Improving Gene-finding in Chlamydomonas reinhardtii:GreenGenie2
1 Department of Computer Science and Engineering, Washington University in Saint Louis, Campus Box 1045, One Brookings Drive, Saint Louis, MO, USA
2 Department of Genetics, Washington University School of Medicine, Campus Box 8232, 660 S. Euclid Avenue, Saint Louis, MO, USA
3 Department of Computer Science, University of Massachusetts, 140 Governors Drive, Amherst, MA, USA
BMC Genomics 2009, 10:210 doi:10.1186/1471-2164-10-210Published: 7 May 2009
The availability of whole-genome sequences allows for the identification of the entire set of protein coding genes as well as their regulatory regions. This can be accomplished using multiple complementary methods that include ESTs, homology searches and ab initio gene predictions. Previously, the Genie gene-finding algorithm was trained on a small set of Chlamydomonas genes and shown to improve the accuracy of gene prediction in this species compared to other available programs. To improve ab initio gene finding in Chlamydomonas, we assemble a new training set consisting of over 2,300 cDNAs by assembling over 167,000 Chlamydomonas EST entries in GenBank using the EST assembly tool PASA.
The prediction accuracy of our cDNA-trained gene-finder, GreenGenie2, attains 83% sensitivity and 83% specificity for exons on short-sequence predictions. We predict about 12,000 genes in the version v3 Chlamydomonas genome assembly, most of which (78%) are either identical to or significantly overlap the published catalog of Chlamydomonas genes . 22% of the published catalog is absent from the GreenGenie2 predictions; there is also a fraction (23%) of GreenGenie2 predictions that are absent from the published gene catalog. Randomly chosen gene models were tested by RT-PCR and most support the GreenGenie2 predictions.
These data suggest that training with EST assemblies is highly effective and that GreenGenie2 is a valuable, complementary tool for predicting genes in Chlamydomonas reinhardtii.