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I have a BWA alignment of reads to a small (50 kb) reference sequence. It is a transgenic sequence inserted into a host cell genome. I want to be able to locate the insert's position in the host cell genome. There are reads at the ends (pointing outwards) which have their pairs unmapped. These mates would presumably be in the flanking genomic sequence that I want to identify. Is there an easy way to get the unmapped mates? I suppose I could make a list of the reads and write a script to parse the original fastQ files, but I am hoping there is a tool already available for this (seemingly common) purpose. Any help would be greatly appreciated.
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See this thread for the info you need: http://seqanswers.com/forums/showthread.php?t=12283 -
You could parse the .bam for unmapped reads whose mates mapped close to your boundaries in the correct orientation.
You could also align the fastqs to the 50kb genome and the host genome, then filter for reads that aligned to the host whose mates aligned to the insert. That's probably the best solution. You'd want the mapping position of the reads that aligned to host anyway, so this way you'd have them.Comment
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Thanks for the suggestions. I'll look at the other thread and try your suggestions.Comment
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Covid-19
I am confused on the issue:
the service provider company provide AmpliSeq for Illumina On-Demand, Custom, and Community Panels. for COVID diagnostic, the library was prepared, the issue started @ sample sheet, manifest file - covid- successfully added, genome (we try our level best to integrate the genome file but no use, after creating multipath the genome was integrated in sample sheet and run started, output was 93.8=Q score, Cluster passing 96.7%, Cluster density 774K) but analysis failed (Sunday) till now we try all possible methods with illumina support but no use. initially RNA amplicon was downloaded and added in sample sheet, the sample sheet was headed by DNA amplicon (no use) & now PCR amplicon was added in sample sheet but same error. plz guide.
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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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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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