Hey guys,
we are about to perform a transcriptome assembly approach. Here we want to apply de novo assemblers like Trinity, Oases and Trans-ABySS as well as reference genome guided assemblers like Cufflinks and Genome-guided Trinity. The aim is to build a global transcriptome of all assemblers by making a consensus. Several workflows were discussed in this thread http://seqanswers.com/forums/showthread.php?t=33452
We are about to perform paired-end sequencing but are not sure about the sequencing platform. We were offered two alternatives. The first one is the sequencing on a HiSeq2500, which produces 2x100 reads with an amount of 100million up to 180million read pairs. The second alternative is to sequence on a MiSeq, which would produce 2x250 reads with an amount of about 10million read pairs.
My question is which alternative you guys would prefer for a transcriptome assembly approach? Personally, I would prefer the first alternative for getting a high coverage, which is for example a filter criterion in Oases for the de Bruijn graph simplification and is also helping to capture more low abundant transcripts.
I`m looking forward to your recommendations.
Best regards
Mchicken
we are about to perform a transcriptome assembly approach. Here we want to apply de novo assemblers like Trinity, Oases and Trans-ABySS as well as reference genome guided assemblers like Cufflinks and Genome-guided Trinity. The aim is to build a global transcriptome of all assemblers by making a consensus. Several workflows were discussed in this thread http://seqanswers.com/forums/showthread.php?t=33452
We are about to perform paired-end sequencing but are not sure about the sequencing platform. We were offered two alternatives. The first one is the sequencing on a HiSeq2500, which produces 2x100 reads with an amount of 100million up to 180million read pairs. The second alternative is to sequence on a MiSeq, which would produce 2x250 reads with an amount of about 10million read pairs.
My question is which alternative you guys would prefer for a transcriptome assembly approach? Personally, I would prefer the first alternative for getting a high coverage, which is for example a filter criterion in Oases for the de Bruijn graph simplification and is also helping to capture more low abundant transcripts.
I`m looking forward to your recommendations.
Best regards
Mchicken
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