000114890 001__ 114890
000114890 005__ 20181203021032.0
000114890 0247_ $$2doi$$a10.1006/geno.2002.6781
000114890 037__ $$aARTICLE
000114890 245__ $$aNineteen additional unpredicted transcripts from human chromosome 21
000114890 269__ $$a2002
000114890 260__ $$c2002
000114890 336__ $$aJournal Articles
000114890 500__ $$aDivision of Medical Genetics, University of Geneva Medical School, 1211 Geneva, Switzerland.
000114890 520__ $$aThe identification of all human chromosome 21 (HC21) genes is a necessary step in understanding the molecular pathogenesis of trisomy 21 (Down syndrome). The first analysis of the sequence of 21q included 127 previously characterized genes and predicted an additional 98 novel anonymous genes. Recently we evaluated the quality of this annotation by characterizing a set of HC21 open reading frames (C21orfs) identified by mapping spliced expressed sequence tags (ESTs) and predicted genes (PREDs), identified only in silico. This study underscored the limitations of in silico-only gene prediction, as many PREDs were incorrectly predicted. To refine the HC21 annotation, we have developed a reliable algorithm to extract and stringently map sequences that contain bona fide 3' transcript ends to the genome. We then created a specific 21q graphical display allowing an integrated view of the data that incorporates new ESTs as well as features such as CpG islands, repeats, and gene predictions. Using these tools we identified 27 new putative genes. To validate these, we sequenced previously cloned cDNAs and carried out RT-PCR, 5'- and 3'-RACE procedures, and comparative mapping. These approaches substantiated 19 new transcripts, thus increasing the HC21 gene count by 9.5%. These transcripts were likely not previously identified because they are small and encode small proteins. We also identified four transcriptional units that are spliced but contain no obvious open reading frame. The HC21 data presented here further emphasize that current gene prediction algorithms miss a substantial number of transcripts that nevertheless can be identified using a combination of experimental approaches and multiple refined algorithms.
000114890 700__ $$aReymond, A.
000114890 700__ $$aCamargo, A. A.
000114890 700__ $$aDeutsch, S.
000114890 700__ $$aStevenson, B. J.
000114890 700__ $$aParmigiani, R. B.
000114890 700__ $$aUcla, C.
000114890 700__ $$aBettoni, F.
000114890 700__ $$aRossier, C.
000114890 700__ $$aLyle, R.
000114890 700__ $$aGuipponi, M.
000114890 700__ $$ade Souza, S.
000114890 700__ $$aIseli, C.
000114890 700__ $$aJongeneel, C. V.
000114890 700__ $$0244404$$aBucher, P.$$g113607
000114890 700__ $$aSimpson, A. J.
000114890 700__ $$aAntonarakis, S. E.
000114890 773__ $$j79$$k6$$q824-32$$tGenomics
000114890 909C0 $$0252244$$pGR-BUCHER$$xU11780
000114890 909CO $$ooai:infoscience.tind.io:114890$$pSV$$particle
000114890 937__ $$aGR-BUCHER-ARTICLE-2002-006
000114890 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000114890 980__ $$aARTICLE