Biomedical subjects

Geir Skogerbø

Publications and source records attributed to Geir Skogerbø.

9 recordsLinked to original sources

PubMed2006

Conserved distances between vertebrate highly conserved elements.

High numbers of sequence element with very high (>95%) sequence conservation between the human and other vertebrate genomes have been reported and ascribed putative cis-regulatory functions. We have investigated the structural relationships between such elements in mammalian genomes and find that not only their sequences, but also the distances between them are significantly (P<2.2x10(-16)) more conserved than corresponding distances between orthologous protein-coding genes or between exons within these genes. Regions of largely conserved distance between consecutive highly conserved elements (HCE) generally overlap previously identified HCE clusters, but may be far longer (up to 20 Mb) and possibly cover close to 25% of the human genome sequence. Similar conservation of distance is found between bird (chicken) and mammalian genomes and is also discernible in comparisons between fish and mammals. The data suggest either that a substantial amount of essential (functionally active) elements with lower sequence conservation occupy the space between the HCEs or that distance itself is an important factor in transcriptional regulation or chromatin modelling.

Animals↗

PubMed2006

Prediction of structured non-coding RNAs in the genomes of the nematodes Caenorhabditis elegans and Caenorhabditis briggsae.

We present a survey for non-coding RNAs and other structured RNA motifs in the genomes of Caenorhabditis elegans and Caenorhabditis briggsae using the RNAz program. This approach explicitly evaluates comparative sequence information to detect stabilizing selection acting on RNA secondary structure. We detect 3,672 structured RNA motifs, of which only 678 are known non-translated RNAs (ncRNAs) or clear homologs of known C. elegans ncRNAs. Most of these signals are located in introns or at a distance from known protein-coding genes. With an estimated false positive rate of about 50% and a sensitivity on the order of 50%, we estimate that the nematode genomes contain between 3,000 and 4,000 RNAs with evolutionary conserved secondary structures. Only a small fraction of these belongs to the known RNA classes, including tRNAs, snoRNAs, snRNAs, or microRNAs. A relatively small class of ncRNA candidates is associated with previously observed RNA-specific upstream elements.

Animals↗

PubMed2006

Profiling Caenorhabditis elegans non-coding RNA expression with a combined microarray.

Small non-coding RNAs (ncRNAs) are encoded by genes that function at the RNA level, and several hundred ncRNAs have been identified in various organisms. Here we describe an analysis of the small non-coding transcriptome of Caenorhabditis elegans, microRNAs excepted. As a substantial fraction of the ncRNAs is located in introns of protein-coding genes in C.elegans, we also analysed the relationship between ncRNA and host gene expression. To this end, we designed a combined microarray, which included probes against ncRNA as well as host gene mRNA transcripts. The microarray revealed pronounced differences in expression profiles, even among ncRNAs with housekeeping functions (e.g. snRNAs and snoRNAs), indicating distinct developmental regulation and stage-specific functions of a number of novel transcripts. Analysis of ncRNA-host mRNA relations showed that the expression of intronic ncRNA loci with conserved upstream motifs was not correlated to (and much higher than) expression levels of their host genes. Even promoter-less intronic ncRNA loci, though showing a clear correlation to host gene expression, appeared to have a surprising amount of 'expressional freedom', depending on host gene function. Taken together, our microarray analysis presents a more complete and detailed picture of a non-coding transcriptome than hitherto has been presented for any other multicellular organism.

Animals↗

PubMed2006

Dynamic changes in subgraph preference profiles of crucial transcription factors.

Transcription factors with a large number of target genes--transcription hub(s), or THub(s)--are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (A(N)) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns.

Cell Cycle↗

PubMed2006

Phylophenetic properties of metabolic pathway topologies as revealed by global analysis.

BACKGROUND: As phenotypic features derived from heritable characters, the topologies of metabolic pathways contain both phylogenetic and phenetic components. In the post-genomic era, it is possible to measure the "phylophenetic" contents of different pathways topologies from a global perspective. RESULTS: We reconstructed phylophenetic trees for all available metabolic pathways based on topological similarities, and compared them to the corresponding 16S rRNA-based trees. Similarity values for each pair of trees ranged from 0.044 to 0.297. Using the quartet method, single pathways trees were merged into a comprehensive tree containing information from a large part of the entire metabolic networks. This tree showed considerably higher similarity (0.386) to the corresponding 16S rRNA-based tree than any tree based on a single pathway, but was, on the other hand, sufficiently distinct to preserve unique phylogenetic information not reflected by the 16S rRNA tree. CONCLUSION: We observed that the topology of different metabolic pathways provided different phylogenetic and phenetic information, depicting the compromise between phylogenetic information and varying evolutionary pressures forming metabolic pathway topologies in different organisms. The phylogenetic information content of the comprehensive tree is substantially higher than that of any tree based on a single pathway, which also gave clues to constraints working on the topology of the global metabolic networks, information that is only partly reflected by the topologies of individual metabolic pathways.

Chromosome Mapping↗

PubMed2006

NPInter: the noncoding RNAs and protein related biomacromolecules interaction database.

The noncoding RNAs and protein related biomacromolecules interaction database (NPInter; http://bioinfo.ibp.ac.cn/NPInter or http://www.bioinfo.org.cn/NPInter) is a database that documents experimentally determined functional interactions between noncoding RNAs (ncRNAs) and protein related biomacromolecules (PRMs) (proteins, mRNAs or genomic DNAs). NPInter intends to provide the scientific community with a comprehensive and integrated tool for efficient browsing and extraction of information on interactions between ncRNAs and PRMs. Beyond cataloguing details of these interactions, the NPInter will be useful for understanding ncRNA function, as it adds a very important functional element, ncRNAs, to the biomolecule interaction network and sets up a bridge between the coding and the noncoding kingdoms.

Animals↗

PubMed2005

Organization of the Caenorhabditis elegans small non-coding transcriptome: genomic features, biogenesis, and expression.

Recent evidence points to considerable transcription occurring in non-protein-coding regions of eukaryote genomes. However, their lack of conservation and demonstrated function have created controversy over whether these transcripts are functional. Applying a novel cloning strategy, we have cloned 100 novel and 61 known or predicted Caenorhabditis elegans full-length ncRNAs. Studying the genomic environment and transcriptional characteristics have shown that two-thirds of all ncRNAs, including many intronic snoRNAs, are independently transcribed under the control of ncRNA-specific upstream promoter elements. Furthermore, the transcription levels of at least 60% of the ncRNAs vary with developmental stages. We identified two new classes of ncRNAs, stem-bulge RNAs (sbRNAs) and snRNA-like RNAs (snlRNAs), both featuring distinct internal motifs, secondary structures, upstream elements, and high and developmentally variable expression. Most of the novel ncRNAs are conserved in Caenorhabditis briggsae, but only one homolog was found outside the nematodes. Preliminary estimates indicate that the C. elegans transcriptome contains approximately 2700 small non-coding RNAs, potentially acting as regulatory elements in nematode development.

Animals↗

PubMed2005

NONCODE: an integrated knowledge database of non-coding RNAs.

NONCODE is an integrated knowledge database dedicated to non-coding RNAs (ncRNAs), that is to say, RNAs that function without being translated into proteins. All ncRNAs in NONCODE were filtered automatically from literature and GenBank, and were later manually curated. The distinctive features of NONCODE are as follows: (i) the ncRNAs in NONCODE include almost all the types of ncRNAs, except transfer RNAs and ribosomal RNAs. (ii) All ncRNA sequences and their related information (e.g. function, cellular role, cellular location, chromosomal information, etc.) in NONCODE have been confirmed manually by consulting relevant literature: more than 80% of the entries are based on experimental data. (iii) Based on the cellular process and function, which a given ncRNA is involved in, we introduced a novel classification system, labeled process function class, to integrate existing classification systems. (iv) In addition, some 1100 ncRNAs have been grouped into nine other classes according to whether they are specific to gender or tissue or associated with tumors and diseases, etc. (v) NONCODE provides a user-friendly interface, a visualization platform and a convenient search option, allowing efficient recovery of sequence, regulatory elements in the flanking sequences, secondary structure, related publications and other information. The first release of NONCODE (v1.0) contains 5339 non-redundant sequences from 861 organisms, including eukaryotes, eubacteria, archaebacteria, virus and viroids. Access is free for all users through a web interface at http://noncode.bioinfo.org.cn.

Base Sequence↗

PubMed2004

The interactome as a tree--an attempt to visualize the protein-protein interaction network in yeast.

The refinement and high-throughput of protein interaction detection methods offer us a protein-protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.

Algorithms↗