In recent years, there have been increasing numbers of transcripts identified that do not encode proteins, many of which are developmentally regulated and appear to have regulatory functions. Here, we describe the construction of a comprehensive mammalian noncoding RNA database (RNAdb) which contains over 800 unique experimentally studied non-coding RNAs (ncRNAs), including many associated with diseases and/or developmental processes. The database is available at http://research.imb.uq.edu.au/RNAdb and is searchable by many criteria. It includes microRNAs and snoRNAs, but not infrastructural RNAs, such as rRNAs and tRNAs, which are catalogued elsewhere. The database also includes over 1100 putative antisense ncRNAs and almost 20 000 putative ncRNAs identified in high-quality murine and human cDNA libraries, with more to be added in the near future. Many of these RNAs are large, and many are spliced, some alternatively. The database will be useful as a foundation for the emerging field of RNomics and the characterization of the roles of ncRNAs in mammalian gene expression and regulation.
The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseudogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.
Backgroundβ2-Microglobulin (β2M) is the light chain of major histocompatibility class I (MHC I) that binds non-covalently with the α heavy chain. Both proteins attach to the antigen peptide, presenting a complex to the T cell to be destroyed via the immune mechanism.Methodology/Principal FindingsIn this study, a cDNA sequence encoding β2M in the Asian seabass (Lates calcarifer) was identified and analyzed using in silico approaches to predict and characterize its functional domain. The β2M cDNA contains an open reading frame (ORF) of 351 bases with a coding capacity of 116 amino acids. A large portion of the protein consists of the IG constant domain (IGc1), similar to β2M sequences from other species studied thus far. Alignment of the IGc1 domains of β2M from L. calcarifer and other species shows a high degree of overall conservation. Seven amino acids were found to be conserved across taxa whereas conservation between L. calcarifer and other fish species was restricted to 14 amino acids at identical conserved positions.Conclusion/SignificanceAs the L. calcarifer β2M protein analyzed in this study contains a functional domain similar to that of β2M proteins in other species, it can be postulated that the β2M proteins from L. calcarifer and other organisms are derived from a common ancestor and thus have a similar immune function. Interestingly, fish β2M genes could also be classified according to the ecological habitat of the species, i.e. whether it is from a freshwater, marine or euryhaline environment.
Integration into National StrategiesEarly government initiatives were centered on developing a strong biotechnology core with bioinformatics playing a supporting role [10]. Clusters of technology parks were deemed the best launch strategy. These clusters were intended to comprise biotechnology research institutions, universities, and specialized compa-
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