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<div style="direction: ltr;font-family: Tahoma;color: #000000;font-size: 10pt;">Hey,<br>
<br>
I used the MAKER model to retrain MAKER itself. I read somewhere it improves MAKER's predictions.
<br>
<br>
I did train abinitio gene predictors using MAKERs output, but I wanted to identify the best prediction before using it to train other gene predictors.<br>
<br>
Thanks,<br>
Ranjani<br>
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<div style="direction: ltr;" id="divRpF742812"><font color="#000000" face="Tahoma" size="2"><b>From:</b> Daniel Ence [dence@genetics.utah.edu]<br>
<b>Sent:</b> Tuesday, September 11, 2012 11:46 AM<br>
<b>To:</b> Sivaranjani Namasivayam; maker-devel@yandell-lab.org<br>
<b>Subject:</b> RE: MAKER training<br>
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<div style="direction:ltr; font-family:Tahoma; color:#000000; font-size:10pt">Hi Ranjani,
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<div>It is fine to include all three of those transcriptome datatsets. The more (relevant) evidence the better. </div>
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<div>I'm not certain what you mean when you say "you used the above model to retrain". Did you train an abinitio gene predictor using the results from your first maker run?<br>
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<div class="BodyFragment"><font size="2">
<div class="PlainText">Daniel Ence<br>
Graduate Student<br>
Eccles Institute of Human Genetics<br>
University of Utah<br>
15 North 2030 East, Room 2100<br>
Salt Lake City, UT 84112-5330</div>
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<div id="divRpF441968" style="direction:ltr"><font color="#000000" face="Tahoma" size="2"><b>From:</b> maker-devel-bounces@yandell-lab.org [maker-devel-bounces@yandell-lab.org] on behalf of Sivaranjani Namasivayam [ranjani@uga.edu]<br>
<b>Sent:</b> Tuesday, September 11, 2012 9:38 AM<br>
<b>To:</b> maker-devel@yandell-lab.org<br>
<b>Subject:</b> [maker-devel] MAKER training<br>
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<div style="direction:ltr; font-family:Tahoma; color:#000000; font-size:10pt">Hi,<br>
<br>
I am using MAKER to annotate a newly sequenced genome. I have trained and retrained with datasets but I would like some advice on assessing the output and how this is affected by the input provided.<br>
<br>
- I have transcriptome data from 454 and Illumina platforms. Illumina is from a single time point and 454 from multiple time point. 454 was assembled using Newbler(dataset 1) and Illumina using Tophat-Cufflinks (dataset 2) and the denovo Trinity pipeline (dataset
3). I now have3 assemblies - 454 and Illumina will have some redunant transcripts (because of one overlapping time point); TopHat-Cufflinks and Trinity will have highly redundant transcripts (because they use same raw reads). Is it OK to provide all 3 datasets
as EST evidence, how does it affect the quality of annotation. (For now I have used dataset 1 and dataset 2 as EST evidence)<br>
<br>
- I used the above model to retrain, I passed through everything except the abinitio gene predictions. I also provided a set a manually annotated genes , many of which have EST evidence. Is this OK to do? [ For proteins evidence, I gave a set from related organisms,
same as above]<br>
<br>
- In my third retraining, I used the above retrained model, but this time I only provided the genome_gff but did not pass through any other data. However I did provide the manually annotated genes as EST evidence and related proteins as protein_evidence.
<br>
<br>
Can you please give me some advice on which of these could give me the best prediction, or if I can alter something to get a better prediction.<br>
<br>
- A quick question about Augustus - I used a Augustus model (trained for a closely related organism) for ab-initio prediction. Does MAKER adjust this model based on the evidence provided, or use the model as such for a prediction.<br>
<br>
Greatly appreciate your help!<br>
Thanks!<br>
Ranjani<br>
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