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| Clone Mass | Clones in CloneSet | Parameter Count | Clone Similarity | Syntax Category [Sequence Length] |
|---|---|---|---|---|
| 4 | 11 | 4 | 0.966 | or_test |
| Clone Abstraction | Parameter Bindings |
| Clone Instance (Click to see clone) | Line Count | Source Line | Source File |
|---|---|---|---|
| 1 | 4 | 202 | Bio/Align/Applications/_Mafft.py |
| 2 | 4 | 70 | Bio/Align/Applications/_Muscle.py |
| 3 | 4 | 78 | Bio/Align/Applications/_Muscle.py |
| 4 | 5 | 102 | Bio/Align/Applications/_Muscle.py |
| 5 | 5 | 113 | Bio/Align/Applications/_Muscle.py |
| 6 | 5 | 222 | Bio/Align/Applications/_Muscle.py |
| 7 | 5 | 228 | Bio/Align/Applications/_Muscle.py |
| 8 | 5 | 237 | Bio/Align/Applications/_Muscle.py |
| 9 | 5 | 245 | Bio/Align/Applications/_Muscle.py |
| 10 | 5 | 291 | Bio/Align/Applications/_Muscle.py |
| 11 | 5 | 314 | Bio/Align/Applications/_Muscle.py |
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#BLOSUM number matrix (Henikoff and Henikoff 1992) is used.
#number=30, 45, 62 or 80. Default: 62
_Option(["--bl","bl"],["input"],
lambda x:x in BLOSUM_MATRICES,0,"BLOSUM number matrix is used. Default: 62",0)
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#cluster1 upgma upgmb Clustering method.
_Option(["-cluster1","cluster1"],["input"],
lambda x:x in CLUSTERING_ALGORITHMS,0,"Clustering method used in iteration 1",0)
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#cluster2 upgmb cluster1 is used in
# neighborjoining iteration 1 and 2,
# cluster2 in later
# iterations.
_Option(["-cluster2","cluster2"],["input"],
lambda x:x in CLUSTERING_ALGORITHMS,0,"Clustering method used in iteration 2",0)
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#distance1 kmer6_6 Kmer6_6 (amino) or Distance measure for
# kmer20_3 Kmer4_6 (nucleo) iteration 1.
# kmer20_4
# kbit20_3
# kmer4_6
_Option(["-distance1","distance1"],["input"],
lambda x:x in DISTANCE_MEASURES_ITER1,0,"Distance measure for iteration 1",0)
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#distance2 kmer6_6 pctid_kimura Distance measure for
# kmer20_3 iterations 2, 3 ...
# kmer20_4
# kbit20_3
# pctid_kimura
# pctid_log
_Option(["-distance2","distance2"],["input"],
lambda x:x in DISTANCE_MEASURES_ITER2,0,"Distance measure for iteration 2",0)
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#objscore sp spm Objective score used by
# ps tree dependent
# dp refinement.
# xp sp=sum-of-pairs score.
# spf spf=sum-of-pairs score
# spm (dimer approximation)
# spm=sp for < 100 seqs,
# otherwise spf
# dp=dynamic programming
# score.
# ps=average profile-
# sequence score.
# xp=cross profile score.
_Option(["-objscore","objscore"],["input"],
lambda x:x in OBJECTIVE_SCORES,0,"Objective score used by tree dependent refinement",0)
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#root1 pseudo psuedo Method used to root
_Option(["-root1","root1"],["input"],
lambda x:x in TREE_ROOT_METHODS,0,"Method used to root tree in iteration 1",0)
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#root2 midlongestspan tree; root1 is used in
# minavgleafdist iteration 1 and 2,
# root2 in later
# iterations.
_Option(["-root2","root2"],["input"],
lambda x:x in TREE_ROOT_METHODS,0,"Method used to root tree in iteration 2",0)
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#seqtype protein auto Sequence type.
# nucleo
# auto
_Option(["-seqtype","seqtype"],["input"],
lambda x:x in SEQUENCE_TYPES,0,"Sequence type",0)
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#weight1 none clustalw Sequence weighting
_Option(["-weight1","weight1"],["input"],
lambda x:x in WEIGHTING_SCHEMES,0,"Weighting scheme used in iteration 1",0)
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#weight2 henikoff scheme.
# henikoffpb weight1 is used in
# gsc iterations 1 and 2.
# clustalw weight2 is used for
# threeway tree-dependent
# refinement.
# none=all sequences have
# equal weight.
# henikoff=Henikoff &
# Henikoff weighting
# scheme.
# henikoffpb=Modified
# Henikoff scheme as used
# in PSI-BLAST.
# clustalw=CLUSTALW
# method.
# threeway=Gotoh three-
# way method.
_Option(["-weight2","weight2"],["input"],
lambda x:x in WEIGHTING_SCHEMES,0,"Weighting scheme used in iteration 2",0)
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#BLOSUM number matrix (Henikoff and Henikoff 1992) is used. #number=30, 45, 62 or 80. Default: 62 #weight2 henikoff scheme. # henikoffpb weight1 is used in # gsc iterations 1 and 2. # clustalw weight2 is used for # threeway tree-dependent # refinement. # none=all sequences have # equal weight. # henikoff=Henikoff & # Henikoff weighting # scheme. # henikoffpb=Modified # Henikoff scheme as used # in PSI-BLAST. # clustalw=CLUSTALW # method. # threeway=Gotoh three- # way method. #weight1 none clustalw Sequence weighting #seqtype protein auto Sequence type. # nucleo # auto #root2 midlongestspan tree; root1 is used in # minavgleafdist iteration 1 and 2, # root2 in later #cluster2 upgmb cluster1 is used in # neighborjoining iteration 1 and 2, # cluster2 in later # iterations. #root1 pseudo psuedo Method used to root #objscore sp spm Objective score used by # ps tree dependent # dp refinement. # xp sp=sum-of-pairs score. # spf spf=sum-of-pairs score # spm (dimer approximation) # spm=sp for < 100 seqs, # otherwise spf # dp=dynamic programming # score. # ps=average profile- # sequence score. # xp=cross profile score. #distance2 kmer6_6 pctid_kimura Distance measure for # kmer20_3 iterations 2, 3 ... #distance1 kmer6_6 Kmer6_6 (amino) or Distance measure for # kmer20_3 Kmer4_6 (nucleo) iteration 1. # kmer20_4 # kbit20_3 # pctid_kimura # pctid_log # kmer4_6 #cluster1 upgma upgmb Clustering method. _Option([ [[#variable5f9ec800]], [[#variable5f9ec7a0]]],["input"], lambda x:x in [[#variable5f9ec740]],0, [[#variable5f9ec6a0]],0) |
| CloneAbstraction |
| Parameter Index | Clone Instance | Parameter Name | Value |
|---|---|---|---|
| 1 | 1 | [[#5f9ec800]] | "--bl" |
| 1 | 2 | [[#5f9ec800]] | "-weight2" |
| 1 | 3 | [[#5f9ec800]] | "-weight1" |
| 1 | 4 | [[#5f9ec800]] | "-seqtype" |
| 1 | 5 | [[#5f9ec800]] | "-root2" |
| 1 | 6 | [[#5f9ec800]] | "-root1" |
| 1 | 7 | [[#5f9ec800]] | "-objscore" |
| 1 | 8 | [[#5f9ec800]] | "-distance2" |
| 1 | 9 | [[#5f9ec800]] | "-distance1" |
| 1 | 10 | [[#5f9ec800]] | "-cluster2" |
| 1 | 11 | [[#5f9ec800]] | "-cluster1" |
| 2 | 1 | [[#5f9ec7a0]] | "bl" |
| 2 | 2 | [[#5f9ec7a0]] | "weight2" |
| 2 | 3 | [[#5f9ec7a0]] | "weight1" |
| 2 | 4 | [[#5f9ec7a0]] | "seqtype" |
| 2 | 5 | [[#5f9ec7a0]] | "root2" |
| 2 | 6 | [[#5f9ec7a0]] | "root1" |
| 2 | 7 | [[#5f9ec7a0]] | "objscore" |
| 2 | 8 | [[#5f9ec7a0]] | "distance2" |
| 2 | 9 | [[#5f9ec7a0]] | "distance1" |
| 2 | 10 | [[#5f9ec7a0]] | "cluster2" |
| 2 | 11 | [[#5f9ec7a0]] | "cluster1" |
| 3 | 1 | [[#5f9ec740]] | BLOSUM_MATRICES |
| 3 | 2 | [[#5f9ec740]] | WEIGHTING_SCHEMES |
| 3 | 3 | [[#5f9ec740]] | WEIGHTING_SCHEMES |
| 3 | 4 | [[#5f9ec740]] | SEQUENCE_TYPES |
| 3 | 5 | [[#5f9ec740]] | TREE_ROOT_METHODS |
| 3 | 6 | [[#5f9ec740]] | TREE_ROOT_METHODS |
| 3 | 7 | [[#5f9ec740]] | OBJECTIVE_SCORES |
| 3 | 8 | [[#5f9ec740]] | DISTANCE_MEASURES_ITER2 |
| 3 | 9 | [[#5f9ec740]] | DISTANCE_MEASURES_ITER1 |
| 3 | 10 | [[#5f9ec740]] | CLUSTERING_ALGORITHMS |
| 3 | 11 | [[#5f9ec740]] | CLUSTERING_ALGORITHMS |
| 4 | 1 | [[#5f9ec6a0]] | "BLOSUM number matrix is used. Default: 62" |
| 4 | 2 | [[#5f9ec6a0]] | "Weighting scheme used in iteration 2" |
| 4 | 3 | [[#5f9ec6a0]] | "Weighting scheme used in iteration 1" |
| 4 | 4 | [[#5f9ec6a0]] | "Sequence type" |
| 4 | 5 | [[#5f9ec6a0]] | "Method used to root tree in iteration 2" |
| 4 | 6 | [[#5f9ec6a0]] | "Method used to root tree in iteration 1" |
| 4 | 7 | [[#5f9ec6a0]] | "Objective score used by tree dependent refinement" |
| 4 | 8 | [[#5f9ec6a0]] | "Distance measure for iteration 2" |
| 4 | 9 | [[#5f9ec6a0]] | "Distance measure for iteration 1" |
| 4 | 10 | [[#5f9ec6a0]] | "Clustering method used in iteration 2" |
| 4 | 11 | [[#5f9ec6a0]] | "Clustering method used in iteration 1" |