The restriction enzyme package is situated in Bio.Restriction. This package will allow you to work with restriction enzymes and realise restriction analysis on your sequence. Restriction make use of the facilities offered by REBASE and contains classes for more than 800 restriction enzymes. This chapter will lead you through a quick overview of the facilities offered by the Restriction package of Biopython. The chapter is constructed as an interactive Python session and the best way to read it is with a Python shell open alongside you.
To import the enzymes, open a Python shell and type:
>>> from Bio import Restriction
>>> dir()
['Restriction', '__builtins__', '__doc__', '__name__', '__package__']
>>> Restriction.EcoRI
EcoRI
>>> Restriction.EcoRI.site
'GAATTC'
>>>You will certainly notice that the package is quite slow to load. This is normal as each enzyme possess its own class and there is a lot of them. This will not affect the speed of Python after the initial import.
I don't know for you but I find it quite cumbersome to have to prefix each operation with Restriction., so here is another way to import the package.
>>> from Bio.Restriction import *
>>> EcoRI
EcoRI
>>> EcoRI.site
'GAATTC'
>>>However, this method has one big disadvantage: It is almost impossible to use the command dir() anymore as there is so much enzymes the results is hardly readable. A workaround is provided at the end of this tutorial. I let you decide which method you prefer. But in this tutorial I will use the second. If you prefer the first method you will need to prefix each call to a restriction enzyme with Restriction. in the remaining of the tutorial.
To access an enzyme simply enter it's name. You must respect the usual naming convention with the upper case letters and Latin numbering (in upper case as well):
>>> EcoRI
EcoRI
>>> ecori
Traceback (most recent call last):
File "<pyshell#25>", line 1, in -toplevel-
ecori
NameError: name 'ecori' is not defined
>>> EcoR1
Traceback (most recent call last):
File "<pyshell#26>", line 1, in -toplevel-
EcoR1
NameError: name 'EcoR1' is not defined
>>> KpnI
KpnI
>>>ecori or EcoR1 are not enzymes, EcoRI and KpnI are.
So what can we do with these restriction enzymes? To see that we will need a DNA sequence. Restriction enzymes support both Bio.Seq.MutableSeqand Bio.Seq.Seq objects. You can use any DNA alphabet which complies with the IUPAC alphabet.
>>> from Bio.Seq import Seq
>>> from Bio.Alphabet.IUPAC import IUPACAmbiguousDNA
>>> amb = IUPACAmbiguousDNA()
>>> my_seq = Seq('AAAAAAAAAAAAAA', amb)
>>> my_seq
Seq('AAAAAAAAAAAAAA', IUPACAmbiguousDNA())Searching a sequence for the presence of restriction site for your preferred enzyme is as simple as:
>>> EcoRI.search(my_seq)
[]The results is a list. Here the list is empty since there is obviously no EcoRI site in my_seq. Let's try to get a sequence with an EcoRI site.
>>> ecoseq = my_seq + Seq(EcoRI.site, amb) + my_seq
>>> ecoseq
Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())
>>> EcoRI.search(ecoseq)
[16]We therefore have a site at position 16 of the sequence ecoseq. The position returned by the method search is the first base of the downstream segment produced by a restriction (i.e. the first base after the position where the enzyme will cut). The Restriction package follows biological convention (the first base of a sequence is base 1). No need to make difficult conversions between your recorded biological data and the results produced by the enzymes in this package.
Seq objects as all Python sequences, have different conventions and the first base of a sequence is base 0. Therefore to get the sequences produced by an EcoRI digestion of ecoseq, one should do the following:
>>> ecoseq[:15], ecoseq[15:]
(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
biguousDNA()))I hear you thinking "this is a cumbersome and error prone method to get these sequences". To simplify your life, Restriction provides another method to get these sequences without hassle: catalyse. This method will return a tuple containing all the fragments produced by a complete digestion of the sequence. Using it is as simple as before:
>>> EcoRI.catalyse(ecoseq)
(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
biguousDNA()))BTW, you can also use spell it the American way catalyze:
>>> EcoRI.catalyze(ecoseq)
(Seq('AAAAAAAAAAAAAAG', IUPACAmbiguousDNA()), Seq('AATTCAAAAAAAAAAAAAA', IUPACAm
biguousDNA()))Now, if you have entered the previous command in your shell you may have noticed that both search and catalyse can take a second argument linear which defaults to True. Using this will allow you to simulate circular sequences such as plasmids. Setting linear to False informs the enzyme to make the search over a circular sequence and to search for potential sites spanning over the boundaries of the sequence.
>>> EcoRI.search(ecoseq, linear=False)
[16]
>>> EcoRI.catalyse(ecoseq, linear=False)
(Seq('AATTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAG', IUPACAmbiguousDNA()),)
>>> ecoseq # for memory
Seq('AAAAAAAAAAAAAAGAATTCAAAAAAAAAAAAAA', IUPACAmbiguousDNA())OK, this is quite a difference, we only get one fragment, which correspond to the linearised sequence. The beginning sequence has been shifted to take this fact into account. Moreover we can see another difference:
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> EcoRI.search(new_seq)
[]
>>> EcoRI.search(new_seq, linear=False)
[33]As you can see using linear=False, make a site appearing in the sequence new_seq. This site does not exist in a linear sequence as the EcoRI site is split into two halves at the start and the end of the sequence. In a circular sequence however, the site is effectively present when the beginning and end of the sequence are joined.
Restriction enzymes define 4 comparative operators ==, !=, >> and %. All these operator compares two enzymes together and either return True or False.
== (test identity)True if the two sides of the operator are the same. *Same" is defined as: same name, same site, same overhang (i.e. the only thing which is equal to EcoRI is EcoRI).
!= (test for different site or cutting)True if the two sides of the operator are different. Two enzymes are not different if the result produced by one enzyme will always be the same as the result produced by the other (i.e. true isoschizomers will not being the same enzymes, are not different since they are interchangeable).
>> (test for neoschizomer)True if the enzymes recognise the same site, but cut it in a different way (i.e. the enzymes are neoschizomers).
% (test compatibilty)True if the fragments produced with one of the enzyme can directly be ligated to fragments produced by the other).
Let's use Acc65I and its isoschizomers as example:
>>> Acc65I.isoschizomers()
[Asp718I, KpnI]
>>> Acc65I.elucidate()
'G^GTAC_C'
>>> Asp718I.elucidate()
'G^GTAC_C'
>>> KpnI.elucidate()
'G_GTAC^C'
>>> # Asp718I and Acc65I are true isoschizomers,
>>> # they recognise the same site and cut it the
>>> # same way.
>>> # KpnI is a neoschizomers of the 2 others.
>>> # Here are the results of the 4 operators
>>> # for each pair of enzymes:
>>>
>>> ############# x == y (x is y)
>>> Acc65I == Acc65I # same enzyme => True
True
>>> Acc65I == KpnI # all other cases => False
False
>>> Acc65I == Asp718I
False
>>> Acc65I == EcoRI
False
>>> ############ x != y (x and y are not true isoschizomers)
>>> Acc65I != Acc65I # same enzyme => False
False
>>> Acc65I != Asp718I # different enzymes, but cut same manner => False
False
>>> Acc65I != KpnI # all other cases => True
True
>>> Acc65I != EcoRI
True
>>> ########### x >> y (x is neoschizomer of y)
>>> Acc65I >> Acc65I # same enzyme => False
False
>>> Acc65I >> Asp718I # same site, same cut => False
False
>>> Acc65I >> EcoRI # different site => False
False
>>> Acc65I >> KpnI # same site, different cut => True
True
>>> ########### x % y (fragments produced by x and fragments produced by y
>>> # can be directly ligated to each other)
>>> Acc65I % Asp718I
True
>>> Acc65I % Acc65I
True
>>> Acc65I % KpnI # KpnI -> '3 overhang, Acc65I-> 5' overhang => False
False
>>>
>>> SunI.elucidate()
'C^GTAC_G'
>>> SunI == Acc65I
False
>>> SunI != Acc65I
True
>>> SunI >> Acc65I
False
>>> SunI % Acc65I # different site, same overhang (5' GTAC) => True
True
>>> SmaI % EcoRV # 2 Blunt enzymes, all blunt enzymes are compatible => True
TrueThe Restriction class provides quite a number of others methods. We will not go through all of them, but only have a quick look to the most useful ones.
Not all enzymes possess the same properties when it comes to the way they digest a DNA. If you want to know more about the way a particular enzyme cut you can use the three following methods. They are fairly straightforward to understand and refer to the ends that the enzyme produces: blunt, 5' overhanging (also called 3' recessed) sticky end and 3' overhanging (or 5' recessed) sticky end.
>>> EcoRI.is_blunt()
False
>>> EcoRI.is_5overhang()
True
>>> EcoRI.is_3overhang()
FalseA more detailled view of the restriction site can be produced using the elucidate() method. The ^ refers to the position of the cut in the sense strand of the sequence, _ to the cut on the antisense or complementary strand. ^_ means blunt.
>>> EcoRI.elucidate()
'G^AATT_C'
>>> KpnI.elucidate()
'G_GTAC^C'
>>> EcoRV.elucidate()
'GAT^_ATC'The method frequency() will give you the statistical frequency of the enzyme site.
>>> EcoRI.frequency()
4096
>>> XhoII.elucidate()
'R^GATC_Y'
>>> XhoII.frequency()
1024To get the length of a the recognition sequence of an enzyme use the built-in function len():
>>> len(EcoRI)
6
>>> BstXI.elucidate()
'CCAN_NNNN^NTGG'
>>> len(BstXI)
12
>>> FokI.site
'GGATG'
>>> FokI.elucidate() # FokI cut well outside its recognition site
'GGATGNNNNNNNNN^NNNN_N'
>>> len(FokI) # its length is the length of the recognition site
5Also interesting are the methods dealing with isoschizomers. For memory, two enzymes are isoschizomers if they share a same recognition site. A further division is made between isoschizomers (same name, recognise the same sequence and cut the same way) and neoschizomers which cut at different positions. Equischizomer is an arbitrary choice to design "isoschizomers_that_are_not_neoschizomers" as this last one was a bit long. Another set of method one_enzyme.is_*schizomers(one_other_enzyme), allow to test 2 enzymes against each other.
>>> Acc65I.isoschizomers()
[Asp718I, KpnI]
>>> Acc65I.neoschizomers()
[KpnI]
>>> Acc65I.equischizomers()
[Asp718I]
>>> KpnI.elucidate()
'G_GTAC^C'
>>> Acc65I.elucidate()
'G^GTAC_C'
>>> KpnI.is_neoschizomer(Acc65I)
True
>>> KpnI.is_neoschizomer(KpnI)
False
>>> KpnI.is_isoschizomer(Acc65I)
True
>>> KpnI.is_isoschizomer(KpnI)
True
>>> KpnI.is_equischizomer(Acc65I)
False
>>> KpnI.is_equischizomer(KpnI)
Truesuppliers() will get you the list of all the suppliers of the enzyme. all_suppliers() will give you all the suppliers in the database.
If you want to make a restriction map of a sequence, using individual enzymes can become tedious and will endures a big overhead due to the repetitive conversion of the sequence to a FormattedSeq (see Chapter 5). Restriction provides a class to make easier the use of large number of enzymes in one go: RestrictionBatch. RestrictionBatch will help you to manipulate lots of enzymes with a single command. Moreover all the enzymes in the restriction batch will share the same converted sequence, reducing the overhead.
You can initiate a restriction batch by passing it a list of enzymes or enzyme names as argument.
>>> rb = RestrictionBatch([EcoRI])
>>> rb
RestrictionBatch(['EcoRI'])
>>> rb2 = RestrictionBatch(['EcoRI'])
>>> rb2
RestrictionBatch(['EcoRI'])
>>> rb == rb2
TrueAdding a new enzyme to a restriction batch is easy:
>>> rb.add(KpnI)
>>> rb
RestrictionBatch(['EcoRI', 'KpnI'])
>>> rb += EcoRV
>>> rb
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])])Another way to create a RestrictionBatch is by simply adding restriction enzymes together, this is particularly useful for small batches:
>>> rb3 = EcoRI + KpnI + EcoRV
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])The Restriction package is based upon the REBASE database. This database gives a list of suppliers for each enzyme. It would be a shame not to make use of this facility. You can produce a RestrictionBatch containing only enzymes from one or a few supplier(s). Here is how to do it:
>>> rb_supp = RestrictionBatch(first=[], suppliers=['C','B','E','I','K','J','M',
'O','N','Q','S','R','V','Y','X'])
>>> # This will create a RestrictionBatch with the all enzymes which possess a s
upplier.
>>> len(rb_supp) # May 2016
622The argument suppliers take a list of one or several single letter codes corresponding to the supplier(s). The codes are the same as defined in REBASE. As it would be a pain to have to remember each supplier code, RestrictionBatch provides a method which show the pair code <=> supplier:
>>> RestrictionBatch.show_codes() # as of May 2016 REBASE release.
C = Minotech Biotechnology
B = Life Technologies
E = Agilent Technologies
I = SibEnzyme Ltd.
K = Takara Bio Inc.
J = Nippon Gene Co., Ltd.
M = Roche Applied Science
O = Toyobo Biochemicals
N = New England Biolabs
Q = Molecular Biology Resources - CHIMERx
S = Sigma Chemical Corporation
R = Promega Corporation
V = Vivantis Technologies
Y = SinaClon BioScience Co.
X = EURx Ltd.
>>> # You can now choose a code and built your RestrictionBatchThis way of producing a RestrictionBatch can drastically reduce the amount of useless output from a restriction analysis, limiting the search to enzymes that you can get hold of and limiting the risks of nervous breakdown. Nothing is more frustrating than to get the perfect enzyme for a sub-cloning only to find it's not commercially available.
Adding an enzyme to a batch if the enzyme is already present will not raise an exception, but will have no effects. Sometimes you want to get an enzyme from a RestrictionBatch or add it to the batch if it is not present. You will use the get method setting the second argument add to True.
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
>>> rb3.add(EcoRI)
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
>>> rb3.get(EcoRI)
EcoRI
>>> rb3.get(SmaI)
Traceback (most recent call last):
File "<pyshell#4>", line 1, in -toplevel-
rb3.get(SmaI)
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1800, in get
raise ValueError, 'enzyme %s is not in RestrictionBatch'%e.__name__
ValueError: enzyme SmaI is not in RestrictionBatch
>>> rb3.get(SmaI, add=True)
SmaI
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])Removing enzymes from a batch is done using the remove() method. If the enzyme is not present in the batch this will raise a KeyError. If the value you want to remove is not an enzyme this will raise a ValueError.
>>> rb3.remove(EcoRI)
>>> rb3
RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
>>> rb3.remove(EcoRI)
Traceback (most recent call last):
File "<pyshell#14>", line 1, in -toplevel-
rb3.remove('EcoRI')
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
return Set.remove(self, self.format(other))
File "/usr/lib/Python2.3/sets.py", line 534, in remove
del self._data[element]
KeyError: EcoRI
>>> rb3 += EcoRI
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'SmaI'])
>>> rb3.remove('EcoRI')
>>> rb3
RestrictionBatch(['EcoRV', 'KpnI', 'SmaI'])
>>> rb3.remove('spam')
Traceback (most recent call last):
File "<pyshell#18>", line 1, in -toplevel-
rb3.remove('spam')
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1839, in remove
return Set.remove(self, self.format(other))
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
raise ValueError, '%s is not a RestrictionType'%y.__class__
ValueError: <type 'str'> is not a RestrictionTypeYou can not, however, add batches together, as they are Python sets. You must use the pipe operator | instead. You can find the intersection between 2 batches using & (see the Python documentation about sets for more information.
>>> rb3 = EcoRI + KpnI + EcoRV
>>> rb3
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI'])
>>> rb4 = SmaI + PstI
>>> rb4
RestrictionBatch(['PstI', 'SmaI'])
>>> rb3 + rb4
Traceback (most recent call last):
File "<pyshell#23>", line 1, in -toplevel-
rb3 + rb4
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1829, in __add__
new.add(other)
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1848, in add
return Set.add(self, self.format(other))
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1871, in format
raise ValueError, '%s is not a RestrictionType'%y.__class__
ValueError: <class 'Bio.Restriction.Restriction.RestrictionBatch'> is not a RestrictionType
>>> rb3 | rb4
RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI', 'PstI', 'SmaI'])
>>> rb3 & rb4
RestrictionBatch([])
>>> rb4 += EcoRI
>>> rb4
RestrictionBatch(['EcoRI', 'PstI', 'SmaI'])
>>> rb3 & rb4
RestrictionBatch(['EcoRI'])To analyse a sequence for potential site, you can use the search method of the batch, the same way you did for restriction enzymes. The results is no longer a list however, but a dictionary. The keys of the dictionary are the names of the enzymes and the value a list of position site. RestrictionBatch does not implement a catalyse method, as it would not have a real meaning when used with large batch.
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> rb.search(new_seq)
{'KpnI': [], 'EcoRV': [], 'EcoRI': []}
>>> rb.search(new_seq, linear=False)
{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}Amongst the other methods provided by RestrictionBatch, elements() which return a list of all the element names alphabetically sorted, is certainly the most useful.
>>> rb = EcoRI + KpnI + EcoRV
>>> rb.elements()
['EcoRI', 'EcoRV', 'KpnI']If you don't care about the alphabetical order use the method as_string(), to get the same thing a bit faster. The list is not sorted. The order is random as Python sets are dictionary.
>>> rb = EcoRI + KpnI + EcoRV
>>> rb.as_string()
['EcoRI', 'KpnI', 'EcoRV']Other RestrictionBatch methods are generally used for particular purposes and will not be discussed here. See the source if you are interested.
While it is sometime practical to produce a RestrictionBatch of your own you will certainly more frequently use the two batches provided with the Restriction packages: AllEnzymes and CommOnly. These two batches contain respectively all the enzymes in the database and only the enzymes which have a commercial supplier. They are rather big, but that's what make them useful. With these batch you can produce a full description of a sequence with a single command. You can use these two batch as any other batch.
>>> len(AllEnzymes)
778
>>> len(CommOnly)
622
>>> AllEnzymes.search(new_seq) ...There is not a lot to say about them apart the fact that they are present. They are really normal batches, and you can use them as any other batch.
RestrictionBatch can give you a dictionary with the sites for all the enzymes in a batch. However, it is sometime nice to get something a bit easier to read than a Python dictionary. Complex restriction analysis are not easy with RestrictionBatch. Some refinements in the way to search a sequence for restriction sites will help. Analysis provides a serie of command to customise the results obtained from a pair restriction batch/sequence and some facilities to make the output sligthly more human readable.
To build a restriction analysis you will need a RestrictionBatch and a sequence and to tell it if the sequence is linear or circular. The first argument Analysis takes is the restriction batch, the second is the sequence. If the third argument is not provided, Analysis will assume the sequence is linear.
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
>>> Ana = Analysis(rb, new_seq, linear=False)
>>> Ana
Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAA
AAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)Once you have created your new Analysis, you can use it to get a restriction analysis of your sequence. The way to make a full restriction analysis of the sequence is:
>>> Ana.full()
{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}This is much the same as the output of a RestrictionBatch.search method. You will get a more easy to read output with print_that used without argument:
>>> # let's create a something a bit more complex to analyse.
>>>
>>> rb = RestrictionBatch([], ['C']) # we will explain the meaning of the
>>> # double list argument later.
>>>
>>> multi_site = Seq.Seq('AAA' + EcoRI.site + 'G' + KpnI.site + EcoRV.site +
'CT' + SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' +
EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
>>> Analong = Analysis(rb, multi_site)
>>> Analong.full()
{BglI: [], BstEII: [], AsuII: [], HinfI: [], SfiI: [], PspPI: [], BsiSI: [27], S
alI: [], SlaI: [], NcoI: [], NotI: [], PstI: [], StyI: [], BseBI: [], PvuII: [],
HindIII: [], BglII: [], ApaLI: [], TaqI: [], BssAI: [], AluI: [], SstI: [], Bse
CI: [], Sau3AI: [], HpaI: [], SnaBI: [], NheI: [], BclI: [], KpnI: [16], NruI: [
], MspCI: [], BshFI: [], CspAI: [], RsaI: [14], EcoRV: [20], SphI: [], BamHI: []
, MboI: [], SgrBI: [], SspI: [], ScaI: [], XbaI: [], SseBI: [], NaeI: [], EcoRI:
[5, 47], SmaI: [28], BseAI: []}
>>>
>>> # The results are here but it is difficult to read. let's try print_that
>>>
>>> Analong.print_that()
BsiSI : 27.
RsaI : 14.
EcoRI : 5, 47.
EcoRV : 20.
KpnI : 16.
SmaI : 28.
Enzymes which do not cut the sequence.
AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
SseBI SspI SstI StyI XbaI BstEII NotI BglI
SfiIMuch clearer, is'nt ? The output is optimised for a shell 80 columns wide. If the output seems odd, check that the width of your shell is at least 80 columns.
You can provide a title to the analysis and modify the sentence 'Enzymes which do not cut the sequence', by setting the two optional arguments of print_that, title and s1. No formatting will be done on these strings so if you have to include the newline (\n) as you see fit:
>>> Analong.print_that(None, title='sequence = multi_site\n\n')
sequence = multi_site
BsiSI : 27.
RsaI : 14.
EcoRI : 5, 47.
EcoRV : 20.
KpnI : 16.
SmaI : 28.
Enzymes which do not cut the sequence.
AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
SseBI SspI SstI StyI XbaI BstEII NotI BglI
SfiI
>>> Analong.print_that(None, title='sequence = multi_site\n\n',
s1='\n no site:\n\n')
sequence = multi_site
BsiSI : 27.
RsaI : 14.
EcoRI : 5, 47.
EcoRV : 20.
KpnI : 16.
SmaI : 28.
no site:
AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
SseBI SspI SstI StyI XbaI BstEII NotI BglI
SfiIYou can modify some aspects of the output interactively. There is three main type of output, two listing types (alphabetically sorted and sorted by number of site) and map-like type. To change the output, use the method print_as() of Analysis. The change of output is permanent for the instance of Analysis (that is until the next time you use print_as()). The argument of print_as() are strings: 'map', 'number' or 'alpha'. As you have seen previously the default behaviour is an alphabetical list ('alpha').
>>> Analong.print_as('map')
>>> Analong.print_that()
5 EcoRI
|
| 14 RsaI
| |
| | 16 KpnI
| | |
| | | 20 EcoRV
| | | |
| | | | 27 BsiSI
| | | | |
| | | | |28 SmaI
| | | | ||
| | | | || 47 EcoRI
| | | | || |
AAAGAATTCGGGTACCGATATCCTCCCGGGGTGGATGGAAAGGGCGAATTCACGT
|||||||||||||||||||||||||||||||||||||||||||||||||||||||
TTTCTTAAGCCCATGGCTATAGGAGGGCCCCACCTACCTTTCCCGCTTAAGTGCA
1 55
Enzymes which do not cut the sequence.
AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
SseBI SspI SstI StyI XbaI BstEII NotI BglI
SfiI
>>> Analong.print_as('number')
>>> Analong.print_that()
enzymes which cut 1 times :
BsiSI : 27.
RsaI : 14.
EcoRV : 20.
KpnI : 16.
SmaI : 28.
enzymes which cut 2 times :
EcoRI : 5, 47.
Enzymes which do not cut the sequence.
AluI BshFI MboI Sau3AI TaqI BseBI HinfI PspPI
ApaLI AsuII BamHI BclI BglII BseAI BseCI BssAI
CspAI HindIII HpaI MspCI NaeI NcoI NheI NruI
PstI PvuII SalI ScaI SgrBI SlaI SnaBI SphI
SseBI SspI SstI StyI XbaI BstEII NotI BglI
SfiI
>>>To come back to the previous behaviour:
>>> Analong.print_as('alpha')
>>> Analong.print_that()
BsiSI : 27.
RsaI : 14.
EcoRI : 5, 47.
EcoRV : 20.
etc ...I will not go into the detail for each single method, here are all the functions that are available. Most are perfectly self explanatory and the others are fairly well documented (use help('Analysis.command_name')). The methods are:
full(self,linear=True)
blunt(self,dct = None)
overhang5(self, dct=None)
overhang3(self, dct=None)
defined(self,dct=None)
with_sites(self, dct=None)
without_site(self, dct=None)
with_N_sites(self, N, dct=None)
with_number_list(self, list, dct=None)
with_name(self, names, dct=None)
with_site_size(self, site_size, dct=None)
only_between(self, start, end, dct=None)
between(self,start, end, dct=None)
show_only_between(self, start, end, dct=None)
only_outside(self, start, end, dct =None)
outside(self, start, end, dct=None)
do_not_cut(self, start, end, dct =None)Using these methods is simple:
>>> new_seq = Seq('TTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAGAA', IUPACAmbiguousDNA())
>>> rb = RestrictionBatch([EcoRI, KpnI, EcoRV])
>>> Ana = Analysis(rb, new_seq, linear=False)
>>> Ana
Analysis(RestrictionBatch(['EcoRI', 'EcoRV', 'KpnI']),Seq('TTCAAAAAAAAAAAAAAAAAA
AAAAAAAAAAGAA', IUPACAmbiguousDNA()),False)
>>> Ana.blunt() # output only the result for enzymes which cut blunt
{'EcoRV': []}
>>> Ana.full() # all the enzymes in the RestrictionBatch
{'KpnI': [], 'EcoRV': [], 'EcoRI': [33]}
>>> Ana.with_sites() # output only the result for enzymes which have a site
{'EcoRI': [33]}
>>> Ana.without_site() # output only the enzymes which have no site
{'KpnI': [], 'EcoRV': []}
>>> Ana.only_between(1, 20) # the enzymes which cut between position 1 and 20
{}
>>> Ana.only_between(20, 34) # etc...
{'EcoRI': [33]}
>>> Ana.only_outside(20, 34)
{}
>>> Ana.with_name([EcoRI])
{'EcoRI': [33]}
>>>To get a nice output, you still use print_that but this time with the command you want executed as argument.
>>> Ana.print_that(Ana.blunt())
Enzymes which do not cut the sequence.
EcoRV
>>> pt = Ana.print_that
>>> pt(Ana.with_sites())
EcoRI : 33.
>>> pt(Ana.without_site())
Enzymes which do not cut the sequence.
EcoRV KpnI
>>> # etc ...All of these methods (except full() which, well ... do a full restriction analysis) can be supplied with an additional dictionary. If no dictionary is supplied a full restriction analysis is used as starting point. Otherwise the dictionary provided by the argument dct is used. The dictionary must be formatted as the result of RestrictionBatch.search. Therefore of the form {'enzyme_name': [position1, position2],...}, where position1 and position2 are integers. All methods list previously output such dictionaries and can be used as starting point.
Using this method you can build really complex query by chaining several method one after the other. For example if you want all the enzymes which are 5' overhang and cut the sequence only once, you have two ways to go:
The hard way consist to build a restriction batch containing only 5' overhang enzymes and use this batch to create a new Analysis instance and then use the method with_N_sites() as follow:
>>> rbov5 = RestrictionBatch([x for x in rb if x.is_5overhang()])
>>> Anaov5 = Analysis(rbov5, new_seq, linear=False)
>>> Anaov5.with_N_sites(1)
{'EcoRI' : [33]}The easy solution is to chain several Analysis methods. This is possible since each method return a dictionary as results and is able to take a dictionary as input:
>>> Ana.with_N_sites(1, Ana.overhang5())
{'EcoRI': [33]}The dictionary is always the last argument whatever the command you use.
The way to prefer certainly depends of the conditions you will use your Analysis instance. If you are likely to frequently reuse the same batch with different sequences, using a dedicated RestrictionBatch might be faster as the batch is likely to be smaller. Chaining methods is generally quicker when working with an interactive shell. In a script, the extended syntax may be easier to understand in a few months.
Restriction enzymes require a much more strict formatting of the DNA sequences than Bio.Seq object provides. For example, the restriction enzymes expect to find an ungapped (no space) upper-case sequence, while Bio.Seq object allow sequences to be in lower-case separated by spaces. Therefore when a restriction enzyme analyse a Bio.Seq object (be it a Seq or a MutableSeq), the object undergoes a conversion. The class FormattedSeq ensure the smooth conversion from a Bio.Seq object to something which can be safely be used by the enzyme.
While this conversion is done automatically by the enzymes if you provide them with a Seq or a MutableSeq, there is time where it will be more efficient to realise the conversion before hand. Each time a Seq object is passed to an enzyme for analysis you pay a overhead due to the conversion. When analysing the same sequence over and over, it will be faster to convert the sequence, store the conversion and then use only the converted sequence.
Creating a FormattedSeq from a Bio.Seq object is simple. The first argument of FormattedSeq is the sequence you wish to convert. You can specify a shape with the second argument linear, if you don't the FormattedSeq will be linear:
>>> from Bio.Restriction import *
>>> from Bio.Seq import Seq
>>> seq = Seq('TTCAAAAAAAAAAGAATTCAAAAGAA')
>>> linear_fseq = FormattedSeq(seq, linear=True)
>>> default_fseq = FormattedSeq(seq)
>>> circular_fseq = FormattedSeq(seq, linear=False)
>>> linear_fseq
FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
>>> linear_fseq.is_linear()
True
>>> default_fseq.is_linear()
True
>>> circular_fseq.is_linear()
False
>>> circular_fseq
FormattedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)FormattedSeq retains information about the shape of the sequence. Therefore unlike with Seq and MutableSeq you don't need to specify the shape of the sequence when using search() or catalyse():
>>> EcoRI.search(linear_fseq)
[15]
>>> EcoRI.search(circular_fseq) # no need to specify the shape
[15, 25]In fact, the shape of a FormattedSeq is not altered by the second argument of the commands search() and catalyse():
>>> # In fact the shape is blocked.
>>> # The 3 following commands give the same results
>>> # which correspond to a circular sequence
>>> EcoRI.search(circular_fseq)
[15, 25]
>>> EcoRI.search(circular_fseq, linear=True)
[15, 25]
>>> EcoRI.search(circular_fseq, linear=False)
[15, 25]You can however change the shape of the FormattedSeq. The command to use are:
FormattedSeq.to_circular() => new FormattedSeq, shape will be circular.
FormattedSeq.to_linear() => new FormattedSeq, shape will be linear
FormattedSeq.circularise() => change the shape of FormattedShape to circular
FormattedSeq.linearise() => change the shape of FormattedShape to linear>>> circular_fseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
>>> circular_fseq.is_linear()
False
>>> circular_fseq == linear_fseq
False
>>> newseq = circular_fseq.to_linear()
>>> circular_fseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=False)
>>> newseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
>>> circular_fseq.linearise()
>>> circular_fseq
FormatedSeq(Seq('TTCAAAAAAAAAAGAATTCAAAAGAA', Alphabet()), linear=True)
>>> circular_fseq.is_linear()
True
>>> circular_fseq == linear_fseq
True
>>> EcoRI.search(circular_fseq) # which is now linear
[15]Not having to specify the shape of the sequence to analyse gives you the opportunity to use the shorthand '/' and '//' with restriction enzymes:
>>> EcoRI/linear_fseq # <=> EcoRI.search(linear_fseq)
[15]
>>> linear_fseq/EcoRI # <=> EcoRI.search(linear_fseq)
[15]
>>> EcoRI//linear_fseq # <=> linear_fseq//EcoRI <=> EcoRI.catalyse(linear_fseq)
(Seq('TTCAAAAAAAAAAG', Alphabet()), Seq('AATTCAAAAGAA', Alphabet()))Another way to avoid the overhead due to a repetitive conversion from a Seq object to a FormattedSeq is to use a RestrictionBatch.
To conclude, the performance gain achieved when using a FormattedSeq instead of a Seq is not huge. The analysis of a 10 kb sequence by all the enzymes in AllEnzymes (for x in AllEnzymes: x.search(seq), 867 enzymes) is 7 % faster when using a FormattedSeq than a Seq. Using a RestrictionBatch (AllEnzymes.search(seq)) is about as fast as using a FormattedSeq the first time the search is run. This however is dramatically reduced in subsequent runs with the same sequence (RestrictionBatch keeps in memory the result of their last run while the sequence is not changed).
This chapter addresses some more advanced features of the packages, most users can safely ignore it.
Most people will certainly not need to update the enzymes. The restriction enzyme package will be updated in with each new release of Biopython. But if you wish to get an update in between Biopython-releases here is how to do it.
First, you have to download the two scripts rebase_update.py and ranacompile.py: Go to https://github.com/biopython/biopython/tree/master/Scripts/Restriction, click on the respective file and press the 'Raw' button on the top right of the code window. Then, with right-click, save the file. Both scripts must be in the same directory.
Each month, REBASE release a new compilation of data about restriction enzymes. While the enzymes do not change so frequently, you may wish to update the restriction enzymes classes. The first thing to do is to get the last rebase file. You can find the release of REBASE at http://rebase.neb.com/rebase/rebase.files.html. The file you are interested in are in the EMBOSS format. You can download the files directly from the REBASE ftp server using your browser. The file are situated at ftp://ftp.neb.com/pub/rebase. You will have to download 3 files: emboss_e.###, emboss_r.###, and emboss_s.###. The ### is a 3 digit number corresponding to the year and month of the release. The first digit is the year, the two last are the month: so July 2015 will be: 507; October 2016: 610, etc... Download the three file corresponding to the current month and place them in the same folder as your rebase_update.py and ranacompiler.py scripts.
Another way to do the same thing is to use the rebase_update.py script. It will connect directly to the rebase ftp server and download the last batch of emboss files. From a DOS or Unix shell do the following:
$ cd path_to_the_update_script
$ rebase_update.py -p http://www.somewhere.com:8000
Please wait, trying to connect to Rebase
copying ftp://ftp.neb.com/pub/rebase/emboss_e.407
to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_e.407
copying ftp://ftp.neb.com/pub/rebase/emboss_s.407
to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_s.407
copying ftp://ftp.neb.com/pub/rebase/emboss_r.407
to /cvsroot/bioPython/Bio/Restriction/Scripts/emboss_r.407Some explanation are needed: -p is the switch to indicate to the script you are using a proxy. If you use a ftp proxy enter its address and the connection port after the ':'.
Once you have got the recent emboss files you can compile a new module containing the data necessary to create restriction enzyme.
Note: if the emboss files are not present in the current directory or if they are not up to date, ranacompiler.py will invoke the script rebase_update.py, which needs to be installed in the same folder. You will need to use the same options as before (ie -m and -p). See the previous paragraph on rebase_update.py for more details.
For simplicity let's assume we have put the emboss files in the same folder as the files which contains the script ranacompiler.py. You may have the change the mode of the file to make it executable:
$ cd path_to_the_ranacompiler_script
$ chmod '+x' ranacompiler.pyNow execute the script:
$ Python ranacompiler.py # or ./ranacompiler.pyYou get normally the following message:
$ ./ranacompiler.py
Using the files : emboss_e.407, emboss_r.407, emboss_s.407
WARNING : HaeIV cut twice with different overhang length each time.
Unable to deal with this behaviour.
This enzyme will not be included in the database. Sorry.
Checking :
Anyway, HaeIV is not commercially available.
WARNING : HpyUM037X has two different sites.
The new database contains 867 enzymes.
Writing the dictionary containing the new Restriction classes...
OK.
Writing the dictionary containing the suppliers datas...
OK.
Writing the dictionary containing the Restriction types....
OK.
******************************************************************************
Compilation of the new dictionary : OK.
Installation : No.
You will find the newly created 'Restriction_Dictionary.py' file
in the :
/path/where/you/run/ranacompiler.py
Make a copy of 'Restriction_Dictionary.py' and place it with
the other Restriction libraries.
note :
This folder should be :
path_to_python/site-packages/Bio/Restriction
******************************************************************************The first line indicate which emboss files have been used for the present compilation. You can safely ignore the warnings as long as the compilation of the new dictionary : OK. is present in the last part of the output. They are here for debugging purpose. The number of enzymes in the new module is indicated as well as a list of the dictionary which have been compiled. The last part indicate that the module has been succesfully created but not installed. To finish the update you must copy the file Restriction_Dictionary.py into the folder /your_python_path/site-packages/Bio/Restriction/ as indicated by the script. Looking into the present folder, you will see to new files: the newly created dictionary Restriction_Dictionary.py and Restriction_Dictionary.old. This last file containing the old dictionary to which you can revert in case anything the new file is corrupted (this should not happen since the script is happy enough the new dictionary is good, but if there is a problem it is always nice to know you can revert to the previous setting without having to reinstall the whole thing.
If you whish, the script may install the folder for you as well, but you will have to run it as root if your normal user has no write access to your Python installation (and it should'nt). Use the command ranacompiler.py -i or ranacompiler.py --install for this.
If anything goes wrong (you have no write access to the destination folder for example) the script will let you know it did not perform the installation. It will however still save the new module in the current directory.
As you can see the script is not very bright and will redo the compilation each time it is invoked, no matter if a previous version of the module is already present.
As seen previously, you can modify some aspects of the Analysis output interactively. However if you want to write your own Analysis class, you may wish to provide others output facilities than is given in this package. Depending on what you want to do you may get away with simply changing the make_format method of your derived class or you will need to provide new methods. Rather than get into a long explanation, here is the implementation of a rather useless Analysis class:
>>> class UselessAnalysis(Analysis):
def __init__(self, rb=RestrictionBatch(), seq=Seq(''), lin=True):
"""UselessAnalysis -> A class that waste your time"""
#
# Unless you want to do something more fancy all
# you need to do here is instantiate Analysis.
# Don't forget the self in __init__
#
Analysis.__init__(self, rb, seq, lin)
def make_format(self, cut=[], t='', nc=[], s=''):
"""not funny"""
#
# Generally, you don't need to do anything else here
# This will tell to your new class to default to the
# _make_joke format.
#
return self._make_joke(cut, t, nc, s)
def print_as(self, what='joke'):
"""Never know somebody might want to change the behaviour of
this class."""
#
# add your new option to print_as
#
if what == 'joke':
self.make_format = self._make_joke
return
else:
#
# The other options will be treated as before
#
return Analysis.print_as(self, what)
def _make_joke(self, cut=[], title='', nc=[], s1=''):
"""UA._make_joke(cut, t, nc, s) -> new analysis output"""
#
# starting your new method with '_make_'
# will give a hint to what it is suppose to do
#
# We will not process the non-cutting enzymes
# Their names are in nc
# s1 is the string printed before them
#
if not title:
title = '\nYou have guessed right the following enzymes:\n\n'
for name, sites in cut:
#
# cut contains:
# - the name of the enzymes which cut the sequence (name)
# - a list of the site positions (sites)
#
guess = raw_input("next enzyme is %s, Guess how many sites ?\n>>> "%name)
try:
guess = int(guess)
except:
guess = None
if guess == len(sites):
print 'You did guess right. Good. Next.'
result = '%i site' % guess
if guess > 1:
result += 's'
#
# now we format the line. See the PrintFormat module
# for some examples
# PrintFormat.__section_list and _make_map are good start.
#
title=''.join((title, str(name).ljust(self.NameWidth),
' : ', result, '.\n'))
print '\nNo more enzyme.'
return title
#
# I you want to print the non cutting enzymes use
# the following return instead of the previous one:
#
#return title + t + self._make_nocut_only(nc,s1)
>>> # You initiate and use it as before
>>> rb = RestrictionBatch([], ['A'])
>>> multi_site = Seq('AAA' + EcoRI.site +'G' + KpnI.site + EcoRV.site + 'CT' +\
SmaI.site + 'GT' + FokI.site + 'GAAAGGGC' + EcoRI.site + 'ACGT', IUPACAmbiguousDNA())
>>>
>>> b = UselessAnalysis(rb, multi_site)
>>> b.print_that() # Well, I let you discover if you haven't already guessedUsing this example, as a template you should now be able to subclass Analysis as you wish. You will found more implementation details in the module Bio.Restriction.PrintFormat which contains the class providing all the _make_* methods.
Particularly, the class Analysis is a quick and dirty implementation based on the facilities furnished by the package. Please check your results and report any fault.
On a more general basis, Restriction have some other limitations:
No facility to work with methylated DNA has been implemented yet. As far as the enzyme classes are concerned all DNA is non methylated DNA. Implementation of methylation sensibility will possibly occur in the future. But for now, if your sequence is methylated, you will have to check if the site is methylated using other means.
As before no support has been yet implemented to find site mis-recognised by enzymes under high salt concentration conditions, the so-called star activity. This will be implemented as soon as I can get a good source of information for that.
It is safe to use degenerated DNA as input for the query. You will not be flooded with meaningless results. But this come at a price: GAANTC will not be recognised as a potential EcoRI site for example, in fact it will not be recognised at all. Degenerated sequences will not be analysed. If your sequence is not fully sequenced, you will certainly miss restriction sites:
>>> a = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAATTCrrrrrrrrrrr', IUPACAmbiguou
sDNA())
>>> EcoRI.search(a)
[36]
>>> b = Seq('nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnGAAnTCrrrrrrrrrrr', IUPACAmbiguou
sDNA())
>>> EcoRI.search(b)
[]While you can use degenerated DNA, using non standard base alphabet will make the enzymes choke, even if Bio.Seq.Seq accepts them. However, space-like characters (' ', '', '', ...) and digit will be removed but will not stop the enzyme analysing the sequence. You can use them but the fragments produced by catalyse will have lost any formatting. catalyse tries to keep the original case of the sequence (i.e lower case sequences will generate lower case fragments, upper case sequences upper case fragments), but mixed case will return upper case fragments:
>>> c = Seq('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxGAANTCrrrrrrrrrrr', IUPACAmbiguou
sDNA())
>>> EcoRI.search(c)
Traceback (most recent call last):
File "<pyshell#110>", line 1, in -toplevel-
EcoRI.search(b)
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 3
96, in search
cls.dna = FormatedSeq(dna, linear)
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1
37, in __init__
self.format()
File "/usr/lib/Python2.3/site-packages/Bio/Restriction/Restriction.py", line 1
53, in format
raise AlphabetError, " '%s' is not in the IUPAC alphabet" % s
AlphabetError: 'X' is not in the IUPAC alphabet
>>> d = Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n\
26 nnnnn nnnnG AATTC rrrrr rrrrr \n\
51 r', IUPACAmbiguousDNA())
>>> d
Seq('1 nnnnn nnnnn nnnnn nnnnn nnnnn \n26 nnnnn nnnnG AATTC rrrrr rrrrr \n51 r',
IUPACAmbiguousDNA())
>>> EcoRI.search(d)
[36]
>>> EcoRI.catalyse(d)
(Seq('AATTCRRRRRRRRRRR', IUPACAmbiguousDNA()), Seq('NNNNNNNNNNNNNNNNNNNNNNNNNNNN
NNNNNNG', IUPACAmbiguousDNA()))
>>> e = Seq('nnnnGAATTCrr', IUPACAmbiguousDNA())
>>> f = Seq('NNNNGAATTCRR', IUPACAmbiguousDNA())
>>> g = Seq('nnnngaattcrr', IUPACAmbiguousDNA())
>>> EcoRI.catalyse(e)
(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
>>> EcoRI.catalyse(f)
(Seq('NNNNG', IUPACAmbiguousDNA()), Seq('AATTCRR', IUPACAmbiguousDNA()))
>>> EcoRI.catalyse(g)
(Seq('nnnng', IUPACAmbiguousDNA()), Seq('aattcrr', IUPACAmbiguousDNA()))Not allowing other letters than IUPAC might seems drastic but this is really to limit errors. It is not totally fool proof but it does help.
While sites clearly outsides a sequence will not be reported, nothing has been done to try to determine if a restriction site at the end of a linear sequence is valid:
>>> d = Seq('GAATTCAAAAAAAAAAAAAAAAAAAAAAAAAAGGATG', IUPACAmbiguousDNA())
>>> FokI.site # site present
'GGATG'
>>> FokI.elucidate() # but cut outside the sequence
'GGATGNNNNNNNNN^NNNN_N'
>>> FokI.search(d) # therefore no site found
[]
>>> EcoRI.search(d)
[2]EcoRI finds a site at position 2 even if it is highly unlikely that EcoRI accepts to cut this site in a tube. It is generally considered that at about 5 nucleotides must separate the site from the edge of the sequence to be reasonably sure the enzyme will work correctly. This "security margin" is variable from one enzyme to the other. In doubt consult the documentation for the enzyme.
Some enzymes will cut twice each time they encounter a restriction site. The enzymes in this package report both cut not the site. Other software may only reports restriction sites. Therefore the output given for some enzymes might seems to be the double when compared with the results of these software. It is not a bug.
>>> AloI.cut_twice()
True
>>> AloI.fst5 # first cut
-7
>>> AloI.scd5 # second cut
25
>>> AloI.site
'GAACNNNNNNTCC'
>>> b = Seq('AAAAAAAAAAA'+ AloI.site + 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA')
>>> b
Seq('AAAAAAAAAAAGAACNNNNNNTCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA', Alphabet())
>>> AloI.search(b) # one site, two cuts -> two positions
[5, 37]Having all the enzymes imported directly in the shell is useful when working in an interactive shell (even if it is not recommended by the purists). Here is a little hack to get some sanity back when using dir() in those conditions:
>>> # we will change the builtin dir() function to get ride of the enzyme names.
>>> import sys
>>> def dir(object=None):
"""dir([object]) -> list of string.
over-ride the built-in function to get some clarity."""
if object:
# we only want to modify dir(),
# so here we return the result of the builtin function.
return __builtins__.dir(object)
else:
# now the part we want to modify.
# All the enzymes are in a RestrictionBatch (we will talk about
# that later, for the moment simply believe me).
# So if we remove from the results of dir() everything which is
# in AllEnzymes we will get a much shorter list when we do dir()
#
# the current level is __main__ ie dir() is equivalent to
# ask what's in __main__ at the moment.
# we can't access __main__ directly.
# so we will use sys.modules['__main__'] to reach it.
# the following list comprehension remove from the result of
# dir() everything which is also present in AllEnzymes.
#
return [x for x in __builtins__.dir(sys.modules['__main__'])
if not x in AllEnzymes]
>>> # now let's see if it works.
>>> dir()
['AllEnzymes', 'Analysis', 'CommOnly', 'NonComm', 'PrintFormat', 'RanaConfig',
'Restriction', 'RestrictionBatch', 'Restriction_Dictionary', '__builtins__',
'__doc__', '__name__', 'dir', 'sys']
>>> # ok that's much better.
>>> # The enzymes are still there
>>> EcoRI.site
'GAATTC'