Biomedical subjects

Tim J P Hubbard

Publications and source records attributed to Tim J P Hubbard.

8 recordsLinked to original sources

PubMed2005

Adding some SPICE to DAS.

UNLABELLED: The distributed annotation system (DAS) defines a communication protocol used to exchange biological annotations. It is motivated by the idea that annotations should not be provided by single centralized databases but instead be spread over multiple sites. Data distribution, performed by DAS servers, is separated from visualization, which is carried out by DAS clients. The original DAS protocol was designed to serve annotation of genomic sequences. We have extended the protocol to be applicable to macromolecular structures. Here we present SPICE, a new DAS client that can be used to visualize protein sequence and structure annotations. AVAILABILITY: http://www.efamily.org.uk/software/dasclients/spice/

Algorithms↗

PubMed2005

NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence.

NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of a newly developed inference strategy called Nested Sampling means NestedMICA is able to find optimal solutions without the need for a problematic initialization or seeding step. We investigate the performance of NestedMICA in a range scenario, on synthetic data and a well-characterized set of muscle regulatory regions, and compare it with the popular MEME program. We show that the new method is significantly more sensitive than MEME: in one case, it successfully extracted a target motif from background sequence four times longer than could be handled by the existing program. It also performs robustly on synthetic sequences containing multiple significant motifs. When tested on a real set of regulatory sequences, NestedMICA produced motifs which were good predictors for all five abundant classes of annotated binding sites.

Base Sequence↗

PubMed2004

What can we learn from noncoding regions of similarity between genomes?

BACKGROUND: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to assume that these conserved regions are more likely to contain functional elements than less-conserved portions of the genome. METHODS: Here we used a motif-oriented machine learning method based on the Relevance Vector Machine algorithm to extract the strongest signal from a set of non-coding conserved sequences. RESULTS: We successfully fitted models to reflect the non-coding sequences, and showed that the results were quite consistent for repeated training runs. Using the learned models to scan genomic sequence, we found that they often made predictions close to the start of annotated genes. We compared this method with other published promoter-prediction systems, and showed that the set of promoters which are detected by this method is substantially similar to that detected by existing methods. CONCLUSIONS: The results presented here indicate that the promoter signal is the strongest single motif-based signal in the non-coding functional fraction of the genome. They also lend support to the belief that there exists a substantial subset of promoter regions which share several common features including, but not restricted to, a relative abundance of CpG dinucleotides. This subset is detectable by a variety of distinct computational methods.

Animals↗

PubMed2004

SCOP database in 2004: refinements integrate structure and sequence family data.

The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.

Animals↗

PubMed2003

CASP5 target classification.

This report summarizes the Critical Assessment of Protein Structure Prediction (CASP5) target proteins, which included 67 experimental models submitted from various structural genomics efforts and independent research groups. Throughout this special issue, CASP5 targets are referred to with the identification numbers T0129-T0195. Several of these targets were excluded from the assessment for various reasons: T0164 and T0166 were cancelled by the organizers; T0131, T0144, T0158, T0163, T0171, T0175, and T0180 were not available in time; T0145 was "natively unfolded"; the T0139 structure became available before the target expired; and T0194 was solved for a different sequence than the one submitted. Table I outlines the sequence and structural information available for CASP5 proteins in the context of existing folds and evolutionary relationships. This information provided the basis for a domain-based classification of the target structures into three assessment categories: comparative modeling (CM), fold recognition (FR), and new fold (NF). The FR category was further subdivided into homologues [FR(H)] and analogs [FR(A)] based on evolutionary considerations, and the overlap between assessment categories was classified as CM/FR(H) and FR(A)/NF. CASP5 domains are illustrated in Figure 1. Examples of nontrivial links between CASP5 target domains and existing structures that support our classifications are provided.

Binding Sites↗

PubMed2002

SCOP database in 2002: refinements accommodate structural genomics.

The SCOP (Structural Classification of Proteins) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are grouped into species and hierarchically classified into families, superfamilies, folds and classes. Recently, we introduced a new set of features with the aim of standardizing access to the database, and providing a solid basis to manage the increasing number of experimental structures expected from structural genomics projects. These features include: a new set of identifiers, which uniquely identify each entry in the hierarchy; a compact representation of protein domain classification; a new set of parseable files, which fully describe all domains in SCOP and the hierarchy itself. These new features are reflected in the ASTRAL compendium. The SCOP search engine has also been updated, and a set of links to external resources added at the level of domain entries. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.

Animals↗

PubMed2002

MaxBench: evaluation of sequence and structure comparison methods.

SUMMARY: MaxBench is a web-based system available for evaluating the results of sequence and structure comparison methods, based on the SCOP protein domain classification. The system makes it easy for developers to both compare the overall performance of their methods to standard algorithms and investigate the results of individual comparisons. AVAILABILITY: http://www.sanger.ac.uk/Users/lp1/MaxBench/

Algorithms↗

PubMed2002

Computational detection and location of transcription start sites in mammalian genomic DNA.

Transcription, the process whereby RNA copies are made from sections of the DNA genome, is directed by promoter regions. These define the transcription start site, and also the set of cellular conditions under which the promoter is active. At least in more complex species, it appears to be common for genes to have several different transcription start sites, which may be active under different conditions. Eukaryotic promoters are complex and fairly diffuse structures, which have proven hard to detect in silico. We show that a novel hybrid machine-learning method is able to build useful models of promoters for >50% of human transcription start sites. We estimate specificity to be >70%, and demonstrate good positional accuracy. Based on the structure of our learned models, we conclude that a signal resembling the well known TATA box, together with flanking regions of C-G enrichment, are the most important sequence-based signals marking sites of transcriptional initiation at a large class of typical promoters.

Animals↗