Hi, I wondering is it possible to reduce the fastq redundancy for the downstream analysis.
For RNAseq, because the cost decreased quickly, large number of data was produced for one sample, which may be not necessary, but always prefer by biologiest in the name for the low level expressed transcripts. However, in the same time, the highly expressed transcripts have sequenced many many times. So, there will be many many reads exactly same. But for the alignment tools, such as bowtie/tophat, all these reads will be processed and aligned to the big genome reference, although many many reads are exactly same.
The question is, is it possible to reduce the redundancy of the raw fastq reads while retain the copy information. When you do alignment, the multiple same reads only needed to align once. The quantity information will be integrated to measure the expression level. By doing so, the calculation will de decreased significantly.
For RNAseq, because the cost decreased quickly, large number of data was produced for one sample, which may be not necessary, but always prefer by biologiest in the name for the low level expressed transcripts. However, in the same time, the highly expressed transcripts have sequenced many many times. So, there will be many many reads exactly same. But for the alignment tools, such as bowtie/tophat, all these reads will be processed and aligned to the big genome reference, although many many reads are exactly same.
The question is, is it possible to reduce the redundancy of the raw fastq reads while retain the copy information. When you do alignment, the multiple same reads only needed to align once. The quantity information will be integrated to measure the expression level. By doing so, the calculation will de decreased significantly.
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