Saad I. Sheikh

KINALYZER, a computer program for reconstructing sibling groups

A software suite KINALYZER reconstructs full‐sibling groups without parental information using data from codominant marker loci such as microsatellites. KINALYZER utilizes a new algorithm for sibling reconstruction in diploid organisms based on combinatorial optimization. KINALYZER makes use of a Minimum 2‐Allele Set Cover approach based on Mendelian inheritance rules and finds the smallest number of sibling groups that contain all the individuals in the sample. Also available is a ‘Greedy Consensus’ approach that reconstructs sibgroups using subsets of loci and finds the consensus of the partial solutions. Unlike likelihood methods for sibling reconstruction, KINALYZER does not require information about population allele frequencies and it makes no assumptions regarding the mating system of the species. KINALYZER is freely available as a web‐based service.

Estimating execution time for parallel tasks in heterogeneous processing (HP) environment

Mapping of application program tasks onto a suite of heterogeneous machines requires the estimation of execution times of the tasks on these machines. In this paper, an efficient methodology for estimating the execution times of a given program on various machines available in an HP environment is presented. The methodology uses parametric code profiling and parametric analytical benchmarking techniques and incorporates the concept of an architecture-independent computation model to estimate the execution times.<<ETX>>

Abstract: using the fast fourier transform to accelerate the computational search for RNA conformational switches

We describe the broad outline of a new thermodynamics-based algorithm, FFTbor, that uses the fast Fourier transform to perform polynomial interpolation to compute the Boltzmann probability that secondary structures differ by k base pairs from an arbitrary reference structure of a given RNA sequence. The algorithm, which runs in quartic time O(n4) and quadratic space O(n2), is used to determine the correlation between kinetic folding speed and the ruggedness of the energy landscape, and to predict the location of riboswitch expression platform candidates. The full paper appears in PLoS ONE (2012) 19 Dec 2012. A web server is available at http://bioinformatics.bc.edu/clotelab/FFTbor/.

Error Tolerant Sibship Reconstruction in Wild Populations

Kinship analysis using genetic data is important for many biological applications, including many in conservation biology. Wide availability of microsatellites has boosted studies in wild populations that rely on the knowledge of kinship, particularly sibling relationships (sibship). While there exist many methods for reconstructing sibling relationships, almost none account for errors and mutations in microsatellite data, which are prevalent and affect the quality of reconstruction. We present an error-tolerant method for reconstructing sibling relationships based on the concept of consensus methods. We test our approach on both real and simulated data, with both pre-existing and introduced errors. Our method is highly accurate on almost all simulations, giving over 90% accuracy in most cases. Ours is the first method designed to tolerate errors while making no assumptions about the population or the sampling.

New Optimization Model and Algorithm for Sibling Reconstruction from Genetic Markers

With improved tools for collecting genetic data from natural and experimental populations, new opportunities arise to study fundamental biological processes, including behavior, mating systems, adaptive trait evolution, and dispersal patterns. Full use of the newly available genetic data often depends upon reconstructing genealogical relationships of individual organisms, such as sibling reconstruction. This paper presents a new optimization framework for sibling reconstruction from single generation microsatellite genetic data. Our framework is based on assumptions of parsimony and combinatorial concepts of Mendels inheritance rules. Here, we develop a novel optimization model for sibling reconstruction as a large-scale mixed-integer program (MIP), shown to be a generalization of the set covering problem. We propose a new heuristic approach to efficiently solve this large-scale optimization problem. We test our approach on real biological data as presented in other studies as well as simulated data, and compare our results with other state-of-the-art sibling reconstruction methods. The empirical results show that our approaches are very efficient and outperform other methods while providing the most accurate solutions for two benchmark data sets. The results suggest that our framework can be used as an analytical and computational tool for biologists to better study ecological and evolutionary processes involving knowledge of familial relationships in a wide variety of biological systems.

Combinatorial reconstruction of half-sibling groups from microsatellite data

While full-sibling group reconstruction from microsatellite data is a well-studied problem, reconstruction of half-sibling groups is much less studied, theoretically challenging, and computationally demanding. In this paper, we present a formulation of the half-sibling reconstruction problem and prove its APX-hardness. We also present exact solutions for this formulation and develop heuristics. Using biological and synthetic datasets we present experimental results and compare them with the leading alternative software COLONY. We show that our results are competitive and allow half-sibling group reconstruction in the presence of polygamy, which is prevalent in nature.

A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins

BackgroundTransmembrane β-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane β-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane β-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis.ResultsWe present a novel graph-theoretic model for classification and structure prediction of transmembrane β-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize β-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane β-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject α-helical bundles with 100% accuracy and β-barrel lipocalins with 97% accuracy.ConclusionsWe show that it is possible to design models for classification and structure prediction for transmembrane β-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

Impact of the energy model on the complexity of RNA folding with pseudoknots

Predicting the folding of an RNA sequence, while allowing general pseudoknots (PK), consists in finding a minimal free-energy matching of its n positions. Assuming independently contributing base-pairs, the problem can be solved in Θ(n3)-time using a variant of the maximal weighted matching. By contrast, the problem was previously proven NP-Hard in the more realistic nearest-neighbor energy model. In this work, we consider an intermediate model, called the stacking-pairs energy model. We extend a result by Lyngso, showing that RNA folding with PK is NP-Hard within a large class of parametrization for the model. We also show the approximability of the problem, by giving a practical Θ(n3) algorithm that achieves at least a 5-approximation for any parametrization of the stacking model. This contrasts nicely with the nearest-neighbor version of the problem, which we prove cannot be approximated within any positive ratio, unless P=NP.

Stability analysis of phylogenetic trees

MOTIVATION Phylogenetics, or reconstructing the evolutionary relationships of organisms, is critical for understanding evolution. A large number of heuristic algorithms for phylogenetics have been developed, some of which enable estimates of trees with tens of thousands of taxa. Such trees may not be robust, as small changes in the input data can cause major differences in the optimal topology. Tools that can assess the quality and stability of phylogenetic tree estimates and identify the most reliable parts of the tree are needed. RESULTS We define measures that assess the stability of trees, subtrees and individual taxa with respect to changes in the input sequences. Our measures consider changes at the finest granularity in the input data (i.e. individual nucleotides). We demonstrate the effectiveness of our measures on large published datasets. Our measures are computationally feasible for phylogenetic datasets consisting of tens of thousands of taxa. AVAILABILITY This software is available at http://bioinformatics.cise.ufl.edu/phylostab CONTACT sheikh@cise.ufl.edu

Energy-based classification and structure prediction of transmembrane beta-barrel proteins

Transmembrane β-barrel (TMB) proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of TMB proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of TMB proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. We present a novel graph-theoretic model for classification and structure prediction of TMB proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize β-barrels by assessing the pseudo free energy. We assess the structure prediction on 42 proteins gathered from existing databases on experimentally validated TMB proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject β-helical bundles with 100% accuracy and β-barrel lipocalins with 97% accuracy.