Thank you for your reply. I have done GTDB-tk. I was simply curious, so I built a custom Kraken database using MAGs and contigs and then ran the scripts. I have used other bbtools but not bbsketch. I appreciate the post you provided and will read it soon.
I have 2 paired shot gun sequencing data sets of human gut microbiome using Hiseq. I re-constructed MAGs using metaSpades, metabat2, checkM. For the 2 data sets, 55-65% paired reads concordantly aligned 1 time to the contigs with 75-88% overall alignment rate using bowtie2. Everything looks fairly Ok to me so far.
Then, I built a custom database using Kraken2 to classify the reads that were used for MAG re-constructing. <1% reads were classified by Kraken2. When I used a group of published genomes of human gut microbiome, ~50% reads could be classified.
My question: Can I expect that most reads are classified by Kraken2? Since MAGs are built with the same reads, I would guess most of them can be successfully classified. Thank you. Appreciate your ideas.
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I am curious: why do you need to classify the reads that are already assembled into MAGs, when you can classify the MAGs? MAGs are much longer and therefore contain stronger and more reliable signals.
To classify MAGs: https://github.com/Ecogenomics/GTDBTk
I would understand if you wanted to classify unmapped reads. While this is not the answer to your question, you may want to try sketching, which has been introduced on Biostars in this thread.
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Thank you for letting me know. I am sorry that I didn't see this message before I submit to close this post.
I figured it out that the fasta sequences have not be recognized by Kraken2. After reformatting the fasta files of my MAGs, ~88% reads aligned to them.
The fasta file format I used before:
Correct fasta file format for Kraken2 custom database: