-h [ --help ]
produce help message
-i [ --inFile ] arg
Contigs in (gzipped) fasta file format [Mandatory]
-o [ --outFile ] arg
Base file name for each bin. The default output is fasta format. Use -l option to output only
contig names [Mandatory]
-a [ --abdFile ] arg
A file having mean and variance of base coverage depth (tab delimited; the first column should be
contig names, and the first row will be considered as the header and be skipped) [Optional]
--cvExt
When a coverage file without variance (from third party tools) is used instead of abdFile from
jgi_summarize_bam_contig_depths
-p [ --pairFile ] arg
A file having paired reads mapping information. Use it to increase sensitivity. (tab delimited;
should have 3 columns of contig index (ordered by), its mate contig index, and supporting mean
read coverage. The first row will be considered as the header and be skipped) [Optional]
--p1 arg (=0)
Probability cutoff for bin seeding. It mainly controls the number of potential bins and their
specificity. The higher, the more (specific) bins would be. (Percentage; Should be between 0 and
100)
--p2 arg (=0)
Probability cutoff for secondary neighbors. It supports p1 and better be close to p1. (Percentage;
Should be between 0 and 100)
--minProb arg (=0)
Minimum probability for binning consideration. It controls sensitivity. Usually it should be >=
75. (Percentage; Should be between 0 and 100)
--minBinned arg (=0)
Minimum proportion of already binned neighbors for one's membership inference. It contorls
specificity. Usually it would be <= 50 (Percentage; Should be between 0 and 100)
--verysensitive
For greater sensitivity, especially in a simple community. It is the shortcut for --p1 90 --p2 85
--pB 20 --minProb 75 --minBinned 20 --minCorr 90
--sensitive
For better sensitivity [default]. It is the shortcut for --p1 90 --p2 90 --pB 20 --minProb 80
--minBinned 40 --minCorr 92
--specific
For better specificity. Different from --sensitive when using correlation binning or ensemble
binning. It is the shortcut for --p1 90 --p2 90 --pB 30 --minProb 80 --minBinned 40 --minCorr 96
--veryspecific
For greater specificity. No correlation binning for short contig recruiting. It is the shortcut
for --p1 90 --p2 90 --pB 40 --minProb 80 --minBinned 40
--superspecific
For the best specificity. It is the shortcut for --p1 95 --p2 90 --pB 50 --minProb 80 --minBinned
20
--minCorr arg (=0)
Minimum pearson correlation coefficient for binning missed contigs to increase sensitivity
(Helpful when there are many samples). Should be very high (>=90) to reduce contamination.
(Percentage; Should be between 0 and 100; 0 disables)
--minSamples arg (=10)
Minimum number of sample sizes for considering correlation based recruiting
-x [ --minCV ] arg (=1)
Minimum mean coverage of a contig to consider for abundance distance calculation in each library
--minCVSum arg (=2)
Minimum total mean coverage of a contig (sum of all libraries) to consider for abundance distance
calculation
-s [ --minClsSize ] arg (=200000) Minimum size of a bin to be considered as the output
-m [ --minContig ] arg (=2500)
Minimum size of a contig to be considered for binning (should be >=1500; ideally >=2500). If # of
samples >= minSamples, small contigs (>=1000) will be given a chance to be recruited to existing
bins by default.
--minContigByCorr arg (=1000)
Minimum size of a contig to be considered for recruiting by pearson correlation coefficients
(activated only if # of samples >= minSamples; disabled when minContigByCorr > minContig)
-t [ --numThreads ] arg (=0)
Number of threads to use (0: use all cores)
--minShared arg (=50)
Percentage cutoff for merging fuzzy contigs
--fuzzy
Binning with fuzziness which assigns multiple memberships of a contig to bins (activated only with
--pairFile at the moment)
-l [ --onlyLabel ]
Output only sequence labels as a list in a column without sequences
-S [ --sumLowCV ]
If set, then every sample that falls below the minCV will be used in an aggregate sample
-V [ --maxVarRatio ] arg (=0)
Ignore any contigs where variance / mean exceeds this ratio (0 disables)
--saveTNF arg
File to save (or load if exists) TNF matrix for each contig in input
--saveDistance arg
File to save (or load if exists) distance graph at lowest probability cutoff
--saveCls
Save cluster memberships as a matrix format
--unbinned
Generate [outFile].unbinned.fa file for unbinned contigs
--noBinOut
No bin output. Usually combined with --saveCls to check only contig memberships
-B [ --B ] arg (=20)
Number of bootstrapping for ensemble binning (Recommended to be >=20)
--pB arg (=50)
Proportion of shared membership in bootstrapping. Major control for sensitivity/specificity. The
higher, the specific. (Percentage; Should be between 0 and 100)
--seed arg (=0)
For reproducibility in ensemble binning, though it might produce slightly different results. (0:
use random seed)
--keep Keep the intermediate files for later usage
-d [ --debug ]
Debug output
-v [ --verbose ]
Verbose output
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