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oh, sorry. I found repetitive elements which are reverses transcriptases, located at 3' UTR of different genes. How can I differenciate the origin of my blast results?
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Because if you haven't a large coverage and the same repetitive elements could appears in different genes, how do I know which protein has been translated? So, I would mask these elements.
The low coverage has been my problem with Standard GS de novo assembler. Length contigs aprox 200 bp and a coverage from 4X to 6X.
Thanks!
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Originally posted by jordi View PostHi all!
I'm doing the annotation of a transcriptome of a non reference organism, something similar like you. My assembly was made with GS de novo assembler, but I had short contigs...
I'm trying the assembly with Mosaik but prior I have another problem: what about transposable elements? Have you tried to use windowmasker? Or RepeatMasker? For an organism without a database for these repetitives elements, which program do you think is better?
Thanks!
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Hi all!
I'm doing the annotation of a transcriptome of a non reference organism, something similar like you. My assembly was made with GS de novo assembler, but I had short contigs...
I'm trying the assembly with Mosaik but prior I have another problem: what about transposable elements? Have you tried to use windowmasker? Or RepeatMasker? For an organism without a database for these repetitives elements, which program do you think is better?
Thanks!
Leave a comment:
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A year ago, de novo transcriptome sequencing solely based on Illumina GAII is a bad idea. With 72bp PE reads and higher coverage, nothing is impossible now.
Like what Rao suggested, EST data will be helpful for the assembly. But, the fact is most organisms of interest don’t have comprehensive EST information. No available reference genome/ transcriptome (not even from a related species). You don’t know the exact size of the transcriptome, repeats, paralogous genes and isoforms problem. It’s tricky to tell even if your assembly went wrong. Like I said, it depends on the purpose of sequencing. Things is a lot easier if the goal is to discover SNPs. If the results are not satisfying, try other alternatives like sequencing using longer reads.
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Though I haven't finished the project (reads aren't all in yet), I'm doing something similar right now: no reference transcriptome, but looking for SNPs in cDNA reads of two subspecies. The first was sequenced with single-ended reads, and resulted in pretty short contigs, and only roughly 1/10 of the trancriptome total was assembled. I'm recommending paired-ends for the second sample, so I may have a quantitative answer for you in a couple of weeks.
The transcriptome may have more unique, assemblable sequence than the genome .. but homologous domains will be a problem, and paired-ends would definitely help there. That's why I'd guess that a small insert library should help quite a bit.
I'd recommend velvet - seems to still be the best option out there for Illumina reads. Not sure on simulation ...
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Check for ESTs may help you in assembly
de novo assembly of transcriptome.... what about misassemblies...
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De Novo Assembly of a transcriptome
Hi all,
We are planning to perform an mRNA-seq run using the Illumina GAII platform. We are worried about assembling the transcriptome when we get our data back. Most of the RNA-seq papers I read are assembling to a reference genome/transcriptome, we don't have either of these! Is there anyone out there that has assembled cDNA short reads de novo? If so, are paired reads as important as they are with genome assembly?
Is there an example database of mRNA-seq short-pair reads that i can download to simulate assembly?
also, what software would you recommend for this?
hope someone can help
best regards
neil
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The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist on Modified Bases...-
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