MedWorm: Bioinformatics

Hadamard phylogenetic methods and the n-taxon process.

Bulletin of Mathematical Biology — Fri, 10 Oct 2008 04:00:00 +0100

Hadamard Phylogenetic Methods and the n-taxon Process.

Bull Math Biol. 2008 Oct 10;

Authors: Bryant D

The Hadamard transform (Hendy and Penny, Syst. Zool. 38(4):297-309, 1989; Hendy, Syst. Zool. 38(4):310-321, 1989) provides a way to work with stochastic models for sequence evolution without having to deal with the complications of tree space and the graphical structure of trees. Here we demonstrate that the transform can be expressed in terms of the familiar P[tau]=e ( Q[tau]) formula for Markov chains. The key idea is to study the evolution of vectors of states, one vector entry for each taxa; we call this the n-taxon process. We derive transition probabilities for the process. Significantly, the findings show that tree-based models are indeed in the family of (multi-variate) exponential distributions.

PMID: 18846403 [PubMed - as supplied by publisher]

(Source: Bulletin of Mathematical Biology)

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Structural segments and residue propensities in protein-rna interfaces: comparison with protein-protein and protein-dna complexes.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

Structural segments and residue propensities in protein-RNA interfaces: Comparison with protein-protein and protein-DNA complexes.

Bioinformation. 2008;2(10):422-7

Authors: Biswas S, Guharoy M, Chakrabarti P

The interface of a protein molecule that is involved in binding another protein, DNA or RNA has been characterized in terms of the number of unique secondary structural segments (SSSs), made up of stretches of helix, strand and non-regular (NR) regions. On average 10-11 segments define the protein interface in protein-protein (PP) and protein-DNA (PD) complexes, while the number is higher (14) for protein-RNA (PR) complexes. While the length of helical segments in PP interaction increases with the interface area, this is not the case in PD and PR complexes. The propensities of residues to occur in the three types of secondary structural elements (SSEs) in the interface relative to the corresponding elements in the protein tertiary structures have been calculated. Arg, Lys, Asn, Tyr, His and Gln are preferred residues in PR complexes; in addition, Ser and Thr are also favoured in PD interfaces.

PMID: 18841236 [PubMed - in process]

(Source: Bioinformation)

David gene id conversion tool.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

DAVID gene ID conversion tool.

Bioinformation. 2008;2(10):428-30

Authors: Huang da W, Sherman BT, Stephens R, Baseler MW, Lane HC, Lempicki RA

Our current biological knowledge is spread over many independent bioinformatics databases where many different types of gene and protein identifiers are used. The heterogeneous and redundant nature of these identifiers limits data analysis across different bioinformatics resources. It is an even more serious bottleneck of data analysis for larger datasets, such as gene lists derived from microarray and proteomic experiments. The DAVID Gene ID Conversion Tool (DICT), a web-based application, is able to convert user's input gene or gene product identifiers from one type to another in a more comprehensive and high-throughput manner with a uniquely enhanced ID-ID mapping database. AVAILABILITY: http://david.abcc.ncifcrf.gov/conversion.jsp.

PMID: 18841237 [PubMed - in process]

(Source: Bioinformation)

Stif: identification of stress-upregulated transcription factor binding sites in arabidopsis thaliana.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

STIF: Identification of stress-upregulated transcription factor binding sites in Arabidopsis thaliana.

Bioinformation. 2008;2(10):431-7

Authors: Sundar AS, Varghese SM, Shameer K, Karaba N, Udayakumar M, Sowdhamini R

The expressions of proteins in the cell are carefully regulated by transcription factors that interact with their downstream targets in specific signal transduction cascades. Our understanding of the regulation of functional genes responsive to stress signals is still nascent. Plants like Arabidopsis thaliana, are convenient model systems to study fundamental questions related to regulation of the stress transcriptome in response to stress challenges. Microarray results of the Arabidopsis transcriptome indicate that several genes could be upregulated during multiple stresses, such as cold, salinity, drought etc. Experimental biochemical validations have proved the involvement of several transcription factors could be involved in the upregulation of these stress responsive genes. In order to follow the intricate and complicated networks of transcription factors and genes that respond to stress situations in plants, we have developed a computer algorithm that can identify key transcription factor binding sites upstream of a gene of interest. Hidden Markov models of the transcription factor binding sites enable the identification of predicted sites upstream of plant stress genes. The search algorithm, STIF, performs very well, with more than 90% sensitivity, when tested on experimentally validated positions of transcription factor binding sites on a dataset of 60 stress upregulated genes. AVAILABILITY: Supplementary data is available at http://caps.ncbs.res.in/download/stif.

PMID: 18841238 [PubMed - in process]

(Source: Bioinformation)

Ontoslug: a dynamic visual front-end program for ontologies.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

OntoSlug: a dynamic visual front-end program for ontologies.

Bioinformation. 2008;2(10):438-40

Authors: Telefont M, Liu Y

The display of ontological information has become a crucial factor over the last decade in systems biology. The possibility to compare different ontological systems in a single application has however not been answered with an appropriate application. OntoSlug is an easy to use application that tries to fill this need. OntoSlug has been developed for use in classroom settings and scientific laboratory environment.

PMID: 18841239 [PubMed - in process]

(Source: Bioinformation)

Distinguishing compounds with anticancer activity by ann using inductive qsar descriptors.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

Distinguishing compounds with anticancer activity by ANN using inductive QSAR descriptors.

Bioinformation. 2008;2(10):441-51

Authors: Jaiswal K, Naik PK

This article describes a method developed for predicting anticancer/non-anticancer drugs using artificial neural network (ANN). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. Using 30 'inductive' QSAR descriptors alone, we have been able to achieve 84.28% accuracy for correct separation of compounds with- and without anticancer activity. For the complete set of 30 inductive QSAR descriptors, ANN based method reveals a superior model (accuracy = 84.28%, Q(pred) = 74.28%, sensitivity = 0.9285, specificity = 0.7857, Matthews correlation coefficient (MCC) = 0.6998). The method was trained and tested on a non redundant data set of 380 drugs (122 anticancer and 258 non-anticancer). The elaborated QSAR model based on the Artificial Neural Networks approach has been extensively validated and has confidently assigned anticancer character to a number of trial anticancer drugs from the literature.

PMID: 18841240 [PubMed - in process]

(Source: Bioinformation)

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A comparison of msa tools.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

A comparison of MSA tools.

Bioinformation. 2008;2(10):452-5

Authors: Essoussi N, Boujenfa K, Limam M

Multiple sequence alignment (MSA) is essential in phylogenetic, evolutionary and functional analysis. Several MSA tools are available in the literature. Here, we use several MSA tools such as ClustalX, Align-m, T-Coffee, SAGA, ProbCons, MAFFT, MUSCLE and DIALIGN to illustrate comparative phylogenetic trees analysis for two datasets. Results show that there is no single MSA tool that consistently outperforms the rest in producing reliable phylogenetic trees.

PMID: 18841241 [PubMed - in process]

(Source: Bioinformation)

Its-2 secondary structures and phylogeny of anopheles culicifacies species.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

ITS-2 secondary structures and phylogeny of Anopheles culicifacies species.

Bioinformation. 2008;2(10):456-60

Authors: Dassanayake RS, Gunawardene YI, Silva BD

BACKGROUND: Second internal transcribed spacer (ITS2) has proven to contain useful biological information at higher taxonomic levels. OBJECTIVES: This study was carried out to unravel the biological information in the ITS2 region of An. culicifacies and the internal relationships between the five species of Anopheles culicifacies. METHODOLOGY: In achieving these objectives, twenty two ITS2 sequences (~370bp) of An. culicifacies species were retrieved from GenBank and secondary structures were generated. For the refinement of the primary structures, i.e. nucleotide sequence of ITS2 sequences, generated secondary structures were used. The improved ITS2 primary structures sequences were then aligned and used for the construction of phylogenetic trees. RESULTS AND DISCUSSIONS: ITS2 secondary structures of culicifacies closely resembled near universal eukaryotes secondary structure and had three helices, and the structures of helix II and distal region of helix III of ITS2 of An. culicifacies were strikingly similar to those regions of other organisms strengthening possible involvement of these regions in rRNA biogenesis. Phylogenetic analysis of improved ITS2 sequences revealed two main clades one representing sibling B, C and E and A and D in the other. CONCLUSIONS: Near sequence identity of ITS2 regions of the members in a particular clade indicate that this region is undergoing parallel evolution to perform clade specific RNA biogenesis. The divergence of certain isolates of An. culicifacies from main clades in phylogenetic analyses suggests the possible existence of camouflaged sub-species within the complex of culicifacies. Using the fixed nucleotide differences, we estimate that these two clades have diverged nearly 3.3 million years ago, while the sibling species in clade 2 are under less evolutionary pressure, which may have evolved much later than the members in clade 1.

PMID: 18841242 [PubMed - in process]

(Source: Bioinformation)

Evolutionary analysis of wd40 super family proteins involved in spindle checkpoint and rna export: molecular evolution of spindle checkpoint.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

Evolutionary analysis of WD40 super family proteins involved in spindle checkpoint and RNA export: Molecular evolution of spindle checkpoint.

Bioinformation. 2008;2(10):461-8

Authors: Reddy DM, Aspatwar A, Dholakia BB, Gupta VS

The spindle checkpoint delays sister chromatid separation until all chromosomes have undergone bipolar spindle attachment. Previous studies have revealed BUB3, as an essential spindle checkpoint protein and its extensive sequence similarity with Rae1 (Gle2), a highly conserved member of WD40 repeat protein family throughout their length which was first shown to be involved in mRNA export. However, the recent discovery of Rae1 as an essential mitotic checkpoint protein, based on the studies from mouse and drosophila, has renewed the interest in its function during cell division. Study of evolution of proteins involved in checkpoint might throw light on evolution of eukaryotic cell cycle regulation. Here we report the evolutionary relationships between these two WD40 repeat family proteins. Amino acid sequences of BUB3 and Rae1 homologs were retrieved from various databases and phylogenetic analysis was performed with the MEGA program. Multiple sequence alignments of these two protein homologues with the ClustalX software revealed specific amino acid signatures corresponding to the protein function and also few amino acids, which are conserved in BUB3 and Rae1 indicating some common overlapping function. Data indicated a common ancestral origin of these two important proteins and further suggest that, BUB3 mediated cell cycle checkpoint might have evolved with compartmentalization of genetic material into the nucleus in eukaryotes.

PMID: 18841243 [PubMed - in process]

(Source: Bioinformation)

Snpinprobe_1.0: a database for filtering out probes in the affymetrix genechip(r) human exon 1.0 st array potentially affected by snps.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

SNPinProbe_1.0: A database for filtering out probes in the Affymetrix GeneChip(R) Human Exon 1.0 ST array potentially affected by SNPs.

Bioinformation. 2008;2(10):469-70

Authors: Duan S, Zhang W, Bleibel WK, Cox NJ, Dolan ME

The Affymetrix GeneChip(R) Human Exon 1.0 ST array (exon array) is designed to measure both gene-level and exon-level expression in human samples. This exon array contains ~1.4 million probesets consisting of ~5.4 million probes and profiles over 17,000 well-annotated gene transcripts in the human genome. As with all expression arrays, the exon array is vulnerable to SNPs within probes, because these SNPs can affect the hybridization of the probes and thus produce misleading expression values. In some cases, this could result in dramatic fluctuations of the exon-level expression. For this reason, we performed a genome-wide search for SNPs within regions that hybridize to probes by evaluating approximately 18 million SNPs in dbSNP (Build 129) and about 5.4 million probes in the exon array. We identified 597,068 probes within 350,382 probe sets that hybridized to regions containing SNPs. These affected probes and/or probesets can be filtered in the data processing procedure thus controlling for potential false expression phenotypes when using this exon array. AVAILABILITY: http://cid-fb2a64e541add2be.skydrive.live.com/browse.aspx/Affy%7C_HuEx%7C_1.0ST?uc=2.

PMID: 18841244 [PubMed - in process]

(Source: Bioinformation)

Evolutionary analysis of phlpp1 gene in humans and non-human primates.

Bioinformation — Thu, 09 Oct 2008 20:10:04 +0100

Evolutionary analysis of PHLPP1 gene in humans and non-human primates.

Bioinformation. 2008;2(10):471-4

Authors: Anbazhagan P, Purushottam M, Kumar HB, Kubendran S, Mukherjee O, Brahmachari SK, Jain S, Sowdhamini R

The chromosome 18q22-23 region has been shown to be implicated in bipolar disorder (BPAD) by several studies. PHLPP1 gene, in the locus (chromosome 18q22-23), is involved in circadian pathways and bears modules like 'PH domain and leucine rich repeat protein phosphatase'. This gene also contains a polyglutamine (CAG or PolyQ) repeat motif at the carboxyl terminal end. A comparative analysis of the PolyQ repeats of the PHLPP1 gene in humans, non-human primates and other species has been attempted in order to investigate the possible significance of repeat length as seen in other triplet-repeat associated diseases. Sequencing of the CAG repeat in humans and in non-human primates revealed that the CAG repeat is not polymorphic in humans; whereas, in other species it shows an area of high variability, both in length and sequence composition. Despite the conservation of circadian clock components in different species, there is remarkable diversity in the protein structure, regulation and biochemical functions of the circadian orthologs. These can be due to specific adaptations in accordance with the physiology of the particular species providing a species-specific biological advantage.

PMID: 18841245 [PubMed - in process]

(Source: Bioinformation)

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Mechanical-statistical modeling in ecology: from outbreak detections to pest dynamics.

Bulletin of Mathematical Biology — Thu, 09 Oct 2008 04:00:00 +0100

Mechanical-Statistical Modeling in Ecology: From Outbreak Detections to Pest Dynamics.

Bull Math Biol. 2008 Oct 9;

Authors: Soubeyrand S, Neuvonen S, Penttinen A

Knowledge about large-scale and long-term dynamics of (natural) populations is required to assess the efficiency of control strategies, the potential for long-term persistence, and the adaptability to global changes such as habitat fragmentation and global warming. For most natural populations, such as pest populations, large-scale and long-term surveys cannot be carried out at a high resolution. For instance, for population dynamics characterized by irregular abundance explosions, i.e., outbreaks, it is common to report detected outbreaks rather than measuring the population density at every location and time event. Here, we propose a mechanical-statistical model for analyzing such outbreak occurrence data and making inference about population dynamics. This spatio-temporal model contains the main mechanisms of the dynamics and describes the observation process. This construction enables us to account for the discrepancy between the phenomenon scale and the sampling scale. We propose the Bayesian method to estimate model parameters, pest densities and hidden factors, i.e., variables involved in the dynamics but not observed. The model was specified and used to learn about the dynamics of the European pine sawfly (Neodiprion sertifer Geoffr., an insect causing major defoliation of pines in northern Europe) based on Finnish sawfly data covering the years 1961-1990. In this application, a dynamical Beverton-Holt model including a hidden regime variable was incorporated into the model to deal with large variations in the population densities. Our results gave support to the idea that pine sawfly dynamics should be studied as metapopulations with alternative equilibria. The results confirmed the importance of extreme minimum winter temperatures for the occurrence of European pine sawfly outbreaks. The strong positive connection between the ratio of lake area over total area and outbreaks was quantified for the first time.

PMID: 18843520 [PubMed - as supplied by publisher]

(Source: Bulletin of Mathematical Biology)

A mass action model of a fibroblast growth factor signaling pathway and its simplification.

Bulletin of Mathematical Biology — Thu, 09 Oct 2008 04:00:00 +0100

A Mass Action Model of a Fibroblast Growth Factor Signaling Pathway and Its Simplification.

Bull Math Biol. 2008 Oct 9;

Authors: Gaffney EA, Heath JK, Kwiatkowska MZ

We consider a kinetic law of mass action model for Fibroblast Growth Factor (FGF) signaling, focusing on the induction of the RAS-MAP kinase pathway via GRB2 binding. Our biologically simple model suffers a combinatorial explosion in the number of differential equations required to simulate the system. In addition to numerically solving the full model, we show that it can be accurately simplified. This requires combining matched asymptotics, the quasi-steady state hypothesis, and the fact subsets of the equations decouple asymptotically. Both the full and simplified models reproduce the qualitative dynamics observed experimentally and in previous stochastic models. The simplified model also elucidates both the qualitative features of GRB2 binding and the complex relationship between SHP2 levels, the rate SHP2 induces dephosphorylation and levels of bound GRB2. In addition to providing insight into the important and redundant features of FGF signaling, such work further highlights the usefulness of numerous simplification techniques in the study of mass action models of signal transduction, as also illustrated recently by Borisov and co-workers (Borisov et al. in Biophys. J. 89, 951-966, 2005, Biosystems 83, 152-166, 2006; Kiyatkin et al. in J. Biol. Chem. 281, 19925-19938, 2006). These developments will facilitate the construction of tractable models of FGF signaling, incorporating further biological realism, such as spatial effects or realistic binding stoichiometries, despite a more severe combinatorial explosion associated with the latter.

PMID: 18841420 [PubMed - as supplied by publisher]

(Source: Bulletin of Mathematical Biology)

Mining protein networks for synthetic genetic interactions

BMC Bioinformatics - Latest articles — Thu, 09 Oct 2008 04:00:00 +0100

Background: The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified. Results: We design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs. Conclusions: Our analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein interaction networks could plausibly be rich sources of information about epistatic effects among genes. (Source: BMC Bioinformatics - Latest articles)

Preprint: a monte carlo em algorithm for de novo motif discovery in biomolecular sequences

IEEE/ACM Transactions on Computational Biology and Bioinformatics — Wed, 08 Oct 2008 13:26:55 +0100

Motif discovery methods play pivotal roles in deciphering the genetic regulatory codes in genome as well as in locating conserved domains in protein sequences. The Expectation Maximization (EM) motif-finding algorithm is one of the most popular de novo motif discovery methods. Based on the position weight matrix (PWM) updating technique, this paper presents a Monte Carlo version of the EM motif algorithm The newly implemented algorithm is named as Monte Carlo EM Motif Discovery Algorithm (MCEMDA). MCEMDA starts from an initial model, and then it iteratively performs Monte Carlo simulation and parameter update until convergence. A log-likelihood profiling technique together with the top-k strategy is introduced to cope with the phase shifts and multiple modal issues in motif discovery problem. A novel grouping motif alignments (GMA) algorithm is designed to select motifs by clustering a population of candidate local alignments and successfully applied to subtle motif discovery. MCEMDA compares favorably to other popular PWM-based and word enumerative motif algorithms tested using simulated motif cases, documented prokaryotic and eukaryotic DNA motif sequences. Finally MCEMDA is applied to detect large blocks of conserved domains in protein benchmarks and compared with other multiple sequence alignment methods. (Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics)

Bringing web 2.0 to bioinformatics.

Briefings in Bioinformatics — Wed, 08 Oct 2008 04:00:00 +0100

Bringing Web 2.0 to bioinformatics.

Brief Bioinform. 2008 Oct 8;

Authors: Zhang Z, Cheung KH, Townsend JP

Enabling deft data integration from numerous, voluminous and heterogeneous data sources is a major bioinformatic challenge. Several approaches have been proposed to address this challenge, including data warehousing and federated databasing. Yet despite the rise of these approaches, integration of data from multiple sources remains problematic and toilsome. These two approaches follow a user-to-computer communication model for data exchange, and do not facilitate a broader concept of data sharing or collaboration among users. In this report, we discuss the potential of Web 2.0 technologies to transcend this model and enhance bioinformatics research. We propose a Web 2.0-based Scientific Social Community (SSC) model for the implementation of these technologies. By establishing a social, collective and collaborative platform for data creation, sharing and integration, we promote a web services-based pipeline featuring web services for computer-to-computer data exchange as users add value. This pipeline aims to simplify data integration and creation, to realize automatic analysis, and to facilitate reuse and sharing of data. SSC can foster collaboration and harness collective intelligence to create and discover new knowledge. In addition to its research potential, we also describe its potential role as an e-learning platform in education. We discuss lessons from information technology, predict the next generation of Web (Web 3.0), and describe its potential impact on the future of bioinformatics studies.

PMID: 18842678 [PubMed - as supplied by publisher]

(Source: Briefings in Bioinformatics)

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A multi-type hpv transmission model.

Bulletin of Mathematical Biology — Wed, 08 Oct 2008 04:00:00 +0100

A Multi-Type HPV Transmission Model.

Bull Math Biol. 2008 Oct 8;

Authors: Elbasha EH, Dasbach EJ, Insinga RP

A prophylactic quadrivalent (types 6/11/16/18) vaccine against oncogenic and warts-causing genital Human papillomavirus (HPV) types was approved by the US Food and Drug Administration in 2006. This paper presents a nonlinear, deterministic, age-structured, mathematical model of the transmission dynamics of HPV and disease occurrence in a US population stratified by gender and sexual activity group. The model can assess both the epidemiologic consequences and cost effectiveness of alternative vaccination strategies in a setting of organized cervical cancer screening in the United States. Inputs for the model were obtained from public data sources, published literature, and analyses of clinical trial data. The results suggest that a prophylactic quadrivalent HPV vaccine can: (i) substantially reduce the incidence of disease, (ii) increase survival among females, (iii) improve quality of life for both males and females, (iv) be cost-effective when administered to females age 12-24 years, and (v) be cost-effective when implemented as a strategy that combines vaccination of both females and males before age 12 vaccination with a 12 to 24 years of age catch-up vaccination program.

PMID: 18841421 [PubMed - as supplied by publisher]

(Source: Bulletin of Mathematical Biology)

Lc-mssim - a simulation software for liquid chromatography mass spectrometry data

BMC Bioinformatics - Latest articles — Wed, 08 Oct 2008 04:00:00 +0100

Background: Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms. Results: We present LC-MSsim, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, LC-MSsim writes the simulated data to public XML formats (mzXML or mzData). The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files. Conclusions: LC-MSsim generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that LC-MSsim will be useful to the wider community to perform benchmark studies and comparisons between computational tools. (Source: BMC Bioinformatics - Latest articles)

Preprint: a study of hierarchical and flat classification of proteins

IEEE/ACM Transactions on Computational Biology and Bioinformatics — Tue, 07 Oct 2008 10:00:03 +0100

Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes exist and can potentially be exploited to improve classification performance. In this article we investigate empirically whether this is the case for two such hierarchies. We compare multi-class classification techniques that exploit the information in those class hierarchies and those that do not, using logistic regression, decision trees, bagged decision trees, and support vector machines as the underlying base learners. In particular, we compare hierarchical and flat variants of ensembles of nested dichotomies. The latter have been shown to deliver strong classification performance in multi-class settings. We present experimental results for synthetic, fold recognition, enzyme classification, and remote homology detection data. Our results show that exploiting the class hierarchy improves performance on the synthetic data, but not in the case of the protein classification problems. Based on this we recommend that strong flat multi-class methods be used as a baseline to establish the benefit of exploiting class hierarchies in this area. (Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics)

Probability fold change: a robust computational approach for identifying differentially expressed gene lists.

Computer Methods and Programs in Biomedicine — Mon, 06 Oct 2008 04:00:00 +0100

Probability fold change: A robust computational approach for identifying differentially expressed gene lists.

Comput Methods Programs Biomed. 2008 Oct 6;

Authors: Deng X, Xu J, Hui J, Wang C

Identifying genes that are differentially expressed under different experimental conditions is a fundamental task in microarray studies. However, different ranking methods generate very different gene lists, and this could profoundly impact follow-up analyses and biological interpretation. Therefore, developing improved ranking methods are critical in microarray data analysis. We developed a new algorithm, the probabilistic fold change (PFC), which ranks genes based on a confidence interval estimate of fold change. We performed extensive testing using multiple benchmark data sources including the MicroArray Quality Control (MAQC) data sets. We corroborated our observations with MAQC data sets using qRT-PCR data sets and Latin square spike-in data sets. Along with PFC, we tested six other popular ranking algorithms including Mean Fold Change (FC), SAM, t-statistic (T), Bayesian-t (BAYT), Intensity-Conditional Fold Change (CFC), and Rank Product (RP). PFC achieved reproducibility and accuracy that are consistently among the best of the seven ranking algorithms while other ranking algorithms would show weakness in some cases. Contrary to common belief, our results demonstrated that statistical accuracy will not translate to biological reproducibility and therefore both quality aspects need to be evaluated.

PMID: 18842321 [PubMed - as supplied by publisher]

(Source: Computer Methods and Programs in Biomedicine)

Bayesian ranking of biochemical system models

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

(Source: Bioinformatics)

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Comments on 'on correcting the overestimation of the permutation-based false discovery rate estimator'

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Contact: Yang.xie@utsouthwestern.edu (Source: Bioinformatics)

Cneviewer: a database of conserved non-coding elements for studies of tissue-specific gene regulation

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: There are thousands of strongly conserved non-coding elements (CNEs) in vertebrate genomes, and their functions remain largely unknown. However, without biologically relevant criteria for prioritizing them, selecting a particular CNE sequences to study can be haphazard. To address this problem, we present cneViewer—a database and webtool that systematizes information on conserved non-coding DNA elements in zebrafish. A key feature here is the ability to search for CNEs that may be relevant to tissue-specific gene regulation, based on known developmental expression patterns of nearby genes. cneViewer provides this and other organizing features that significantly facilitate experimental design and CNE analysis. Availability: http://cneviewer.zebrafishcne.org Contact: chuangj@bc.edu (Source: Bioinformatics)

Itfp: an integrated platform of mammalian transcription factors

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: Investigation of transcription factors (TFs) and their downstream regulated genes (targets) is a significant issue in post-genome era, which can provide a brand new vision for some vital biological process. However, information of TFs and their targets in mammalian is far from sufficient. Here, we developed an integrated TF platform (ITFP), which included abundant TFs and their targets of mammalian. In current release, ITFP includes 4105 putative TFs and 69 496 potential TF-target pairs for human, 3134 putative TFs and 37 040 potential TF-target pairs for mouse, and 1114 putative TFs and 18 055 potential TF-target pairs for rat. In short, ITFP will serve as an important resource for the research community of transcription and provide strong support for regulatory network study. Availability: ITFP can be accessed at http://itfp.biosino.org/itfp Contact: yyzhu@fudan.edu.cn; yxli@sibs.ac.cn (Source: Bioinformatics)

Spoltools: online utilities for analyzing spoligotypes of the mycobacterium tuberculosis complex

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

spolTools is a collection of online programs designed to manipulate and analyze spoligotype datasets of the Mycobacterium tuberculosis complex. These tools are integrated into a repository currently containing 1179 spoligotypes and 6278 isolates across 30 datasets. Users can search this database to export for external use or to pass on to the integrated tools. These tools include the computation of basic population genetic quantities, the visualization of clusters of spoligotype patterns based on an estimated evolutionary history and a procedure to predict emerging strains – genotypes associated with elevated transmission. Availability: Database, programs and documentation may be accessed online at http://www.emi.unsw.edu.au/spolTools. Contact: j.reyes@student.unsw.edu.au; m.tanaka@unsw.edu.au (Source: Bioinformatics)

Mpsq: a web tool for protein-state searching

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: MPSQ (multi-protein-states query) is a web-based tool for the discovery of protein states (e.g. biological interactions, covalent modifications, cellular localizations). In particular, large sets of genes can be used to search for enriched state transition network maps (NMs) and features facilitating the interpretation of genomic-scale experiments such as microarrays. One NM collects all the catalogued states of a protein as well as the mutual transitions between the states. For the returned NM, graph visualization is provided for easy understanding and to guide further analysis. Availability: MPSQ is freely available via the web at http://mpsq.biosino.org/. Contact: phao@sibs.ac.cn; liulei@scbit.org; yxli@sibs.ac.cn (Source: Bioinformatics)

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Dbnei2.0: building multilayer network for drug-nei-disease

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: The neuro-endocrine-immune (NEI) system plays a critical regulatory role in modulating host homeostasis and optimizing health. We created the dbNEI 2 years ago to collect NEI molecules and interactions. For transferring the conceptual NEI to the systematic NEI network and uncovering the NEI's medical function, we updated the dbNEI 2.0 in three ways: (i) extended NEI molecules to 2242 genes and 7657 chemical compounds by using gene ontology-based (GO-based) data mining strategy, (ii) added multilayer interactions of NEI molecules including KEGG signal transduction and metabolic pathways, HPRD protein–protein interactions (PPI), transcription factor and microRNA regulations and (iii) connected 611 drugs and 823 diseases through multilayer NEI interactions. The reconstructed drug–NEI–disease network will facilitate the systematic study of NEI system. Availability: http://bioinfo.au.tsinghua.edu.cn/dbNEIweb/ Contact: shaoli@mail.tsinghua.edu.cn (Source: Bioinformatics)

Exactfdr: exact computation of false discovery rate estimate in case-control association studies

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: Genome-wide association studies require accurate and fast statistical methods to identify relevant signals from the background noise generated by a huge number of simultaneously tested hypotheses. It is now commonly accepted that exact computations of association probability value (P-value) are preferred to 2 and permutation-based approximations. Following the same principle, the ExactFDR software package improves speed and accuracy of the permutation-based false discovery rate (FDR) estimation method by replacing the permutation-based estimation of the null distribution by the generalization of the algorithm used for computing individual exact P-values. It provides a quick and accurate non-conservative estimator of the proportion of false positives in a given selection of markers, and is therefore an efficient and pragmatic tool for the analysis of genome-wide association studies. Availability: A Java 1.6 (1.5-compatible) version is available on SourceForge: http://sourceforge.net/projects/exactfdr. Contact: Jerome.wojcik@merckserono.net Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Sidrm: an effective and generally applicable online sirna design tool

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: Small interfering RNAs (siRNAs) have become an indispensable tool for the investigation of gene functions. Most existing siRNA design tools were trained on datasets assembled from confined origins, incompatible with the diverse siRNA laboratory practice to which these tools will ultimately be applied. We have performed an updated analysis using the disjunctive rule merging (DRM) approach on a large and diverse dataset compiled from siRecords, and implemented the resulting rule sets in siDRM, a new online siRNA design tool. siDRM also implements a few high-sensitivity rule sets and fast rule sets, links to siRecords, and uses several filters to check unwanted detrimental effects, including innate immune responses, cell toxic effects and off-target activities in selecting siRNAs. A performance comparison using an independent dataset indicated that siDRM outperforms 19 existing siRNA design tools in identifying effective siRNAs. Availability: siDRM can be accessed at http://siRecords.umn.edu/siDRM/. Contact: toli@biocompute.umn.edu Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Statalign: an extendable software package for joint bayesian estimation of alignments and evolutionary trees

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Bayesian analysis is one of the most popular methods in phylogenetic inference. The most commonly used methods fix a single multiple alignment and consider only substitutions as phylogenetically informative mutations, though alignments and phylogenies should be inferred jointly as insertions and deletions also carry informative signals. Methods addressing these issues have been developed only recently and there has not been so far a user-friendly program with a graphical interface that implements these methods. Results: We have developed an extendable software package in the Java programming language that samples from the joint posterior distribution of phylogenies, alignments and evolutionary parameters by applying the Markov chain Monte Carlo method. The package also offers tools for efficient on-the-fly summarization of the results. It has a graphical interface to configure, start and supervise the analysis, to track the status of the Markov chain and to save the results. The background model for insertions and deletions can be combined with any substitution model. It is easy to add new substitution models to the software package as plugins. The samples from the Markov chain can be summarized in several ways, and new postprocessing plugins may also be installed. Availability: The code is available from http://phylogeny-cafe.elte.hu/StatAlign/ Contact: miklosi@ramet.elte.hu (Source: Bioinformatics)

Profdists: (profile-) distance based phylogeny on sequence--structure alignments

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: The Profile Neighbor Joining (PNJ) algorithm as implemented in the software ProfDist is computationally efficient in reconstructing very large trees. Besides the huge amount of sequence data the structure is important in RNA alignment analysis and phylogenetic reconstruction. Results: For this ProfDistS provides a phylogenetic workflow that uses individual RNA secondary structures in reconstructing phylogenies based on sequence-structure alignments—using PNJ with manual or iterative and automatic profile definition. Moreover, ProfDistS can deal also with protein sequences. Availability: ProfDistS is freely available for non-commercial use for Windows, Linux and MAC operating systems at http://profdist.bioapps.biozentrum.uni-wuerzburg.de. Contact: tobias.mueller@biozentrum.uni-wuerzburg.de; matthias.wolf@biozentrum.uni-wuerzburg.de (Source: Bioinformatics)

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Epos: a modular software framework for phylogenetic analysis

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: Estimating Phylogenies of Species (EPoS) is a modular software framework for phylogenetic analysis, visualization and data management. It provides a plugin-based system that integrates a storage facility, a rich user interface and the ability to easily incorporate new methods, functions and visualizations. EPoS ships with persistent data management, a set of well-known phylogenetic algorithms and a multitude of tree visualization methods and layouts. Implemented algorithms cover distance-based tree construction, consensus trees and various graph-based supertree methods. The rendering system can be customized for, say, different edge and node styles. Availability: Executables and source code are available under the LGPL license at http://www.bio.informatik.uni-jena.de/epos. Contact: thasso@minet.uni-jena.de Supplementary information: The homepage contains tutorials and documentation for both users and programmers who want to develop plugins and extensions. (Source: Bioinformatics)

Snap predicts effect of mutations on protein function

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: Many non-synonymous single nucleotide polymor-phisms (nsSNPs) in humans are suspected to impact protein function. Here, we present a publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions. SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold. Each prediction is associated with a reliability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions. Availability: Web-server: http://www.rostlab.org/services/SNAP; downloadable program available upon request. Contact: bromberg@rostlab.org Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Seqmap: mapping massive amount of oligonucleotides to the genome

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: SeqMap is a tool for mapping large amount of short sequences to the genome. It is designed for finding all the places in a reference genome where each sequence may come from. This task is essential to the analysis of data from ultra high-throughput sequencing machines. With a carefully designed index-filtering algorithm and an efficient implementation, SeqMap can map tens of millions of short sequences to a genome of several billions of nucleotides. Multiple substitutions and insertions/deletions of the nucleotide bases in the sequences can be tolerated and therefore detected. SeqMap supports FASTA input format and various output formats, and provides command line options for tuning almost every aspect of the mapping process. A typical mapping can be done in a few hours on a desktop PC. Parallel use of SeqMap on a cluster is also very straightforward. Contact: whwong@stanford.edu (Source: Bioinformatics)

Protein function prediction and annotation in an integrated environment powered by web services (afawe)

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: Many sequenced genes are mainly annotated through automatic transfer of annotation from similar sequences. Manual comparison of results or intermediate results from different tools can help avoid wrong annotations and give hints to the function of a gene even if none of the automated tools could return any result. AFAWE simplifies the task of manual functional annotation by running different tools and workflows for automatic function prediction and displaying the results in a way that facilitates comparison. Because all programs are executed as web services, AFAWE is easily extensible and can directly query primary databases, thereby always using the most up-to-date data sources. Visual filters help to distinguish trustworthy results from non-significant results. Furthermore, an interface to add detailed manual annotation to each gene is provided, which can be displayed to other users. Availability: AFAWE is available at http://bioinfo.mpiz-koeln.mpg.de/afawe/ Contact: afawe-admin@mpiz-koeln.mpg.de Supplementary information: SIFTER pipeline (S1), AFAWE tutorial (S2). (Source: Bioinformatics)

Rtreemix: an r package for estimating evolutionary pathways and genetic progression scores

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Summary: In genetics, many evolutionary pathways can be modeled by the ordered accumulation of permanent changes. Mixture models of mutagenetic trees have been used to describe disease progression in cancer and in HIV. In cancer, progression is modeled by the accumulation of chromosomal gains and losses in tumor cells; in HIV, the accumulation of drug resistance-associated mutations in the viral genome is known to be associated with disease progression. From such evolutionary models, genetic progression scores can be derived that assign measures for the disease state to single patients. Rtreemix is an R package for estimating mixture models of evolutionary pathways from observed cross-sectional data and for estimating associated genetic progression scores. The package also provides extended functionality for estimating confidence intervals for estimated model parameters and for evaluating the stability of the estimated evolutionary mixture models. Availability: Rtreemix is an R package that is freely available from the Bioconductor project at http://www.bioconductor.org and runs on Linux and Windows. Contact: jasmina@mpi-inf.mpg.de (Source: Bioinformatics)

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Novel pathway compendium analysis elucidates mechanism of pro-angiogenic synthetic small molecule

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Computational techniques have been applied to experimental datasets to identify drug mode-of-action. A shortcoming of existing approaches is the requirement of large reference databases of compound expression profiles. Here, we developed a new pathway-based compendium analysis that couples multi-timepoint, controlled microarray data for a single compound with systems-based network analysis to elucidate drug mechanism more efficiently. Results: We applied this approach to a transcriptional regulatory footprint of phthalimide neovascular factor 1 (PNF1)—a novel synthetic small molecule that exhibits significant in vitro endothelial potency—spanning 1–48 h post-supplementation in human micro-vascular endothelial cells (HMVEC) to comprehensively interrogate PNF1 effects. We concluded that PNF1 first induces tumor necrosis factor-alpha (TNF-) signaling pathway function which in turn affects transforming growth factor-beta (TGF-β) signaling. These results are consistent with our previous observations of PNF1-directed TGF-β signaling at 24 h, including differential regulation of TGF-β-induced matrix metalloproteinase 14 (MMP14/MT1-MMP) which is implicated in angiogenesis. Ultimately, we illustrate how our pathway-based compendium analysis more efficiently generates hypotheses for compound mechanism than existing techniques. Availability: The microarray data generated as part of this study are available in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/). Contact: botchwey@virginia.edu; papin@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Local coherence in genetic interaction patterns reveals prevalent functional versatility

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Epistatic or genetic interactions, representing the effects of mutating one gene on the phenotypes caused by mutations in one or moredistinct genes, can be very helpful for uncovering functional relationships between genes. Recently, the epistatic miniarray profiles (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. For E-MAP data analysis, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles, and the resulting descriptions assign each gene to only one group, thereby ignoring the multifunctional roles played by most genes. Results: Here, we present the original local coherence detection (LCD) algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast Saccharomyces cerevisiae. In addition to recapitulating the majority of the functional modules and many protein complexes reported previously, our algorithm uncovers many recently documented and novel multifunctional relationships between genes and gene groups. Our algorithm hence represents a valuable tool for uncovering new roles for genes with annotated functions and for mapping groups of genes and proteins into pathways. Availability: A Java implementation of the LCD algorithm is available at URL http://genepro.ccb.sickkids.ca/biclustering.html Contact: shuyepu@sickkids.ca Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Computational prediction of human proteins that can be secreted into the bloodstream

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

We present a novel computational method for predicting which proteins from highly and abnormally expressed genes in diseased human tissues, such as cancers, can be secreted into the bloodstream, suggesting possible marker proteins for follow-up serum proteomic studies. A main challenging issue in tackling this problem is that our understanding about the downstream localization after proteins are secreted outside the cells is very limited and not sufficient to provide useful hints about secretion to the bloodstream. To bypass this difficulty, we have taken a data mining approach by first collecting, through extensive literature searches, human proteins that are known to be secreted into the bloodstream due to various pathological conditions as detected by previous proteomic studies, and then asking the question: ‘what do these secreted proteins have in common in terms of their physical and chemical properties, amino acid sequence and structural features that can be used to predict them?’ We have identified a list of features, such as signal peptides, transmembrane domains, glycosylation sites, disordered regions, secondary structural content, hydrophobicity and polarity measures that show relevance to protein secretion. Using these features, we have trained a support vector machine-based classifier to predict protein secretion to the bloodstream. On a large test set containing 98 secretory proteins and 6601 non-secretory proteins of human, our classifier achieved ~90% prediction sensitivity and ~98% prediction specificity. Several additional datasets are used to further assess the performance of our classifier. On a set of 122 proteins that were found to be of abnormally high abundance in human blood due to various cancers, our program predicted 62 as blood-secreted proteins. By applying our program to abnormally highly expressed genes in gastric cancer and lung cancer tissues detected through microarray gene expression studies, we predicted 13 and 31 as blood secreted, respectively, suggesting that they could serve as potential biomarkers for these two cancers, respectively. Our study demonstrated that our method can provide highly useful information to link genomic and proteomic studies for disease biomarker discovery. Our software can be accessed at http://csbl1.bmb.uga.edu/cgi-bin/Secretion/secretion.cgi. Contact: xyn@bmb.uga.edu Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Model-based analysis of interferon-{beta} induced signaling pathway

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Interferon-β induced JAK-STAT signaling pathways contribute to mucosal immune recognition and an anti-viral state. Though the main molecular mechanisms constituting these pathways are known, neither the detailed structure of the regulatory network, nor its dynamics has yet been investigated. The objective of this work is to build a mathematical model for the pathway that would serve two purposes: (1) to reproduce experimental results in simulation of both early and late response to Interferon-β stimulation and (2) to explain experimental phenomena generating new hypotheses about regulatory mechanisms that cannot yet be tested experimentally. Results: Experimentally determined time dependent changes in the major components of this pathway were used to build a mathematical model describing pathway dynamics in the form of ordinary differential equations. The experimental results suggested existence of unknown negative control mechanisms that were tested numerically using the model. Together, experimental and numerical data show that the epithelial JAK-STAT pathway might be subjected to previously unknown dynamic negative control mechanisms: (1) activation of dormant phosphatases and (2) inhibition of nuclear import of IRF1. Availability: The model, written in Matlab, is available online at www.stat.rice.edu/~jsmieja/IFN Contact: jaroslaw.smieja@polsl.pl Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

A semiparametric test to detect associations between quantitative traits and candidate genes in structured populations

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Although population-based association mapping may be subject to the bias caused by population stratification, alternative methods that are robust to population stratification such as family-based linkage analysis have lower mapping resolution. Recently, various statistical methods robust to population stratification were proposed for association studies, using unrelated individuals to identify associations between candidate genes and traits of interest. The association between a candidate gene and a quantitative trait is often evaluated via a regression model with inferred population structure variables as covariates, where the residual distribution is customarily assumed to be from a symmetric and unimodal parametric family, such as a Gaussian, although this may be inappropriate for the analysis of many real-life datasets. Results: In this article, we proposed a new structured association (SA) test. Our method corrects for continuous population stratification by first deriving population structure and kinship matrices through a set of random genetic markers and then modeling the relationship between trait values, genotypic scores at a candidate marker and genetic background variables through a semiparametric model, where the error distribution is modeled as a mixture of Polya trees centered around a normal family of distributions. We compared our model to the existing SA tests in terms of model fit, type I error rate, power, precision and accuracy by application to a real dataset as well as simulated datasets. Contact: meijuanl@biostat.umn.edu (Source: Bioinformatics)

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On the frequency of copy number variants

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Estimating the frequency distribution of copy number variants (CNVs) is an important aspect of the effort to characterize this new type of genetic variation. Currently, most studies report a strong skew toward low-frequency CNVs. In this article, our goal is to investigate the frequencies of CNVs. We employ a two-step procedure for the CNV frequency estimation process. We use family information a posteriori to select only the most reliable CNV regions, i.e. those showing high rates of Mendelian transmission. Results: Our results suggest that the current skew toward low-frequency CNVs may not be representative of the true frequency distribution, but may be due, among other reasons, to the non-negligible false negative rates that characterize CNV detection methods. Moreover, false positives are also likely, as low-frequency CNVs are hard to detect with small sample sizes and technologies that are not ideally suited for their detection. Without appropriate validation methods, such as incorporation of biologically relevant information (for example, in our case, the transmission of heritable CNVs from parents to offspring), it is difficult to assess the validity of specific CNVs, and even harder to obtain reliable frequency estimates. Availability: Software implementing the methods described in this article is available for download at the following address: http://www.isites.harvard.edu/icb/icb.do?keyword=k36162 Contact: iionita@hsph.harvard.edu Supplementary informantion: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

An hmm approach to genome-wide identification of differential histone modification sites from chip-seq data

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Epigenetic modifications are one of the critical factors to regulate gene expression and genome function. Among different epigenetic modifications, the differential histone modification sites (DHMSs) are of great interest to study the dynamic nature of epigenetic and gene expression regulations among various cell types, stages or environmental responses. To capture the histone modifications at whole genome scale, ChIP-seq technology is becoming a robust and comprehensive approach. Thus the DHMSs are potentially identifiable by comparing two ChIP-seq libraries. However, little has been addressed on this issue in literature. Results: Aiming at identifying DHMSs, we propose an approach called ChIPDiff for the genome-wide comparison of histone modification sites identified by ChIP-seq. Based on the observations of ChIP fragment counts, the proposed approach employs a hidden Markov model (HMM) to infer the states of histone modification changes at each genomic location. We evaluated the performance of ChIPDiff by comparing the H3K27me3 modification sites between mouse embryonic stem cell (ESC) and neural progenitor cell (NPC). We demonstrated that the H3K27me3 DHMSs identified by our approach are of high sensitivity, specificity and technical reproducibility. ChIPDiff was further applied to uncover the differential H3K4me3 and H3K36me3 sites between different cell states. Interesting biological discoveries were achieved from such comparison in our study. Availability: http://cmb.gis.a-star.edu.sg/ChIPSeq/tools.htm Contact: asflin@ntu.edu.sg; sungk@gis.a-star.edu.sg Supplementary information: Supplementary methods and data are available at Bioinformatics online. (Source: Bioinformatics)

Optimal design of thermally stable proteins

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: For many biotechnological purposes, it is desirable to redesign proteins to be more structurally and functionally stable at higher temperatures. For example, chemical reactions are intrinsically faster at higher temperatures, so using enzymes that are stable at higher temperatures would lead to more efficient industrial processes. We describe an innovative and computationally efficient method called Improved Configurational Entropy (ICE), which can be used to redesign a protein to be more thermally stable (i.e. stable at high temperatures). This can be accomplished by systematically modifying the amino acid sequence via local structural entropy (LSE) minimization. The minimization problem is modeled as a shortest path problem in an acyclic graph with nonnegative weights and is solved efficiently using Dijkstra's method. Contact: mitchell@biochem.wisc.edu (Source: Bioinformatics)

Accurate sequence-based prediction of catalytic residues

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Prediction of catalytic residues provides useful information for the research on function of enzymes. Most of the existing prediction methods are based on structural information, which limits their use. We propose a sequence-based catalytic residue predictor that provides predictions with quality comparable to modern structure-based methods and that exceeds quality of state-of-the-art sequence-based methods. Results: Our method (CRpred) uses sequence-based features and the sequence-derived PSI-BLAST profile. We used feature selection to reduce the dimensionality of the input (and explain the input) to support vector machine (SVM) classifier that provides predictions. Tests on eight datasets and side-by-side comparison with six modern structure- and sequence-based predictors show that CRpred provides predictions with quality comparable to current structure-based methods and better than sequence-based methods. The proposed method obtains 15–19% precision and 48–58% TP (true positive) rate, depending on the dataset used. CRpred also provides confidence values that allow selecting a subset of predictions with higher precision. The improved quality is due to newly designed features and careful parameterization of the SVM. The features incorporate amino acids characterized by the highest and the lowest propensities to constitute catalytic residues, Gly that provides flexibility for catalytic sites and sequence motifs characteristic to certain catalytic reactions. Our features indicate that catalytic residues are on average more conserved when compared with the general population of residues and that highly conserved amino acids characterized by high catalytic propensity are likely to form catalytic sites. We also show that local (with respect to the sequence) hydrophobicity contributes towards the prediction. Availability: http://biomine.ece.ualberta.ca/CRpred/CRpred.htm Contact: lkurgan@ece.ualberta.ca Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Optimal contact map alignment of protein-protein interfaces

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

The long-standing problem of constructing protein structure alignments is of central importance in computational biology. The main goal is to provide an alignment of residue correspondences, in order to identify homologous residues across chains. A critical next step of this is the alignment of protein complexes and their interfaces. Here, we introduce the program CMAPi, a two-dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces: it produces polynomial-time near-optimal alignments in the case of multiple complexes. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity. While previous methods that use an all-atom based representation of the interface have been successful, CMAPi's use of a contact map representation allows it to be more tolerant to conformational changes and thus to align more of the interaction surface. These improved interface alignments should enhance homology modeling and threading methods for predicting PPIs by providing a basis for generating template profiles for sequence–structure alignment. Contact: bab@mit.edu; jbienkowska@gmail.com Supplementary information: Supplementary data are available at http://theory.csail.mit.edu/cmapi (Source: Bioinformatics)

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Empirical profile mixture models for phylogenetic reconstruction

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Previous studies have shown that accounting for site-specific amino acid replacement patterns using mixtures of stationary probability profiles offers a promising approach for improving the robustness of phylogenetic reconstructions in the presence of saturation. However, such profile mixture models were introduced only in a Bayesian context, and are not yet available in a maximum likelihood (ML) framework. In addition, these mixture models only perform well on large alignments, from which they can reliably learn the shapes of profiles, and their associated weights. Results: In this work, we introduce an expectation–maximization algorithm for estimating amino acid profile mixtures from alignment databases. We apply it, learning on the HSSP database, and observe that a set of 20 profiles is enough to provide a better statistical fit than currently available empirical matrices (WAG, JTT), in particular on saturated data. Availability: We have implemented these models into two currently available Bayesian and ML phylogenetic reconstruction programs. The two implementations, PhyloBayes, and PhyML, are freely available on our web site (http://atgc.lirmm.fr/cat). They run under Linux and MaxOSX operating systems. Contact: nicolas.lartillot@lirmm.fr Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Improving position-specific predictions of protein functional sites using phylogenetic motifs

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Accurate computational prediction of protein functional sites is critical to maximizing the utility of recent high-throughput sequencing efforts. Among the available approaches, position-specific conservation scores remain among the most popular due to their accuracy and ease of computation. Unfortunately, high false positive rates remain a limiting factor. Using phylogenetic motifs (PMs), we have developed two combined (conservation + PMs) prediction schemes that significantly improve prediction accuracy. Results: Our first approach, called position-specific MINER (psMINER), rank orders alignment columns by conservation. Subsequently, positions that are also not identified as PMs are excluded from the prediction set. This approach improves prediction accuracy, in a statistically significant way, compared to the underlying conservation scores. Increased accuracy is a general result, meaning improvement is observed over several different conservation scores that span a continuum of complexity. In addition, a hybrid MINER (hMINER) that quantitatively considers both scoring regimes provides further improvement. More importantly, it provides critical insight into the relative importance of phylogeny versus alignment conservation. Both methods outperform other common prediction algorithms that also utilize phylogenetic concepts. Finally, we demonstrate that the presented results are critically sensitive to functional site definition, thus highlighting the need for more complete benchmarks within the prediction community. Availability: Our benchmark datasets are available for download at http://www.cs.uncc.edu/~drlivesa/dataset.html. Contact: drlivesa@uncc.edu Supplementary information: Supplementary data is available at Bioinformatics online. (Source: Bioinformatics)

Seeder: discriminative seeding dna motif discovery

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches. Results: We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements. Availability: The Seeder Perl distribution includes four modules. It is available for download on the Comprehensive Perl Archive Network (CPAN) at http://www.cpan.org. Contact: martina.stromvik@mcgill.ca Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)

Markov model plus k-word distributions: a synergy that produces novel statistical measures for sequence comparison

Bioinformatics — Mon, 06 Oct 2008 04:00:00 +0100

Motivation: Many proposed statistical measures can efficiently compare biological sequences to further infer their structures, functions and evolutionary information. They are related in spirit because all the ideas for sequence comparison try to use the information on the k-word distributions, Markov model or both. Motivated by adding k-word distributions to Markov model directly, we investigated two novel statistical measures for sequence comparison, called wre.k.r and S2.k.r. Results: The proposed measures were tested by similarity search, evaluation on functionally related regulatory sequences and phylogenetic analysis. This offers the systematic and quantitative experimental assessment of our measures. Moreover, we compared our achievements with these based on alignment or alignment-free. We grouped our experiments into two sets. The first one, performed via ROC (receiver operating curve) analysis, aims at assessing the intrinsic ability of our statistical measures to search for similar sequences from a database and discriminate functionally related regulatory sequences from unrelated sequences. The second one aims at assessing how well our statistical measure is used for phylogenetic analysis. The experimental assessment demonstrates that our similarity measures intending to incorporate k-word distributions into Markov model are more efficient. Availability: The software, data and supplement material are freely available at http://math.dlut.edu.cn/daiqi/mplusd.html. Contact: daiailiu2004@yahoo.com.cn Supplementary information: Supplementary data are available at Bioinformatics online. (Source: Bioinformatics)