Protein motif extraction with neuro-fuzzy optimization

MOTIVATION: It is attempted to improve the speed and flexibility of protein motif identification. The proposed algorithm is able to extract both rigid and flexible protein motifs. RESULTS: In this work, we present a new algorithm for extracting the consensus pattern, or motif, from a group of related protein sequences. This algorithm involves a statistical method to find short patterns with high frequency and then neural network training to optimize the final classification accuracies. Fuzzy logic is used to increase the flexibility of protein motifs. C2H2 Zinc Finger Protein and epidermal growth factor protein sequences are used to demonstrate the capability of the proposed algorithm in finding motifs. AVAILABILITY: This program is freely available for academic use by request.     

On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.     

Clustering of diverse genomic data using information fusion

MOTIVATION: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. RESULTS: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. AVAILABILITY: Software code is available by request from the first author. CONTACT: jkasturi@cse.psu.edu.     

A CART-based approach to discover emerging patterns in microarray data

MOTIVATION: Cancer diagnosis using gene expression profiles requires supervised learning and gene selection methods. Of the many suggested approaches, the method of emerging patterns (EPs) has the particular advantage of explicitly modeling interactions among genes, which improves classification accuracy. However, finding useful (i.e. short and statistically significant) EP is typically very hard. METHODS: Here we introduce a CART-based approach to discover EPs in microarray data. The method is based on growing decision trees from which the EPs are extracted. This approach combines pattern search with a statistical procedure based on Fisher's exact test to assess the significance of each EP. Subsequently, sample classification based on the inferred EPs is performed using maximum-likelihood linear discriminant analysis. RESULTS: Using simulated data as well as gene expression data from colon and leukemia cancer experiments we assessed the performance of our pattern search algorithm and classification procedure. In the simulations, our method recovers a large proportion of known EPs while for real data it is comparable in classification accuracy with three top-performing alternative classification algorithms. In addition, it assigns statistical significance to the inferred EPs and allows to rank the patterns while simultaneously avoiding overfit of the data. The new approach therefore provides a versatile and computationally fast tool for elucidating local gene interactions as well as for classification. AVAILABILITY: A computer program written in the statistical language R implementing the new approach is freely available from the web page http://www.stat.uni-muenchen.de/~socher/     

Probabilistic alignment of motifs with sequences

MOTIVATION: Motif detection is an important component of the classification and annotation of protein sequences. A method for aligning motifs with an amino acid sequence is introduced. The motifs can be described by the secondary (i.e. functional, biophysical, etc.) characteristics of a signal or pattern to be detected. The results produced are based on the statistical relevance of the alignment. The method was targeted to avoid the problems (i.e. over-fitting, biological interpretation and mathematical soundness) encountered in other methods currently available. RESULTS: The method was tested on lipoprotein signals in B. subtilis yielding stable results. The results of signal prediction were consistent with other methods where literature was available. AVAILABILITY: An implementation of the motif alignment, refining and bootstrapping is available for public use online at http://www.expasy.org/tools/patoseq/     

A novel approach to the detection of genomic approximate tandem repeats in the Levenshtein metric.

An efficient algorithm for detecting approximate tandem repeats in genomic sequences is presented. The algorithm is based on innovative statistical criteria to detect candidate regions which may include tandem repeats; these regions are subsequently verified by alignments based on dynamic programming. No prior information about the period size or pattern is needed. Also, the algorithm is virtually capable of detecting repeats with any period. An implementation of the algorithm is compared with the two state-of-the-art tandem repeats detection tools to demonstrate its effectiveness both on natural and synthetic data. The algorithm is available at www.cs.brown.edu/people/domanic/tandem/.     

PipeOnline 2.0: automated EST processing and functional data sorting

Expressed sequence tags (ESTs) are generated and deposited in the public domain, as redundant, unannotated, single-pass reactions, with virtually no biological content. PipeOnline automatically analyses and transforms large collections of raw DNA-sequence data from chromatograms or FASTA files by calling the quality of bases, screening and removing vector sequences, assembling and rewriting consensus sequences of redundant input files into a unigene EST data set and finally through translation, amino acid sequence similarity searches, annotation of public databases and functional data. PipeOnline generates an annotated database, retaining the processed unigene sequence, clone/file history, alignments with similar sequences, and proposed functional classification, if available. Functional annotation is automatic and based on a novel method that relies on homology of amino acid sequence multiplicity within GenBank records. Records are examined through a function ordered browser or keyword queries with automated export of results. PipeOnline offers customization for individual projects (MyPipeOnline), automated updating and alert service. PipeOnline is available at http://stress-genomics.org.     

AMASS: a structured pattern matching approach to shotgun sequence assembly.

In this paper, we propose an efficient, reliable shotgun sequence assembly algorithm based on a fingerprinting scheme that is robust to both noise and repetitive sequences in the data, two primary roadblocks to effective whole-genome shotgun sequencing. Our algorithm uses exact matches of short patterns randomly selected from fragment data to identify fragment overlaps, construct an overlap map, and deliver a consensus sequence. We show how statistical clues made explicit in our approach can easily be exploited to correctly assemble results even in the presence of extensive repetitive sequences. Our approach is both accurate and exceptionally fast in practice: e.g., we have correctly assembled the whole Mycoplasma genitalium genome (approximately 580 kbp) is roughly 8 minutes of 64MB 200MHz Pentium Pro CPU time from real shotgun data, where most existing algorithms can be expected to run for several hours to a day on the same data. Moreover, experiments with artificially-shotgunned data prepared from real DNA sequences from a wide range of organisms (including human DNA) and containing complex repeating regions demonstrate our algorithm's robustness to input noise and the presence of repetitive sequences. For example, we have correctly assembled a 238-kbp human DNA sequence in less than 3 min of 64-MB 200-MHz Pentium Pro CPU time.     

SQID: an intensity-incorporated protein identification algorithm for tandem mass spectrometry

To interpret LC-MS/MS data in proteomics, most popular protein identification algorithms primarily use predicted fragment m/z values to assign peptide sequences to fragmentation spectra. The intensity information is often undervalued, because it is not as easy to predict and incorporate into algorithms. Nevertheless, the use of intensity to assist peptide identification is an attractive prospect and can potentially improve the confidence of matches and generate more identifications. On the basis of our previously reported study of fragmentation intensity patterns, we developed a protein identification algorithm, SeQuence IDentfication (SQID), that makes use of the coarse intensity from a statistical analysis. The scoring scheme was validated by comparing with Sequest and X!Tandem using three data sets, and the results indicate an improvement in the number of identified peptides, including unique peptides that are not identified by Sequest or X!Tandem. The software and source code are available under the GNU GPL license at http://quiz2.chem.arizona.edu/wysocki/bioinformatics.htm.     

Identifying DNA and protein patterns with statistically significant alignments of multiple sequences.

MOTIVATION: Molecular biologists frequently can obtain interesting insight by aligning a set of related DNA, RNA or protein sequences. Such alignments can be used to determine either evolutionary or functional relationships. Our interest is in identifying functional relationships. Unless the sequences are very similar, it is necessary to have a specific strategy for measuring-or scoring-the relatedness of the aligned sequences. If the alignment is not known, one can be determined by finding an alignment that optimizes the scoring scheme. RESULTS: We describe four components to our approach for determining alignments of multiple sequences. First, we review a log-likelihood scoring scheme we call information content. Second, we describe two methods for estimating the P value of an individual information content score: (i) a method that combines a technique from large-deviation statistics with numerical calculations; (ii) a method that is exclusively numerical. Third, we describe how we count the number of possible alignments given the overall amount of sequence data. This count is multiplied by the P value to determine the expected frequency of an information content score and, thus, the statistical significance of the corresponding alignment. Statistical significance can be used to compare alignments having differing widths and containing differing numbers of sequences. Fourth, we describe a greedy algorithm for determining alignments of functionally related sequences. Finally, we test the accuracy of our P value calculations, and give an example of using our algorithm to identify binding sites for the Escherichia coli CRP protein. AVAILABILITY: Programs were developed under the UNIX operating system and are available by anonymous ftp from ftp://beagle.colorado.edu/pub/consensus.     

Pathway analysis using random forests classification and regression

MOTIVATION: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. RESULTS: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. AVAILABILITY: Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.     

Optimal classification of protein sequences and selection of representative sets from multiple alignments: application to homologous families and lessons for structural genomics

Hierarchical classification is probably the most popular approach to group related proteins. However, there are a number of problems associated with its use for this purpose. One is that the resulting tree showing a nested sequence of groups may not be the most suitable representation of the data. Another is that visual inspection is the most common method to decide the most appropriate number of subsets from a tree. In fact, classification of proteins in general is bedevilled with the need for subjective thresholds to define group membership (e.g., 'significant' sequence identity for homologous families). Such arbitrariness is not only intellectually unsatisfying but also has important practical consequences. For instance, it hinders meaningful identification of protein targets for structural genomics. I describe an alternative approach to cluster related proteins without the need for an a priori threshold: one, through its use of dynamic programming, which is guaranteed to produce globally optimal solutions at all levels of partition granularity. Grouping proteins according to weights assigned to their aligned sequences makes it possible to delineate dynamically a 'core-periphery' structure within families. The 'core' of a protein family comprises the most typical sequences while the 'periphery' consists of the atypical ones. Further, a new sequence weighting scheme that combines the information in all the multiply aligned positions of an alignment in a novel way is put forward. Instead of averaging over all positions, this procedure takes into account directly the distribution of sequence variability along an alignment. The relationships between sequence weights and sequence identity are investigated for 168 families taken from HOMSTRAD, a database of protein structure alignments for homologous families. An exact solution is presented for the problem of how to select the most representative pair of sequences for a protein family. Extension of this approach by a greedy algorithm allows automatic identification of a minimal set of aligned sequences. The results of this analysis are available on the Web at http://mathbio.nimr.mrc.ac.uk/~amay.     

Analysis of machine-selected cells with an image analysis system in normal and abnormal cervical specimens

An analysis has been performed of visual diagnostic criteria used in cervical cytology applied to machine selected cells in relation to automated classification based on variables, which can be recorded in an image system with automated cell search and segmentation, feature extraction and classification. A 98% accuracy could be obtained with the choice of the most ideal statistical methods for discrimination and the use of the most powerful variables recorded in the image system when compared with consensus of the visual diagnoses based on established cytological criteria for diagnosis of cancer and precancer of the cervix uteri. The most powerful discriminatory variables in the image system (of 17 recorded) for discrimination between normal and abnormal epithelial cells were, in addition to nuclear extinction, cytoplasmic extinction and cytoplasmic shape. It is concluded that the visual classification of cervical cells is highly accurate with experienced observers and that imaging microscopes can be trained to nearly equal this accuracy with appropriate statistical methods of discrimination. The problem of creating fully automated systems, however, also requires the inclusion of even more effective discriminatory variables and also the solution of such problems as automatic cell search, segmentation, artifact rejection, feature extraction, classification and electronic stability in order to become cost-effective.     

iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components

Recombination hotspots in a genome are unevenly distributed. Hotspots are regions in a genome that show higher rates of meiotic recombinations. Computational methods for recombination hotspot prediction often use sophisticated features that are derived from physico-chemical or structure based properties of nucleotides. In this paper, we propose iRSpot-SF that uses sequence based features which are computationally cheap to generate. Four feature groups are used in our method: k-mer composition, gapped k-mer composition, TF-IDF of k-mers and reverse complement k-mer composition. We have used recursive feature elimination to select 17 top features for hotspot prediction. Our analysis shows the superiority of gapped k-mer composition and reverse complement k-mer composition features over others. We have used SVM with RBF kernel as a classification algorithm. We have tested our algorithm on standard benchmark datasets. Compared to other methods iRSpot-SF is able to produce significantly better results in terms of accuracy, Mathew's Correlation Coefficient and sensitivity which are 84.58%, 0.6941 and 84.57%. We have made our method readily available to use as a python based tool and made the datasets and source codes available at: https://github.com/abdlmaruf/iRSpot-SF. An web application is developed based on iRSpot-SF and freely available to use at: http://irspot.pythonanywhere.com/server.html.     

Clustering of multi‐domain protein sequences

The overall function of a multi‐domain protein is determined by the functional and structural interplay of its constituent domains. Traditional sequence alignment‐based methods commonly utilize domain‐level information and provide classification only at the level of domains. Such methods are not capable of taking into account the contributions of other domains in the proteins, and domain‐linker regions and classify multi‐domain proteins. An alignment‐free protein sequence comparison tool, CLAP (CLAssification of Proteins) was previously developed in our laboratory to especially handle multi‐domain protein sequences without a requirement of defining domain boundaries and sequential order of domains. Through this method we aim to achieve a biologically meaningful classification scheme for multi‐domain protein sequences. In this article, CLAP‐based classification has been explored on 5 datasets of multi‐domain proteins and we present detailed analysis for proteins containing (1) Tyrosine phosphatase and (2) SH3 domain. At the domain‐level CLAP‐based classification scheme resulted in a clustering similar to that obtained from an alignment‐based method. CLAP‐based clusters obtained for full‐length datasets were shown to comprise of proteins with similar functions and domain architectures. Our study demonstrates that multi‐domain proteins could be classified effectively by considering full‐length sequences without a requirement of identification of domains in the sequence.     

A genotype calling algorithm for affymetrix SNP arrays

MOTIVATION: A classification algorithm, based on a multi-chip, multi-SNP approach is proposed for Affymetrix SNP arrays. Current procedures for calling genotypes on SNP arrays process all the features associated with one chip and one SNP at a time. Using a large training sample where the genotype labels are known, we develop a supervised learning algorithm to obtain more accurate classification results on new data. The method we propose, RLMM, is based on a robustly fitted, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variance is reduced through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as across thousands of SNPs for accurate classification. In this paper, we apply RLMM to Affymetrix 100 K SNP array data, present classification results and compare them with genotype calls obtained from the Affymetrix procedure DM, as well as to the publicly available genotype calls from the HapMap project.