.
doi:10.1093/bioinformatics/btq280
PMCID: PMC3073232  PMID: 20507892
14.  Mining experimental evidence of molecular function claims from the literature 
Bioinformatics (Oxford, England)  2007;23(23):3232-3240.
Motivation
The rate at which gene-related findings appear in the scientific literature makes it difficult if not impossible for biomedical scientists to keep fully informed and up to date. The importance of these findings argues for the development of automated methods that can find, extract and summarize this information. This article reports on methods for determining the molecular function claims that are being made in a scientific article, specifically those that are backed by experimental evidence.
Results
The most significant result is that for molecular function claims based on direct assays, our methods achieved recall of 70.7% and precision of 65.7%. Furthermore, our methods correctly identified in the text 44.6% of the specific molecular function claims backed up by direct assays, but with a precision of only 0.92%, a disappointing outcome that led to an examination of the different kinds of errors. These results were based on an analysis of 1823 articles from the literature of Saccharomyces cerevisiae (budding yeast).
Availability
The annotation files for S.cerevisiae are available from ftp://genome-ftp.stanford.edu/pub/yeast/data_download/literature_curation/gene_association.sgd.gz. The draft protocol vocabulary is available by request from the first author.
Contact
crangle@converspeech.com
doi:10.1093/bioinformatics/btm495
PMCID: PMC3041023  PMID: 17942445
15.  GO::TermFinder—open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes 
Bioinformatics (Oxford, England)  2004;20(18):3710-3715.
Summary
GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology (GO) information and evaluating and visualizing the collective annotation of a list of genes to GO terms. It can be used to draw conclusions from microarray and other biological data, calculating the statistical significance of each annotation. GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script.
Availability
The full source code and documentation for GO::TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/
Contact
sherlock@genome.stanford.edu
doi:10.1093/bioinformatics/bth456
PMCID: PMC3037731  PMID: 15297299
16.  The Protein Feature Ontology: A Tool for the Unification of Protein Annotations 
Bioinformatics (Oxford, England)  2008;24(23):2767-2772.
The advent of sequencing and structural genomics projects has provided a dramatic boost in the number of protein structures and sequences. Due to the high-throughput nature of these projects, many of the molecules are uncharacterised and their functions unknown. This, in turn, has led to the need for a greater number and diversity of tools and databases providing annotation through transfer based on homology and prediction methods. Though many such tools to annotate protein sequence and structure exist, they are spread throughout the world, often with dedicated individual web pages. This situation does not provide a consensus view of the data and hinders comparison between methods. Integration of these methods is needed. So far this has not been possible since there was no common vocabulary available that could be used as a standard language. A variety of terms could be used to describe any particular feature ranging from different spellings to completely different terms. The Protein Feature Ontology (http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=BS) is a structured controlled vocabulary for features of a protein sequence or structure. It provides a common language for tools and methods to use, so that integration and comparison of their annotations is possible. The Protein Feature Ontology comprises approximately 100 positional terms (located in a particular region of the sequence), which have been integrated into the Sequence Ontology (SO). 40 non-positional terms which describe general protein properties have also been defined and, in addition, post-translational modifications are described by using an already existing ontology, the Protein Modification Ontology (MOD). The Protein Feature Ontology has been used by the BioSapiens Network of Excellence, a consortium comprising 19 partner sites in 14 European countries generating over 150 distinct annotation types for protein sequences and structures.
doi:10.1093/bioinformatics/btn528
PMCID: PMC2912506  PMID: 18936051
17.  Comments on ‘Bayesian hierarchical error model for analysis of gene expression data’ 
Bioinformatics (Oxford, England)  2006;22(19):2446-2452.
doi:10.1093/bioinformatics/btl173
PMCID: PMC2904753  PMID: 16731698
18.  Visualizing Information across Multidimensional Post-Genomic Structured and Textual Databases 
Bioinformatics (Oxford, England)  2004;21(8):1659-1667.
Motivation
Visualizing relations among biological information to facilitate understanding is crucial to biological research during the post-genomic era. Although different systems have been developed to view gene-phenotype relations for specific databases, very few have been designed specifically as a general flexible tool for visualizing multidimensional genotypic and phenotypic information together. Our goal is to develop a method for visualizing multidimensional genotypic and phenotypic information and a model that unifies different biological databases in order to present the integrated knowledge using a uniform interface.
Results
We developed a novel, flexible and generalizable visualization tool, called PhenoGenesviewer (PGviewer), which in this paper was used to display gene-phenotype relations from a human-curated database (OMIM) and from an automatic method using a Natural Language Processing tool called BioMedLEE. Data obtained from multiple databases were first integrated into a uniform structure and then organized by PGviewer. PGviewer provides a flexible query interface that allows dynamic selection and ordering of any desired dimension in the databases. Based on users’ queries, results can be visualized using hierarchical expandable trees that present views specified by users according to their research interests. We believe that this method, which allows users to dynamically organize and visualize multiple dimensions, is a potentially powerful and promising tool that should substantially facilitate biological research.
doi:10.1093/bioinformatics/bti210
PMCID: PMC2901923  PMID: 15598839
19.  Improved Recognition of Figures containing Fluorescence Microscope Images in Online Journal Articles using Graphical Models 
Bioinformatics (Oxford, England)  2007;24(4):569-576.
Motivation
There is extensive interest in automating the collection, organization, and analysis of biological data. Data in the form of images in online literature present special challenges for such efforts. The first steps in understanding the contents of a figure are decomposing it into panels and determining the type of each panel. In biological literature, panel types include many kinds of images collected by different techniques, such as photographs of gels or images from microscopes. We have previously described the SLIF system (http://slif.cbi.cmu.edu) that identifies panels containing fluorescence microscope images among figures in online journal articles as a prelude to further analysis of the subcellular patterns in such images. This system contains a pretrained classifier that uses image features to assign a type (class) to each separate panel. However, the types of panels in a figure are often correlated, so that we can consider the class of a panel to be dependent not only on its own features but also on the types of the other panels in a figure.
Results
In this paper, we introduce the use of a type of probabilistic graphical model, a factor graph, to represent the structured information about the images in a figure, and permit more robust and accurate inference about their types. We obtain significant improvement over results for considering panels separately.
Availability
The code and data used for the experiments described here are available from http://murphylab.web.cmu.edu/software. Contact: murphy@cmu.edu
doi:10.1093/bioinformatics/btm561
PMCID: PMC2901545  PMID: 18033795
20.  Building chromosome-wide LD maps 
Bioinformatics (Oxford, England)  2006;22(16):1933-1934.
Summary
BMapBuilder builds maps of pairwise linkage disequilibrium (LD) in either two or three dimensions. The optimized resolution allows for graphical display of LD for single nucleotide polymorphisms (SNPs) in a whole chromosome.
doi:10.1093/bioinformatics/btl288
PMCID: PMC2893229  PMID: 16782726
21.  Information theory applied to the sparse gene ontology annotation network to predict novel gene function 
Bioinformatics (Oxford, England)  2007;23(13):i529-i538.
Motivation
Despite advances in the gene annotation process, the functions of a large portion of the gene products remain insufficiently characterized. In addition, the “in silico” prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or function genomics approaches.
Results
We propose a novel approach, Information Theory-based Semantic Similarity (ITSS), to automatically predict molecular functions of genes based on Gene Ontology annotations. We have demonstrated using a 10-fold cross-validation that the ITSS algorithm obtains prediction accuracies (Precision 97%, Recall 77%) comparable to other machine learning algorithms when applied to similarly dense annotated portions of the GO datasets. In addition, such method can generate highly accurate predictions in sparsely annotated portions of GO, in which previous algorithm failed to do so. As a result, our technique generates an order of magnitude more gene function predictions than previous methods. Further, this paper presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions for an evaluation than generally used cross-validations type of evaluations. By manually assessing a random sample of 100 predictions conducted in a historical roll-back evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43%–58%) can be achieved for the human GO Annotation file dated 2003.
Availability
The program is available on request. The 97,732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset are available at http://phenos.bsd.uchicago.edu/mphenogo/prediction_result_2005.txt.
doi:10.1093/bioinformatics/btm195
PMCID: PMC2882681  PMID: 17646340
22.  Genomix 
Bioinformatics (Oxford, England)  2007;23(12):1468-1475.
Motivation
Correct gene predictions are crucial for most analyses of genomes. However, in the absence of transcript data, gene prediction is still challenging. One way to improve gene-finding accuracy in such genomes is to combine the exons predicted by several gene-finders, so that gene-finders that make uncorrelated errors can correct each other.
Results
We present a method for combining gene-finders called Genomix. Genomix selects the predicted exons that are best conserved within and/or between species in terms of sequence and intron–exon structure, and combines them into a gene structure. Genomix was used to combine predictions from four gene-finders for Caenorhabditis elegans, by selecting the predicted exons that are best conserved with C.briggsae and C.remanei. On a set of ~1500 confirmed C.elegans genes, Genomix increased the exon-level specificity by 10.1% and sensitivity by 2.7% compared to the best input gene-finder.
doi:10.1093/bioinformatics/btm133
PMCID: PMC2880447  PMID: 17483502
23.  Bio-Ontologies and Text: Bridging the Modeling Gap Between 
Bioinformatics (Oxford, England)  2006;22(19):2421-2429.
Motivation
Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. To facilitate the computational reuse and integration of information buried in unstructured text, we propose a schema that represents a comprehensive set of biological entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides links from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination, and knowledge management of heterogeneous information. A comprehensive representation for otherwise heterogeneous datasets, such as the one proposed, are critical for advancing systems biology because they allow for acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text.
Results
A novel representational schema, PGschema, was developed that enables translation of information in textual narratives to a well-defined data structure comprising genotypic and phenotypic concepts from established ontologies along with modifiers and relationships. Initial evaluation for coverage of a selected set of entities showed that 85% of the information could be represented. Moreover, PGschema can be realized automatically in an XML format by using natural language techniques to process the text.
doi:10.1093/bioinformatics/btl405
PMCID: PMC2879055  PMID: 16870928
24.  NvMap: automated analysis of NMR chemical shift perturbation data 
Bioinformatics (Oxford, England)  2006;23(3):378-380.
Summary
NMR chemical shift perturbation experiments are widely used to define binding sites in biomolecular complexes. Especially in the case of high throughput screening of ligands, rapid analysis of NMR spectra is essential. NvMap extends NMRViewJ and provides a means for rapid assignments and book-keeping of NMR titration spectra. Our module offers options to analyze multiple titration spectra both separately and sequentially, where the sequential spectra are analyzed either two at a time or all simultaneously. The first option is suitable for slow or intermediate exchange rates between free and bound proteins. The latter option is particularly useful for fast exchange situations and can compensate for the lack of indicators for overlapped peaks. Our module also provides a simple user interface to automate the analysis process from dataset to peak list. We demonstrate the effectiveness of our program using NMR spectra of SUMO in complexes with three different peptides.
doi:10.1093/bioinformatics/btl585
PMCID: PMC2862991  PMID: 17118956
25.  PedMine – A simulated annealing algorithm to identify maximally unrelated individuals in population isolates 
Bioinformatics (Oxford, England)  2008;24(8):1106-1108.
Summary
In family-based genetic studies, it is often useful to identify a subset of unrelated individuals. When such studies are conducted in population isolates, however, most if not all individuals are often detectably related to each other. To identify a set of maximally unrelated (or equivalently, minimally related) individuals, we have implemented simulated annealing, a general-purpose algorithm for solving difficult combinatorial optimization problems. We illustrate our method on data from a genetic study in the Old Order Amish of Lancaster County, Pennsylvania, a population isolate derived from a modest number of founders. Given one or more pedigrees, our program automatically and rapidly extracts a fixed number of maximally unrelated individuals.
doi:10.1093/bioinformatics/btn087
PMCID: PMC2862369  PMID: 18321883

Results 1-25 (126)