K Malone1, M M Sohocki, L S Sullivan, S P Daiger. 1. Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA. kmalone@gsbs3.gs.uth.tmc.edu
Abstract
PURPOSE: The goal of this study was to develop efficient methods to identify tissue-specific expressed sequence tags (ESTs) and to map their locations in the human genome. Through a combination of database analysis and laboratory investigation, unique retina-specific ESTs were identified and mapped as candidate genes for inherited retinal diseases. METHODS: DNA sequences from retina-specific EST clusters were obtained from the TIGR Human Gene Index Database. Further processing of the EST sequence data was necessary to ensure that each EST cluster represented a novel, non-redundant mapping candidate. Processing involved screening for homologies to known genes and proteins using BLAST, excluding known human gene sequences and repeat sequences, and developing primers for PCR amplification of the gene encoding each cDNA cluster from genomic DNA. The EST clusters were mapped using the GeneBridge 4.0 Radiation Hybrid Mapping Panel with standard PCR conditions. RESULTS: A total of 83 retinal-expressed EST clusters were examined as potential novel, non-redundant mapping candidates. Fifty-five clusters were mapped successfully and their locations compared to the locations of known retinal disease genes. Fourteen EST clusters localize to candidate regions for inherited retinal diseases. CONCLUSIONS: This pilot study developed methodology for mapping uniquely expressed retinal ESTs and for identifying potential candidate genes for inherited retinal disorders. Despite the overall success, several complicating factors contributed to the high failure rate (33%) for mapping EST-clustered sequences. These include redundancy in the sequence data, widely dispersed sequences, ambiguous nucleotides within the sequences, the possibility of amplifying through introns and the presence of repetitive elements within the sequence. However, the combination of database analysis and laboratory mapping is a powerful method for identification of candidate genes for inherited diseases.
PURPOSE: The goal of this study was to develop efficient methods to identify tissue-specific expressed sequence tags (ESTs) and to map their locations in the human genome. Through a combination of database analysis and laboratory investigation, unique retina-specific ESTs were identified and mapped as candidate genes for inherited retinal diseases. METHODS: DNA sequences from retina-specific EST clusters were obtained from the TIGRHuman Gene Index Database. Further processing of the EST sequence data was necessary to ensure that each EST cluster represented a novel, non-redundant mapping candidate. Processing involved screening for homologies to known genes and proteins using BLAST, excluding known human gene sequences and repeat sequences, and developing primers for PCR amplification of the gene encoding each cDNA cluster from genomic DNA. The EST clusters were mapped using the GeneBridge 4.0 Radiation Hybrid Mapping Panel with standard PCR conditions. RESULTS: A total of 83 retinal-expressed EST clusters were examined as potential novel, non-redundant mapping candidates. Fifty-five clusters were mapped successfully and their locations compared to the locations of known retinal disease genes. Fourteen EST clusters localize to candidate regions for inherited retinal diseases. CONCLUSIONS: This pilot study developed methodology for mapping uniquely expressed retinal ESTs and for identifying potential candidate genes for inherited retinal disorders. Despite the overall success, several complicating factors contributed to the high failure rate (33%) for mapping EST-clustered sequences. These include redundancy in the sequence data, widely dispersed sequences, ambiguous nucleotides within the sequences, the possibility of amplifying through introns and the presence of repetitive elements within the sequence. However, the combination of database analysis and laboratory mapping is a powerful method for identification of candidate genes for inherited diseases.
Authors: S J Mitchell; D P McHale; D A Campbell; N J Lench; R F Mueller; S E Bundey; A F Markham Journal: Am J Hum Genet Date: 1998-05 Impact factor: 11.025
Authors: E Dias Neto; R G Correa; S Verjovski-Almeida; M R Briones; M A Nagai; W da Silva; M A Zago; S Bordin; F F Costa; G H Goldman; A F Carvalho; A Matsukuma; G S Baia; D H Simpson; A Brunstein; P S de Oliveira; P Bucher; C V Jongeneel; M J O'Hare; F Soares; R R Brentani; L F Reis; S J de Souza; A J Simpson Journal: Proc Natl Acad Sci U S A Date: 2000-03-28 Impact factor: 11.205
Authors: R T Miller; A G Christoffels; C Gopalakrishnan; J Burke; A A Ptitsyn; T R Broveak; W A Hide Journal: Genome Res Date: 1999-11 Impact factor: 9.043
Authors: Nicholas Katsanis; Kim C Worley; Guillermo Gonzalez; Stephen J Ansley; James R Lupski Journal: Proc Natl Acad Sci U S A Date: 2002-10-21 Impact factor: 11.205
Authors: Natalie E Freeman; Justin P Templeton; William E Orr; Lu Lu; Robert W Williams; Eldon E Geisert Journal: Mol Vis Date: 2011-05-25 Impact factor: 2.367