(Translated by https://www.hiragana.jp/)
A GENERALISED PROFILE SYNTAX FOR PROTEIN AND NUCLEIC ACID
SEQUENCE MOTIFS
Version 1.31, December 2001
Contact information (November 2012)
-----------------------------------
Philipp Bucher
EPFL SV ISREC GR-BUCHER
AAB 0 17 (Batiment AAB)
Station 15
CH-1015 Lausanne
Switzerland
Telephone: [+41 21 69] 30956, 38358
Email address: philipp.bucher@epfl.ch
This document may be copied and redistributed freely, without advance per-
mission, provided that this statement is reproduced with any copy.
INTRODUCTION
This document describes a general syntax to express a quantitative, pri-
mary structure-based protein or nucleic acid sequence motif. The designa-
tion `quantitative' means that a motif description assigns a degree of
similarity to a potential match rather than a binary status of true or
false. The restriction `primary structure-based' implies that the proba-
bility of finding a specific residue at one position is independent of any
residue occurring at another position.
The generalised profile syntax has been designed for and will be used in
future releases of the PROSITE database. In addition, it will be used in
a similar database of nucleic acid sequence motifs currently under
development by the author. Other researchers working on sequence motifs
are encouraged to use the same format for their own motif collections, and
may include this document in a public distribution release.
The term `generalised profile syntax' is meant to indicate that the pro-
posed data structure represents a generalisation of the profile type
described by Gribskov et al. [1]. However, similar motif descriptors have
been introduced by others under different names, e.g. weight matrices [2]
or flexible patterns [3].
The following terminology is adopted in this document:
- The term `profile' refers to a quantitative motif description based on
the generalised profile syntax.
- The term `pattern' refers to a qualitative motif description based on a
regular expression-like syntax such as the one currently used in PRO-
SITE entries marked as PATTERN.
- The term `motif' refers to the biological object one attempts to ap-
proximate by a pattern or a profile.
Note that the PROSITE database reserves the token MATRIX to identify
entries containing profiles.
* * *
The design of the new profile structure has been guided by various biolog-
ical and technical considerations. High priority has been given to the
following principles:
A) Syntactic versatility
The syntax should be versatile enough to cover a large variety of biologi-
cally relevant motifs. In particular, it should be be possible to accu-
rately represent the following objects:
- Signatures for various types and levels of protein taxons.
- Highly degenerate protein structural and functional domains such as the
immunoglobulin domains, or the SH2 and SH3 domains.
- Consensus sequences of interspersed repetitive DNA elements (SINEs and
LINEs).
- Basic gene expression signals, e.g. promoter elements, RNA processing
signals, translational initiation sites.
- Recognition motifs of a large variety of sequence-specific DNA-binding
proteins.
- Protein and nucleic acid compositional domains, e.g. glutamine-rich ac-
tivation domains, CpG islands.
B) Determinative search instructions
The profile syntax should have the capacity to encode precise and complete
instructions for a motif search. Ideally, the result of a motif search
should be determined by the profile and a sequence alone, i.e. not depend
on parameters of the search method. In practice, this goal may only be ap-
proximately achieved due to ambiguities arising with multiple locally op-
timal profile-sequence alignments (see Section 2).
Notes:
- In other implementations of similar methods, e.g. GCG profiles or HMMER
Hidden Markov models software, different search methods are available
as options and parameters of the search programs rather than as syntac-
tic features of the motif description itself. For the profiles in PRO-
SITE, inclusion of determinative search instructions is a necessity be-
cause otherwise the information given on the NR lines (statistics of
true and false positives/negatives) would have no meaning.
- The notion of determinative search instructions is not meant to imply a
specific search algorithm. There is space for different technical
solutions to achieve the same result.
C) Openness to different interpretations
A profile syntax is situated at the interface between a motif definition
and a motif search method. As such it can serve as a melting pot for in-
tegrating complementary efforts. While a rigid meaning vis-a-vis a search
method is desirable, flexibility with regard to motif definition methods
is equally important. In order to achieve such flexibility, it is essen-
tial that the profile parameters remain open to a variety of different
theoretical interpretations implicit in different methodologies.
Relevant motif definition techniques may include both comparative struc-
tural and wet biochemical approaches. It is thus conceivable that the
same type of numeric profile parameter may reflect log-probabilities in
one case, and binding energies in another. A profile syntax must be neu-
tral in this respect in order to be generally acceptable to a heterogene-
ous research community relying on different rationales for motif defini-
tion.
D) Compatibility with existing search methods
As a profile is required to encode determinative directives for a motif
search, the underlying syntax should have the capacity to emulate most of
the commonly used motif search techniques, such as:
- Search for PROSITE patterns.
- Search for fixed-length weight matrices without gaps [2].
- Search for complex motifs defined by multiple weight matrices and
variable-length linkers [4].
- Gribskov's profile alignment algorithm [1].
- Barton's alignment algorithm for flexible patterns [3].
- Viterbi algorithm for the hidden Markov model architecture described in
[5].
- The domain and fragment search algorithms implemented in the HMMER pro-
grams hmmls and hmmfs, respectively [10].
This requirement stems from two beliefs: (i) that the bewildering variety
of motif search methods described in the literature can be understood and
reformulated as special cases of a more general method; (ii) that such an
exercise will facilitate communication between different groups and will
lead to new theoretical insights.
Notes:
- With the exception of Barton's algorithm for flexible patterns, the
capacity of the generalised profile syntax to emulate the search tech-
niques listed above has been verified by experiment.
* * *
The remaining part of this document is organised as follows:
- Section 1 explains the basic components of a profile which are likely
to remain stable for several years.
- Section 2 presents accessory features necessary to encode complete in-
structions for a motif search. This part of the syntax may be gradually
expanded in the future.
- Section 3 describes a specific machine-readable format which will be
used in PROSITE and a similar data bank of nucleic acid sequence
motifs.
- Section 4 shows two illustrative examples, one from the nucleic acid
and one from the protein world.
1) BASIC PROFILE STRUCTURE AND FUNCTION
In abstract terms, a generalised profile can be described as an alternat-
ing sequence of `match' and `insert' positions. Match and insert posi-
tions contain complementary sets of numeric parameters called profile
scores. The values assigned to these parameters may be different at each
position. In reality, a profile resembles a two-dimensional table of
numbers.
From an other perspective, a profile may also be viewed as a degenerate
molecular sequence. The match positions correspond to residues which typ-
ically occur in such a sequence. The insert positions represent places
where additional residues can optionally be inserted.
The function of a profile is to align itself to a real sequence and to as-
sign a number to such an alignment. This number is called similarity
score or alignment score and serves to evaluate the significance of a po-
tential motif occurrence.
The notion of a profile match cannot be separated from that of an align-
ment. The alignment is not only a prerequisite for computing a similarity
score, it also expresses a specific interpration of the sequence match.
For instance, if the profile involved represent a protein domain where
certain positions are associated with specific functions, e.g. metal
ion-binding capacities or catalytic roles, then the alignment will map
these functions onto individual residues of the sequence.
The basic components of a profile are those which are necessary for com-
puting a similarity score. To prevent possible misunderstandings, it has
to be stressed that a profile defines a score for any alignment, not just
for an optimal alignment. The concept of an optimal alignment relates to
motif search strategies and is totally irrelevant in this Section.
1.1) Definition of a profile-sequence alignment
It is useful to introduce a profile-sequence alignment with the aid of the
path matrix representation. The following diagram defines an alignment
between a sequence and a profile.
S E Q U E N C E
. . . . . . . . .
p
. . . . . . . . .
r \
. . _ . _ . . . . . .
o \
. . . . . . . . .
f \
. . . . . . . . .
i |
. . . . . . . . .
l \
. . . . . . . . .
e
. . . . . . . . .
The capital letters represent sequence residues, the lower-case letters
represent profile match positions. Profile insert positions are not
marked by symbols. They occur at the beginning, at the end, and between
any pair of consecutive match positions of the profile.
The path marked by horizontal, vertical, and diagonal bars defines the
following alignment:
S E Q U E - N
r - - o f i l
Such an alignment can also be defined by a sequence of path matrix co-
ordinates. By convention, the upper left corner of the matrix is assigned
co-ordinates (0,0). Note that path matrix co-ordinates correspond to pro-
file insert positions rather than match positions. Likewise, they fall
between consecutive residues of the sequence. The above alignment is de-
fined by the following co-ordinate sequence.
(1,0) , (2,1) , (2,2) , (2,3) , (3,4) , (4,5) , (5,5) , (6,6)
In general, a co-ordinate sequence
(i ,j ) , (i ,j ) , ... , (i ,j )
0 0 1 1 L L
defines a valid sequence alignment between a profile of length N and a se-
quence of length M if and only if:
{ 0 <= i <= N AND 0 <= j <= M } for 0 <= k <= L
k k
AND
{ ( i + 1 = i AND j + 1 = j
k k+1 k k+1
OR ( i = i AND j + 1 = j
k k+1 k k+1
OR ( i + 1 = i AND j = j } for 0 <= k <= L-1 .
k k+1 k k+1
Notes:
- The above definition encompasses both global and local types of align-
ments. In the following, it is not necessary to distinguish between
these two alternatives. A global alignment may simply be viewed as a
limit case of a local alignment.
- The alignment definition underlying generalised profiles is equivalent
to the path definition of hidden Markon models in the following sense:
(i) for a sequence and a profile of given lengths M,N, the number of
possible alignments is exactly identical to the number of paths through
which an HMM of length N can generate sequences of length M, (ii) there
is an obvious one-to-one mapping between profile-sequence alignments
and paths through HMMs; see also [9].
1.2) Definition of the similarity score
The similarity score of a profile sequence-alignment is the sum of the
scores assigned to its scorable components. The scorable components of an
alignment are:
- the beginning
- each extension step
- each state transition
- the end
Some of these terms need further explanation.
An extension step occurs between any pair of consecutive path matrix co-
ordinates. There are three different types of extension steps: `match',
`insert', and `deletion' steps. In the above diagram, diagonal bars
correspond to match extension steps, horizontal bars correspond to insert
extension steps, and vertical bars correspond to deletion extension steps.
The number of extension steps defines the length of the alignment.
The type of an extension step is also called a state. Each extension step
is thus associated with a match, insert, or deletion state. At the begin-
ning, an alignment is in `initiation' state. At the end, it is in `termi-
nation' state. Initiation, match, insert, deletion, and termination
states will be symbolised by the letters B, M, I, D, and E, respectively.
A state transition occurs between any two consecutive alignment components
associated with a state. Thus, there is one state transition for each co-
ordinate pair of the alignment, including the first and the last. Note
that this definition implies that state transitions also occur between
identical states.
In summary, an alignment of length L has:
1 beginning
L extensions steps
L+1 state transitions
1 termination
--------------------------------------------------------------------------
2L+3 scorable components in total.
All component scores are provided by the profile in a position-specific
manner. Therefore, the similarity score does not depend on any parameter
of an alignment method. The types and functions of profile scores are now
explained.
The scores assigned to the beginning and end of the alignment are called
`initiation' and `termination' scores. These scores are distinct from
those assigned to the first and last state transition though they
correspond to the same path matrix co-ordinates. There are two types of
scores for each class. The `external' initiation score applies to co-
ordinates at the beginning of the sequence. The `internal' initiation
score applies to all other co-ordinates. External and internal termina-
tion scores are defined analogously. The function of these scores is to
flexibly encode local or global alignment scoring modes. In addition,
they may serve to anchor a motif at the beginning or at the end of a se-
quence.
The scores for extension steps comprise three classes: match extension
scores, insert extension scores, and deletion extension scores. Match and
insert extension scores are residue-specific because the corresponding
alignment steps span one sequence residue. By contrast, there is only one
deletion extension score per profile position because deletion steps do
not involve sequence residues.
There are 16 different types of state transition scores for all possible
transitions from an element of {B,M,I,D} to an element of {M,I,D,E}. State
transition scores serve similar functions as gap opening penalties in a
sequence-sequence alignment.
1.3) Basic profile structure
The basic profile structure follows almost conclusively from the forgoing
definitions of a profile-sequence alignment and its similarity score.
What remains to be clarified are a few details.
A profile is based on a particular alphabet. The alphabet is considered a
basic constituent of the profile because it determines the exact number of
parameters per insert and match position.
The two standard character sets for biomolecular patterns are:
- {A,C,G,T} for nucleic acid motifs.
- {A,B,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,Z} for protein motifs.
Other alphabets, e.g. alphabets including ambiguous codes for nucleotides,
may be useful in particular circumstances.
There is one insert and one match extension score for each character of
the alphabet. In practice, it is useful to define one additional insert
and match extension score to deal with unexpected characters appearing in
real sequences.
Some of the previously introduced profile scores are associated with in-
sert positions, others with match positions. A look at the path matrix
diagram makes clear which type of score is associated with which type of
profile position.
An insert position of a profile based on a K-letter alphabet contains the
following parameters:
1 external initiation score
1 internal initiation score
16 state transition scores for all transitions between
elements of {B,M,I,D} and {M,I,D,E}
K insert extension scores for each character of the alphabet
1 insert extension score for an unexpected character
1 internal termination score
1 external termination score
--------------------------------------------------------------------------
K+21 insert position scores in total
A match position of a profile based on a K-letter alphabet contains the
following parameters.
K match extension scores for each letter of the alphabet
1 match extension score for an unexpected character
1 deletion extension score
--------------------------------------------------------------------------
K+ 2 match position scores in total
Admissible values for profile scores are any integer or real number plus a
special value representing a forbidden alignment step. This value will be
called `low-value' and behaves like minus infinity in mathematical opera-
tions.
A profile has also a defined topology, either linear or circular. Most
profiles will be linear. Circular profiles may represent motifs which
consist of a variable number of tandemly repeated units. Note that a
linear profile begins and ends with an insert position.
Notes:
- The above list of position-specific profile scores represents the max-
imum number of supported features. Real profiles derived with an exist-
ing method will rarely use all of them. Concise representation of a
profile can be achieved through specification of appropriate defaults;
see examples in Section 4.
- There is some redundancy in the implemented parameter set allowing for
alternative representations of functionally equivalent profiles. This
freedom could be used for scaling profile scores in units related to a
particular mathematical or physical interpretation, e.g. probabilities
of a hidden Markov model or thermodynamic quantities.
- The above definition of a sequence alignment assumes linear topology
for both the profile and the sequence. Generalisation to circular to-
pology is straightforward. An alignment between a circular profile and
a linear sequence, or between a linear profile and a circular sequence,
corresponds to a path on a cylindrical surface. An alignment between a
circular profile and a circular sequence corresponds to a path on a
torus.
2) PROFILE ACCESSORIES
The primary purpose of a profile is to identify as reliably as possible
biologically relevant motif occurrences in new sequences. The basic pro-
file structure described in the previous Section is not sufficient to de-
fine a rational search strategy to this end. The accessory profile
features presented here fill this gap. Appropriately interpreted, they
complement the position-specific profile scores to provide determinative
instruction for a motif search. In addition, they guide the interpration
of potential matches.
2.1) Cut-off value
For a profile and a sequence of typical lengths, there is a very large
number of possible alignments. At most a few of them will be biologically
relevant. The function of a cut-off value is to a priori exclude a large
number of alignments from further consideration by a profile search algo-
rithm. The fate of the remaining alignments with similarity scores greater
than or equal to the cut-off value depends on a specific disjointness de-
finition applied; see below.
An important aspect of a cut-off value is that it gives a qualitative
meaning to a profile. This is a prerequisite for statistics on false po-
sitives and false negatives obtained in a database search, as currently
provided by PROSITE.
In certain situations, it is useful to supply more than one cut-off value,
partitioning the range of alignment scores into multiple areas. The areas
may correspond to different degrees of certainty, ranges of evolutionary
distance, or levels of physiological activity.
2.2) Score normalisation instructions
The profile-alignment scores defined in the previous Section are called
raw scores. In most cases, they will not lend themselves to meaningful
biological interpretations and will therefore not be very helpful in the
interpretation of a potential match. In practice, one is interested in
questions like: What is the probability of finding a match of a certain
score in a random sequence? How does the similarity score relate to a
measurable property of the biological object? The purpose of normalisation
instructions is to convert the raw score into directly interpretable un-
its.
There may be multiple normalisation modes for the same profile, each one
associated with a different mathematical, physical, or biological in-
terpretation; see examples in Section 4.
Normalisation functions are required to preserve the ranking of scores
pertaining to alternative alignments between the same profile and the same
sequence. However, since normalisation functions may depend on sequence
parameters such as length and residue composition, they will generally not
preserve the order of scores pertaining to matches from different se-
quences arising in a database search.
Notes:
- Cut-off values may be defined in raw score units or normalised score
units.
- Programs may rely on normalised rather than raw scores for various
operation, e.g. sorting of accepted matches in a database search.
- An expanding list of normalisation functions is presented in Appendix
B.
2.3) Disjointness definitions
There are situations where only a single best alignment and its similarity
score are of interest. This arises for instance with a profile serving
exclusively as a signature for a protein family. More frequently, the
same motif may occur more than once in a given sequence, and each oc-
currence will be of interest.
In the first case, the motif search problem is simple and can be solved by
a standard optimal alignment algorithm such as described in [1]. In the
second case, the task is more difficult and needs to be explained in more
detail.
At first glance, the problem seems to be to find all profile-sequence
alignments with similarity scores greater than or equal to the cut-off
value. However, such an approach would not yield useful results because a
high scoring alignment typically belongs to a large group of very similar
alignments with comparable scores. Two members of such a group may differ
only by an additional extension step at one end of one alignment. In
sequence-sequence comparisons, a cluster of related alignments is
represented by a single highest scoring member. This seems a reasonable
procedure for profiles too.
As a second approximation, one could therefore define the task of finding
multiple profile matches in the same sequence as one of finding as many as
possible, locally optimal, but mutually disjoint alignments with scores
greater than or equal to the cut-off value. What is still missing in such
a statement of the problem is a precise definition of disjointness and a
tie-braking rule to choose between equally high-scoring alignments. The
former is of fundamental importance and needs to be addressed here. The
latter may be considered a property of a specific algorithm and thus is
beyond the scope of this document.
There are many possible ways to define disjointness of two alignments.
The algorithms described for finding multiple locally optimal alignments
between pairs of sequences consider two alignments disjoint if they have
no extension step in common [6,7]. The two alignments specified in the
path matrix diagram below illustrate this notion of disjointness.
S E Q U E N C E
. . . . . . . . .
p \
. . . _ . . . . . .
r \ \
. . . . . . . . .
o | \
. . . . . . . . .
f \ \
. . . _ . . . . _ . _ .
i \
. . . . . . . . .
l \
. . . . . . . . .
e \
. . . . . . . . .
However, such a definition will not be adequate for many motif search ap-
plication because it allows the same sequence residue to be matched with
different profile positions. Imagine the case of a protein structural
domain. There, it is inconceivable that the same residue simultaneously
participates in the formation of two physically distinct domains, occupy-
ing different places within these domains.
There may be no single disjointness definition adequate for all kinds of
biological sequence motifs which can be characterised by a profile. For
this and other reasons, a specific notion of disjointness is viewed and
implemented as a profile-inherent property rather than a variable of the
alignment method. In some cases, a particular definition may even be
derived from a measurable property of the biological object. The conclud-
ing example illustrates this point.
The DNA recognition site of mammalian transcription factor Sp1 is about 14
bp long and can be fairly accurately represented by a conventional weight
matrix. Experiments have shown that the minimal center-to-center distance
for two sites to be simultaneously occupied by two proteins is 10 bp. For
a profile representing an Sp1 binding site, an appropriate criterion for
disjointness would require that the sequence segments aligned with the
central 10 bp region of the recognition motif do not overlap.
Notes:
- The problems related to disjoint alignments are not specific to pro-
files. They also occur with qualitative variable-length patterns based
on a regular expression-like syntax.
- An expanding list of alternative disjointness definitions is presented
in Appendix A.
- The algorithms for multiple pairwise sequence alignments described in
[6,7] can easily be adjusted to the disjointness definitions proposed
in Appendix A.
- Another principle for parsing multiple matches between an HMM and a se-
quence is implemented in the HMMER programs hmmls and hmmfs [10].
3) A MACHINE-READABLE TEXT FILE FORMAT
This Section describes the format conventions used in the PROSITE data
bank for representation of profiles
3.1) General format of the MA line
The current PROSITE database reserves the MA line code for information
specific to matrix entries.
A profile typically extends over many MA lines. The general format of a
block of consecutive MA lines is as follows:
MA /KEYWORD: parameter=value; parameter=value; ... ; /KEYWORD:
MA parameter=value; parameter=value; ... ; /KEYWORD: ...
The text is substructured into so-called data blocks, each one beginning
with a keyword followed by a list of parameter specifications. Keywords
identify different types of data blocks with characteristic parameter sub-
sets. The keywords at the beginning of each data block are enclosed by
slash on the left side and by colon on the right side. Individual parame-
ter specifications are delimited by semicolon. There is also a semicolon
at the end of each data block containing at least one parameter specifica-
tion.
A single word, quoted string, or number must be contained within one line.
Otherwise, there are no rules guiding the placement of text units onto
physical lines. Within one block, the parameter specifications can appear
in any order.
The following keywords define valid data block types:
/GENERAL_SPEC: General specifications.
/DISJOINT: Disjointness definition for multiple matches.
/NORMALIZATION: Score normalisation instructions.
/CUT_OFF: Recommended cut-off value(s).
/DEFAULT: Defaults for position specific profile parameters.
/I: Profile insert position.
/M: Profile match position.
3.2) The formats of different data block types
3.2.1) The GENERAL_SPEC data block
The GENERAL_SPEC data block provides general information about the pro-
file. It has the following format:
/GENERAL_SPEC: ALPHABET=string;
[ LENGTH=length; TOPOLOGY=topology; BEGIN=begin; END=end ]
[ LOG_BASE=log_base; P0=p0; P=random_model ]
where:
- `string' is a quoted character string defining the character set for
which, and the order in which, position-specific match and insert ex-
tension scores are provided in subsequent M and I data blocks.
- `length' is the length of the profile defined as the total number of
match positions.
- `topology' is one of the alternative words LINEAR or CIRCULAR.
- `begin' is an integer indicating the match position withing the profile
where the described biological object begins (implying that positions
before `begin' characterise contextual constraints). This together
with the END feature may be useful for profiles characterising biologi-
cal objects such as transmembrane helices in proteins, or exons in gene
sequences. As an instruction to software, this parameter means that
sequence residues matching profile positions before the `begin' posi-
tion should not be reported as being part of the biological object.
- `end' is an integer indicating the match position within the profile
where the biological object ends; see also remarks on previous parame-
ter.
- `log_base' is the logarithmic base that should be used when translating
the generalised profile (back) into an HMM, see [9]. Popular loga-
rithmic bases for the representation of HMMs, null-models, substitution
matrices, etc. are tabulated in APPENDIX C.
- `p0' is a real number between 0 and 1 defining the insert-to-insert
state transition probability of the null-model that should be used for
translating the generalised profile (back) into an HMM; see [9]. This
parameter defines a geometric length distribution over the sequence
space.
- `random-model' is a real number, or comma separated list of real
numbers, defining the residue emission probabilities of the null-model
that should be used for translating the generalised profile (back) into
an HMM; see [9]. These numbers are not required to sum to 1 and thus
should be renormalised by programs on input. In PROSITE, the random
model is usually given as percent amino acid frequencies.
The GENERAL_SPEC data block is mandatory and precedes any DEFAULT, M or I
data block.
Implicit defaults:
- TOPOLOGY=LINEAR;
Example:
MA /GENERAL_SPEC: ALPHABET='ACGT';
Notes:
- The optional LENGTH parameter is purely informative and redundant. The
actual length of the profile is given by the sequence of subsequent I
and M data blocks.
3.2.2) The DISJOINT data block
The DISJOINT data block provides a definition of disjointness for multiple
profile-sequence alignments, or indicates that only one globally optimal
alignment is of interest. It has the following format:
/DISJOINT: DEFINITION=name; parameters;
where:
- `name' is a word from a controlled vocabulary identifying one of the
supported disjoint definitions listed in Appendix A.
- `parameters' is a list of parameter specifications for the correspond-
ing disjointness definition. Note that different disjointness defini-
tions depend on different parameter sets; see APPENDIX A.
The DISJOINT data block is mandatory.
Example:
MA /DISJOINT: DEFINITION=PROTECT; N1=12; N2=40;
Notes:
- Some disjointness definitions are parameter-free. In this case, the
list of parameter specifications is empty.
- The list of supported disjoint definitions constitutes a dynamic
feature of the format. New functions may be added in the future.
Suggestions are welcome.
3.2.3) The NORMALIZATION data block
A NORMALIZATION data block describes a specific normalisation mode for
alignment scores. It has the following format:
/NORMALIZATION: FUNCTION=name; parameters;
[ MODE=mode-nr; PRIORITY=rank; TEXT=string; ]
where:
- `name' is a word form a controlled vocabulary identifying one of the
supported normalisation functions listed in Appendix B.
- `parameters' is a list of parameter specifications providing values for
all parameters of the corresponding normalisation function listed in
APPENDIX B.
- `mode-nr' is an integer by which the normalisation mode can be referred
to in CUT_OFF data blocks.
- `rank' is an integer assigning a relative priority to the normalisation
mode with regard to various pattern search operations.
- `string' is a quoted string describing a score normalised according to
this mode.
NORMALIZATION data blocks are optional. There may be several NORMALIZA-
TION data blocks per profile.
The optional MODE parameter either appears in all or in none of the NOR-
MALIZATION data blocks. If specified, the mode numbers form a contiguous
integer range starting with 1. If not specified, mode numbers are implied
by the order in which the NORMALIZATION data blocks appear in the profile.
The optional PRIORITY parameter either appears in all or none of the NOR-
MALIZATION data blocks. If not specified, priorities are equal to the mode
numbers. The lower the priority number, the higher the priority of the
normalisation mode, and vice-versa.
Example:
MA /NORMALIZATION: MODE=1; FUNCTION=LINEAR; TEXT='Homology Score';
MA R1=-90.558; R2=0.57225;
Notes:
- Normalisation functions may, in addition to the parameters listed in
APPENDIX B, depend on characteristics of the sequence such as length
and residue composition.
3.2.4) The CUT_OFF data block
A CUT_OFF data block defines a cut-off level. It has the following for-
mat:
/CUT_OFF: SCORE=rscore;
[ LEVEL=level; TEXT=string; N_SCORE=nscore; MODE=mode-nr ]
where:
- `rscore' is an integer defining the cut-off value in raw score units.
- `level' is an integer identifying a cut-off level.
- `string' is a quoted character string characterising profile matches
with scores greater than or equal to the corresponding cut-off value
(but lower than any higher cut-off value specified).
- `nscore' is a real number, or a comma separated list of real numbers,
defining the cut-off value(s) in normalised score units calculated ac-
cording to the mode(s) identified by mode-nr.
- `mode-nr' is an integer, or a comma separated list of integers, refer-
ring to one or several normalisation modes defined in NORMALIZATION
data blocks.
The CUT_OFF data block for level 0 is mandatory. There may be multiple
CUT_OFF data blocks, one for each level.
The LEVEL parameter is optional for level 0. All other levels are speci-
fied explicitly. The levels assigned to alternative cut-off values, in-
cluding level 0, form a contiguous integer range.
The N_SCORE and MODE parameters are either both present or both absent.
If present, they contain the same number of elements.
Example:
MA /CUT_OFF: LEVEL=0; SCORE=237; N_SCORE=7.5; MODE=1;
Notes:
- Cut-off values in raw score units may be used by programs which do not
support a given normalisation mode.
- The list of supported normalisation functions constitutes a dynamic
feature of the format. New functions may be added in the future.
Suggestions are welcome.
3.2.5) The DEFAULT data block
The DEFAULT data block redefines defaults for position-specific profile
parameters and has the following format:
/DEFAULT: [ SY_I=char1; SY_M=char2; parameters; ]
where:
- `char1' is a quoted character representing a profile insert position in
a profile-sequence alignment.
- `char2' is a quoted character representing a profile match position in
a profile-sequence alignment.
- `parameters' is a list of parameter specifications defining default
values for one or several of the profile scores listed in the tables at
the end of this Section.
DEFAULT data blocks are optional. There may be multiple DEFAULT data
blocks per profile.
Implicit defaults:
- SY_I='-'; SY_M='X';
Example:
MA /DEFAULT: B0= *; B1= *; E0= *; E1= *;
Notes:
- The first DEFAULT data block redefines the implicit defaults given in
the tables at the end of this Section. Subsequent DEFAULT data blocks
consecutively redefine each other.
- Asterisk represents low-value in the example; see next Subsection.
3.2.6) The I and M data blocks
The I and M data blocks contain position-specific profile scores for in-
sert and match positions. They have the following formats:
/I: [ SY=char1; parameters; ]
/M: [ SY=char2; parameters; ]
where:
- `char1' is a quoted character representing the corresponding profile
insert position in a profile-sequence alignment.
- `char2' is a quoted character representing the corresponding profile
match position in a profile-sequence alignment.
- `parameters' is a list of parameter specifications assigning values to
one or several of the position-specific profile scores listed in the
tables at the end of this Section.
The profile scores specified in I and M data blocks overwrite the current
default values set by a preceding DEFAULT data block or initialised as
shown in the tables at the end of this Section.
The values assigned to profile scores may be integers, reals, or low-value
represented by an asterisk. Most profile scores are assigned one value.
The exceptions are the residue-specific insert and match extension scores.
These scores can either be assigned one value or a comma separated list of
values, one for each character of the alphabet. The correspondence between
scores and characters is defined by the order in which the alphabet is
presented in the GENERAL_SPEC data block. A single value is equivalent to
a list of identical values.
Each I data block characterises one insert position of the profile. Each
M data block characterises one match position of the profile. The physi-
cal order of the M and I data blocks defines the logical order of the
corresponding profile positions. Default match and insert positions are
not always specified explicitly. This requires further explanation.
Remember that a profile consists of an alternating sequence of insert and
match positions, and that a linear profile starts and ends with an insert
position. Default I or M data block are implied wherever the physical
order of I and M data blocks does not conform to these rules.
Example:
MA /I: B0= 0; B1= 0; /M: M= 11, 3, 3, 4;
In case the above line describes a complete linear profile, a default in-
sert position is implied at the end. In case it describes a circular pro-
file, no additional profile position is implied.
Notes:
- There has been some debate (and no decision so far) whether profile
scores should be required to be integers. In PROSITE, all profile
scores are represented as integers and existing software supporting
this format actually requires integer representation. Integer represen-
tation has thus become a `de facto' standard.
- A linear normalisation function implicitly defines an integer to real
conversion of profile scores; see protein example in Section 4.
Profile scores of insert positions and implicit defaults:
Name Default Parameter description
--------------------------------------------------------------------------
B0 B0= 0 External initiation score
B1 B1= 0 Internal initiation score
E0 E0= 0 External termination score
E1 E1= 0 Internal termination score
BM BM= 0 State transition score from state B to M
BI BI= * State transition score form state B to I
BD BD= * State transition score from state B to D
BE BE= * State transition score from state B to E
MM MM= 0 State transition score from state M to M
MI MI= * State transition score from state M to I
MD MD= * State transition score from state M to D
ME ME= 0 State transition score from state M to E
IM IM= * State transition score from state I to M
II II= 0 State transition score from state I to I
ID ID= * State transition score from state I to D
IE IE= * State transition score from state I to E
DM DM= * State transition score from state D to M
DI DI= * State transition score from state D to I
DD DD= 0 State transition score from state D to D
DE DE= * State transition score from state D to E
I I = 0 Insert extension score(s) for characters included in the
alphabet
I0 I0= 0 Insert extension score for a character not included in the
alphabet
--------------------------------------------------------------------------
Profile parameters of match positions and implicit defaults:
Name Default Parameter description
--------------------------------------------------------------------------
M M = 0 Match extension score(s) for characters included in the
alphabet
M0 M0= 0 Match extension score for a character not included in the
alphabet
D D = 0 Deletion extension score
--------------------------------------------------------------------------
4) EXAMPLES
4.1) E. coli promoters
The profile shown below describes the major class of E. coli promoters
recognised by RNA polymerase-sigma factor 70. It is based on work pub-
lished in [4] and emulates the functionality of the promoter search pro-
gram TARGSEARCH.
MA /GENERAL_SPEC: ALPHABET='ACGT';
MA /DISJOINT: DEFINITION=PROTECT; N1=37; N2=42;
MA /NORMALIZATION: MODE=1; FUNCTION=LINEAR; R1=-90.558; R2=0.57225;
MA TEXT='Homology Score';
MA /NORMALIZATION: MODE=2; FUNCTION=LINEAR; R1=-10.198; R2=0.06215;
MA TEXT='Log KBk2';
MA /CUT_OFF: LEVEL=0; SCORE=237; N_SCORE=45.0; MODE=1;
MA /DEFAULT: B0=*; B1=*; E0=*; E1=*;
MA /I: B0= 0; B1= 0;
MA /M: M= 11, 3, 3, 4;
MA /M: M= 8, 4, 2, 7;
MA /M: M= 8, 2, 4, 7;
MA /M: M= 7, 4, 2, 8;
MA /M: M= 8, 4, 4, 5;
MA /M: M= 7, 3, 5, 6;
MA /M: M= 3, 5, 5, 8;
MA /M: M= 5, 2, 5, 9;
MA /M: M= 5, 8, 5, 3;
MA /M: M= 0, 1, 2,17; SY='T';
MA /M: M= 1, 1, 1,18; SY='T';
MA /M: M= 0, 2,17, 2; SY='G';
MA /M: M= 14, 3, 1, 4; SY='A';
MA /M: M= 5,11, 2, 5; SY='C';
MA /M: M= 9, 2, 3, 7; SY='A';
MA /M: M= 5, 5, 3, 9;
MA /M:/M:/M:/M:/M:/M:/M:/M:/M:
MA /I: MD=0; MM=1;/I: DM=1;/I: DM=1;/I: DM=6;/I: DM=14;/I: DM=6;/I: DM=1;
MA /M: M= 4, 5, 2,10;
MA /M: M= 5, 4, 5, 6;
MA /M: M= 3, 5, 5, 8;
MA /M: M= 4, 4, 8, 5;
MA /M: M= 4, 5, 7, 6;
MA /M: M= 0, 2, 2,17; SY='T';
MA /M: M= 20, 0, 0, 1; SY='A';
MA /M: M= 5, 3, 3, 9; SY='T';
MA /M: M= 12, 3, 3, 3; SY='A';
MA /M: M= 11, 4, 3, 4; SY='A';
MA /M: M= 0, 1, 0,20; SY='T';
MA /M: M= 7, 2, 6, 6;
MA /M: M= 4, 7, 5, 5;
MA /M: M= 6, 6, 6, 4;
MA /I: E0=0; E1= 0;
The profile is substructured into four operationally distinct modules
totaling 45 match positions.
Position Module
--------------------------------------------------------------------------
1-16 Weight matrix for the -35 region including the core hexamer box
TTGACA at pos. 10-15.
17-25 Fixed length linker module encoded by 9 dummy match positions.
This module is defined on the first of the two indented MA
lines.
26-31 Variable length linker scoring module encoded by 7 consecutive
insert positions. This module is defined on the second of two
indented MA lines.
32 -45 Weight matrix for the -10 region including the core hexamer box
TATAAT at pos. 37-42.
--------------------------------------------------------------------------
The variable length linker scoring module defines the following scoring
scheme.
# of bp between core score
hexamers boxes
15 1
16 6
17 14
18 6
19 1
20 1
21 1
These scores are achieved as follows. Format-proprietary default values
for state transition scores (not over-written by local defaults) make sure
that deletions and insertions can only occur at positions where
corresponding scores are explicitly specified. Insertion gaps are thus
generally forbidden. A deletion gap can only be opened at the beginning
of the linker length scoring module and must be closed within or at the
end of this module. A promoter with the maximal linker length of 21 can be
aligned without gap to the profile. In this case, the linker length score
is provided by MM=1 at insert position 25. Promoters with linker lengths
15 to 20 require a deletion gap in their alignment to the profile. The
corresponding scores are provided by DM=1, 1, 6, 14, 6, 1 at insert posi-
tions 26, 27, 28, 29, 30, 31, respectively.
Notes:
- The default low-values assigned to parameters B0, B1, E0, E1, together
with the exceptions B0, B1 = 0 at the beginning and E0, E1 = 0 at the
end of the profile, define a global alignment algorithm with endgap
weighting in the profile but not in the sequence.
- Two normalisation modes are defined in this profile. The names of the
corresponding scores, `Homology score' and `log KBk2', are taken from
the original publication. The parameters of the second normalisation
function were derived by a linear regression analysis between homology
scores and enzyme selectivities (defined as log KBk2) of 31 transcrip-
tionally assayed promoters.
- The cut-off homology score of 45 has been proposed by the authors as
lower limit for effective promoters.
- The disjointness definition protecting only the TATAAT box region from
sequence overlap, is motivated by a known promoter example where two
adjacent TATAAT boxes direct transcription from two distinct initiation
sites six bp apart from each other.
- This profile is not supposed to represent the most accurate E. coli
promoter prediction method available today. It primarily serves to il-
lustrate that the proposed syntax is flexible enough to express the
functionality of a specialised search algorithm developed for a partic-
ular object.
4.2) Src homology domain SH3
The profile shown on the next page describes the Src homology domain SH3
as defined by sequence similarity. It has been constructed by a recently
described extension of Gribskov's method incorporating several improve-
ments [8].
The SH3 profile consists of three homology blocks separated by two gap re-
gions. Within the homology blocks, small insertions and deletions are not
totally forbidden but strongly impeded by high gap costs defined in the
DEFAULT data block: MI=-26, I=-3, MD=-26, D=-3. These numbers are over-
written by more permissive values in the two gap regions.
Notes:
- The SH3 profile uses only features which are compatible with Gribskov's
methodology. As a consequence, it can be automatically reformatted for
use with the existing profile alignment programs implemented in the GCG
package.
- The second normalisation mode defines a real number conversion of the
integer profile scores.
MA /GENERAL_SPEC: ALPHABET='ACDEFGHIKLMNPQRSTVWY';
MA /DISJOINT: DEFINITION=PROTECT; N1=1; N2=53;
MA /NORMALIZATION: MODE=1; FUNCTION=GLE_ZSCORE; R1=44.55; R2=-0.0035;
MA R3=0.7386; R4=1.001; R5=0.208; TEXT='ZScore';
MA /NORMALIZATION: MODE=2; FUNCTION=LINEAR; R1=0.0; R2=0.1;
MA TEXT='OrigScore';
MA /CUT_OFF: LEVEL=0; SCORE=90; N_SCORE=7.0; MODE=1;
MA /DEFAULT: MI=-26; I=-3; IM=0; MD=-26; D=-3; DM=0;
MA /M: SY='F';M=-2,-3,-3,-4,2,-3,-2,1,-2,0,-1,-2,-3,-3,-4,-2,-1,0,-5,2;
MA /M: SY='I';M=-1,-5,-2,-3,-2,-3,0,1,1,-1,1,-1,-2,-1,1,-1,0,1,-4,-4;
MA /M: SY='A';M=2,-3,1,0,-5,2,-2,-1,-1,-3,-2,1,1,0,-2,2,2,0,-8,-5;
MA /M: SY='L';M=-3,-8,-5,-4,2,-6,-2,2,-4,6,4,-3,-3,-2,-3,-3,-2,1,-3,0;
MA /M: SY='Y';M=-4,-2,-6,-6,9,-7,0,-1,-5,-1,-3,-3,-6,-5,-6,-4,-4,-4,-1,11;
MA /M: SY='D';M=1,-6,3,3,-7,0,0,-2,-1,-4,-3,2,0,1,-2,0,0,-2,-9,-6;
MA /M: SY='Y';M=-5,-3,-6,-6,10,-7,-1,-1,-2,-1,-2,-3,-6,-5,-5,-4,-4,-4,-1,11;
MA /M: SY='K';M=-1,-6,1,1,-4,-2,0,-2,2,-3,-1,1,-1,1,1,0,0,-3,-7,-6;
MA /M: SY='A';M=1,-4,1,0,-5,1,-1,-1,0,-3,-1,1,0,0,0,1,1,-1,-7,-6;
MA /M: SY='R';M=0,-5,0,0,-5,-1,0,-1,1,-3,-1,1,0,1,1,0,0,-2,-5,-5;
MA /M: SY='R';M=0,-5,1,1,-6,0,1,-2,1,-4,-2,1,0,1,2,1,0,-2,-5,-5;
MA /M: SY='E';M=1,-6,2,2,-6,0,0,-2,-1,-4,-2,1,1,1,-1,0,0,-3,-8,-6;
MA /M: SY='D';M=0,-6,2,2,-6,0,1,-3,0,-5,-3,2,-1,2,-1,0,0,-4,-7,-4;
MA /M: SY='D';M=0,-8,4,3,-6,0,0,-2,-1,-3,-2,2,-2,2,-2,0,-1,-3,-9,-6;
MA /M: SY='L';M=-2,-8,-5,-5,2,-5,-3,3,-4,7,5,-4,-3,-3,-4,-3,-2,3,-4,-2;
MA /M: SY='S';M=1,-4,1,1,-5,1,0,-2,1,-4,-2,1,0,0,0,1,1,-2,-6,-5;
MA /M: SY='F';M=-3,-7,-6,-6,6,-5,-3,3,-2,5,3,-4,-5,-4,-5,-4,-3,1,-3,3;
MA /M: SY='Q';M=-1,-6,0,0,-3,-2,1,-1,1,-2,0,0,-1,1,1,-1,0,-1,-6,-4;
MA /M: SY='K';M=-1,-8,0,1,-3,-2,0,-2,3,-3,0,1,0,2,2,0,0,-3,-6,-6;
MA /M: SY='G';M=2,-5,1,0,-7,7,-3,-4,-2,-6,-4,1,-1,-2,-4,2,0,-2,-10,-8;
MA /M: SY='D';M=1,-7,5,4,-8,1,1,-3,0,-5,-3,2,-1,2,-2,0,0,-4,-10,-6;
MA /M: SY='I';M=0,-5,-1,-2,-2,-2,-1,2,0,0,1,-1,-2,0,0,-1,0,1,-6,-5;
MA /M: SY='L';M=-2,-6,-5,-5,3,-5,-3,4,-3,6,4,-4,-4,-3,-4,-3,-2,3,-5,0;
MA /M: SY='Q';M=-1,-5,-1,-1,-3,-2,0,0,0,-2,-1,0,-1,0,0,-1,0,-1,-6,-3;
MA /M: SY='V';M=0,-4,-3,-4,-1,-3,-3,5,-3,3,3,-2,-2,-2,-3,-2,0,5,-8,-4;
MA /M: SY='L';M=-1,-6,-3,-3,-1,-3,-2,2,-3,3,2,-2,-2,-2,-3,-2,-1,2,-5,-3;
MA /M: SY='D';M=0,-6,3,3,-6,0,1,-3,2,-5,-2,2,-1,2,1,0,0,-4,-7,-5;
MA /M: SY='K';M=-1,-6,0,0,-2,-1,0,-3,3,-4,-1,1,-1,0,1,0,0,-3,-6,-4;
MA /M: SY='N';M=1,-4,1,1,-5,0,0,-2,0,-3,-2,1,1,0,-1,1,1,-1,-7,-5;
MA /I: MI=0; I=-1; MD=0; /M: SY='X'; M=0; D=-1;
MA /M: SY='G';M=1,-5,0,0,-5,1,-2,-1,-2,-3,-2,0,0,-1,-2,0,0,-1,-8,-6;
MA /M: SY='G';M=1,-6,3,3,-7,3,0,-4,-1,-5,-4,2,-1,1,-2,1,0,-3,-10,-6;
MA /M: SY='W';M=-9,-12,-9,-11,1,-11,-4,-8,-5,-3,-6,-6,-8,-7,3,-4,-8,-9,26,0;
MA /M: SY='W';M=-7,-9,-9,-9,0,-9,-4,-5,-5,-1,-4,-6,-7,-6,2,-3,-6,-6,18,-1;
MA /M: SY='K';M=-1,-7,0,0,-3,-2,0,-2,2,-3,-1,1,-1,1,2,0,-1,-3,-5,-5;
MA /M: SY='G';M=2,-3,0,-1,-6,3,-3,-2,-3,-4,-3,0,0,-2,-3,1,0,0,-10,-6;
MA /M: SY='Q';M=-2,-6,0,0,-3,-3,1,-2,0,-2,-1,0,-2,1,1,-1,-1,-3,-5,-3;
MA /I: MI=0; I=-2; MD=0; /M: SY='X'; M=0; D=-2;
MA /M: SY='T';M=0,-4,-1,-1,-4,0,-2,0,-1,-2,0,0,-1,-1,-1,0,1,0,-7,-5;
MA /M: SY='T';M=0,-5,0,0,-3,-1,-1,-1,1,-3,-1,1,-1,0,0,1,1,-1,-6,-4;
MA /M: SY='G';M=0,-5,0,-1,-5,3,-2,-3,-1,-5,-3,0,-1,-1,-1,1,0,-2,-7,-6;
MA /M: SY='K';M=0,-6,1,1,-5,-1,1,-2,2,-4,-1,1,-1,2,2,0,0,-3,-6,-6;
MA /M: SY='R';M=-1,-6,-1,-1,-5,-3,1,-1,1,-3,-1,0,-1,1,3,-1,-1,-2,-2,-6;
MA /M: SY='G';M=1,-5,0,0,-6,6,-3,-3,-3,-5,-4,0,-1,-2,-4,1,0,-2,-10,-6;
MA /M: SY='W';M=-5,-5,-5,-5,2,-6,-2,-2,-4,-1,-3,-3,-6,-5,-3,-3,-4,-4,4,3;
MA /M: SY='F';M=-3,-5,-6,-6,6,-5,-3,4,-1,3,2,-4,-4,-5,-4,-3,-2,2,-4,3;
MA /M: SY='P';M=2,-4,-1,-1,-7,-1,0,-3,-2,-4,-3,-1,8,0,0,1,0,-2,-8,-7;
MA /M: SY='G';M=1,-3,0,0,-4,2,-1,-2,0,-3,-2,0,0,-1,-1,1,1,-1,-6,-5;
MA /M: SY='N';M=1,-5,2,1,-5,0,1,-2,1,-4,-2,2,0,0,0,1,1,-2,-7,-4;
MA /M: SY='Y';M=-5,-1,-7,-7,10,-8,-1,-1,-5,-1,-3,-3,-7,-6,-6,-4,-4,-5,0,13;
MA /M: SY='V';M=0,-3,-3,-5,-2,-2,-3,5,-3,2,2,-2,-2,-3,-4,-1,0,5,-8,-5;
MA /M: SY='E';M=1,-6,2,3,-6,0,0,-2,1,-4,-2,1,0,2,0,0,0,-3,-8,-6;
MA /M: SY='P';M=0,-5,-1,-1,-2,-2,-1,-2,-1,-3,-2,0,1,-1,-2,0,-1,-2,-6,-3;
Acknowledgements:
I thank Roland Luethy, Michael Gribskov, Stephen Altschul, David Haussler,
Sean Eddy, Kevin Karplus, and Ewan Birney for valuable comments and dis-
cussions. The text file format described in Section 3 has been designed
in collaboration with Amos Bairoch. Ioannis Xenarios has contributed the
SH3 profile shown with minor modifications in Section 4.
REFERENCES
1 Gribskov M., Luethy R., Eisenberg D.
Profile analysis.
Meth. Enzymol. 183:146-159(1990).
2 Staden R.
Searching for patterns in protein and nucleic acid sequences.
Meth. Enzymol. 183:193-211(1990).
3 Barton G.J., Sternberg M.J.E.
Flexible protein sequence patterns: a sensitive method to detect weak
structural similarities.
J. Mol. Biol. 212:389-402(1990).
4 Mulligan M.E., Hawley D.K., Entriken R., McClure W.R.
Escherichia coli promoter sequences predict in vitro RNA polymerase
selectivity.
Nucleic Acids Res. 12:789-800(1984).
5 Krogh A., Brown M., Mian I.S., Sjoelander K., Haussler D.
Hidden Markov models in computational biology.
J. Mol. Biol. 235:1501-1531(1994).
6 Waterman M.S., Eggert M.
A new algorithm for best subsequence alignments with application to
tRNA-rRNA comparisons.
J. Mol. Biol. 197:723-728(1987).
7 Huang X., Miller W.
A time-efficient, linear-space local similarity algorithm.
Adv. Appl. Math. 12:337-357(1991).
8 Luethy R., Xenarios I., Bucher P.
Improving the sensitivity of the sequence profile method.
Protein Sci. 3:139-146(1994).
9 Bucher P., Karplus K., Moeri N., Hofmann K.
A flexible motif search technique based on generalized profiles.
Comput. Chem. 20:3-24(1996).
10 Eddy S.R.
Hidden Markov models.
Curr. Opin. in Struct. Biol. 6:361-365(1996).
APPENDIX A): DISJOINTNESS DEFINITIONS
The following notions of disjointness of two alignments between the same
profile and the same sequence are currently defined:
Name Parameters Description
--------------------------------------------------------------------------
UNIQUE Multiple profile-sequence alignment between the same
profile and the same sequence are not permitted. The
result of a profile search consists of a single best
alignment.
PROTECT N1 (int) Two profile-sequence alignments are disjoint if the
N2 (int) two sequence segments associated with the `protected'
profile area do not overlap. The protected profile
area extends from match position N1 to match position
N2 inclusive.
--------------------------------------------------------------------------
APPENDIX B): NORMALIZATION FUNCTIONS
The following score normalisation functions are currently defined.
Name Parameters Formula (X = raw score, Y= normalised score)
--------------------------------------------------------------------------
LINEAR R1 (real) Y = R1 + R2 * X
R2 (real)
GLE_ZSCORE R1 (real) X/[R1*(1.0-exp(R2*SeqLen-R3))]-R4
R2 (real) Y = ---------------------------------
R3 (real) R5
R4 (real)
R5 (real)
--------------------------------------------------------------------------
Sequence-dependent variables:
- SeqLen (integer) is the length of the sequence.
Notes:
- The score normalisation function named GLE_ZSCORE has been described by
Gribskov, Luethy, and Eisenberg in [1].
APPENDIX C): FREQUENTLY USED LOGARITHMIC BASES
Logarithmic base Name of units Used e.g. in:
--------------------------------------------------------------------------
10.000000000 Log10 units PROSITE profile -log P-values
2.718281828 nats SAM HMM log-odds scores
2.000000000 bits BLAST, HMMER HMM log-odds scores
1.414213562 1/2 bit units BLAST blosum62 matrix
1.259921050 1/3 bit units BLAST blosum45, pam250 matrices
1.258925412 1/10 Log10 units Dayhoff MDM78 matrix
1.189207115 1/4 bit units BLAST blosum35, blosum40 matrices
1.148698355 1/5 bit units BLAST blosum30 matrix
1.000693387 1/1000 bit units HMMER integer arithmetics
--------------------------------------------------------------------------