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Abstract: Computational analysis of genomic sequence databases has recently revealed a striking abundance of Natural Antisense Transcripts within the mouse and human genomes. Since antisense transcription is increasingly recognized as a molecular mechanism involved in the regulation of gene expression, a significant proportion of human disease genes could potentially display antisense-mediated abnormal patterns of gene expression. Cancer is a pathological phenotype that could represent a potential target for such gene regulation mechanism, given the high number of genes governing cancer-related cellular functions such as proliferation, differentiation and apoptosis. Preliminary experimental evidence has been reported recently for the occurrence of natural antisense transcript for several genes whose function has been implicated in cancer pathogenesis. Therefore, a targeted in silico survey of antisense transcription, coupled with a detailed inspection of annotated gene features, could represent a novel tool for the identification of candidate cancer-related genes. We have performed an in silico search for “sense-antisense gene clusters” within two regions from human chromosome 6 (6q21 and 6q27) that have long been reported to carry cancer-associated deletions and rearrangements, but for which no tumor suppressor genes has been unambiguously identified. Experimental validation of each sense-antisense cluster detected in this study, followed by definition of bona fide tumor suppressor candidates based on the available annotation features, confirmed the feasibility of this approach to better define candidate cancer-associated genes.

Abstract: The antigen receptor repertoire is generated through rearrangements of Immunoglobulin (Ig) and T-cell receptor (TCR) gene segments into functional genes by a mechanism named V(D)J recombination. This is a two-step process: a cleavage step in which double-strand DNA cuts are made at specific sequences, followed by a joining step to repair the breaks. The coding segments of Ig and TCR genes are flanked by short recombination signal sequences (RSS). The RSS are recognized by a complex of the lymphocyte specific recombination proteins RAG1 and RAG2, which cleave the DNA between the coding sequence and the RSS. The broken coding strands are then rejoined to produce a rearranged gene. Mistakes in this process can generate chromosomal translocations that are involved in acute lymphoid leukemias and non-Hodgkin lymphomas. These errors include cleavage of DNA sequences, termed cryptic RSS, similar to functional RSS but located outside the Ig or TCR loci. We have screened the human and mouse genomes for the presence of putative cryptic RSS. To provide an initial list enriched in cryptic RSS, we used an original search algorithm. This primary set was then further filtered for biologically functional sequences using a published method. We have created a web-accessible database containing these putative recombination signals in the genome context. This is the first repository containing a genome-wide collection of RSS sequences. These sequence tags can be retrieved from a number of starting points including RSS type (with 12- or 23 bp spacer), chromosomal region, cytoband and gene identity. For visualization of our RSS search results, we have chosen to rely on an existing genome annotation knowledgebase and correlate our results with the gene structure, analysis, annotation and browsing features of the UCSC Genome Browser. Sequences of interest may also be searched for the occurrence of RSS and the corresponding tracks searched from within the genome database.

Abstract: Expressed Sequence Tags (ESTs) constitute an important source of information for laboratories interested in the identification of novel gene sequences. We developed bioinformatic strategies and tools which rely on dbEST sequence data mining in order to support the effort of disease gene identification both at TIGEM and worldwide. One example of systematic human EST analysis is the DRES (Drosophila Related Expressed Sequences) project. As a starting strategy, we applied the power of Drosophila genetics to identify novel human genes of high biological interest. Sixty-six human cDNAs (called DRES clones) showing significant homology to Drosophila mutant genes were identified by screening dbEST with keywords, and their map position was determined experimentally. Based on this approach we developed the "DRES Search Engine", a tool for the systematic identification of human cDNAs homologous to Drosophila genes through an automated sequence database searching procedure. The homepage of the DRES project is at the WWW address http://www.tigem.it/LOCAL/drosophila/dros.html. Other tools of interest to the researchers interested in maximizing the information associated with a single cDNA sequence are freely available at the WWW address http://www.tigem.it/LOCAL/sequtils.html : - the "In Situ Blast" server performs a library-specific (and consequently tissue-specific) Blast search against one or more given cDNA libraries belonging to the UniGene EST cluster database; - the "UniBlast" server performs a local Blast search against the UniGene database or against UniNewGene, a locally generated version of UniGene devoid of all the clusters containing an already known mRNA or coding sequence; - the "EST Assembly Machine" and the "EST Extractor" will build sequence contigs (corresponding to "virtual transcripts") from the UniGene EST cluster database or from dbEST respectively, starting from a sequence Accession Number or a plain DNA/Protein sequence. This procedure extends a given cDNA sequence information through repeated cycles of sequence comparison, ideally providing the sequence of a full-length transcript starting from a single query sequence.

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Abstract: We describe here the rationale, implementation and results of a bioinformatic strategy for large-scale identification and annotation of chromosomal translocations in tumours, based on sequence and annotation comparison between human transcriptome and EST partial cDNA sequences derived from tissues or cell lines. We also illustrate how the sequencing and subsequent careful bioinformatic analysis of a number of identified candidate translocation cDNAs revealed the complexity of distinguishing recombination from true translocation events. Finally, we suggest some EST filtering and cleaning strategy for pursuing EST-based “in silico” translocation identification projects.

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