4 ms·

These are FASTQ files[0], which are the actual raw bases read by the sequencer, and a quality score for each base. This is upstream of comparison to a known gen

These are FASTQ files[0], which are the actual raw bases read by the sequencer, and a quality score for each base. This is upstream of comparison to a known genome any other processing. You're correct that there's a lot of redundancy in these files, not least because each area of the genome is generally covered several times over. Removing this redundancy (or using it for error correction) is a big part of the downstream processing of this file. [0]: https://en.wikipedia.org/wiki/FASTQ_format https://en.wikipedia.org/wiki/FASTQ_format

cjbgkagh 4y ago
These snippets can be used to reconstructing the whole genome sequence but it's done statistically so they're not 100% confident. Some sections have more coverage than others. You want to keep the raw reads around just in case. Longer higher quality reads have been developed and for them less coverage will be needed and this will produce smaller files with higher degrees of confidence.
- biotinker 4y ago
  Depends on the use case. If one is trying to broadly sequence a genome or exome, all of that is correct. For something like a liquid biopsy test that's looking for circulating tumor DNA, a given mutation may only exist in 0.5% of the reads that cover a particular locus. Then you have to deduplicate to tell which are PCR duplicates of the same original strand, and which have different progenitor fragments. Low-coverage long reads are bad for this.
  - cjbgkagh 4y ago
    I had not thought of that use case but that does make sense.
otherme123 4y ago
The whole point behind Next Generation Sequencing is to get a lot of redundant and cheap raw data, but with some errors. This way you sequence the same base of the genome say 10 times or more. If 9 of them are the same as the reference, and 1 is another base (and this base is low quality), the Variant Caller can be confident that was a sequencing error, and the position is the same base as the reference. But if you have 5 reference and 5 of other base, it's probably heterozigous for that position. If you have more of other base, is homozigous for that other base.

cjbgkagh 4y ago

These snippets can be used to reconstructing the whole genome sequence but it's done statistically so they're not 100% confident. Some sections have more coverage than others. You want to keep the raw reads around just in case. Longer higher quality reads have been developed and for them less coverage will be needed and this will produce smaller files with higher degrees of confidence.

biotinker 4y ago
Depends on the use case. If one is trying to broadly sequence a genome or exome, all of that is correct. For something like a liquid biopsy test that's looking for circulating tumor DNA, a given mutation may only exist in 0.5% of the reads that cover a particular locus. Then you have to deduplicate to tell which are PCR duplicates of the same original strand, and which have different progenitor fragments. Low-coverage long reads are bad for this.
- cjbgkagh 4y ago
  I had not thought of that use case but that does make sense.

otherme123 4y ago

The whole point behind Next Generation Sequencing is to get a lot of redundant and cheap raw data, but with some errors. This way you sequence the same base of the genome say 10 times or more. If 9 of them are the same as the reference, and 1 is another base (and this base is low quality), the Variant Caller can be confident that was a sequencing error, and the position is the same base as the reference. But if you have 5 reference and 5 of other base, it's probably heterozigous for that position. If you have more of other base, is homozigous for that other base.