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Updated: Jun 23, 2026
High-Throughput Robotically Assisted Isolation of Temperature-sensitive Lethal Mutants in Chlamydomonas reinhardtii
Published on: December 5, 2016
Improving gene-finding in Chlamydomonas reinhardtii:GreenGenie2
Alan L Kwan1, Linya Li, David C Kulp
1Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, MO, USA. alan@ural.wustl.edu
Background:
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.
Results:
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 1. 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.
Conclusion:
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.
