Recent genome-wide studies on nucleosome positioning in model organisms have shown strong evidence that nucleosome landscapes in the proximity of protein-coding genes exhibit regular characteristic patterns. Here, we propose a computational framework to discover novel genes in the human malaria parasite genome P. falciparum using nucleosome positioning inferred from MAINE-seq data. We rely on a classifier trained on the nucleosome landscape profiles of experimentally verified genes, and then used to discover new genes (without considering the primary DNA sequence). Cross-validation experiments show that our classifier is very accurate. About two thirds of the locations reported by the classifier match experimentally determined expressed sequence tags in GenBank, for which no gene has been annotated in the human malaria parasite.
There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity. Our focus is on methods that determine binding site similarity. Although several such methods exist, it still remains a challenging problem to quickly find all functionally-related matches for structural motifs in large data sets with high specificity. In this context, a structural motif is a set of 3D points annotated with physicochemical information that characterize a molecular function. We propose a new method called LabelHash that creates hash tables of n-tuples of residues for a set of targets. Using these hash tables, we can quickly look up partial matches to a motif and expand those matches to complete matches. We show that by applying only very mild geometric constraints we can find statistically significant matches with extremely high specificity in very large data sets and for very general structural motifs. We demonstrate that our method requires a reasonable amount of storage when employing a simple geometric filter and further improves on the specificity of our previous work while maintaining very high sensitivity. Our algorithm is evaluated on 20 homolog classes and a non-redundant version of the Protein Data Bank as our background data set. We use cluster analysis to analyze why certain classes of homologs are more difficult to classify than others. The LabelHash algorithm is implemented on a web server at http://kavrakilab.org/labelhash/.
Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.
The recent explosion in the number of clinical studies involving microarray data calls for novel computational methods for their dissection. Human protein interaction networks are rapidly growing and can assist in the extraction of functional modules from microarray data. We describe a novel methodology for extraction of connected network modules with coherent gene expression patterns that are correlated with a specific clinical parameter. Our approach suits both numerical (e.g., age or tumor size) and logical parameters (e.g., gender or mutation status). We demonstrate the method on a large breast cancer dataset, where we identify biologically-relevant modules related to nine clinical parameters including patient age, tumor size, and metastasis-free survival. Our method is capable of detecting disease-relevant pathways that could not be found using other methods. Our results support some previous hypotheses regarding the molecular pathways underlying diversity of breast tumors and suggest novel ones.
We improve on guided genome halving algorithms so that several thousand gene sets, each containing two paralogs in the descendant T of the doubling event and their single ortholog from an undoubled reference genome R, can be analyzed to reconstruct the ancestor A of T at the time of doubling. At the same time, large numbers of defective gene sets, either missing one paralog from T or missing their ortholog in R, may be incorporated into the analysis in a consistent way. We apply this genomic rearrangement distance-based approach to the recently sequenced poplar (Populus trichocarpa) and grapevine (Vitis vinifera) genomes, as T and R respectively.
Solvent accessibility is an important structural feature for a protein. We propose a new method for solvent accessibility prediction that uses known structure and sequence information more efficiently. We first estimate the relative solvent accessibility of the query protein using fuzzy mean operator from the solvent accessibilities of known structure fragments that have similar sequences to the query protein. We then integrate the estimated solvent accessibility and the position specific scoring matrix of the query protein using a neural network. We tested our method on a large data set consisting of 3386 non-redundant proteins. The comparison with other methods show slightly improved prediction accuracies with our method. The resulting system does need not be re-trained when new data is available. We incorporated our method into the MUPRED system, which is available as a web server at http://digbio.missouri.edu/mupred.
In order to improve the prediction of protein-ligand binding sites through homology modeling, we incorporate knowledge of the binding residues into the modeling framework. Residues are identified as binding or nonbinding based on their true labels as well as labels predicted from structure and sequence. The sequence predictions were made using a support vector machine framework which employs a sophisticated window-based kernel. Binding labels are used with a very sensitive sequence alignment method to align the target and template. Relevant parameters governing the alignment process are searched for optimal values. Based on our results, homology models of the binding site can be improved if a priori knowledge of the binding residues is available. For target-template pairs with low sequence identity and high structural diversity our sequence-based prediction method provided sufficient information to realize this improvement.
Pathways show how different biochemical entities interact with each other to perform vital functions for the survival of organisms. Similarities between pathways indicate functional similarities that are difficult to identify by comparing the individual entities that make up those pathways. When interacting entities are of single type, the problem of identifying similarities reduces to graph isomorphism problem. However, for pathways with varying types of entities, such as metabolic pathways, alignment problem is more challenging. Existing methods, often, address the metabolic pathway alignment problem by ignoring all the entities except for one type. This kind of abstraction reduces the relevance of the alignment significantly as it causes losses in the information content. In this paper, we develop a method to solve the pairwise alignment problem for metabolic pathways. One distinguishing feature of our method is that it aligns reactions, compounds and enzymes without abstraction of pathways. We pursue the intuition that both pairwise similarities of entities (homology) and their organization (topology) are crucial for metabolic pathway alignment. In our algorithm, we account for both by creating an eigenvalue problem for each entity type. We enforce the consistency by considering the reachability sets of the aligned entities. Our experiments show that, our method finds biologically and statistically significant alignments in the order of seconds for pathways with approximately 100 entities.
We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The problem is to build a consensus map, which includes and is consistent with all (or, the vast majority of) the markers in the individual maps. When markers in the input maps have ordering conflicts, the resulting consensus map will contain cycles. We formulate the problem of resolving cycles in a combinatorial optimization framework, which in turn is expressed as an integer linear program. A faster approximation algorithm is proposed, and an additional speed-up heuristic is developed. According to an extensive set of experimental results, our tool is consistently better than JOINMAP, both in terms of accuracy and running time.
Establishing structural or functional relationship between sequences, for instance to infer the structural class of an unannotated protein, is a key task in biological sequence analysis. Recent computational methods such as profile and neighborhood mismatch kernels have shown very promising results for protein sequence classification, at the cost of high computational complexity. In this study we address the multi-class sequence classification problems using a class of string-based kernels, the sparse spatial sample kernels (SSSK), that are both biologically motivated and efficient to compute. The proposed methods can work with very large databases of protein sequences and show substantial improvements in computing time over the existing methods. Application of the SSSK to the multi-class protein prediction problems (fold recognition and remote homology detection) yields significantly better performance than existing state-of-the-art algorithms.
We present two heuristics for speeding up a time series alignment algorithm that is related to dynamic time warping (DTW). In previous work, we developed our multisegment alignment algorithm to answer similarity queries for toxicogenomic time-series data. Our multisegment algorithm returns more accurate alignments than DTW at the cost of time complexity; the multisegment algorithm is O(n(5)) whereas DTW is O(n(2)). The first heuristic we present speeds up our algorithm by a constant factor by restricting alignments to a cone shape in alignment space. The second heuristic restricts the alignments considered to those near one returned by a DTW-like method. This heuristic adjusts the time complexity to O(n(3)). Importantly, neither heuristic results in a loss in accuracy.
With the advent of high-throughput gene perturbation screens (e.g., RNAi assays, genome-wide deletion mutants), modeling the complex relationship between genes and phenotypes has become a paramount problem. One broad class of methods uses 'guilt by association' methods to impute phenotypes to genes based on the interactions between the given gene and other genes with known phenotypes. But these methods are inadequate for genes that have no cataloged interactions but which nevertheless are known to result in important phenotypes. In this paper, we present an approach to first model relationships between phenotypes using the notion of 'relative importance' and subsequently use these derived relationships to make phenotype predictions. Besides improved accuracy on S. cerevisiae deletion mutants and C. elegans knock-down datasets, we show how our approach sheds insight into relations between phenotypes.
With the rapidly expanding field of medical genetics and genetic counseling, genealogy information is becoming increasingly abundant. An important computation on pedigree data is the calculation of identity coefficients, which provide a complete description of the degree of relatedness of a pair of individuals. The areas of application of identity coefficients are numerous and diverse, from genetic counseling to disease tracking, and thus, the computation of identity coefficients merits special attention. However, the computation of identity coefficients is not done directly, but rather as the final step after computing a set of generalized kinship coefficients. In this paper, we first propose a novel Path-Counting Formula for calculating generalized kinship coefficients, which is motivated by Wright's path-counting method for computing inbreeding coefficient. We then present an efficient and scalable scheme for calculating generalized kinship coefficients on large pedigrees using NodeCodes, a special encoding scheme for expediting the evaluation of queries on pedigree graph structures. Furthermore, we propose an improved scheme using Family NodeCodes for the computation of generalized kinship coefficients, which is motivated by the significant improvement of using Family NodeCodes for inbreeding coefficient over the use of NodeCodes. We also perform experiments for evaluating the efficiency of our method, and compare it with the performance of the traditional recursive algorithm for three individuals. Experimental results demonstrate that the resulting scheme is more scalable and efficient than the traditional recursive methods for computing generalized kinship coefficients.
We study the haplotype inference problem from pedigree data under the zero recombination assumption, which is well supported by real data for tightly linked markers (i.e., single nucleotide polymorphisms (SNPs)) over a relatively large chromosome segment. We solve the problem in a rigorous mathematical manner by formulating genotype constraints as a linear system of inheritance variables. We then utilize disjoint-set structures to encode connectivity information among individuals, to detect constraints from genotypes, and to check consistency of constraints. On a tree pedigree without missing data, our algorithm can output a general solution as well as the number of total specific solutions in a nearly linear time O (mn x alpha(n)), where m is the number of loci, n is the number of individuals and alpha is the inverse Ackermann function, which is a further improvement over existing ones. We also extend the idea to looped pedigrees and pedigrees with missing data by considering existing (partial) constraints on inheritance variables. The algorithm has been implemented in C++ and will be incorporated into our PedPhase package. Experimental results show that it can correctly identify all 0-recombinant solutions with great efficiency. Comparisons with other two popular algorithms show that the proposed algorithm achieves 10 to 10(5)-fold improvements over a variety of parameter settings. The experimental study also provides empirical evidences on the complexity bounds suggested by theoretical analysis.
High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn3 + tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol eta) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 A and all-heavy-atom RMSD < 2.5 A from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.
The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA) for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network, and then, breaks each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log-fold changes in the microarray data, until the fragment subgraphs are sufficiently small. Our tests on three widely used protein-protein interaction data sets and comparisons with several latest methods for protein complex identification demonstrate the strong performance of our method in predicting novel protein complexes in terms of its specificity and efficiency. Given the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than 200 novel protein complexes.
Detecting non-coding RNAs (ncRNAs) in genomic DNA is an important part of annotation. However, the most widely used tool for modeling ncRNA families, the covariance model (CM), incurs a high computational cost when used for search. This cost can be reduced by using a filter to exclude sequence that is unlikely to contain the ncRNA of interest, applying the CM only where it is likely to match strongly. Despite recent advances, designing an efficient filter that can detect nearly all ncRNA instances while excluding most irrelevant sequences remains challenging. This work proposes a systematic procedure to convert a CM for an ncRNA family to a secondary structure profile (SSP), which augments a conservation profile with secondary structure information but can still be efficiently scanned against long sequences. We use dynamic programming to estimate an SSP's sensitivity and FP rate, yielding an efficient, fully automated filter design algorithm. Our experiments demonstrate that designed SSP filters can achieve significant speedup over unfiltered CM search while maintaining high sensitivity for various ncRNA families, including those with and without strong sequence conservation. For highly structured ncRNA families, including secondary structure conservation yields better performance than using primary sequence conservation alone.
This paper presents an original Quantum Genetic algorithm for Multiple sequence ALIGNment (QGMALIGN) that combines a genetic algorithm and a quantum algorithm. A quantum probabilistic coding is designed for representing the multiple sequence alignment. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The features of implicit parallelism and state superposition in quantum mechanics and the global search capability of the genetic algorithm are exploited to get efficient computation. A set of well known test cases from BAliBASE2.0 is used as reference to evaluate the efficiency of the QGMALIGN optimization. The QGMALIGN results have been compared with the most popular methods (CLUSTALX, SAGA, DIALIGN, SB_PIMA, and QGMALIGN) results. The QGMALIGN results show that QGMALIGN performs well on the presenting biological data. The addition of genetic operators to the quantum algorithm lowers the cost of overall running time.
We have developed a feedback algorithm for protein structure alignment that uses a series of phases to improve the global alignment between two protein backbones. The method implements a self-improving learning strategy by sending the output of one phase, the global alignment, to the next phase as an input. A web portal implementing this method has been constructed and is freely available for use at http://fpsa.cs.uno.edu/. Based on hundreds of test cases, we compare our algorithm with three other, commonly used methods: CE, Dali, and SSM. Our results show that, in most cases, our algorithm outputs a larger number of aligned positions when the (C(alpha)) RMSD is comparable. Also, in many cases where the number of aligned positions is larger or comparable to the other methods, our learning method is able to achieve a smaller (C(alpha)) RMSD than the other methods tested.
UNLABELLED Finding motifs in many sequences is an important problem in computational biology, especially in identification of regulatory motifs in DNA sequences. Let c be a motif sequence. Given a set of sequences, each is planted with a mutated version of c at an unknown position, the motif finding problem is to find these planted motifs and the original c. In this paper, we study the VM model of the planted motif problem, which is proposed by Pevzner and Sze. We give a simple Selecting One Voting algorithm and a more powerful Selecting k Voting algorithm. When the length of motif and the number of input sequences are large enough, we prove that the two algorithms can find the unknown motif consensus with high probability. In the proof, we show why a large number of input sequences is so important for finding motifs, which is believed by most researchers. Experimental results on simulated data also support the claim. Selecting k Voting algorithm is powerful, but computational intensive. To speed up the algorithm, we propose a progressive filtering algorithm, which improves the running time significantly and has good accuracy in finding motifs. Our experimental results show that Selecting k Voting algorithm with progressive filtering performs very well in practice and it outperforms some best known algorithms. AVAILABILITY The software is available upon request.
