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PubMed2000

The PWWP domain: a potential protein-protein interaction domain in nuclear proteins influencing differentiation?

Upon characterization of WHSC1, a gene mapping to the Wolf-Hirschhorn syndrome critical region and at its C-terminus similar to the Drosophila ASH1/trithorax group proteins, we identified a novel protein domain designated PWWP domain. To gain insight into its structure, evolutionary conservation and its potential functional role, we performed database searches to identify other PWWP domain-containing proteins. We retrieved 39 proteins, and a multiple alignment shows that the domain spans some 70 amino acids. It is present in proteins of nuclear origin and plays a role in cell growth and differentiation. Due to its position, the composition of amino acids close to the PWWP motif and the pattern of other domains present, we hypothesize that the domain is involved in protein-protein interactions.

Amino Acid Motifs↗

PubMed2005

LOVD: easy creation of a locus-specific sequence variation database using an "LSDB-in-a-box" approach.

The completion of the human genome project has initiated, as well as provided the basis for, the collection and study of all sequence variation between individuals. Direct access to up-to-date information on sequence variation is currently provided most efficiently through web-based, gene-centered, locus-specific databases (LSDBs). We have developed the Leiden Open (source) Variation Database (LOVD) software approaching the "LSDB-in-a-Box" idea for the easy creation and maintenance of a fully web-based gene sequence variation database. LOVD is platform-independent and uses PHP and MySQL open source software only. The basic gene-centered and modular design of the database follows the recommendations of the Human Genome Variation Society (HGVS) and focuses on the collection and display of DNA sequence variations. With minimal effort, the LOVD platform is extendable with clinical data. The open set-up should both facilitate and promote functional extension with scripts written by the community. The LOVD software is freely available from the Leiden Muscular Dystrophy pages (www.DMD.nl/LOVD/). To promote the use of LOVD, we currently offer curators the possibility to set up an LSDB on our Leiden server.

Computational Biology↗

PubMed2004

The ASTRAL Compendium in 2004.

The ASTRAL Compendium provides several databases and tools to aid in the analysis of protein structures, particularly through the use of their sequences. Partially derived from the SCOP database of protein structure domains, it includes sequences for each domain and other resources useful for studying these sequences and domain structures. The current release of ASTRAL contains 54,745 domains, more than three times as many as the initial release 4 years ago. ASTRAL has undergone major transformations in the past 2 years. In addition to several complete updates each year, ASTRAL is now updated on a weekly basis with preliminary classifications of domains from newly released PDB structures. These classifications are available as a stand-alone database, as well as integrated into other ASTRAL databases such as representative subsets. To enhance the utility of ASTRAL to structural biologists, all SCOP domains are now made available as PDB-style coordinate files as well as sequences. In addition to sequences and representative subsets based on SCOP domains, sequences and subsets based on PDB chains are newly included in ASTRAL. Several search tools have been added to ASTRAL to facilitate retrieval of data by individual users and automated methods. ASTRAL may be accessed at http://astral.stanford. edu/.

Animals↗

PubMed2003

PlasmoDB: exploring genomics and post-genomics data of the malaria parasite, Plasmodium falciparum.

The recent completion of the genome sequence of Plasmodium falciparum 3D7 provides the foundation for genome-wide analysis of the parasite. In addition to DNA and gene sequence data, postgenomic methods including microarray-based transcript profiling and high-throughput proteomics are now accessible to Plasmodium researchers. The Plasmodium Genome database ( ) was developed to provide rapid and convenient access to the terabytes of genomic-scale data now being generated around the world. All data are available in a relational framework, permitting convenient downloading, browsing, and analysis. Combinatorial use of data analysis tools enables powerful data mining queries, such as combining gene and protein expression data to monitor changes through various life-cycle stages. Functional predictions can be used to explore potential targets for antimalarial drug development. This report outlines the use of PlasmoDB to examine redox-active functions in Plasmodium.

Animals↗

PubMedDate not supplied

[Prediction of protein domain boundaries based on statistics of appearance of amino acid residues].

We have created a database of two-domain proteins with homology less than 25% (452 proteins). Based on one half of this set of proteins statistics of appearance of amino acid residues on the domain boundaries of multiple domain proteins has been obtained. Small and hydrophilic amino acids (proline, glycine, asparagine, glutamic acid, arginine and others) appear on the domain boundaries more often than in the whole protein. Opposite, hydrophobic amino acid residues (tryptophane, methionine, phenylalanine and others) appear on the domain boundaries more rarely. The obtained scales of the appearance of amino acid residues on the boundary regions from the statistics have been used for calculation of domain boundaries in the proteins of the second half of the database. The probability scale obtained by averaging the appearance of amino acid residues on the domain boundary region including 8 residues (+/-4 residues from the real domain boundary) gives the best result: for 57% of proteins the predicted boundary was closer than 40 residues to the boundary assigned from three-dimensional structures, for 41% it was closer than 20 residues from the real boundary. The probability scale was used to predict domain boundaries for proteins with unknown three-dimensional structure (international competition CASP6).

Algorithms↗

PubMed2004

RTCGD: retroviral tagged cancer gene database.

Retroviral insertional mutagenesis in mouse hematopoietic tumors provides a potent cancer gene discovery tool in the post-genome-sequence era. To manage multiple high-throughput insertional mutagenesis screening projects, we developed the Retroviral Tagged Cancer Gene Database (RTCGD; http://RTCGD.ncifcrf.gov). A sequence analysis pipeline determines the genomic position of each retroviral integration site cloned from a mouse tumor, the distance between it and the nearest candidate disease gene(s) and its orientation with respect to the candidate gene(s). The pipeline also identifies genomic regions that are targets of retroviral integration in more than one tumor (common integration sites, CISs) and are thus likely to encode a disease gene. Users can search the database using a specified gene symbol, chromosome number or tumor model to identify both CIS genes and unique viral integration sites or compare the integration sites cloned by different laboratories using different models. As a default setting, users first review the CIS Lists and then Clone Lists. CIS Lists describe CISs and their candidate disease genes along with links to other public databases and clone lists. Clone Lists describe the viral integration site clones along with the tumor model and tumor type from which they were cloned, candidate disease gene(s), genomic position and orientation of the integrated provirus with respect to the candidate gene(s). It also provides a pictorial view of the genomic location of each integration site relative to neighboring genes and markers. Researchers can identify integrations of interest and compare their results with those for multiple tumor models and tumor types using RTCGD.

Animals↗

PubMed1998

Alternate polyadenylation in human mRNAs: a large-scale analysis by EST clustering.

Alternate polyadenylation is an important post-transcriptional regulatory process now open to large-scale analysis by use of cDNA databases. We clustered 164,000 expressed sequence tags (ESTs) into approximately 15,000 groups and aligned each group to a putative mRNA 3' end. By use of stringent criteria to discard artifactual mRNA extremities, clear evidence for alternate polyadenylation was obtained in 189 of the 1000 EST clusters studied. A number of previously unreported polyadenylation sites were identified, together with possible instances of tissue-specific differential polyadenylation. This study demonstrates that, besides quantitative aspects of gene expression, the distribution of alternate mRNA forms can be analyzed through EST sampling.

Computational Biology↗

PubMed2003

Neuroscience data and tool sharing: a legal and policy framework for neuroinformatics.

The requirements for neuroinformatics to make a significant impact on neuroscience are not simply technical--the hardware, software, and protocols for collaborative research--they also include the legal and policy frameworks within which projects operate. This is not least because the creation of large collaborative scientific databases amplifies the complicated interactions between proprietary, for-profit R&D and public "open science." In this paper, we draw on experiences from the field of genomics to examine some of the likely consequences of these interactions in neuroscience. Facilitating the widespread sharing of data and tools for neuroscientific research will accelerate the development of neuroinformatics. We propose approaches to overcome the cultural and legal barriers that have slowed these developments to date. We also draw on legal strategies employed by the Free Software community, in suggesting frameworks neuroinformatics might adopt to reinforce the role of public-science databases, and propose a mechanism for identifying and allowing "open science" uses for data whilst still permitting flexible licensing for secondary commercial research.

Computational Biology↗

PubMed1996

Company strategies for using bioinformatics.

Bioinformatics enables biotechnology companies to access and analyse their growing databases of experimental results, and to exploit public data from genome programmes and other sources. Traditionally occupying the domain of a 'guru' supplying answers to infrequent research questions, corporate bioinformatics is breaking down under the flood of data. New, more robust, professional and expandable systems will give scientists effective access to new tools. This review outlines how companies have evolved beyond the 'guru', and have organized their bioinformatics by acquiring or developing bioinformatics resources. It also describes why the biologist must be central to this process, and why this is a problem for computer professionals to solve, not for 'gurus'.

Biotechnology↗

PubMed2004

Evidence and characteristics of putative human alpha recombination hotspots.

Understanding recombination rate variation is very important for studying genome diversity and evolution, and for investigation of phenotypic association and genetic diseases. Recombination hotspots have been observed in many species and are well studied in yeast. Recent study demonstrated that recombination hotspots are also a ubiquitous feature of the human genome. But the nature of human hotspots remains largely unknown. We have developed and validated a novel computational method for testing the existence of hotspots as well as for localizing them with either unphased or phased genotyping data. To study the characteristics of hotspots within or close to genes, we scanned for unusually high levels of recombination using the European population samples in the SeattleSNPs database, and found evidence for the existence of human alpha hotspots similar to those of yeast. This type of hotspots, found at promoter regions, accounts for about half of the total detected and appears to depend on some specific transcription factor binding sites (such as CGCCCCCGC). These characteristics can explain the observed weak correlation between hotspots and GC-content, and their variation may contribute to the diversity of hotspot distribution among different individuals and species. These long-sought putative human alpha recombination hotspots should deserve further experimental investigations.

Binding Sites↗

PubMed2004

Development of a mass fingerprinting tool for automated interpretation of oligosaccharide fragmentation data.

The bioinformatic tool GlycosidIQ was developed for computerized interpretation of oligosaccharide mass spectrometric fragmentation based on matching experimental data with theoretically fragmented oligosaccharides generated from the database GlycoSuiteDB. This use of the software for glycofragment mass fingerprinting obviates a large part of the manual, labor intensive, and technically challenging interpretation of oligosaccharide fragmentation. Using 130 negative ion electrospray ionization-tandem mass spectrometry fragment spectra from identified oligosaccharide structures, it was shown that the GlycosidIQ scoring algorithms were able to correctly identify oligosaccharides in the great majority of cases (correct structure top ranked in 78% of the cases and an additional 17% were ranked second highest in the sample set).

Algorithms↗

PubMed1998

DIVCLUS: an automatic method in the GEANFAMMER package that finds homologous domains in single- and multi-domain proteins.

MOTIVATION: Large-scale determination of relationships between the proteins produced by genome sequences is now common. All protein sequences are matched and those that have high match scores are clustered into families. In cases where the proteins are built of several domains or duplication modules, this can lead to misleading results. Consider the very simple example of three proteins: 1, formed by duplication modules A and B; 2, formed by duplication modules B' and C; and 3, formed by duplication modules C' and D. Duplication modules B and B' are homologous, as are C and C'. Matching the sequences of 1, 2 and 3 followed by simple single-linkage clustering would put all three in the same family, even though proteins 1 and 3 are not related. This is because the different parts of 2 match 1 and 3. This paper describes a procedure, DIVCLUS, that divides such complex clusters of partially related sequences into simple clusters that contain only related duplication modules. In the example just given, it would produce two groups of sequences: the first with domains B of sequence 1 and B of sequence 2, and the second with domain C of sequence 2 and C of sequence 3. DIVCLUS is part of a package called GEANFAMMER, for GEnome ANalysis and protein FAMily MakER. The package automates the detection of families of duplication modules from a protein sequence database. RESULTS: DIVCLUS has been applied to the division of single-linkage clusters generated from the protein sequences of six completely sequenced bacterial genomes. Out of 12 013 genes in these six genomes, 4563 single- and multi-domain sequences formed 1071 complex clusters. Application of the DIVCLUS program resolved these clusters into 2113 clusters corresponding to single duplication modules. AVAILABILITY: The perl5 program and its documentation are available at the following address: http://www.mrc-lmb.cam.ac.uk/genomes/ and by anonymous ftp at ftp.mrc-lmb.cam.ac.uk in the directory /pub/genomes/Software/. CONTACT: sat@mrc-lmb.cam.ac.uk; jong@mrc-lmb. cam.ac.uk

Algorithms↗

PubMed2000

Data acquisition, data storage, and data presentation in a modern genetics laboratory.

Modern genetic analysis can be divided into three main areas of investigation. The first is data acquisition, in the form of genomic sequence and the cataloguing of polymorphism data of the single nucleotide polymorphism variety (so called SNPs). Once identified, such genetic information can be adapted into high throughput tests to examine genetic information in large populations, making the analysis of sufficiently large numbers both cost and time effective so that relatively low-penetrant genetic effects can be accurately detected. The third step is correlating variation with phenotype (e.g. disease susceptibility or resistance) for a variety of disorders is paramount in our motivation and indeed is a common goal of modern human genetic analysis. While the technology to acquire vast amounts of genetic data is now well established and continues to expand, the ability to deal with such data, from the process of acquisition, storage, and analysis depends fundamentally on a solid informatics infrastructure as an essential component. Indeed, most of the major gains in productivity in this field are to be realized on the informatics front, and involve automating data acquisition, defining and sorting data in databases for quality control and analysis and facilitating access to data for the large variety of data analyses. Informatics-related issues including those relating to data acquisition, database structure, and analysis tools are summarized here in an effort to define some of the issues relevant to establishing informatics infrastructure in a small genetics laboratory focused on resequencing human immune response genes. From inherited diseases to drug efficacy to the specific genetic changes occurring during tumor development, this new field of medical genetics promises a profound impact on the state of human health. Ultimately, any and all advances in this field will continue to depend on major investments in informatics.

Computational Biology↗

PubMed2006

Bioinformatics and microbial biodiversity: analysis of vibrios by the GenEnv system.

Sequence-based approaches to prokaryotic systematics and typing represent a modern and promising strategy in epidemiology and environmental microbiology. GenEnv, a database-driven system for bacterial typing, was developed in order to provide user friendly tools for supporting biomolecular analysis of bacteria. The family Vibrionaceae represents a heterogeneous taxon of aquatic microrganisms, harbouring a plethora of genomes currently analyzed by different molecular techniques. Under the query "Vibrio", GenEnv retrieved 256 organisms, included in a total number of 19 families. Overall, 548 sequences, comprising 16S rRNA (n=402), rpoB (n=1), gyrB (n=145) were available. In addition, GenEnv system allowed primer design, homology analysis and restriction maps, for immediate applications to the study of Vibrionaceae.

Bacterial Typing Techniques↗

PubMed1996

Analysis of chaperonin-containing TCP-1 subunits in the human keratinocyte two-dimensional protein database: further characterisation of antibodies to individual subunits.

The chaperonin-containing TCP-1 (CCT), found in the eukaryotic cytosol, is currently the focus of extensive research. CCT consists of at least eight different subunit types encoded by independent but related genes, and a set of antibodies that recognise individual subunits has proved useful in the characterisation and functional analysis of CCT. These antibodies were used to identify subunits of CCT in the human keratinocyte two-dimensional protein database. Accurate values for the pI and molecular mass of human CCT subunits were determined from the database, and biological data was obtained regarding changes in subunit levels in response to extracellular agents and growth conditions. The second part of the study describes the characterisation of seven monoclonal antibodies raised against mouse TCP-1, also known as CCT alpha, using a combination of epitope mapping and immunoblot analysis of protein extracts from different species and tissue types. Some antibodies were not monospecific for TCP-1, and a number of epitope-related proteins were identified.

Animals↗

PubMed2005

Prediction of beta-strand packing interactions using the signature product.

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].

Amino Acid Sequence↗

PubMed2006

PUMA2--grid-based high-throughput analysis of genomes and metabolic pathways.

The PUMA2 system (available at http://compbio.mcs.anl.gov/puma2) is an interactive, integrated bioinformatics environment for high-throughput genetic sequence analysis and metabolic reconstructions from sequence data. PUMA2 provides a framework for comparative and evolutionary analysis of genomic data and metabolic networks in the context of taxonomic and phenotypic information. Grid infrastructure is used to perform computationally intensive tasks. PUMA2 currently contains precomputed analysis of 213 prokaryotic, 22 eukaryotic, 650 mitochondrial and 1493 viral genomes and automated metabolic reconstructions for >200 organisms. Genomic data is annotated with information integrated from >20 sequence, structural and metabolic databases and ontologies. PUMA2 supports both automated and interactive expert-driven annotation of genomes, using a variety of publicly available bioinformatics tools. It also contains a suite of unique PUMA2 tools for automated assignment of gene function, evolutionary analysis of protein families and comparative analysis of metabolic pathways. PUMA2 allows users to submit batch sequence data for automated functional analysis and construction of metabolic models. The results of these analyses are made available to the users in the PUMA2 environment for further interactive sequence analysis and annotation.

Computational Biology↗

PubMed2004

Progress: simultaneous searching of protein databases by sequence and structure.

We consider the problem of similarity searches on protein databases based on both sequence and structure information simultaneously. Our program extracts feature vectors from both the sequence and structure components of the proteins. These feature vectors are then combined and indexed using a novel multi-dimensional index structure. For a given query, we employ this index structure to find candidate matches from the database. We develop a new method for computing the statistical significance of these candidates. The candidates with high significance are then aligned to the query protein using the Smith-Waterman technique to find the optimal alignment. The experimental results show that our method can classify up to 97% of the superfamilies and up to 100% of the classes correctly according to the SCOP classification. Our method is up to 37 times faster than CTSS, a recent structure search technique, combined with Smith-Waterman technique for sequences.

Abstracting and Indexing↗