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License: Apache License 2.0
BioSpectra: Search, Classify and Inspect metagenomic samples
License: Apache License 2.0
High abundance reads can be found when the reads are shared across many organisms. In this case, we should do post-process to classify them by using 1st-pass results.
No matching reads are basically "unknown".
Currently, k-mer tokenizer has a bug that only handles first 4KB of reads. This need to be fixed as read can bigger than that.
Try spaced k-mers (simple masking) mentioned below paper.
http://bioinformatics.oxfordjournals.org/content/early/2015/08/10/bioinformatics.btv419.full
Drop redundant terms in case described below:
Query: AAAABBBBCCCC (4mers)
to AAAA, AABB, BBBB, BBCC, CCCC
drop AAAB, ABBB, BCCC
when AAAA, AABB, BBBB, BBCC, CCCC returns the same results (meaning no search for dropping kgrams are redundant)
Need to handle command line commands and usage prints.
Current FASTA parser uses String class as a data structure of parsed read.
Take Fasta file as a query file. So that metagenomic sample file will be taken as an input of search system.
This will output:
Need to research what scoring function default lucene uses.
Need to optimize it for better performance and confidence.
Use the same dataset as CLARK.
They used 3 datasets from the Kraken website (HiSeq, MiSeq and simBA-5) which are located here:
https://ccb.jhu.edu/software/kraken/dl/accuracy.tgz
and one additional dataset they made available here:
simHC.20.500
http://clark.cs.ucr.edu/Download/simHC.20.500.fa.gz
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