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Originally posted by Markiyan
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Thank you. I did my sequencing on old platform HiSeq 2500. -
Out of curiosity, why are you joining the read pairs? A lot of the metagenomics software out there now supports paired end reads as input. The metaSPAdes assembler @GenoMax mentioned requires paired end data IIRC.Comment
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Hey, I did try metaSPAdes, less than 1% of total reads assembled. A lot of people tried alternative methods, joined paired-ends and get long reads, but don't assembled reads. Then, use the long merged reads to do BLAST or other annotations.Originally posted by fanli View PostComment
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Would something like kraken or CLARK not be helpful? Are you trying to assemble and annotate de novo genomes? Or trying to figure out the microbial composition and functional content? I guess my point is you would discard ~40% of your data in the joining process, which may not be necessary depending on your task of interest.Comment
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I am not interested in a specific genome in the soil community. Basically, I am just interested in the microbial composition and functional content. There is another way that people usually do. They don't assemble or merge pairs and they just blast using raw data. However, I think blast using ~150bp reads is worse. Some publication shows the intermediate length (merged pair) is better than assembled longer reads or unasembled short reads for the question that I am asking for.Originally posted by fanli View PostComment
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You might find this benchmark to be helpful:
My understanding is that you are better off using newer k-mer based approaches as opposed to BLAST. I've had reasonable success with kraken, although the memory requirements are somewhat onerous. You also have to be extremely careful about removing contaminant (aka human) sequences as these tend to get misclassified.
Another option is kallisto (https://github.com/pachterlab/metakallisto) but I have yet to be able to even build a database due to memory constraints.Comment
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I will try. Can you gimme the Kraken link? Thanks.Originally posted by fanli View PostYou might find this benchmark to be helpful:
My understanding is that you are better off using newer k-mer based approaches as opposed to BLAST. I've had reasonable success with kraken, although the memory requirements are somewhat onerous. You also have to be extremely careful about removing contaminant (aka human) sequences as these tend to get misclassified.
Another option is kallisto (https://github.com/pachterlab/metakallisto) but I have yet to be able to even build a database due to memory constraints.Comment
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