usage: fc_consensus.py [-h] [--n_core N_CORE] [--min_cov MIN_COV]
[--min_cov_aln MIN_COV_ALN] [--max_cov_aln MAX_COV_ALN]
[--min_len_aln MIN_LEN_ALN] [--min_n_read MIN_N_READ]
[--max_n_read MAX_N_READ] [--trim] [--output_full]
[--output_multi] [--min_idt MIN_IDT]
[--edge_tolerance EDGE_TOLERANCE]
[--trim_size TRIM_SIZE]
a simple multi-processor consensus sequence generator
optional arguments:
-h, --help show this help message and exit
--n_core N_CORE number of processes used for generating consensus; 0
for main process only (default: 24)
--min_cov MIN_COV minimum coverage to break the consensus (default: 6)
--min_cov_aln MIN_COV_ALN
minimum coverage of alignment data; a seed read with
less than MIN_COV_ALN average depth of coverage will
be completely ignored (default: 10)
--max_cov_aln MAX_COV_ALN
maximum coverage of alignment data; a seed read with
more than MAX_COV_ALN average depth of coverage of the
longest alignments will be capped, excess shorter
alignments will be ignored (default: 0)
--min_len_aln MIN_LEN_ALN
minimum length of a sequence in an alignment to be
used in consensus; any shorter sequence will be
completely ignored (default: 0)
--min_n_read MIN_N_READ
1 + minimum number of reads used in generating the
consensus; a seed read with fewer alignments will be
completely ignored (default: 10)
--max_n_read MAX_N_READ
1 + maximum number of reads used in generating the
consensus (default: 500)
--trim trim the input sequence with k-mer spare dynamic
programming to find the mapped range (default: False)
--output_full output uncorrected regions too (default: False)
--output_multi output multi correct regions (default: False)
--min_idt MIN_IDT minimum identity of the alignments used for correction
(default: 0.7)
--edge_tolerance EDGE_TOLERANCE
for trimming, the there is unaligned edge leng >
edge_tolerance, ignore the read (default: 1000)
--trim_size TRIM_SIZE
the size for triming both ends from initial sparse
aligned region (default: 50)