Briefings in Bioinformatics - Advance Access

Finding sequence motifs in prokaryotic genomes--a brief practical guide for a microbiologist

Mrazek, J. — 2009-06-24

Finding significant nucleotide sequence motifs in prokaryotic genomes can be divided into three types of tasks: (1) supervised motif finding, where a sample of motif sequences is used to find other similar sequences in genomes; (2) unsupervised motif finding, which typically relates to the task of finding regulatory motifs and protein binding sites and (3) exploratory motif finding, which aims to identify potential functionally significant sequence motifs as those that are unusual in some statistical sense. This article provides a conceptual overview for each type of task, a brief description of basic algorithms used in their solution, and a review of selected relevant software available online.

Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets

2009-06-23

Identification of reliable molecular markers that show differential expression between distinct groups of samples has remained a fundamental research problem in many large-scale profiling studies, such as those based on DNA microarray or mass-spectrometry technologies. Despite the availability of a wide spectrum of statistical procedures, the users of the high-throughput platforms are still facing the crucial challenge of deciding which test statistic is best adapted to the intrinsic properties of their own datasets. To meet this challenge, we recently introduced an adaptive procedure, named ROTS (Reproducibility-Optimized Test Statistic), which learns an optimal statistic directly from the given data, and whose relative benefits have previously been shown in comparison with state-of-the-art procedures for detecting differential expression. Using gene expression microarray and mass-spectrometry (MS)-based protein expression datasets as case studies, we illustrate here the practical usage and advantages of ROTS toward detecting reliable marker lists in clinical transcriptomic and proteomic studies. In a public leukemia microarray dataset, the procedure could improve the sensitivity of the gene marker lists detected with high specificity. When applied to a recent LC-MS dataset, involving plasma samples from severe burn patients, the procedure could identify several peptide markers that remained undetected in the conventional analysis, thus demonstrating the effectiveness of ROTS also for global quantitative proteomic studies. To promote its widespread usage, we have made freely available efficient implementations of ROTS, which are easily accessible either as a stand-alone R-package or as integrated in the open-source data analysis software Chipster.

Architecture, function and prediction of long signal peptides

Hiss, J. A., Schneider, G. — 2009-06-17

Protein targeting in eukaryotic cells is vital for cell survival and development. N-terminal signal peptides guide proteins to the membrane of the endoplasmic reticulum (ER) and initiate translocation into the ER lumen. Here, we review the status of signal peptide architecture and prediction with an emphasis on exceptionally long signal peptides, which often escape the notion of the currently available prediction methods. We benchmark publicly available prediction methods for their ability to correctly identify exceptionally long signal peptides. A set of 136 annotated eukaryotic signals served as reference data. The best prediction tool detected only 63%. A potential reason for the poor performance is the domain architecture of long signal peptides, whose structural peculiarities are insufficiently considered by current prediction algorithms. To overcome this limitation, we motivate a general domain view of long signal peptides, which becomes detectable when both the overall length and secondary structure of long signal peptides are taken into consideration. This concept provides a structural framework for identifying and understanding multiple targeting and post-targeting functions.

Towards accurate human promoter recognition: a review of currently used sequence features and classification methods

Zeng, J., Zhu, S., Yan, H. — 2009-06-16

This review describes important advances that have been made during the past decade for genome-wide human promoter recognition. Interest in promoter recognition algorithms on a genome-wide scale is worldwide and touches on a number of practical systems that are important in analysis of gene regulation and in genome annotation without experimental support of ESTs, cDNAs or mRNAs. The main focus of this review is on feature extraction and model selection for accurate human promoter recognition, with descriptions of what they are, what has been accomplished, and what remains to be done.

Computational methods for the detection of cis-regulatory modules

Loo, P. V., Marynen, P. — 2009-06-04

Metazoan transcription regulation occurs through the concerted action of multiple transcription factors that bind co-operatively to cis-regulatory modules (CRMs). The annotation of these key regulators of transcription is lagging far behind the annotation of the transcriptome itself. Here, we give an overview of existing computational methods to detect these CRMs in metazoan genomes. We subdivide these methods into three classes: CRM scanners screen sequences for CRMs based on predefined models that often consist of multiple position weight matrices (PWMs). CRM builders construct models of similar CRMs controlling a set of co-regulated or co-expressed genes. CRM genome screeners screen sequences or complete genomes for CRMs as homotypic or heterotypic clusters of binding sites for any combination of transcription factors. We believe that CRM scanners are currently the most advanced methods, although their applicability is limited. Finally, we argue that CRM builders that make use of PWM libraries will benefit greatly from future advances and will prove to be most instrumental for the annotation of regulatory regions in metazoan genomes.

Recent advances in computer-aided drug design

Song, C. M., Lim, S. J., Tong, J. C. — 2009-05-11

Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

An Ariadne's thread to the identification and annotation of noncoding RNAs in eukaryotes

2009-04-21

Non-protein coding RNAs (ncRNAs) have emerged as a vast and heterogeneous portion of eukaryotic transcriptomes. Several ncRNA families, either short (<200 nucleotides, nt) or long (>200 nt), have been described and implicated in a variety of biological processes, from translation to gene expression regulation and nuclear trafficking. Most probably, other families are still to be discovered. Computational methods for ncRNA research require different approaches from the ones normally used in the prediction of protein-coding genes. Indeed, primary sequence alone is often insufficient to infer ncRNA functionality, whereas secondary structure and local conservation of portions of the transcript could provide useful information for both the prediction and the functional annotation of ncRNAs. Here we present an overview of computational methods and bioinformatics resources currently available for studying ncRNA genes, introducing the common themes as well as the different approaches required for long and short ncRNA identification and annotation.

Development of biomarker classifiers from high-dimensional data

Baek, S., Tsai, C.-A., Chen, J. J. — 2009-04-03

Recent development of high-throughput technology has accelerated interest in the development of molecular biomarker classifiers for safety assessment, disease diagnostics and prognostics, and prediction of response for patient assignment. This article reviews and evaluates some important aspects and key issues in the development of biomarker classifiers. Development of a biomarker classifier for high-throughput data involves two components: (i) model building and (ii) performance assessment. This article focuses on feature selection in model building and cross validation for performance assessment. A ‘frequency’ approach to feature selection is presented and compared to the ‘conventional’ approach in terms of the predictive accuracy and stability of the selected feature set. The two approaches are compared based on four biomarker classifiers, each with a different feature selection method and well-known classification algorithm. In each of the four classifiers the feature predictor set selected by the frequency approach is more stable than the feature set selected by the conventional approach.

Expression profiling of microRNAs by deep sequencing

Creighton, C. J., Reid, J. G., Gunaratne, P. H. — 2009-03-30

MicroRNAs are short non-coding RNAs that regulate the stability and translation of mRNAs. Profiling experiments, using microarray or deep sequencing technology, have identified microRNAs that are preferentially expressed in certain tissues, specific stages of development, or disease states such as cancer. Deep sequencing utilizes massively parallel sequencing, generating millions of small RNA sequence reads from a given sample. Profiling of microRNAs by deep sequencing measures absolute abundance and allows for the discovery of novel microRNAs that have eluded previous cloning and standard sequencing efforts. Public databases provide in silico predictions of microRNA gene targets by various algorithms. To better determine which of these predictions represent true positives, microRNA expression data can be integrated with gene expression data to identify putative microRNA:mRNA functional pairs. Here we discuss tools and methodologies for the analysis of microRNA expression data from deep sequencing.