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sridharacharya 09-17-2010 08:41 AM

Regarding Unique reads, Unique alignments
I have few questions regarding the best practices that are adopted, in dealing with multiple alignments from a single read and presence of identical reads in the data (from Biology stand point for the later meaningful analysis of data) :

I am curious, how important it is to deal with identical reads.
Having many identical reads in data means something wrong with the

What could be considered as max. cutoff value for the number of identical reads in the data, so as to not consider those reads?

In the other case of a single read aligning at multiple places in a genome, what should be the cutoff value for number of multiple alignments, so as to not consider those reads?

malachig 09-17-2010 08:53 PM

Your first question regarding duplicate/identical reads is an interesting one that has been discussed at great length in this forum. Among the issues discussed are how to identify duplicate reads, where they come from, how many to expect in particular types of libraries, if they should removed, and ways to remove them.

One discussion of read duplicates and links to other posts discussing them further can be found here:
Removing duplicates...

What type of libraries are you sequencing (whole genome, exome, ChIP-Seq, RNA-seq, miRNA, mitochondrial genomes, pooled PCR amplicons, etc.)?? The type of library and the corresponding analysis goals (measure expression, identify mutations, peak finding, etc.) can a have major impact on the way duplicates are handled. If you provide specific details of you libraries, someone with similar data may respond...

sridharacharya 09-20-2010 06:39 AM

Re: Regarding Unique reads, Unique alignments

Thanks for the reply. I'll go through those discussions.

In my case, I am studying 3 types of experiments. I suppose the criteria to handle duplicate reads and handling of reads aligning to multiple places would be different for these three cases.

1. ChIP sequencing to analyze and quantify chromatin modifications

2. Whole transcriptome sequencing to quantify differential gene expression, between different conditions

3. mi-RNA sequencing . To quantify the differential expression of the micro-RNAs.

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