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1.  Efficient whole-genome association mapping using local phylogenies for unphased genotype data 
Bioinformatics  2008;24(19):2215-2221.
Motivation: Recent advances in genotyping technology has made data acquisition for whole-genome association study cost effective, and a current active area of research is developing efficient methods to analyze such large-scale datasets. Most sophisticated association mapping methods that are currently available take phased haplotype data as input. However, phase information is not readily available from sequencing methods and inferring the phase via computational approaches is time-consuming, taking days to phase a single chromosome.
Results: In this article, we devise an efficient method for scanning unphased whole-genome data for association. Our approach combines a recently found linear-time algorithm for phasing genotypes on trees with a recently proposed tree-based method for association mapping. From unphased genotype data, our algorithm builds local phylogenies along the genome, and scores each tree according to the clustering of cases and controls. We assess the performance of our new method on both simulated and real biological datasets.
Availability The software described in this article is available at http://www.daimi.au.dk/~mailund/Blossoc and distributed under the GNU General Public License.
Contact:mailund@birc.au.dk
doi:10.1093/bioinformatics/btn406
PMCID: PMC2553438  PMID: 18667442
2.  iFoldRNA: three-dimensional RNA structure prediction and folding 
Bioinformatics  2008;24(17):1951-1952.
Summary: Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nt) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2–5 Å root mean squre deviations (RMSDs) from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, RMSDs from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses.
Availability: http://iFoldRNA.dokhlab.org.
Contact: dokh@med.unc.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn328
PMCID: PMC2559968  PMID: 18579566
3.  Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence 
Bioinformatics  2008;24(16):1805-1811.
Motivation: A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype–phenotype correlation with a priori evidence of biological relevance.
Results: We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains including statistical evidence of genotype–phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays.
Availability: A comprehensive database of biological prioritization scores for all known SNPs is available at http://zork.wustl.edu/gin. This can be used to prioritize nicotine dependence association studies through a straightforward mathematical formula—no special software is necessary.
Contact: ssaccone@wustl.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn315
PMCID: PMC2610477  PMID: 18565990
4.  Comprehensive in silico mutagenesis highlights functionally important residues in proteins 
Bioinformatics  2008;24(16):i207-i212.
Motivation: Mutating residues into alanine (alanine scanning) is one of the fastest experimental means of probing hypotheses about protein function. Alanine scans can reveal functional hot spots, i.e. residues that alter function upon mutation. In vitro mutagenesis is cumbersome and costly: probing all residues in a protein is typically as impossible as substituting by all non-native amino acids. In contrast, such exhaustive mutagenesis is feasible in silico.
Results: Previously, we developed SNAP to predict functional changes due to non-synonymous single nucleotide polymorphisms. Here, we applied SNAP to all experimental mutations in the ASEdb database of alanine scans; we identified 70% of the hot spots (≥1 kCal/mol change in binding energy); more severe changes were predicted more accurately. Encouraged, we carried out a complete all-against-all in silico mutagenesis for human glucokinase. Many of the residues predicted as functionally important have indeed been confirmed in the literature, others await experimental verification, and our method is ready to aid in the design of in vitro mutagenesis.
Availability: ASEdb and glucokinase scores are available at http://www.rostlab.org/services/SNAP. For submissions of large/whole proteins for processing please contact the author.
Contact: yb2009@columbia.edu
doi:10.1093/bioinformatics/btn268
PMCID: PMC2597370  PMID: 18689826
5.  Systematic biological prioritization after a genome-wide association study 
Bioinformatics (Oxford, England)  2008;24(16):1805-1811.
Motivation
A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of geno-type-phenotype correlation with a priori evidence of biological relevance.
Results
We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains, including statistical evidence of genotype-phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays.
doi:10.1093/bioinformatics/btn315
PMCID: PMC2610477  PMID: 18565990
6.  LOT: a tool for linkage analysis of ordinal traits for pedigree data 
Bioinformatics  2008;24(15):1737-1739.
Summary: Existing linkage-analysis methods address binary or quantitative traits. However, many complex diseases and human conditions, particularly behavioral disorders, are rated on ordinal scales. Herein, we introduce, LOT, a tool that performs linkage analysis of ordinal traits for pedigree data. It implements a latent-variable proportional-odds logistic model that relates inheritance patterns to the distribution of the ordinal trait. The likelihood-ratio test is used for testing evidence of linkage.
Availability: The LOT program is available for download at http://c2s2.yale.edu/software/LOT/
Contact: heping.zhang@yale.edu
doi:10.1093/bioinformatics/btn258
PMCID: PMC2566542  PMID: 18535081
7.  Memory-efficient dynamic programming backtrace and pairwise local sequence alignment 
Bioinformatics (Oxford, England)  2008;24(16):1772-1778.
Motivation
A backtrace through a dynamic programming algorithm’s intermediate results in search of an optimal path, or to sample paths according to an implied probability distribution, or as the second stage of a forward–backward algorithm, is a task of fundamental importance in computational biology. When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis.
Results
Here we present an optimal checkpointing strategy, and demonstrate its utility with pairwise local sequence alignment of sequences of length 10 000.
Availability
Sample C++-code for optimal backtrace is available in the Supplementary Materials.
doi:10.1093/bioinformatics/btn308
PMCID: PMC2668612  PMID: 18558620
8.  Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification 
Bioinformatics (Oxford, England)  2008;24(13):i348-i356.
Motivation
Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms.
Results
We use a hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) approach to address these two problems. We train a set of DBNs on high-confidence peptide-spectrum matches. These DBNs, known collectively as Riptide, comprise a probabilistic model of peptide fragmentation chemistry. Examination of the distributions learned by Riptide allows identification of new trends, such as prevalent a-ion fragmentation at peptide cleavage sites C-term to hydrophobic residues. In addition, Riptide can be used to produce likelihood scores that indicate whether a given peptide-spectrum match is correct. A vector of such scores is evaluated by an SVM, which produces a final score to be used in peptide identification. Using Riptide in this way yields improved discrimination when compared to other state-of-the-art MS/MS identification algorithms, increasing the number of positive identifications by as much as 12% at a 1% false discovery rate.
Availability
Python and C source code are available upon request from the authors. The curated training sets are available at http://noble.gs.washington.edu/proj/intense/. The Graphical Model Tool Kit (GMTK) is freely available at http://ssli.ee.washington.edu/bilmes/gmtk.
Contact
noble@gs.washington.edu
doi:10.1093/bioinformatics/btn189
PMCID: PMC2665034  PMID: 18586734
9.  Comprehensive in silico mutagenesis highlights functionally important residues in proteins 
Bioinformatics (Oxford, England)  2008;24(16):i207-i212.
Motivation
Mutating residues into alanine (alanine scanning) is one of the fastest experimental means of probing hypotheses about protein function. Alanine scans can reveal functional hot spots, i.e. residues that alter function upon mutation. In vitro mutagenesis is cumbersome and costly: probing all residues in a protein is typically as impossible as substituting by all non-native amino acids. In contrast, such exhaustive mutagenesis is feasible in silico.
Results
Previously, we developed SNAP to predict functional changes due to non-synonymous single nucleotide polymorphisms. Here, we applied SNAP to all experimental mutations in the ASEdb database of alanine scans; we identified 70% of the hot spots (≥1kCal/mol change in binding energy); more severe changes were predicted more accurately. Encouraged, we carried out a complete all-against-all in silico mutagenesis for human glucokinase. Many of the residues predicted as functionally important have indeed been confirmed in the literature, others await experimental verification, and our method is ready to aid in the design of in vitro mutagenesis.
Availability
ASEdb and glucokinase scores are available at http://www.rostlab.org/services/SNAP. For submissions of large/whole proteins for processing please contact the author.
Contact: yb2009@columbia.edu
doi:10.1093/bioinformatics/btn268
PMCID: PMC2597370  PMID: 18689826
10.  Powerful fusion: PSI-BLAST and consensus sequences 
Bioinformatics (Oxford, England)  2008;24(18):1987-1993.
Motivation
A typical PSI-BLAST search consists of iterative scanning and alignment of a large sequence database during which a scoring profile is progressively built and refined. Such a profile can also be stored and used to search against a different database of sequences. Using it to search against a database of consensus rather than native sequences is a simple add-on that boosts performance surprisingly well. The improvement comes at a price: we hypothesized that random alignment score statistics would differ between native and consensus sequences. Thus PSI-BLAST-based profile searches against consensus sequences might incorrectly estimate statistical significance of alignment scores. In addition, iterative searches against consensus databases may fail. Here, we addressed these challenges in an attempt to harness the full power of the combination of PSI-BLAST and consensus sequences.
Results
We studied alignment score statistics for various types of consensus sequences. In general, the score distribution parameters of profile-based consensus sequence alignments differed significantly from those derived for the native sequences. PSI-BLAST partially compensated for the parameter variation. We have identified a protocol for building specialized consensus sequences that significantly improved search sensitivity and preserved score distribution parameters. As a result, PSI-BLAST profiles can be used to search specialized consensus sequences without sacrificing estimates of statistical significance. We also provided results indicating that iterative PSI-BLAST searches against consensus sequences could work very well. Overall, we showed how a widely popular and effective method could be used to identify significantly more relevant similarities among protein sequences.
Availability
http://www.rostlab.org/services/consensus/
Contact:
dsp23@columbia.edu
doi:10.1093/bioinformatics/btn384
PMCID: PMC2577777  PMID: 18678588
11.  Efficient Whole-Genome Association Mapping using Local Phylogenies for Unphased Genotype Data 
Bioinformatics (Oxford, England)  2008;24(19):2215-2221.
Motivation
Recent advances in genotyping technology has made data acquisition for whole-genome association study cost effective, and a current active area of research is developing efficient methods to analyze such large-scale data sets. Most sophisticated association mapping methods that are currently available take phased haplotype data as input. However, phase information is not readily available from sequencing methods and inferring the phase via computational approaches is time-consuming, taking days to phase a single chromosome.
Results
In this paper, we devise an efficient method for scanning unphased whole-genome data for association. Our approach combines a recently found linear-time algorithm for phasing genotypes on trees with a recently proposed tree-based method for association mapping. From unphased genotype data, our algorithm builds local phylogenies along the genome, and scores each tree according to the clustering of cases and controls. We assess the performance of our new method on both simulated and real biological data sets.
doi:10.1093/bioinformatics/btn406
PMCID: PMC2553438  PMID: 18667442
12.  LOT 
Bioinformatics (Oxford, England)  2008;24(15):1737-1739.
Summary
Existing linkage-analysis methods address binary or quantitative traits. However, many complex diseases and human conditions, particularly behavioral disorders, are rated on ordinal scales. Herein, we introduce, LOT, a tool that performs linkage analysis of ordinal traits for pedigree data. It implements a latent-variable proportional-odds logistic model that relates inheritance patterns to the distribution of the ordinal trait. The likelihood-ratio test is used for testing evidence of linkage.
doi:10.1093/bioinformatics/btn258
PMCID: PMC2566542  PMID: 18535081
13.  iFoldRNA: Three-dimensional RNA Structure Prediction and Folding 
Bioinformatics (Oxford, England)  2008;24(17):1951-1952.
Summary
Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nucleotides) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2–5 Angstrom root mean square deviations from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, root mean square deviations from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses.
doi:10.1093/bioinformatics/btn328
PMCID: PMC2559968  PMID: 18579566
14.  Powerful fusion: PSI-BLAST and consensus sequences 
Bioinformatics  2008;24(18):1987-1993.
Motivation: A typical PSI-BLAST search consists of iterative scanning and alignment of a large sequence database during which a scoring profile is progressively built and refined. Such a profile can also be stored and used to search against a different database of sequences. Using it to search against a database of consensus rather than native sequences is a simple add-on that boosts performance surprisingly well. The improvement comes at a price: we hypothesized that random alignment score statistics would differ between native and consensus sequences. Thus PSI-BLAST-based profile searches against consensus sequences might incorrectly estimate statistical significance of alignment scores. In addition, iterative searches against consensus databases may fail. Here, we addressed these challenges in an attempt to harness the full power of the combination of PSI-BLAST and consensus sequences.
Results: We studied alignment score statistics for various types of consensus sequences. In general, the score distribution parameters of profile-based consensus sequence alignments differed significantly from those derived for the native sequences. PSI-BLAST partially compensated for the parameter variation. We have identified a protocol for building specialized consensus sequences that significantly improved search sensitivity and preserved score distribution parameters. As a result, PSI-BLAST profiles can be used to search specialized consensus sequences without sacrificing estimates of statistical significance. We also provided results indicating that iterative PSI-BLAST searches against consensus sequences could work very well. Overall, we showed how a very popular and effective method could be used to identify significantly more relevant similarities among protein sequences.
Availability: http://www.rostlab.org/services/consensus/
Contact: dariusz@mit.edu
doi:10.1093/bioinformatics/btn384
PMCID: PMC2577777  PMID: 18678588
15.  Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification 
Bioinformatics  2008;24(13):i348-i356.
Motivation: Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation. Unfortunately, peptide fragmentation is complex and not fully understood, and what is understood is not always exploited by peptide identification algorithms.
Results: We use a hybrid dynamic Bayesian network (DBN)/support vector machine (SVM) approach to address these two problems. We train a set of DBNs on high-confidence peptide-spectrum matches. These DBNs, known collectively as Riptide, comprise a probabilistic model of peptide fragmentation chemistry. Examination of the distributions learned by Riptide allows identification of new trends, such as prevalent a-ion fragmentation at peptide cleavage sites C-term to hydrophobic residues. In addition, Riptide can be used to produce likelihood scores that indicate whether a given peptide-spectrum match is correct. A vector of such scores is evaluated by an SVM, which produces a final score to be used in peptide identification. Using Riptide in this way yields improved discrimination when compared to other state-of-the-art MS/MS identification algorithms, increasing the number of positive identifications by as much as 12% at a 1% false discovery rate.
Availability: Python and C source code are available upon request from the authors. The curated training sets are available at http://noble.gs.washington.edu/proj/intense/. The Graphical Model Tool Kit (GMTK) is freely available at http://ssli.ee.washington.edu/bilmes/gmtk.
Contact:noble@gs.washington.edu
doi:10.1093/bioinformatics/btn189
PMCID: PMC2665034  PMID: 18586734
16.  Memory-efficient dynamic programming backtrace and pairwise local sequence alignment 
Bioinformatics  2008;24(16):1772-1778.
Motivation: A backtrace through a dynamic programming algorithm's intermediate results in search of an optimal path, or to sample paths according to an implied probability distribution, or as the second stage of a forward–backward algorithm, is a task of fundamental importance in computational biology. When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis.
Results: Here we present an optimal checkpointing strategy, and demonstrate its utility with pairwise local sequence alignment of sequences of length 10 000.
Availability: Sample C++-code for optimal backtrace is available in the Supplementary Materials.
Contact: leen@cs.rpi.edu
Supplementary information: Supplementary data is available at Bioinformatics online.
doi:10.1093/bioinformatics/btn308
PMCID: PMC2668612  PMID: 18558620
17.  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
18.  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
19.  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
20.  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
21.  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
22.  Computational analysis of microRNA profiles and their target genes suggests significant involvement in breast cancer antiestrogen resistance 
Bioinformatics  2008;25(4):430-434.
Motivation: Recent evidence shows significant involvement of microRNAs (miRNAs) in the initiation and progression of numerous cancers; however, the role of these in tumor drug resistance remains unknown.
Results: By comparing global miRNA and mRNA expression patterns, we examined the role of miRNAs in resistance to the ‘pure antiestrogen’ fulvestrant, using fulvestrant-resistant MCF7-FR cells and their drug-sensitive parental estrogen receptor (ER)-positive MCF7 cells. We identified 14 miRNAs downregulated in MCF7-FR cells and then used both TargetScan and PITA to predict potential target genes. We found a negative correlation between expression of these miRNAs and their predicted target mRNA transcripts. In genes regulated by multiple miRNAs or having multiple miRNA-targeting sites, an even stronger negative correlation was found. Pathway analyses predicted these miRNAs to regulate specific cancer-associated signal cascades. These results suggest a significant role for miRNA-regulated gene expression in the onset of breast cancer antiestrogen resistance, and an improved understanding of this phenomenon could lead to better therapies for this often fatal condition.
Contact: knephew@indiana.edu; sunkim2@indiana.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn646
PMCID: PMC2642642  PMID: 19091772
23.  Differential dependency network analysis to identify condition-specific topological changes in biological networks 
Bioinformatics  2008;25(4):526-532.
Motivation: Significant efforts have been made to acquire data under different conditions and to construct static networks that can explain various gene regulation mechanisms. However, gene regulatory networks are dynamic and condition-specific; under different conditions, networks exhibit different regulation patterns accompanied by different transcriptional network topologies. Thus, an investigation on the topological changes in transcriptional networks can facilitate the understanding of cell development or provide novel insights into the pathophysiology of certain diseases, and help identify the key genetic players that could serve as biomarkers or drug targets.
Results: Here, we report a differential dependency network (DDN) analysis to detect statistically significant topological changes in the transcriptional networks between two biological conditions. We propose a local dependency model to represent the local structures of a network by a set of conditional probabilities. We develop an efficient learning algorithm to learn the local dependency model using the Lasso technique. A permutation test is subsequently performed to estimate the statistical significance of each learned local structure. In testing on a simulation dataset, the proposed algorithm accurately detected all the genes with network topological changes. The method was then applied to the estrogen-dependent T-47D estrogen receptor-positive (ER+) breast cancer cell line datasets and human and mouse embryonic stem cell datasets. In both experiments using real microarray datasets, the proposed method produced biologically meaningful results. We expect DDN to emerge as an important bioinformatics tool in transcriptional network analyses. While we focus specifically on transcriptional networks, the DDN method we introduce here is generally applicable to other biological networks with similar characteristics.
Availability: The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.
Contact: yuewang@vt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn660
PMCID: PMC2642641  PMID: 19112081
24.  ATOM: a powerful gene-based association test by combining optimally weighted markers 
Bioinformatics  2008;25(4):497-503.
Background: Large-scale candidate-gene and genome-wide association studies genotype multiple SNPs within or surrounding a gene, including both tag and functional SNPs. The immense amount of data generated in these studies poses new challenges to analysis. One particularly challenging yet important question is how to best use all genetic information to test whether a gene or a region is associated with the trait of interest.
Methods: Here we propose a powerful gene-based Association Test by combining Optimally Weighted Markers (ATOM) within a genomic region. Due to variation in linkage disequilibrium, different markers often associate with the trait of interest at different levels. To appropriately apportion their contributions, we assign a weight to each marker that is proportional to the amount of information it captures about the trait locus. We analytically derive the optimal weights for both quantitative and binary traits, and describe a procedure for estimating the weights from a reference database such as the HapMap. Compared with existing approaches, our method has several distinct advantages, including (i) the ability to borrow information from an external database to increase power, (ii) the theoretical derivation of optimal marker weights and (iii) the scalability to simultaneous analysis of all SNPs in candidate genes and pathways.
Results: Through extensive simulations and analysis of the FTO gene in our ongoing genome-wide association study on childhood obesity, we demonstrate that ATOM increases the power to detect genetic association as compared with several commonly used multi-marker association tests.
Contact: mingyao@mail.med.upenn.edu; chun.li@vanderbilt.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn641
PMCID: PMC2642636  PMID: 19074959
25.  Glycan family analysis for deducing N-glycan topology from single MS 
Bioinformatics  2008;25(3):365-371.
Motivation: In the past few years, mass spectrometry (MS) has emerged as the premier tool for identification and quantification of biological molecules such as peptides and glycans. There are two basic strategies: single-MS, which uses a single round of mass analysis, and MS/MS (or higher order MSn), which adds one or more additional rounds of mass analysis, interspersed with fragmentation steps. Single-MS offers higher throughput, broader mass coverage and more direct quantitation, but generally much weaker identification. Single-MS, however, does work fairly well for the case of N-glycan identification, which are more constrained than other biological polymers. We previously demonstrated single-MS identification of N-glycans to the level of ‘cartoons’ (monosaccharide composition and topology) by a system that incorporates an expert's detailed knowledge of the biological sample. In this article, we explore the possibility of ab initio single-MS N-glycan identification, with the goal of extending single-MS, or primarily-single-MS, identification to non-expert users, novel conditions and unstudied tissues.
Results: We propose and test three cartoon-assignment algorithms that make inferences informed by biological knowledge about glycan synthesis. To test the algorithms, we used 71 single-MS spectra from a variety of tissues and organisms, containing more than 2800 manually annotated peaks. The most successful of the algorithms computes the most richly connected subgraph within a ‘cartoon graph’. This algorithm uniquely assigns the correct cartoon to more than half of the peaks in 41 out of the 71 spectra.
Contact: goldberg@parc.com
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btn636
PMCID: PMC2732296  PMID: 19073587

Results 1-25 (278)