Signal-exon trap: a novel method for the identification of signal sequences from genomic DNA

We describe a genomic DNA-based signal sequence trap method, signal-exon trap (SET), for the identification of genes encoding secreted and membrane-bound proteins. SET is based on the coupling of an exon trap to the translation of captured exons, which allows screening of the exon-encoded polypeptides for signal peptide function. Since most signal sequences are expected to be located in the 5′-terminal exons of genes, we first demonstrate that trapping of these exons is feasible. To test the applicability of SET for the screening of complex genomic DNA, we evaluated two critical features of the method. Specificity was assessed by the analysis of random genomic DNA and efficiency was demonstrated by screening a 425 kb YAC known to contain the genes of four secretory or membrane-bound proteins. All trapped clones contained a translation initiation signal followed by a hydrophobic stretch of amino acids representing either a known signal peptide, transmembrane domain or novel sequence. Our results suggest that SET is a potentially useful method for the isolation of signal sequence-containing genes and may find application in the discovery of novel members of known secretory gene clusters, as well as in other positional cloning approaches.     

A novel method for the efficient and selective identification of 5-hydroxymethylcytosine in genomic DNA

Recently, 5-hydroxymethylcytosine (5hmC) was identified in mammalian genomic DNA. The biological role of this modification remains unclear; however, identifying the genomic location of this modified base will assist in elucidating its function. We describe a method for the rapid and inexpensive identification of genomic regions containing 5hmC. This method involves the selective glucosylation of 5hmC residues by the β-glucosyltransferase from T4 bacteriophage creating β-glucosyl-5-hydroxymethylcytosine (β-glu-5hmC). The β-glu-5hmC modification provides a target that can be efficiently and selectively pulled down by J-binding protein 1 coupled to magnetic beads. DNA that is precipitated is suitable for analysis by quantitative PCR, microarray or sequencing. Furthermore, we demonstrate that the J-binding protein 1 pull down assay identifies 5hmC at the promoters of developmentally regulated genes in human embryonic stem cells. The method described here will allow for a greater understanding of the temporal and spatial effects that 5hmC may have on epigenetic regulation at the single gene level.     

ROKU: a novel method for identification of tissue-specific genes

Background  

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes.     

Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier

MOTIVATION: Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known miRNAs from a variety of species. RESULTS: Our study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for miRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.     

iPcc: a novel feature extraction method for accurate disease class discovery and prediction

Gene expression profiling has gradually become a routine procedure for disease diagnosis and classification. In the past decade, many computational methods have been proposed, resulting in great improvements on various levels, including feature selection and algorithms for classification and clustering. In this study, we present iPcc, a novel method from the feature extraction perspective to further propel gene expression profiling technologies from bench to bedside. We define ‘correlation feature space’ for samples based on the gene expression profiles by iterative employment of Pearson’s correlation coefficient. Numerical experiments on both simulated and real gene expression data sets demonstrate that iPcc can greatly highlight the latent patterns underlying noisy gene expression data and thus greatly improve the robustness and accuracy of the algorithms currently available for disease diagnosis and classification based on gene expression profiles.     

Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator

The rapid accumulation of whole-genome data has renewed interest in the study of genomic rearrangements. Comparative genomics, evolutionary biology, and cancer research all require models and algorithms to elucidate the mechanisms, history, and consequences of these rearrangements. However, even simple models lead to NP-hard problems, particularly in the area of phylogenetic analysis. Current approaches are limited to small collections of genomes and low-resolution data (typically a few hundred syntenic blocks). Moreover, whereas phylogenetic analyses from sequence data are deemed incomplete unless bootstrapping scores (a measure of confidence) are given for each tree edge, no equivalent to bootstrapping exists for rearrangement-based phylogenetic analysis. We describe a fast and accurate algorithm for rearrangement analysis that scales up, in both time and accuracy, to modern high-resolution genomic data. We also describe a novel approach to estimate the robustness of results-an equivalent to the bootstrapping analysis used in sequence-based phylogenetic reconstruction. We present the results of extensive testing on both simulated and real data showing that our algorithm returns very accurate results, while scaling linearly with the size of the genomes and cubically with their number. We also present extensive experimental results showing that our approach to robustness testing provides excellent estimates of confidence, which, moreover, can be tuned to trade off thresholds between false positives and false negatives. Together, these two novel approaches enable us to attack heretofore intractable problems, such as phylogenetic inference for high-resolution vertebrate genomes, as we demonstrate on a set of six vertebrate genomes with 8,380 syntenic blocks. A copy of the software is available on demand.     

SCOOP: a simple method for identification of novel protein superfamily relationships

MOTIVATION: Profile searches of sequence databases are a sensitive way to detect sequence relationships. Sophisticated profile-profile comparison algorithms that have been recently introduced increase search sensitivity even further. RESULTS: In this article, a simpler approach than profile-profile comparison is presented that has a comparable performance to state-of-the-art tools such as COMPASS, HHsearch and PRC. This approach is called SCOOP (Simple Comparison Of Outputs Program), and is shown to find known relationships between families in the Pfam database as well as detect novel distant relationships between families. Several novel discoveries are presented including the discovery that a domain of unknown function (DUF283) found in Dicer proteins is related to double-stranded RNA-binding domains. AVAILABILITY: SCOOP is freely available under a GNU GPL license from http://www.sanger.ac.uk/Users/agb/SCOOP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data

High-throughput bisulfite sequencing is widely used to measure cytosine methylation at single-base resolution in eukaryotes. It permits systems-level analysis of genomic methylation patterns associated with gene expression and chromatin structure. However, methods for large-scale identification of methylation patterns from bisulfite sequencing are lacking. We developed a comprehensive tool, CpG_MPs, for identification and analysis of the methylation patterns of genomic regions from bisulfite sequencing data. CpG_MPs first normalizes bisulfite sequencing reads into methylation level of CpGs. Then it identifies unmethylated and methylated regions using the methylation status of neighboring CpGs by hotspot extension algorithm without knowledge of pre-defined regions. Furthermore, the conservatively and differentially methylated regions across paired or multiple samples (cells or tissues) are identified by combining a combinatorial algorithm with Shannon entropy. CpG_MPs identified large amounts of genomic regions with different methylation patterns across five human bisulfite sequencing data during cellular differentiation. Different sequence features and significantly cell-specific methylation patterns were observed. These potentially functional regions form candidate regions for functional analysis of DNA methylation during cellular differentiation. CpG_MPs is the first user-friendly tool for identifying methylation patterns of genomic regions from bisulfite sequencing data, permitting further investigation of the biological functions of genome-scale methylation patterns.     

Application of a colorimetric DNA-DNA hybridization method for identification ofAeromonas genomic species,isolated from diarrheal stools

The colorimetric DNA-DNA hybridization method for the identification of 18 strains ofAeromonas spp. isolated from human stools was used. Bacterial isolates were also examined by phenotypic characteristics. On the basis of biochemical tests 13 strains were included in phenogroupA. caviœ and 5 strains inA. sobria. Identification to the species level was obtained by colorimetric hybridization method. DNA-DNA similarity values showed that isolates ofA. caviœ group belong to hybridization group (HG) 4 whereas isolates ofA. sobria belong to HG 8/10. DNA relatedness results obtained by the colorimetric method showed good agreement with values detected by the spectrophotometric method. The background in the colorimetric method is lower than in the spectrophotometric one. Results of this study indicate the usefulness of the colorimetric DNA-DNA hybridization in microplates method for the identification ofAeromonas genomic species, isolated from human diarrheal stools.     

The selfie trap: A novel camera trap design for accurate small mammal identification

Camera traps are a popular tool for monitoring wildlife though they can fail to capture enough morphological detail for accurate small mammal species identification. Camera trapping small mammals is often limited by the inability of camera models to: (i) record at close distances; and (ii) provide standardised photos. This study aims to provide a camera trapping method that captures standardised images of the faces of small mammals for accurate species identification, with further potential for individual identification. A novel camera trap design coined the ‘selfie trap’ was developed. The selfie trap is a camera contained within an enclosed PVC pipe with a modified lens that produces standardised close images of small mammal species encountered in this study, including: Brown Antechinus (Antechinus stuartii), Bush Rat (Rattus fuscipes) and Sugar Glider (Petaurus breviceps). Individual identification was tested on the common arboreal Sugar Glider. Five individual Sugar Gliders were identified based on unique head stripe pelage. The selfie trap is an accurate camera trapping method for capturing detailed and standardised images of small mammal species. The design described may be useful for wildlife management as a reliable method for surveying small mammal species. However, intraspecies individual identification using the selfie trap requires further testing.     

miRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data

MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci.     

Evaluation of a novel method for the identification of coevolving protein residues

A novel method for the identification of correlated pairs in aligned homologous protein sequences is presented and evaluated against a model of simulated protein evolution incorporating covariation. Our method is shown to be capable of identifying all coevolutionary pairs of sites, with minimal interference by background correlations, in aligned sequence sets containing approximately 60 sequences with a tree depth of at least 30 accepted point mutations. This result is expected even in the presence of a large degree of neutral and non-correlated evolution. It is postulated that, since naturally occurring protein families may be subject to stronger selection pressures and a lesser degree of neutral evolution, this method of covariation analysis may be generally more robust than the model would indicate.     

A novel data mining method to identify assay-specific signatures in functional genomic studies

Background:  

The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies.     

A method for measuring joint kinematics designed for accurate registration of kinematic data to models constructed from CT data

A method for measuring three-dimensional kinematics that incorporates the direct cross-registration of experimental kinematics with anatomic geometry from Computed Tomography (CT) data has been developed. Plexiglas registration blocks were attached to the bones of interest and the specimen was CT scanned. Computer models of the bone surface were developed from the CT image data. Determination of discrete kinematics was accomplished by digitizing three pre-selected contiguous surfaces of each registration block using a three-dimensional point digitization system. Cross-registration of bone surface models from the CT data was accomplished by identifying the registration block surfaces within the CT images. Kinematics measured during a biomechanical experiment were applied to the computer models of the bone surface. The overall accuracy of the method was shown to be at or below the accuracy of the digitization system used. For this experimental application, the accuracy was better than +/-0.1mm for position and 0.1 degrees for orientation for linkage digitization and better than +/-0.2mm and +/-0.2 degrees for CT digitization. Surface models of the radius and ulna were constructed from CT data, as an example application. Kinematics of the bones were measured for simulated forearm rotation. Screw-displacement axis analysis showed 0.1mm (proximal) translation of the radius (with respect to the ulna) from supination to neutral (85.2 degrees rotation) and 1.4mm (proximal) translation from neutral to pronation (65.3 degrees rotation). The motion of the radius with respect to the ulna was displayed using the surface models. This methodology is a useful tool for the measurement and application of rigid-body kinematics to computer models.