Biomedical subjects

Saurabh Sinha

Publications and source records attributed to Saurabh Sinha.

16 recordsLinked to original sources

PubMed2006

Indelign: a probabilistic framework for annotation of insertions and deletions in a multiple alignment.

MOTIVATION: A quantitative study of molecular evolutionary events such as substitutions, insertions and deletions from closely related genomes requires (1) an accurate multiple sequence alignment program and (2) a method to annotate the insertions and deletions that explain the 'gaps' in the alignment. Although the former requirement has been extensively addressed, the latter problem has received little attention, especially in a comprehensive probabilistic framework. RESULTS: Here, we present Indelign, a program that uses a probabilistic evolutionary model to compute the most likely scenario of insertions and deletions consistent with an input multiple alignment. It is also capable of modifying the given alignment so as to obtain a better agreement with the evolutionary model. We find close to optimal performance and substantial improvement over alternative methods, in tests of Indelign on synthetic data. We use Indelign to analyze regulatory sequences in Drosophila, and find an excess of insertions over deletions, which is different from what has been reported for neutral sequences. AVAILABILITY: The Indelign program may be downloaded from the website http://veda.cs.uiuc.edu/indelign/ SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Algorithms↗

PubMed2006

Genome scan for cis-regulatory DNA motifs associated with social behavior in honey bees.

Honey bees (Apis mellifera) undergo an age-related, socially regulated transition from working in the hive to foraging, which is associated with changes in the expression of thousands of genes in the brain. To begin to study the cis-regulatory code underlying this massive social regulation of gene expression, we used the newly sequenced honey bee genome to scan the promoter regions of eight sets of behaviorally related genes differentially expressed in the brain in the context of division of labor among worker bees, for 41 cis-regulatory motifs previously characterized in Drosophila melanogaster. Binding sites for the transcription factors Hairy, GAGA, Adf1, Cf1, Snail, and Dri, known to function in nervous system development, olfactory learning, or hormone binding in Drosophila, were significantly associated with one or more gene sets. The presence of some binding sites also predicted expression patterns for as many as 71% of the genes in some gene sets. These results suggest that there is a robust relationship between cis and social regulation of brain gene expression, especially considering that we studied <15% of all known transcription factors. These results also suggest that transcriptional networks involved in the regulation of development in Drosophila are used to regulate behavioral development in adult honey bees. However, differences in gene regulation between these two processes are suggested by the finding that the promoter regions for the behaviorally related bee genes differed in both motif occurrence and G/C content relative to their Drosophila orthologs.

Animals↗

PubMed2006

On counting position weight matrix matches in a sequence, with application to discriminative motif finding.

MOTIVATION AND RESULTS: The position weight matrix (PWM) is a popular method to model transcription factor binding sites. A fundamental problem in cis-regulatory analysis is to "count" the occurrences of a PWM in a DNA sequence. We propose a novel probabilistic score to solve this problem of counting PWM occurrences. The proposed score has two important properties: (1) It gives appropriate weights to both strong and weak occurrences of the PWM, without using thresholds. (2) For any given PWM, this score can be computed while allowing for occurrences of other, a priori known PWMs, in a statistically sound framework. Additionally, the score is efficiently differentiable with respect to the PWM parameters, which has important consequences for designing search algorithms. The second problem we address is to find, ab initio, PWMs that have high counts in one set of sequences, and low counts in another. We develop a novel algorithm to solve this "discriminative motif-finding problem", using the proposed score for counting a PWM in the sequences. The algorithm is a local search technique that exploits derivative information on an objective function to enhance speed and performance. It is extensively tested on synthetic data, and shown to perform better than other discriminative as well as non-discriminative PWM finding algorithms. It is then applied to cis-regulatory modules involved in development of the fruitfly embryo, to elicit known and novel motifs. We finally use the algorithm on genes predictive of social behavior in the honey bee, and find interesting motifs. AVAILABILITY: The program is available upon request from the author.

Algorithms↗

PubMed2006

Stubb: a program for discovery and analysis of cis-regulatory modules.

Given the DNA-binding specificities (motifs) of one or more transcription factors, an important bioinformatics problem is to discover significant clusters of binding sites for the transcription factors(s). Such clusters often correspond to cis-regulatory modules mediating regulation of an adjacent gene. In earlier work, we developed the Stubb program that uses a probabilistic model and a maximum likelihood approach to efficiently detect cis-regulatory modules over genomic scales. It may optionally exploit a second related genome to improve module prediction accuracy. We describe here the use of a web-based interface for the Stubb program. The interface is equipped with a special post-processing step for in-depth analysis of specific modules, in order to reveal individual binding sites predicted in the module. The web server may be accessed at the URL http://stubb.rockefeller.edu/.

Algorithms↗

PubMed2005

Sequence turnover and tandem repeats in cis-regulatory modules in drosophila.

The path by which regulatory sequence can change, yet preserve function, is an important open question for both evolution and bioinformatics. The recent sequencing of two additional species of Drosophila plus the wealth of data on gene regulation in the fruit fly provides new means for addressing this question. For regulatory sequences, indels account for more base pairs (bp) of change than substitutions (between Drosophila melanogaster and Drosophila yakuba), though they are fewer in number. Using Drosophila pseudoobscura as an out-group, we can distinguish insertions from deletions (with maximum parsimony criteria), and find a ratio between 1 and 5 (insertions to deletions) that is species dependent and much larger than the ratio of 1/8 for neutral sequences (Petrov and Hartl 1998). Because neutral sequence is rapidly cleared from the genome, most noncoding regions which preserve their length between D. melanogaster-D. pseudoobscura and have an excess of insertions over deletions should be functional. A fraction of 15%-18% (i.e., more than 20 standard deviations from random expectation) of the regulatory sequence is covered by low copy number tandem repeats whose repeating unit has an average length of 5-10 bp and which occur preferentially (25%-45% coverage) in indels. All indels may be due to tandem repeats if we extrapolate the detection efficiency of the repeat-finding algorithms using the observed point mutation rate between the species we compare. Sequence creation by local duplication accords with the tendency for multiple copies of transcription factor-binding sites to occur in regulatory modules. Thus, indel events and tandem repeats in particular need to be incorporated into models of regulatory evolution because they can alter the rate at which beneficial variants arise and should also influence bioinformatic algorithms that parse regulatory sequences into binding sites.

Animals↗

PubMed2005

Assessing computational tools for the discovery of transcription factor binding sites.

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

Amino Acid Motifs↗

PubMed2004

PhyME: a probabilistic algorithm for finding motifs in sets of orthologous sequences.

BACKGROUND: This paper addresses the problem of discovering transcription factor binding sites in heterogeneous sequence data, which includes regulatory sequences of one or more genes, as well as their orthologs in other species. RESULTS: We propose an algorithm that integrates two important aspects of a motif's significance - overrepresentation and cross-species conservation - into one probabilistic score. The algorithm allows the input orthologous sequences to be related by any user-specified phylogenetic tree. It is based on the Expectation-Maximization technique, and scales well with the number of species and the length of input sequences. We evaluate the algorithm on synthetic data, and also present results for data sets from yeast, fly, and human. CONCLUSIONS: The results demonstrate that the new approach improves motif discovery by exploiting multiple species information.

Algorithms↗

PubMed2004

Cross-species comparison significantly improves genome-wide prediction of cis-regulatory modules in Drosophila.

BACKGROUND: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. It is important to quantify how comparative genomics can improve computational detection of such modules. RESULTS: We run the Stubb software on the entire D. melanogaster genome, to obtain predictions of modules involved in segmentation of the embryo. Stubb uses a probabilistic model to score sequences for clustering of transcription factor binding sites, and can exploit multiple species data within the same probabilistic framework. The predictions are evaluated using publicly available gene expression data for thousands of genes, after careful manual annotation. We demonstrate that the use of a second genome (D. pseudoobscura) for cross-species comparison significantly improves the prediction accuracy of Stubb, and is a more sensitive approach than intersecting the results of separate runs over the two genomes. The entire list of predictions is made available online. CONCLUSION: Evolutionary conservation of modules serves as a filter to improve their detection in silico. The future availability of additional fruitfly genomes therefore carries the prospect of highly specific genome-wide predictions using Stubb.

Algorithms↗

PubMed2004

Visualization and analysis of white matter structural asymmetry in diffusion tensor MRI data.

This work presents a method that permits the characterization, quantification, and 3D visualization of white matter structural information contained within diffusion tensor MR imaging (DT-MRI) data. In this method, regions within the brain are defined as possessing linear, planar, or spherical diffusion. Visualization of this diffusion metric data is realized by generating streamtube and streamsurface models to represent regions of linear and planar diffusion. Quantification of differences in diffusion anisotropy between different regions of interest (ROIs) is then achieved by analyzing 2D barycentric histograms created from the complete distribution of diffusion metric values measured in each region. In four healthy volunteers, there was only a small degree of asymmetry (epsilon) in the number of linear, planar, or spherical diffusion voxels between the right and left hemispheres (epsilon approximately equal to +/- 2%). However, in a patient with a metastatic brain lesion there was marked asymmetry in both linear (epsilon approximately -10%) and planar (epsilon approximately equal to 5%) diffusion between comparable ipsilateral and contralateral regions, with a significant reduction in the number of linear diffusion voxels and an increase in the number of planar diffusion voxels in the tumor-bearing hemisphere. These results demonstrate the potential of this approach to characterize brain structure in both healthy and diseased subjects.

Adenocarcinoma↗

PubMed2003

Familial posterior fossa arachnoid cyst.

BACKGROUND: Arachnoid cysts are a relatively common incidental finding on CT scans of the brain. They most commonly occur in the middle cranial fossa, where familial occurrence has rarely been reported. Posterior fossa arachnoid cysts are more unusual. CASE HISTORIES: We report the presence of quadrigeminal cistern arachnoid cysts in siblings.

Arachnoid Cysts↗

PubMed2003

YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation.

A fundamental challenge facing biologists is to identify DNA binding sites for unknown regulatory factors, given a collection of genes believed to be coregulated. The program YMF identifies good candidates for such binding sites by searching for statistically overrepresented motifs. More specifically, YMF enumerates all motifs in the search space and is guaranteed to produce those motifs with greatest z-scores. This note describes the YMF web software, available at http://bio.cs.washington.edu/software.html.

Algorithms↗

PubMed2003

Discriminative motifs.

This paper takes a new view of motif discovery, addressing a common problem in existing motif finders. A motif is treated as a feature of the input promoter regions that leads to a good classifier between these promoters and a set of background promoters. This perspective allows us to adapt existing methods of feature selection, a well-studied topic in machine learning, to motif discovery. We develop a general algorithmic framework that can be specialized to work with a wide variety of motif models, including consensus models with degenerate symbols or mismatches, and composite motifs. A key feature of our algorithm is that it measures overrepresentation while maintaining information about the distribution of motif instances in individual promoters. The assessment of a motif's discriminative power is normalized against chance behaviour by a probabilistic analysis. We apply our framework to two popular motif models and are able to detect several known binding sites in sets of co-regulated genes in yeast.

Algorithms↗

PubMed2003

A probabilistic method to detect regulatory modules.

MOTIVATION: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. RESULTS: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.

Algorithms↗

PubMed2002

Discovery of novel transcription factor binding sites by statistical overrepresentation.

Understanding the complex and varied mechanisms that regulate gene expression is an important and challenging problem. A fundamental sub-problem is to identify DNA binding sites for unknown regulatory factors, given a collection of genes believed to be co-regulated. We discuss a computational method that identifies good candidates for such binding sites. Unlike local search techniques such as expectation maximization and Gibbs samplers that may not reach a global optimum, the method discussed enumerates all motifs in the search space, and is guaranteed to produce the motifs with greatest z-scores. We discuss the results of validation experiments in which this algorithm was used to identify candidate binding sites in several well studied regulons of Saccharomyces cerevisiae, where the most prominent transcription factor binding sites are largely known. We then discuss the results on gene families in the functional and mutant phenotype catalogs of S.cerevisiae, where the algorithm suggests many promising novel transcription factor binding sites. The program is available at http://bio.cs.washington.edu/software.html.

Algorithms↗

PubMed2002

Measurements of water diffusion and T1 values in peritumoural oedematous brain.

Using quantitative MR imaging, values for the mean water diffusivity ( ), the diffusion anisotropy and the longitudinal relaxation time (T1) were measured for tumour, oedematous and normal brain in a group of treatment-naive patients with high-grade glioma and low-grade meningioma. The mean values of and T1 for enhancing tumour and oedematous brain were significantly higher in high-grade glioma than meningioma, while the diffusion anisotropy was reduced. Values of and T1 were also positively correlated in oedematous brain in both pathologies. There was, however, no clear evidence of similar correlations in apparently normal contralateral white matter. Such results illustrate the potential of MR imaging to improve not only the characterization of brain oedema, but also the monitoring of treatment response.

Aged↗

PubMed2002

Diffusion tensor MR imaging of high-grade cerebral gliomas.

BACKGROUND AND PURPOSE: Optimizing high-grade glioma treatment requires the delineation of edematous and normal brain from tumor, perhaps by using potential differences in the absolute diffusion parameters of water. Our purpose was to determine whether mean diffusivity and diffusion anisotropic MR imaging data help in this differentiation. METHODS: Nine patients with high-grade cerebral glioblastoma underwent contrast-enhanced structural and diffusion tensor MR imaging before therapy. Tumor, edematous brain, and apparently normal white matter regions were determined on T2-weighted and contrast-enhanced T1-weighted structural images. Fractional anisotropy (FA) and were measured in each tissue type. Differences in these values among the tissue types were assessed with a standard analysis of variance. RESULTS: was highest in the necrotic tumor core (1825.38 +/-404.06) x 10(-6) mm(2)/s, followed by edematous brain (1411.23 +/- 322.31) x 10(-6) mm(2)/s, enhancing tumor core (1308.67 +/- 292.50) x 10(-6) mm(2)/s, enhancing tumor margin (1229.80 +/- 206.80) x 10(-6) mm(2)/s, and normal brain (731.53 +/- 35.21) x 10(-6) mm(2)/s. FA was highest in normal brain (0.47 +/- 0.08) and lowest in the necrotic core (0.09 +/- 0.03). was significantly different in enhancing tumor margins and edematous brain in all patients; FA was significantly different in only seven. These values were significantly different from those of normal brain in all cases in which they were measurable. CONCLUSION: values can be used to differentiate normal white matter, edematous brain, and enhancing tumor margins. Diffusion anisotropic data added no benefit to tissue differentiation. Further studies are required to determine if a value that corresponds to the limit of tumor invasion can be identified.

Adult↗