Cellular RNAs that do not function as messenger RNAs (mRNAs), transfer RNAs (tRNAs) or ribosomal RNAs (rRNAs) comprise a diverse class of molecules that are commonly referred to as non-protein-coding RNAs (ncRNAs). These molecules have been known for quite a while, but their importance was not fully appreciated until recent genome-wide searches discovered thousands of these molecules and their genes in a variety of model organisms. Some of these screens were based on biocomputational prediction of ncRNA candidates within entire genomes of model organisms. Alternatively, direct biochemical isolation of expressed ncRNAs from cells, tissues or entire organisms has been shown to be a powerful approach to identify ncRNAs both at the level of individual molecules and at a global scale. In this review, we will survey several such wet-lab strategies, i.e. direct sequencing of ncRNAs, shotgun cloning of small-sized ncRNAs (cDNA libraries), microarray analysis and genomic SELEX to identify novel ncRNAs, and discuss the advantages and limits of these approaches.

DBTSS was first constructed in 2002 based on precise,experimentallydetermined50 endclones.Several major updates and additions have been made since the last report. First, the number of human clones has drastically increased, going from 190 964 to 1 359 000. Second, information about potential alternative promoters is presented because the number of 50 end clones is now sufficient to determine several promoters for one gene. Namely, we defined putative promoter groups by clustering transcription start sites (TSSs) separated by,500 bases. A total of 8308 human genes and 4276 mouse genes were found to have putative multiple promoters. Third, DBTSS provides detailed sequence comparisons of userspecified TSSs. Finally, we have added TSS information for zebrafish, malaria and schyzon (a red algae model organism). DBTSS is accessible at

The noncoding RNA database (ncRNAdb) was created as a source of information on RNA molecules, which do not possess protein-coding capacity. It is now widely accepted that, in addition to constitutively expressed, housekeeping or infrastructural RNAs, there is a wide variety of RNAs participating in mechanisms involved in regulation of gene expression at all levels of transmission of genetic information from DNA to proteins. Noncoding RNAs’ activities include chromatin structure remodeling, transcriptional and translational regulation of gene expression, modulation of protein function and regulation of subcellular distribution of RNAs as well as proteins. Noncoding transcripts have been identified in organisms belonging to all domains of life. Currently, the ncRNAdb contains>30 000 ncRNA sequences from Eukaryotes, Eubacteria and Archaea, but does not include housekeeping transcripts or microRNAs and snoRNAs for which more specialized databases are available. The contents of the database can be accessed via the WWW at

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Abstract. The ability to locate promoters within a section of DNA is known to be a very difficult and very important task in DNA analysis. We document an approach that incorporates the concept of DNA as a complex molecule using several models of its physico-chemical properties. A support vector machine is trained to recognise promoters by their distinctive physical and chemical properties. We demonstrate that by combining models, we can improve upon the classification accuracy obtained with a single model. We also show that by examining how the predictive accuracy of these properties varies over the promoter, we can reduce the number of attributes needed. Finally, we apply this method to a real-world problem. The results demonstrate that such an approach has significant merit in its own right. Furthermore, they suggest better results from a planned combined approach to promoter prediction using both physicochemical and sequence based techniques. Key words: promoter prediction, support vector machine, SVM, physicochemical, classifier, DNA, transcription.

PURPOSE. To sequence and comprehensively analyze human and mouse lacrimal gland transcriptomes as part of the NEI-Bank project. METHODS. cDNA libraries generated from normal human and mouse lacrimal glands were sequenced and analyzed by PHRED, RepeatMasker, BLAST, and GRIST. Human “lacrimalpreferred genes ” and putative gene regulatory elements were respectively identified in UniGene and ConSite, and gene clustering was analyzed by chromosomal mapping. “Hypothetical proteins, ” identified by keyword search, were verified by genomic alignment and queried in the Conserved Domain database and GEO Profiles. RESULTS. The top six transcripts in human and mouse differed, revealing a previously unappreciated molecular divergence. The human transcriptome is enriched with transcripts from 29

Siglecs are vertebrate cell-surface receptors that recognize sialylated glycans. Here we have identified and characterized a novel Siglec, named Siglec-15. Siglec-15 is a type-I transmembrane protein consisting of: (i) two immunoglobulin (Ig)-like domains, (ii) a transmembrane domain containing a lysine residue, and (iii) a short cytoplasmic tail. Siglec-15 is expressed on macrophages and/or dendritic cells of human spleen and lymph nodes. We show that the extracellular domain of Siglec-15 preferentially recognizes the Neu5Aca2–6GalNAca – structure. Siglec-15 associates with the activating adaptor proteins DNAX activation protein (DAP)12 and DAP10 via its lysine residue in the transmembrane domain, implying that it functions as an activating signaling molecule. Siglec-15 is the second human Siglec identified to have an activating signaling potential; unlike Siglec-14, however, it does not have an inhibitory counterpart. Orthologs of Siglec-15 are present not only in mammals but also in other branches of vertebrates; in contrast, no other known Siglec expressed in the immune system has been conserved throughout vertebrate evolution. Thus, Siglec-15 probably plays a conserved, regulatory role in the immune system of vertebrates. Key words: DAP12/dendritic cells/macrophages/sialyl Tn/ Siglec

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Recent studies of mammalian transcriptomes have identified numerous RNA transcripts that do not code for proteins; their identity, however, is largely unknown. Here we explore an approach based on sequence randomness patterns to discern different RNA classes. The relative z-score we use helps identify the known ncRNA class from the genome, intergene and intron classes. This leads us to a fractional ncRNA measure of putative ncRNA datasets which we model as a mixture of genuine ncRNAs and other transcripts derived from genomic, intergenic and intronic sequences. We use this model to analyze six representative datasets identified by the FANTOM3 project and two computational approaches based on comparative analysis (RNAz and EvoFold). Our analysis suggests fewer ncRNAs than estimated by DNA sequencing and comparative analysis, but the verity of our approach and its prediction requires more extensive experimental RNA data.

Abstract: Recent studies of mammalian transcriptomes have identified numerous RNA transcripts that do not code for proteins; their identity, however, is largely unknown. Here we explore an approach based on sequence randomness patterns to discern different RNA classes. The relative z-score we use helps identify the known ncRNA class from the genome, intergene and intron classes. This leads us to a fractional ncRNA measure of putative ncRNA datasets which we model as a mixture of genuine ncRNAs and other transcripts derived from genomic, intergenic and intronic sequences. We use this model to analyze six representative datasets identified by the FANTOM3 project and two computational approaches based on comparative analysis (RNAz and EvoFold). Our analysis suggests fewer ncRNAs than estimated by DNA sequencing and comparative analysis, but the verity of our approach and its prediction requires more extensive experimental RNA data.