Nineteen additional unpredicted transcripts from human chromosome 21.
ABSTRACT The 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.
[show abstract] [hide abstract]
ABSTRACT: We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified. This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence.Genome biology 02/2006; 7 Suppl 1:S2.1-31. · 6.63 Impact Factor
[show abstract] [hide abstract]
ABSTRACT: The GENCODE consortium was formed to identify and map all protein-coding genes within the ENCODE regions. This was achieved by a combination of initial manual annotation by the HAVANA team, experimental validation by the GENCODE consortium and a refinement of the annotation based on these experimental results. The GENCODE gene features are divided into eight different categories of which only the first two (known and novel coding sequence) are confidently predicted to be protein-coding genes. 5' rapid amplification of cDNA ends (RACE) and RT-PCR were used to experimentally verify the initial annotation. Of the 420 coding loci tested, 229 RACE products have been sequenced. They supported 5' extensions of 30 loci and new splice variants in 50 loci. In addition, 46 loci without evidence for a coding sequence were validated, consisting of 31 novel and 15 putative transcripts. We assessed the comprehensiveness of the GENCODE annotation by attempting to validate all the predicted exon boundaries outside the GENCODE annotation. Out of 1,215 tested in a subset of the ENCODE regions, 14 novel exon pairs were validated, only two of them in intergenic regions. In total, 487 loci, of which 434 are coding, have been annotated as part of the GENCODE reference set available from the UCSC browser. Comparison of GENCODE annotation with RefSeq and ENSEMBL show only 40% of GENCODE exons are contained within the two sets, which is a reflection of the high number of alternative splice forms with unique exons annotated. Over 50% of coding loci have been experimentally verified by 5' RACE for EGASP and the GENCODE collaboration is continuing to refine its annotation of 1% human genome with the aid of experimental validation.Genome biology 02/2006; 7 Suppl 1:S4.1-9. · 6.63 Impact Factor
Article: Gene finding in the chicken genome.[show abstract] [hide abstract]
ABSTRACT: Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which there is not a large collection of known gene sequences, such as the recently published chicken genome. We used the chicken sequence to test comparative and homology-based gene-finding methods followed by experimental validation as an effective genome annotation method. We performed experimental evaluation by RT-PCR of three different computational gene finders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram was computed and each component of it was evaluated. The results showed that de novo comparative methods can identify up to about 700 chicken genes with no previous evidence of expression, and can correctly extend about 40% of homology-based predictions at the 5' end. De novo comparative gene prediction followed by experimental verification is effective at enhancing the annotation of the newly sequenced genomes provided by standard homology-based methods.BMC Bioinformatics 02/2005; 6:131. · 2.75 Impact Factor