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I've been trying to convert my .csfasta and .qual files into a .fastq file for the last two days. I've found two different solid2fastq scripts (one from bfast, I don't remember the other one - might be a link from seqanswers). When I look at my .csfasta file, I see the I have 51 nucleotides for each read (they all start with T for the adapter) and there are 50 characters for each read in my .qual file. When I try to trim one base from the .csfasta files, both scripts trim one character from the quality scores too so my .fastq file has one missing quality score and downstream programs don't like this. What should I use for my conversion that would trim one base from .csfasta but not from .qual?
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There is no quality for the adapter (it is assumed true), hence you should have one fewer quality score than the color sequence (with adapter). -
I realize that. so there is no way to obtain a fastq file with equal numbers of sequence characters and quality score characters?Comment
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i don't mess around with trimming at all and use the solid2fastq script that comes with bfast. which downstream application can't read your fastq?Comment
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I'm trying to use FusionMap currentlyComment
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Why not remove the first "T" away by a simple python/perl script?Comment
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well, I don't know perl/python that well but i ended up editing a script and got what i needed that way in the last hour. so yay.Comment
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ahmetz,did you successfully applied FusionMap on your Solid data? I want to find fusion genes with Solid RNA-seq data, too. And How does FusionMap aligner work, can it built colorspace index? Can you give me email address for more communication? My Email is [email protected],Thank you!Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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