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OPEN ACCESS PLUS
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Contents
8(1): Pp. 37 - 45
Naifang Su, Minping Qian and Minghua Deng
[Open Access Plus] |
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Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important roles in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on the sequence information, which unavoidably have high false detection rates. Recently, some novel approaches are developed to predict miRNA targets by integrating the typical algorithm with the paired expression profiles of miRNA and mRNA. Here we review and discuss these integrative approaches and propose a new algorithm called HCTarget. Applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten specific miRNAs. The experimental verification and a loss of function study validate our predictions. Therefore, the integrative approach is a reliable and effective way to predict miRNA targets, and could improve our comprehensive understanding of gene regulation.
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3(2): Pp. 87 - 97
Sylvain Foissac, Jerome Gouzy, Stephane Rombauts, Catherine Mathe, Joelle Amselem, Lieven Sterck, Yves Van de Peer, Pierre Rouze and Thomas Schiex
[Open Access Plus] |
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In this era of whole genome sequencing, reliable genome annotations (identification of functional regions) are the cornerstones for many subsequent analyses. Not only is careful annotation important for studying the gene and gene family content of a genome and its host, but also for wide-scale transcriptome and proteome analyses attempting to describe a certain biological process or to get a global picture of a cells behavior. Although the number of sequenced genomes is increasing thanks to the application of new technologies, genome-wide analyses will critically depend on the quality of the genome annotations. However, the annotation process is more complicated in the plant field than in the animal field because of the limited funding that leads to much fewer experimental data and less annotation expertise. This situation calls for highly automated annotation platforms that can make the best use of all available data, experimental or not. We discuss how the gene prediction (the process of predicting protein gene structures in genomic sequences) research field increasingly shifts from methods that typically exploited one or two types of data to more integrative approaches that simultaneously deal with various experimental, statistical, or other in silico evidence. We illustrate the importance of integrative approaches for producing high-quality automatic annotations of genomes of plants and algae as well as of fungi that live in close association with plants using the platform EuGène as an example.
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1(3): Pp. 347 - 358
Philip Groth and Bertram Weiss
[Open Access Plus] |
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To a great extent, our phenotype is determined by our genetic material. Many genotypic modifications may ultimately become manifest in more or less pronounced changes in phenotype. Despite the importance of how specific genetic alterations contribute to the development of diseases, surprisingly little effort has been made towards exploiting systematically the current knowledge of genotype-phenotype relationships. In the past, genes were characterized with the help of so-called "forward genetics" studies in model organisms, relating a given phenotype to a genetic modification. Analogous studies in higher organisms were hampered by the lack of suitable high-throughput genetic methods. This situation has now changed with the advent of new screening methods, especially RNA interference (RNAi) which allows to specifically silence gene by gene and to observe the phenotypic outcome. This ongoing large-scale characterization of genes in mammalian in-vitro model systems will increase phenotypic information exponentially in the very near future. But will our knowledge grow equally fast? As in other scientific areas, data integration is a key problem. It is thus still a major bioinformatics challenge to interpret the results of large-scale functional screens, even more so if sets of heterogeneous data are to be combined. It is now time to develop strategies to structure and use these data in order to transform the wealth of information into knowledge and, eventually, into novel therapeutic approaches. In light of these developments, we thoroughly surveyed the available phenotype resources and reviewed different approaches to analyzing their content. We discuss hurdles yet to be overcome, i.e. the lack of data integration, the missing adequate phenotype ontologies and the shortage of appropriate analytical tools. This review aims to assist researchers keen to understand and make effective use of these highly valuable data.
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1(3): Pp. 291 - 300
Michael Kaufmann
[Open Access Plus] |
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A major breakthrough in classifying proteins from different microbial genomes in terms of sequence similarity was the development of the COG concept by Tatusov et al. in 1997. The authors defined clusters of orthologous groups of proteins (COGs) by strictly applying all against all BLAST alignments of protein sequences from completely sequenced microbial genomes. The latest update of the COG database already covered 66 microbial genomes and additionally included the KOG database, an equivalent consisting of seven eukaryotic genomes. Although excellent web-based software tools designed to analyze this huge amount of data were initially provided by the authors, many other groups independently developed more specialized or extended programs making use of COG data for diverse purposes. Here a brief introduction is given to the concept behind COGs and their potentials in the field of comparative and functional genomics are discussed. The review then is focused on the multitude of recently developed web services aimed at mining the COG database. Their capabilities to solve diverse problems in biochemistry are addressed. In order to illustrate the broad field of possible applications, a compilation of recently published findings, implementing information derived from comparative genomics with emphasis on data retrieved from the COG database, is given.
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