Type:	Package
Title:	N-Gram Analysis of Biological Sequences
Version:	1.6.3
LazyData:	true
Date:	2020-03-31
Description:	Tools for extraction and analysis of various n-grams (k-mers) derived from biological sequences (proteins or nucleic acids). Contains QuiPT (quick permutation test) for fast feature-filtering of the n-gram data.
License:	GPL-3
URL:	https://github.com/michbur/biogram
BugReports:	https://github.com/michbur/biogram/issues
VignetteBuilder:	knitr
Depends:	R (≥ 3.0.0), slam
Imports:	combinat, entropy, partitions
Suggests:	ggplot2, knitr, testthat
NeedsCompilation:	no
Repository:	CRAN
Encoding:	UTF-8
RoxygenNote:	7.1.0
Packaged:	2020-03-31 13:55:56 UTC; michal
Author:	Michal Burdukiewicz [cre, aut], Piotr Sobczyk [aut], Chris Lauber [aut], Dominik Rafacz [aut], Katarzyna Sidorczuk [ctb]
Maintainer:	Michal Burdukiewicz <michalburdukiewicz@gmail.com>
Date/Publication:	2020-03-31 14:30:06 UTC

biogram - analysis of biological sequences using n-grams

Description

biogram package is a toolbox for the analysis of nucleic acid and protein sequences using n-grams. Possible applications include motif discovery, feature selection, clustering, and classification.

n-grams

n-grams (k-tuples) are sets of n characters derived from the input sequence(s). They may form continuous sub-sequences or be discontinuous. For example, from the sequence of nucleotides AATA one can extract the following continuous 2-grams (bigrams): AA, AT and TA. Moreover, there are two possible bigrams separated by a single space: A_T and A_A, and one bigram separated by two spaces: A__A.

Another important n-gram parameter is its position. Instead of just counting n-grams, one may want to count how many n-grams occur at a given position in multiple (e.g. related) sequences. For example, in the sequences AATA and AACA there is only one bigram at position 1: AA, but there are two bigrams at position two: AT and AC. The following notation is used for position-specific n-grams: 1_AA, 2_AT, 2_AC.

In the biogram package, the count_ngrams function is used for counting and extracting n-grams. Using the d argument the user can specify the distance between elements of the n-grams. The pos argument can be used to enable position specificity.

n-gram data dimensionality

We note that n-grams suffer from the curse of dimensionality. For example, for a peptide of length 6 20^{n} n-grams and 6 \times 20^{n} positioned n-grams are possible. Data sets of such an enormous size are hard to manage and analyze in R.

The biogram package deals with both of the abovementioned problems. It uses innate properties of the n-gram data which usually can be represented by sparse matrices. Data storage is done using functionalities from the slam package. To ease the selection of significant features, biogram provides the user with QuiPT, a very fast permutation test for binary data (see test_features).

Another way of reducing dimensionality is the aggregation of sequence residues into more general groups. For example, all positively-charged amino acids may be aggregated into one group. This action can be performed using the degenerate function.

Encoding of amino acids can easu sequence analysis, but multidimensional objects as the aggregations of amino acids are not easily comparable. We introduced the encoding distance, a measure defining the distance between encodings. It can be computed using the calc_ed function.

Author(s)

Michal Burdukiewicz, Piotr Sobczyk, Chris Lauber

Examples

# use data set from package
data(human_cleave)
# first nine columns represent subsequent nine amino acids from cleavage sites
# degenerate the sequence to reduce the dimensionality of the problem
# (use five groups instead of 20 amino acids)
deg_seqs <- degenerate(human_cleave[, 1L:9], 
                      list(`a` = c(1, 6, 8, 10, 11, 18), 
                           `b` = c(2, 13, 14, 16, 17), 
                           `c` = c(5, 19, 20), 
                           `d` = c(7, 9, 12, 15), 
                           'e' = c(3, 4)))
# EXAMPLE 1 - extract significant trigrams
# extract trigrams
trigrams <- count_ngrams(deg_seqs, 3, letters[1L:5], pos = TRUE)
# select features that differ between the two target groups using QuiPT
test1 <- test_features(human_cleave[, "tar"], trigrams)
# see a summary of the results
summary(test1)
# aggregate features in groups based on their p-value
gr <- cut(test1)
# get position map of the most significant n-grams
position_ngrams(gr[[1]])
# transform the most significant n-grams to more readable form
decode_ngrams(gr[[1]])

# EXAMPLE 2 - search for specific n-grams
# the n-grams of the interest are a_a (a-gap-a) and e_e (e-gap-e) on the
# 3rd and 4th position
# firstly code n-grams in biogram notation and add position information
coded <- code_ngrams(c("a_a", "c_c"))
# add position information
coded <- c(paste0("3_", coded), paste0("4_", coded))
# count only the features of the interest
bigrams <- count_specified(deg_seqs, coded)
# test which of the features of the interest is significant
test2 <- test_features(human_cleave[, "tar"], bigrams)
cut(test2)

Normalized amino acids properties

Description

Normalized (0-1) 554 amino acid properties as retreived from AAIndex database (release 9.1) enriched with contactivity of amino acids.

Format

A data frames with 20 columns and 600 rows.

Details

Following properties are included (AAIndex key: description of the property)

ANDN920101

alpha-CH chemical shifts (Andersen et al., 1992)

ARGP820101

Hydrophobicity index (Argos et al., 1982)

ARGP820102

Signal sequence helical potential (Argos et al., 1982)

ARGP820103

Membrane-buried preference parameters (Argos et al., 1982)

BEGF750101

Conformational parameter of inner helix (Beghin-Dirkx, 1975)

BEGF750102

Conformational parameter of beta-structure (Beghin-Dirkx, 1975)

BEGF750103

Conformational parameter of beta-turn (Beghin-Dirkx, 1975)

BHAR880101

Average flexibility indices (Bhaskaran-Ponnuswamy, 1988)

BIGC670101

Residue volume (Bigelow, 1967)

BIOV880101

Information value for accessibility; average fraction 35% (Biou et al., 1988)

BIOV880102

Information value for accessibility; average fraction 23% (Biou et al., 1988)

BROC820101

Retention coefficient in TFA (Browne et al., 1982)

BROC820102

Retention coefficient in HFBA (Browne et al., 1982)

BULH740101

Transfer free energy to surface (Bull-Breese, 1974)

BULH740102

Apparent partial specific volume (Bull-Breese, 1974)

BUNA790101

alpha-NH chemical shifts (Bundi-Wuthrich, 1979)

BUNA790102

alpha-CH chemical shifts (Bundi-Wuthrich, 1979)

BUNA790103

Spin-spin coupling constants 3JHalpha-NH (Bundi-Wuthrich, 1979)

BURA740101

Normalized frequency of alpha-helix (Burgess et al., 1974)

BURA740102

Normalized frequency of extended structure (Burgess et al., 1974)

CHAM810101

Steric parameter (Charton, 1981)

CHAM820101

Polarizability parameter (Charton-Charton, 1982)

CHAM820102

Free energy of solution in water, kcal/mole (Charton-Charton, 1982)

CHAM830101

The Chou-Fasman parameter of the coil conformation (Charton-Charton, 1983)

CHAM830102

A parameter defined from the residuals obtained from the best correlation of the Chou-Fasman parameter of beta-sheet (Charton-Charton, 1983)

CHAM830103

The number of atoms in the side chain labelled 1+1 (Charton-Charton, 1983)

CHAM830104

The number of atoms in the side chain labelled 2+1 (Charton-Charton, 1983)

CHAM830105

The number of atoms in the side chain labelled 3+1 (Charton-Charton, 1983)

CHAM830106

The number of bonds in the longest chain (Charton-Charton, 1983)

CHAM830107

A parameter of charge transfer capability (Charton-Charton, 1983)

CHAM830108

A parameter of charge transfer donor capability (Charton-Charton, 1983)

CHOC750101

Average volume of buried residue (Chothia, 1975)

CHOC760101

Residue accessible surface area in tripeptide (Chothia, 1976)

CHOC760102

Residue accessible surface area in folded protein (Chothia, 1976)

CHOC760103

Proportion of residues 95% buried (Chothia, 1976)

CHOC760104

Proportion of residues 100% buried (Chothia, 1976)

CHOP780101

Normalized frequency of beta-turn (Chou-Fasman, 1978a)

CHOP780201

Normalized frequency of alpha-helix (Chou-Fasman, 1978b)

CHOP780202

Normalized frequency of beta-sheet (Chou-Fasman, 1978b)

CHOP780203

Normalized frequency of beta-turn (Chou-Fasman, 1978b)

CHOP780204

Normalized frequency of N-terminal helix (Chou-Fasman, 1978b)

CHOP780205

Normalized frequency of C-terminal helix (Chou-Fasman, 1978b)

CHOP780206

Normalized frequency of N-terminal non helical region (Chou-Fasman, 1978b)

CHOP780207

Normalized frequency of C-terminal non helical region (Chou-Fasman, 1978b)

CHOP780208

Normalized frequency of N-terminal beta-sheet (Chou-Fasman, 1978b)

CHOP780209

Normalized frequency of C-terminal beta-sheet (Chou-Fasman, 1978b)

CHOP780210

Normalized frequency of N-terminal non beta region (Chou-Fasman, 1978b)

CHOP780211

Normalized frequency of C-terminal non beta region (Chou-Fasman, 1978b)

CHOP780212

Frequency of the 1st residue in turn (Chou-Fasman, 1978b)

CHOP780213

Frequency of the 2nd residue in turn (Chou-Fasman, 1978b)

CHOP780214

Frequency of the 3rd residue in turn (Chou-Fasman, 1978b)

CHOP780215

Frequency of the 4th residue in turn (Chou-Fasman, 1978b)

CHOP780216

Normalized frequency of the 2nd and 3rd residues in turn (Chou-Fasman, 1978b)

CIDH920101

Normalized hydrophobicity scales for alpha-proteins (Cid et al., 1992)

CIDH920102

Normalized hydrophobicity scales for beta-proteins (Cid et al., 1992)

CIDH920103

Normalized hydrophobicity scales for alpha+beta-proteins (Cid et al., 1992)

CIDH920104

Normalized hydrophobicity scales for alpha/beta-proteins (Cid et al., 1992)

CIDH920105

Normalized average hydrophobicity scales (Cid et al., 1992)

COHE430101

Partial specific volume (Cohn-Edsall, 1943)

CRAJ730101

Normalized frequency of middle helix (Crawford et al., 1973)

CRAJ730102

Normalized frequency of beta-sheet (Crawford et al., 1973)

CRAJ730103

Normalized frequency of turn (Crawford et al., 1973)

DAWD720101

Size (Dawson, 1972)

DAYM780101

Amino acid composition (Dayhoff et al., 1978a)

DAYM780201

Relative mutability (Dayhoff et al., 1978b)

DESM900101

Membrane preference for cytochrome b: MPH89 (Degli Esposti et al., 1990)

DESM900102

Average membrane preference: AMP07 (Degli Esposti et al., 1990)

EISD840101

Consensus normalized hydrophobicity scale (Eisenberg, 1984)

EISD860101

Solvation free energy (Eisenberg-McLachlan, 1986)

EISD860102

Atom-based hydrophobic moment (Eisenberg-McLachlan, 1986)

EISD860103

Direction of hydrophobic moment (Eisenberg-McLachlan, 1986)

FASG760101

Molecular weight (Fasman, 1976)

FASG760102

Melting point (Fasman, 1976)

FASG760103

Optical rotation (Fasman, 1976)

FASG760104

pK-N (Fasman, 1976)

FASG760105

pK-C (Fasman, 1976)

FAUJ830101

Hydrophobic parameter pi (Fauchere-Pliska, 1983)

FAUJ880101

Graph shape index (Fauchere et al., 1988)

FAUJ880102

Smoothed upsilon steric parameter (Fauchere et al., 1988)

FAUJ880103

Normalized van der Waals volume (Fauchere et al., 1988)

FAUJ880104

STERIMOL length of the side chain (Fauchere et al., 1988)

FAUJ880105

STERIMOL minimum width of the side chain (Fauchere et al., 1988)

FAUJ880106

STERIMOL maximum width of the side chain (Fauchere et al., 1988)

FAUJ880107

N.m.r. chemical shift of alpha-carbon (Fauchere et al., 1988)

FAUJ880108

Localized electrical effect (Fauchere et al., 1988)

FAUJ880109

Number of hydrogen bond donors (Fauchere et al., 1988)

FAUJ880110

Number of full nonbonding orbitals (Fauchere et al., 1988)

FAUJ880111

Positive charge (Fauchere et al., 1988)

FAUJ880112

Negative charge (Fauchere et al., 1988)

FAUJ880113

pK-a(RCOOH) (Fauchere et al., 1988)

FINA770101

Helix-coil equilibrium constant (Finkelstein-Ptitsyn, 1977)

FINA910101

Helix initiation parameter at posision i-1 (Finkelstein et al., 1991)

FINA910102

Helix initiation parameter at posision i,i+1,i+2 (Finkelstein et al., 1991)

FINA910103

Helix termination parameter at posision j-2,j-1,j (Finkelstein et al., 1991)

FINA910104

Helix termination parameter at posision j+1 (Finkelstein et al., 1991)

GARJ730101

Partition coefficient (Garel et al., 1973)

GEIM800101

Alpha-helix indices (Geisow-Roberts, 1980)

GEIM800102

Alpha-helix indices for alpha-proteins (Geisow-Roberts, 1980)

GEIM800103

Alpha-helix indices for beta-proteins (Geisow-Roberts, 1980)

GEIM800104

Alpha-helix indices for alpha/beta-proteins (Geisow-Roberts, 1980)

GEIM800105

Beta-strand indices (Geisow-Roberts, 1980)

GEIM800106

Beta-strand indices for beta-proteins (Geisow-Roberts, 1980)

GEIM800107

Beta-strand indices for alpha/beta-proteins (Geisow-Roberts, 1980)

GEIM800108

Aperiodic indices (Geisow-Roberts, 1980)

GEIM800109

Aperiodic indices for alpha-proteins (Geisow-Roberts, 1980)

GEIM800110

Aperiodic indices for beta-proteins (Geisow-Roberts, 1980)

GEIM800111

Aperiodic indices for alpha/beta-proteins (Geisow-Roberts, 1980)

GOLD730101

Hydrophobicity factor (Goldsack-Chalifoux, 1973)

GOLD730102

Residue volume (Goldsack-Chalifoux, 1973)

GRAR740101

Composition (Grantham, 1974)

GRAR740102

Polarity (Grantham, 1974)

GRAR740103

Volume (Grantham, 1974)

GUYH850101

Partition energy (Guy, 1985)

HOPA770101

Hydration number (Hopfinger, 1971), Cited by Charton-Charton (1982)

HOPT810101

Hydrophilicity value (Hopp-Woods, 1981)

HUTJ700101

Heat capacity (Hutchens, 1970)

HUTJ700102

Absolute entropy (Hutchens, 1970)

HUTJ700103

Entropy of formation (Hutchens, 1970)

ISOY800101

Normalized relative frequency of alpha-helix (Isogai et al., 1980)

ISOY800102

Normalized relative frequency of extended structure (Isogai et al., 1980)

ISOY800103

Normalized relative frequency of bend (Isogai et al., 1980)

ISOY800104

Normalized relative frequency of bend R (Isogai et al., 1980)

ISOY800105

Normalized relative frequency of bend S (Isogai et al., 1980)

ISOY800106

Normalized relative frequency of helix end (Isogai et al., 1980)

ISOY800107

Normalized relative frequency of double bend (Isogai et al., 1980)

ISOY800108

Normalized relative frequency of coil (Isogai et al., 1980)

JANJ780101

Average accessible surface area (Janin et al., 1978)

JANJ780102

Percentage of buried residues (Janin et al., 1978)

JANJ780103

Percentage of exposed residues (Janin et al., 1978)

JANJ790101

Ratio of buried and accessible molar fractions (Janin, 1979)

JANJ790102

Transfer free energy (Janin, 1979)

JOND750101

Hydrophobicity (Jones, 1975)

JOND750102

pK (-COOH) (Jones, 1975)

JOND920101

Relative frequency of occurrence (Jones et al., 1992)

JOND920102

Relative mutability (Jones et al., 1992)

JUKT750101

Amino acid distribution (Jukes et al., 1975)

JUNJ780101

Sequence frequency (Jungck, 1978)

KANM800101

Average relative probability of helix (Kanehisa-Tsong, 1980)

KANM800102

Average relative probability of beta-sheet (Kanehisa-Tsong, 1980)

KANM800103

Average relative probability of inner helix (Kanehisa-Tsong, 1980)

KANM800104

Average relative probability of inner beta-sheet (Kanehisa-Tsong, 1980)

KARP850101

Flexibility parameter for no rigid neighbors (Karplus-Schulz, 1985)

KARP850102

Flexibility parameter for one rigid neighbor (Karplus-Schulz, 1985)

KARP850103

Flexibility parameter for two rigid neighbors (Karplus-Schulz, 1985)

KHAG800101

The Kerr-constant increments (Khanarian-Moore, 1980)

KLEP840101

Net charge (Klein et al., 1984)

KRIW710101

Side chain interaction parameter (Krigbaum-Rubin, 1971)

KRIW790101

Side chain interaction parameter (Krigbaum-Komoriya, 1979)

KRIW790102

Fraction of site occupied by water (Krigbaum-Komoriya, 1979)

KRIW790103

Side chain volume (Krigbaum-Komoriya, 1979)

KYTJ820101

Hydropathy index (Kyte-Doolittle, 1982)

LAWE840101

Transfer free energy, CHP/water (Lawson et al., 1984)

LEVM760101

Hydrophobic parameter (Levitt, 1976)

LEVM760102

Distance between C-alpha and centroid of side chain (Levitt, 1976)

LEVM760103

Side chain angle theta(AAR) (Levitt, 1976)

LEVM760104

Side chain torsion angle phi(AAAR) (Levitt, 1976)

LEVM760105

Radius of gyration of side chain (Levitt, 1976)

LEVM760106

van der Waals parameter R0 (Levitt, 1976)

LEVM760107

van der Waals parameter epsilon (Levitt, 1976)

LEVM780101

Normalized frequency of alpha-helix, with weights (Levitt, 1978)

LEVM780102

Normalized frequency of beta-sheet, with weights (Levitt, 1978)

LEVM780103

Normalized frequency of reverse turn, with weights (Levitt, 1978)

LEVM780104

Normalized frequency of alpha-helix, unweighted (Levitt, 1978)

LEVM780105

Normalized frequency of beta-sheet, unweighted (Levitt, 1978)

LEVM780106

Normalized frequency of reverse turn, unweighted (Levitt, 1978)

LEWP710101

Frequency of occurrence in beta-bends (Lewis et al., 1971)

LIFS790101

Conformational preference for all beta-strands (Lifson-Sander, 1979)

LIFS790102

Conformational preference for parallel beta-strands (Lifson-Sander, 1979)

LIFS790103

Conformational preference for antiparallel beta-strands (Lifson-Sander, 1979)

MANP780101

Average surrounding hydrophobicity (Manavalan-Ponnuswamy, 1978)

MAXF760101

Normalized frequency of alpha-helix (Maxfield-Scheraga, 1976)

MAXF760102

Normalized frequency of extended structure (Maxfield-Scheraga, 1976)

MAXF760103

Normalized frequency of zeta R (Maxfield-Scheraga, 1976)

MAXF760104

Normalized frequency of left-handed alpha-helix (Maxfield-Scheraga, 1976)

MAXF760105

Normalized frequency of zeta L (Maxfield-Scheraga, 1976)

MAXF760106

Normalized frequency of alpha region (Maxfield-Scheraga, 1976)

MCMT640101

Refractivity (McMeekin et al., 1964), Cited by Jones (1975)

MEEJ800101

Retention coefficient in HPLC, pH7.4 (Meek, 1980)

MEEJ800102

Retention coefficient in HPLC, pH2.1 (Meek, 1980)

MEEJ810101

Retention coefficient in NaClO4 (Meek-Rossetti, 1981)

MEEJ810102

Retention coefficient in NaH2PO4 (Meek-Rossetti, 1981)

MEIH800101

Average reduced distance for C-alpha (Meirovitch et al., 1980)

MEIH800102

Average reduced distance for side chain (Meirovitch et al., 1980)

MEIH800103

Average side chain orientation angle (Meirovitch et al., 1980)

MIYS850101

Effective partition energy (Miyazawa-Jernigan, 1985)

NAGK730101

Normalized frequency of alpha-helix (Nagano, 1973)

NAGK730102

Normalized frequency of bata-structure (Nagano, 1973)

NAGK730103

Normalized frequency of coil (Nagano, 1973)

NAKH900101

AA composition of total proteins (Nakashima et al., 1990)

NAKH900102

SD of AA composition of total proteins (Nakashima et al., 1990)

NAKH900103

AA composition of mt-proteins (Nakashima et al., 1990)

NAKH900104

Normalized composition of mt-proteins (Nakashima et al., 1990)

NAKH900105

AA composition of mt-proteins from animal (Nakashima et al., 1990)

NAKH900106

Normalized composition from animal (Nakashima et al., 1990)

NAKH900107

AA composition of mt-proteins from fungi and plant (Nakashima et al., 1990)

NAKH900108

Normalized composition from fungi and plant (Nakashima et al., 1990)

NAKH900109

AA composition of membrane proteins (Nakashima et al., 1990)

NAKH900110

Normalized composition of membrane proteins (Nakashima et al., 1990)

NAKH900111

Transmembrane regions of non-mt-proteins (Nakashima et al., 1990)

NAKH900112

Transmembrane regions of mt-proteins (Nakashima et al., 1990)

NAKH900113

Ratio of average and computed composition (Nakashima et al., 1990)

NAKH920101

AA composition of CYT of single-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920102

AA composition of CYT2 of single-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920103

AA composition of EXT of single-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920104

AA composition of EXT2 of single-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920105

AA composition of MEM of single-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920106

AA composition of CYT of multi-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920107

AA composition of EXT of multi-spanning proteins (Nakashima-Nishikawa, 1992)

NAKH920108

AA composition of MEM of multi-spanning proteins (Nakashima-Nishikawa, 1992)

NISK800101

8 A contact number (Nishikawa-Ooi, 1980)

NISK860101

14 A contact number (Nishikawa-Ooi, 1986)

NOZY710101

Transfer energy, organic solvent/water (Nozaki-Tanford, 1971)

OOBM770101

Average non-bonded energy per atom (Oobatake-Ooi, 1977)

OOBM770102

Short and medium range non-bonded energy per atom (Oobatake-Ooi, 1977)

OOBM770103

Long range non-bonded energy per atom (Oobatake-Ooi, 1977)

OOBM770104

Average non-bonded energy per residue (Oobatake-Ooi, 1977)

OOBM770105

Short and medium range non-bonded energy per residue (Oobatake-Ooi, 1977)

OOBM850101

Optimized beta-structure-coil equilibrium constant (Oobatake et al., 1985)

OOBM850102

Optimized propensity to form reverse turn (Oobatake et al., 1985)

OOBM850103

Optimized transfer energy parameter (Oobatake et al., 1985)

OOBM850104

Optimized average non-bonded energy per atom (Oobatake et al., 1985)

OOBM850105

Optimized side chain interaction parameter (Oobatake et al., 1985)

PALJ810101

Normalized frequency of alpha-helix from LG (Palau et al., 1981)

PALJ810102

Normalized frequency of alpha-helix from CF (Palau et al., 1981)

PALJ810103

Normalized frequency of beta-sheet from LG (Palau et al., 1981)

PALJ810104

Normalized frequency of beta-sheet from CF (Palau et al., 1981)

PALJ810105

Normalized frequency of turn from LG (Palau et al., 1981)

PALJ810106

Normalized frequency of turn from CF (Palau et al., 1981)

PALJ810107

Normalized frequency of alpha-helix in all-alpha class (Palau et al., 1981)

PALJ810108

Normalized frequency of alpha-helix in alpha+beta class (Palau et al., 1981)

PALJ810109

Normalized frequency of alpha-helix in alpha/beta class (Palau et al., 1981)

PALJ810110

Normalized frequency of beta-sheet in all-beta class (Palau et al., 1981)

PALJ810111

Normalized frequency of beta-sheet in alpha+beta class (Palau et al., 1981)

PALJ810112

Normalized frequency of beta-sheet in alpha/beta class (Palau et al., 1981)

PALJ810113

Normalized frequency of turn in all-alpha class (Palau et al., 1981)

PALJ810114

Normalized frequency of turn in all-beta class (Palau et al., 1981)

PALJ810115

Normalized frequency of turn in alpha+beta class (Palau et al., 1981)

PALJ810116

Normalized frequency of turn in alpha/beta class (Palau et al., 1981)

PARJ860101

HPLC parameter (Parker et al., 1986)

PLIV810101

Partition coefficient (Pliska et al., 1981)

PONP800101

Surrounding hydrophobicity in folded form (Ponnuswamy et al., 1980)

PONP800102

Average gain in surrounding hydrophobicity (Ponnuswamy et al., 1980)

PONP800103

Average gain ratio in surrounding hydrophobicity (Ponnuswamy et al., 1980)

PONP800104

Surrounding hydrophobicity in alpha-helix (Ponnuswamy et al., 1980)

PONP800105

Surrounding hydrophobicity in beta-sheet (Ponnuswamy et al., 1980)

PONP800106

Surrounding hydrophobicity in turn (Ponnuswamy et al., 1980)

PONP800107

Accessibility reduction ratio (Ponnuswamy et al., 1980)

PONP800108

Average number of surrounding residues (Ponnuswamy et al., 1980)

PRAM820101

Intercept in regression analysis (Prabhakaran-Ponnuswamy, 1982)

PRAM820102

Slope in regression analysis x 1.0E1 (Prabhakaran-Ponnuswamy, 1982)

PRAM820103

Correlation coefficient in regression analysis (Prabhakaran-Ponnuswamy, 1982)

PRAM900101

Hydrophobicity (Prabhakaran, 1990)

PRAM900102

Relative frequency in alpha-helix (Prabhakaran, 1990)

PRAM900103

Relative frequency in beta-sheet (Prabhakaran, 1990)

PRAM900104

Relative frequency in reverse-turn (Prabhakaran, 1990)

PTIO830101

Helix-coil equilibrium constant (Ptitsyn-Finkelstein, 1983)

PTIO830102

Beta-coil equilibrium constant (Ptitsyn-Finkelstein, 1983)

QIAN880101

Weights for alpha-helix at the window position of -6 (Qian-Sejnowski, 1988)

QIAN880102

Weights for alpha-helix at the window position of -5 (Qian-Sejnowski, 1988)

QIAN880103

Weights for alpha-helix at the window position of -4 (Qian-Sejnowski, 1988)

QIAN880104

Weights for alpha-helix at the window position of -3 (Qian-Sejnowski, 1988)

QIAN880105

Weights for alpha-helix at the window position of -2 (Qian-Sejnowski, 1988)

QIAN880106

Weights for alpha-helix at the window position of -1 (Qian-Sejnowski, 1988)

QIAN880107

Weights for alpha-helix at the window position of 0 (Qian-Sejnowski, 1988)

QIAN880108

Weights for alpha-helix at the window position of 1 (Qian-Sejnowski, 1988)

QIAN880109

Weights for alpha-helix at the window position of 2 (Qian-Sejnowski, 1988)

QIAN880110

Weights for alpha-helix at the window position of 3 (Qian-Sejnowski, 1988)

QIAN880111

Weights for alpha-helix at the window position of 4 (Qian-Sejnowski, 1988)

QIAN880112

Weights for alpha-helix at the window position of 5 (Qian-Sejnowski, 1988)

QIAN880113

Weights for alpha-helix at the window position of 6 (Qian-Sejnowski, 1988)

QIAN880114

Weights for beta-sheet at the window position of -6 (Qian-Sejnowski, 1988)

QIAN880115

Weights for beta-sheet at the window position of -5 (Qian-Sejnowski, 1988)

QIAN880116

Weights for beta-sheet at the window position of -4 (Qian-Sejnowski, 1988)

QIAN880117

Weights for beta-sheet at the window position of -3 (Qian-Sejnowski, 1988)

QIAN880118

Weights for beta-sheet at the window position of -2 (Qian-Sejnowski, 1988)

QIAN880119

Weights for beta-sheet at the window position of -1 (Qian-Sejnowski, 1988)

QIAN880120

Weights for beta-sheet at the window position of 0 (Qian-Sejnowski, 1988)

QIAN880121

Weights for beta-sheet at the window position of 1 (Qian-Sejnowski, 1988)

QIAN880122

Weights for beta-sheet at the window position of 2 (Qian-Sejnowski, 1988)

QIAN880123

Weights for beta-sheet at the window position of 3 (Qian-Sejnowski, 1988)

QIAN880124

Weights for beta-sheet at the window position of 4 (Qian-Sejnowski, 1988)

QIAN880125

Weights for beta-sheet at the window position of 5 (Qian-Sejnowski, 1988)

QIAN880126

Weights for beta-sheet at the window position of 6 (Qian-Sejnowski, 1988)

QIAN880127

Weights for coil at the window position of -6 (Qian-Sejnowski, 1988)

QIAN880128

Weights for coil at the window position of -5 (Qian-Sejnowski, 1988)

QIAN880129

Weights for coil at the window position of -4 (Qian-Sejnowski, 1988)

QIAN880130

Weights for coil at the window position of -3 (Qian-Sejnowski, 1988)

QIAN880131

Weights for coil at the window position of -2 (Qian-Sejnowski, 1988)

QIAN880132

Weights for coil at the window position of -1 (Qian-Sejnowski, 1988)

QIAN880133

Weights for coil at the window position of 0 (Qian-Sejnowski, 1988)

QIAN880134

Weights for coil at the window position of 1 (Qian-Sejnowski, 1988)

QIAN880135

Weights for coil at the window position of 2 (Qian-Sejnowski, 1988)

QIAN880136

Weights for coil at the window position of 3 (Qian-Sejnowski, 1988)

QIAN880137

Weights for coil at the window position of 4 (Qian-Sejnowski, 1988)

QIAN880138

Weights for coil at the window position of 5 (Qian-Sejnowski, 1988)

QIAN880139

Weights for coil at the window position of 6 (Qian-Sejnowski, 1988)

RACS770101

Average reduced distance for C-alpha (Rackovsky-Scheraga, 1977)

RACS770102

Average reduced distance for side chain (Rackovsky-Scheraga, 1977)

RACS770103

Side chain orientational preference (Rackovsky-Scheraga, 1977)

RACS820101

Average relative fractional occurrence in A0(i) (Rackovsky-Scheraga, 1982)

RACS820102

Average relative fractional occurrence in AR(i) (Rackovsky-Scheraga, 1982)

RACS820103

Average relative fractional occurrence in AL(i) (Rackovsky-Scheraga, 1982)

RACS820104

Average relative fractional occurrence in EL(i) (Rackovsky-Scheraga, 1982)

RACS820105

Average relative fractional occurrence in E0(i) (Rackovsky-Scheraga, 1982)

RACS820106

Average relative fractional occurrence in ER(i) (Rackovsky-Scheraga, 1982)

RACS820107

Average relative fractional occurrence in A0(i-1) (Rackovsky-Scheraga, 1982)

RACS820108

Average relative fractional occurrence in AR(i-1) (Rackovsky-Scheraga, 1982)

RACS820109

Average relative fractional occurrence in AL(i-1) (Rackovsky-Scheraga, 1982)

RACS820110

Average relative fractional occurrence in EL(i-1) (Rackovsky-Scheraga, 1982)

RACS820111

Average relative fractional occurrence in E0(i-1) (Rackovsky-Scheraga, 1982)

RACS820112

Average relative fractional occurrence in ER(i-1) (Rackovsky-Scheraga, 1982)

RACS820113

Value of theta(i) (Rackovsky-Scheraga, 1982)

RACS820114

Value of theta(i-1) (Rackovsky-Scheraga, 1982)

RADA880101

Transfer free energy from chx to wat (Radzicka-Wolfenden, 1988)

RADA880102

Transfer free energy from oct to wat (Radzicka-Wolfenden, 1988)

RADA880103

Transfer free energy from vap to chx (Radzicka-Wolfenden, 1988)

RADA880104

Transfer free energy from chx to oct (Radzicka-Wolfenden, 1988)

RADA880105

Transfer free energy from vap to oct (Radzicka-Wolfenden, 1988)

RADA880106

Accessible surface area (Radzicka-Wolfenden, 1988)

RADA880107

Energy transfer from out to in(95%buried) (Radzicka-Wolfenden, 1988)

RADA880108

Mean polarity (Radzicka-Wolfenden, 1988)

RICJ880101

Relative preference value at N" (Richardson-Richardson, 1988)

RICJ880102

Relative preference value at N' (Richardson-Richardson, 1988)

RICJ880103

Relative preference value at N-cap (Richardson-Richardson, 1988)

RICJ880104

Relative preference value at N1 (Richardson-Richardson, 1988)

RICJ880105

Relative preference value at N2 (Richardson-Richardson, 1988)

RICJ880106

Relative preference value at N3 (Richardson-Richardson, 1988)

RICJ880107

Relative preference value at N4 (Richardson-Richardson, 1988)

RICJ880108

Relative preference value at N5 (Richardson-Richardson, 1988)

RICJ880109

Relative preference value at Mid (Richardson-Richardson, 1988)

RICJ880110

Relative preference value at C5 (Richardson-Richardson, 1988)

RICJ880111

Relative preference value at C4 (Richardson-Richardson, 1988)

RICJ880112

Relative preference value at C3 (Richardson-Richardson, 1988)

RICJ880113

Relative preference value at C2 (Richardson-Richardson, 1988)

RICJ880114

Relative preference value at C1 (Richardson-Richardson, 1988)

RICJ880115

Relative preference value at C-cap (Richardson-Richardson, 1988)

RICJ880116

Relative preference value at C' (Richardson-Richardson, 1988)

RICJ880117

Relative preference value at C" (Richardson-Richardson, 1988)

ROBB760101

Information measure for alpha-helix (Robson-Suzuki, 1976)

ROBB760102

Information measure for N-terminal helix (Robson-Suzuki, 1976)

ROBB760103

Information measure for middle helix (Robson-Suzuki, 1976)

ROBB760104

Information measure for C-terminal helix (Robson-Suzuki, 1976)

ROBB760105

Information measure for extended (Robson-Suzuki, 1976)

ROBB760106

Information measure for pleated-sheet (Robson-Suzuki, 1976)

ROBB760107

Information measure for extended without H-bond (Robson-Suzuki, 1976)

ROBB760108

Information measure for turn (Robson-Suzuki, 1976)

ROBB760109

Information measure for N-terminal turn (Robson-Suzuki, 1976)

ROBB760110

Information measure for middle turn (Robson-Suzuki, 1976)

ROBB760111

Information measure for C-terminal turn (Robson-Suzuki, 1976)

ROBB760112

Information measure for coil (Robson-Suzuki, 1976)

ROBB760113

Information measure for loop (Robson-Suzuki, 1976)

ROBB790101

Hydration free energy (Robson-Osguthorpe, 1979)

ROSG850101

Mean area buried on transfer (Rose et al., 1985)

ROSG850102

Mean fractional area loss (Rose et al., 1985)

ROSM880101

Side chain hydropathy, uncorrected for solvation (Roseman, 1988)

ROSM880102

Side chain hydropathy, corrected for solvation (Roseman, 1988)

ROSM880103

Loss of Side chain hydropathy by helix formation (Roseman, 1988)

SIMZ760101

Transfer free energy (Simon, 1976), Cited by Charton-Charton (1982)

SNEP660101

Principal component I (Sneath, 1966)

SNEP660102

Principal component II (Sneath, 1966)

SNEP660103

Principal component III (Sneath, 1966)

SNEP660104

Principal component IV (Sneath, 1966)

SUEM840101

Zimm-Bragg parameter s at 20 C (Sueki et al., 1984)

SUEM840102

Zimm-Bragg parameter sigma x 1.0E4 (Sueki et al., 1984)

SWER830101

Optimal matching hydrophobicity (Sweet-Eisenberg, 1983)

TANS770101

Normalized frequency of alpha-helix (Tanaka-Scheraga, 1977)

TANS770102

Normalized frequency of isolated helix (Tanaka-Scheraga, 1977)

TANS770103

Normalized frequency of extended structure (Tanaka-Scheraga, 1977)

TANS770104

Normalized frequency of chain reversal R (Tanaka-Scheraga, 1977)

TANS770105

Normalized frequency of chain reversal S (Tanaka-Scheraga, 1977)

TANS770106

Normalized frequency of chain reversal D (Tanaka-Scheraga, 1977)

TANS770107

Normalized frequency of left-handed helix (Tanaka-Scheraga, 1977)

TANS770108

Normalized frequency of zeta R (Tanaka-Scheraga, 1977)

TANS770109

Normalized frequency of coil (Tanaka-Scheraga, 1977)

TANS770110

Normalized frequency of chain reversal (Tanaka-Scheraga, 1977)

VASM830101

Relative population of conformational state A (Vasquez et al., 1983)

VASM830102

Relative population of conformational state C (Vasquez et al., 1983)

VASM830103

Relative population of conformational state E (Vasquez et al., 1983)

VELV850101

Electron-ion interaction potential (Veljkovic et al., 1985)

VENT840101

Bitterness (Venanzi, 1984)

VHEG790101

Transfer free energy to lipophilic phase (von Heijne-Blomberg, 1979)

WARP780101

Average interactions per side chain atom (Warme-Morgan, 1978)

WEBA780101

RF value in high salt chromatography (Weber-Lacey, 1978)

WERD780101

Propensity to be buried inside (Wertz-Scheraga, 1978)

WERD780102

Free energy change of epsilon(i) to epsilon(ex) (Wertz-Scheraga, 1978)

WERD780103

Free energy change of alpha(Ri) to alpha(Rh) (Wertz-Scheraga, 1978)

WERD780104

Free energy change of epsilon(i) to alpha(Rh) (Wertz-Scheraga, 1978)

WOEC730101

Polar requirement (Woese, 1973)

WOLR810101

Hydration potential (Wolfenden et al., 1981)

WOLS870101

Principal property value z1 (Wold et al., 1987)

WOLS870102

Principal property value z2 (Wold et al., 1987)

WOLS870103

Principal property value z3 (Wold et al., 1987)

YUTK870101

Unfolding Gibbs energy in water, pH7.0 (Yutani et al., 1987)

YUTK870102

Unfolding Gibbs energy in water, pH9.0 (Yutani et al., 1987)

YUTK870103

Activation Gibbs energy of unfolding, pH7.0 (Yutani et al., 1987)

YUTK870104

Activation Gibbs energy of unfolding, pH9.0 (Yutani et al., 1987)

ZASB820101

Dependence of partition coefficient on ionic strength (Zaslavsky et al., 1982)

ZIMJ680101

Hydrophobicity (Zimmerman et al., 1968)

ZIMJ680102

Bulkiness (Zimmerman et al., 1968)

ZIMJ680103

Polarity (Zimmerman et al., 1968)

ZIMJ680104

Isoelectric point (Zimmerman et al., 1968)

ZIMJ680105

RF rank (Zimmerman et al., 1968)

AURR980101

Normalized positional residue frequency at helix termini N4'(Aurora-Rose, 1998)

AURR980102

Normalized positional residue frequency at helix termini N"' (Aurora-Rose, 1998)

AURR980103

Normalized positional residue frequency at helix termini N" (Aurora-Rose, 1998)

AURR980104

Normalized positional residue frequency at helix termini N'(Aurora-Rose, 1998)

AURR980105

Normalized positional residue frequency at helix termini Nc (Aurora-Rose, 1998)

AURR980106

Normalized positional residue frequency at helix termini N1 (Aurora-Rose, 1998)

AURR980107

Normalized positional residue frequency at helix termini N2 (Aurora-Rose, 1998)

AURR980108

Normalized positional residue frequency at helix termini N3 (Aurora-Rose, 1998)

AURR980109

Normalized positional residue frequency at helix termini N4 (Aurora-Rose, 1998)

AURR980110

Normalized positional residue frequency at helix termini N5 (Aurora-Rose, 1998)

AURR980111

Normalized positional residue frequency at helix termini C5 (Aurora-Rose, 1998)

AURR980112

Normalized positional residue frequency at helix termini C4 (Aurora-Rose, 1998)

AURR980113

Normalized positional residue frequency at helix termini C3 (Aurora-Rose, 1998)

AURR980114

Normalized positional residue frequency at helix termini C2 (Aurora-Rose, 1998)

AURR980115

Normalized positional residue frequency at helix termini C1 (Aurora-Rose, 1998)

AURR980116

Normalized positional residue frequency at helix termini Cc (Aurora-Rose, 1998)

AURR980117

Normalized positional residue frequency at helix termini C' (Aurora-Rose, 1998)

AURR980118

Normalized positional residue frequency at helix termini C" (Aurora-Rose, 1998)

AURR980119

Normalized positional residue frequency at helix termini C"' (Aurora-Rose, 1998)

AURR980120

Normalized positional residue frequency at helix termini C4' (Aurora-Rose, 1998)

ONEK900101

Delta G values for the peptides extrapolated to 0 M urea (O'Neil-DeGrado, 1990)

ONEK900102

Helix formation parameters (delta delta G) (O'Neil-DeGrado, 1990)

VINM940101

Normalized flexibility parameters (B-values), average (Vihinen et al., 1994)

VINM940102

Normalized flexibility parameters (B-values) for each residue surrounded by none rigid neighbours (Vihinen et al., 1994)

VINM940103

Normalized flexibility parameters (B-values) for each residue surrounded by one rigid neighbours (Vihinen et al., 1994)

VINM940104

Normalized flexibility parameters (B-values) for each residue surrounded by two rigid neighbours (Vihinen et al., 1994)

MUNV940101

Free energy in alpha-helical conformation (Munoz-Serrano, 1994)

MUNV940102

Free energy in alpha-helical region (Munoz-Serrano, 1994)

MUNV940103

Free energy in beta-strand conformation (Munoz-Serrano, 1994)

MUNV940104

Free energy in beta-strand region (Munoz-Serrano, 1994)

MUNV940105

Free energy in beta-strand region (Munoz-Serrano, 1994)

WIMW960101

Free energies of transfer of AcWl-X-LL peptides from bilayer interface to water (Wimley-White, 1996)

KIMC930101

Thermodynamic beta sheet propensity (Kim-Berg, 1993)

MONM990101

Turn propensity scale for transmembrane helices (Monne et al., 1999)

BLAM930101

Alpha helix propensity of position 44 in T4 lysozyme (Blaber et al., 1993)

PARS000101

p-Values of mesophilic proteins based on the distributions of B values (Parthasarathy-Murthy, 2000)

PARS000102

p-Values of thermophilic proteins based on the distributions of B values (Parthasarathy-Murthy, 2000)

KUMS000101

Distribution of amino acid residues in the 18 non-redundant families of thermophilic proteins (Kumar et al., 2000)

KUMS000102

Distribution of amino acid residues in the 18 non-redundant families of mesophilic proteins (Kumar et al., 2000)

KUMS000103

Distribution of amino acid residues in the alpha-helices in thermophilic proteins (Kumar et al., 2000)

KUMS000104

Distribution of amino acid residues in the alpha-helices in mesophilic proteins (Kumar et al., 2000)

TAKK010101

Side-chain contribution to protein stability (kJ/mol) (Takano-Yutani, 2001)

FODM020101

Propensity of amino acids within pi-helices (Fodje-Al-Karadaghi, 2002)

NADH010101

Hydropathy scale based on self-information values in the two-state model (5% accessibility) (Naderi-Manesh et al., 2001)

NADH010102

Hydropathy scale based on self-information values in the two-state model (9% accessibility) (Naderi-Manesh et al., 2001)

NADH010103

Hydropathy scale based on self-information values in the two-state model (16% accessibility) (Naderi-Manesh et al., 2001)

NADH010104

Hydropathy scale based on self-information values in the two-state model (20% accessibility) (Naderi-Manesh et al., 2001)

NADH010105

Hydropathy scale based on self-information values in the two-state model (25% accessibility) (Naderi-Manesh et al., 2001)

NADH010106

Hydropathy scale based on self-information values in the two-state model (36% accessibility) (Naderi-Manesh et al., 2001)

NADH010107

Hydropathy scale based on self-information values in the two-state model (50% accessibility) (Naderi-Manesh et al., 2001)

MONM990201

Averaged turn propensities in a transmembrane helix (Monne et al., 1999)

KOEP990101

Alpha-helix propensity derived from designed sequences (Koehl-Levitt, 1999)

KOEP990102

Beta-sheet propensity derived from designed sequences (Koehl-Levitt, 1999)

CEDJ970101

Composition of amino acids in extracellular proteins (percent) (Cedano et al., 1997)

CEDJ970102

Composition of amino acids in anchored proteins (percent) (Cedano et al., 1997)

CEDJ970103

Composition of amino acids in membrane proteins (percent) (Cedano et al., 1997)

CEDJ970104

Composition of amino acids in intracellular proteins (percent) (Cedano et al., 1997)

CEDJ970105

Composition of amino acids in nuclear proteins (percent) (Cedano et al., 1997)

FUKS010101

Surface composition of amino acids in intracellular proteins of thermophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010102

Surface composition of amino acids in intracellular proteins of mesophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010103

Surface composition of amino acids in extracellular proteins of mesophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010104

Surface composition of amino acids in nuclear proteins (percent) (Fukuchi-Nishikawa, 2001)

FUKS010105

Interior composition of amino acids in intracellular proteins of thermophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010106

Interior composition of amino acids in intracellular proteins of mesophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010107

Interior composition of amino acids in extracellular proteins of mesophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010108

Interior composition of amino acids in nuclear proteins (percent) (Fukuchi-Nishikawa, 2001)

FUKS010109

Entire chain composition of amino acids in intracellular proteins of thermophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010110

Entire chain composition of amino acids in intracellular proteins of mesophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010111

Entire chain composition of amino acids in extracellular proteins of mesophiles (percent) (Fukuchi-Nishikawa, 2001)

FUKS010112

Entire chain compositino of amino acids in nuclear proteins (percent) (Fukuchi-Nishikawa, 2001)

AVBF000101

Screening coefficients gamma, local (Avbelj, 2000)

AVBF000102

Screening coefficients gamma, non-local (Avbelj, 2000)

AVBF000103

Slopes tripeptide, FDPB VFF neutral (Avbelj, 2000)

AVBF000104

Slopes tripeptides, LD VFF neutral (Avbelj, 2000)

AVBF000105

Slopes tripeptide, FDPB VFF noside (Avbelj, 2000)

AVBF000106

Slopes tripeptide FDPB VFF all (Avbelj, 2000)

AVBF000107

Slopes tripeptide FDPB PARSE neutral (Avbelj, 2000)

AVBF000108

Slopes dekapeptide, FDPB VFF neutral (Avbelj, 2000)

AVBF000109

Slopes proteins, FDPB VFF neutral (Avbelj, 2000)

YANJ020101

Side-chain conformation by gaussian evolutionary method (Yang et al., 2002)

MITS020101

Amphiphilicity index (Mitaku et al., 2002)

TSAJ990101

Volumes including the crystallographic waters using the ProtOr (Tsai et al., 1999)

TSAJ990102

Volumes not including the crystallographic waters using the ProtOr (Tsai et al., 1999)

COSI940101

Electron-ion interaction potential values (Cosic, 1994)

PONP930101

Hydrophobicity scales (Ponnuswamy, 1993)

WILM950101

Hydrophobicity coefficient in RP-HPLC, C18 with 0.1%TFA/MeCN/H2O (Wilce et al. 1995)

WILM950102

Hydrophobicity coefficient in RP-HPLC, C8 with 0.1%TFA/MeCN/H2O (Wilce et al. 1995)

WILM950103

Hydrophobicity coefficient in RP-HPLC, C4 with 0.1%TFA/MeCN/H2O (Wilce et al. 1995)

WILM950104

Hydrophobicity coefficient in RP-HPLC, C18 with 0.1%TFA/2-PrOH/MeCN/H2O (Wilce et al. 1995)

KUHL950101

Hydrophilicity scale (Kuhn et al., 1995)

GUOD860101

Retention coefficient at pH 2 (Guo et al., 1986)

JURD980101

Modified Kyte-Doolittle hydrophobicity scale (Juretic et al., 1998)

BASU050101

Interactivity scale obtained from the contact matrix (Bastolla et al., 2005)

BASU050102

Interactivity scale obtained by maximizing the mean of correlation coefficient over single-domain globular proteins (Bastolla et al., 2005)

BASU050103

Interactivity scale obtained by maximizing the mean of correlation coefficient over pairs of sequences sharing the TIM barrel fold (Bastolla et al., 2005)

SUYM030101

Linker propensity index (Suyama-Ohara, 2003)

PUNT030101

Knowledge-based membrane-propensity scale from 1D_Helix in MPtopo databases (Punta-Maritan, 2003)

PUNT030102

Knowledge-based membrane-propensity scale from 3D_Helix in MPtopo databases (Punta-Maritan, 2003)

GEOR030101

Linker propensity from all dataset (George-Heringa, 2003)

GEOR030102

Linker propensity from 1-linker dataset (George-Heringa, 2003)

GEOR030103

Linker propensity from 2-linker dataset (George-Heringa, 2003)

GEOR030104

Linker propensity from 3-linker dataset (George-Heringa, 2003)

GEOR030105

Linker propensity from small dataset (linker length is less than six residues) (George-Heringa, 2003)

GEOR030106

Linker propensity from medium dataset (linker length is between six and 14 residues) (George-Heringa, 2003)

GEOR030107

Linker propensity from long dataset (linker length is greater than 14 residues) (George-Heringa, 2003)

GEOR030108

Linker propensity from helical (annotated by DSSP) dataset (George-Heringa, 2003)

GEOR030109

Linker propensity from non-helical (annotated by DSSP) dataset (George-Heringa, 2003)

ZHOH040101

The stability scale from the knowledge-based atom-atom potential (Zhou-Zhou, 2004)

ZHOH040102

The relative stability scale extracted from mutation experiments (Zhou-Zhou, 2004)

ZHOH040103

Buriability (Zhou-Zhou, 2004)

BAEK050101

Linker index (Bae et al., 2005)

HARY940101

Mean volumes of residues buried in protein interiors (Harpaz et al., 1994)

PONJ960101

Average volumes of residues (Pontius et al., 1996)

DIGM050101

Hydrostatic pressure asymmetry index, PAI (Di Giulio, 2005)

WOLR790101

Hydrophobicity index (Wolfenden et al., 1979)

OLSK800101

Average internal preferences (Olsen, 1980)

KIDA850101

Hydrophobicity-related index (Kidera et al., 1985)

GUYH850102

Apparent partition energies calculated from Wertz-Scheraga index (Guy, 1985)

GUYH850103

Apparent partition energies calculated from Robson-Osguthorpe index (Guy, 1985)

GUYH850104

Apparent partition energies calculated from Janin index (Guy, 1985)

GUYH850105

Apparent partition energies calculated from Chothia index (Guy, 1985)

ROSM880104

Hydropathies of amino acid side chains, neutral form (Roseman, 1988)

ROSM880105

Hydropathies of amino acid side chains, pi-values in pH 7.0 (Roseman, 1988)

JACR890101

Weights from the IFH scale (Jacobs-White, 1989)

COWR900101

Hydrophobicity index, 3.0 pH (Cowan-Whittaker, 1990)

BLAS910101

Scaled side chain hydrophobicity values (Black-Mould, 1991)

CASG920101

Hydrophobicity scale from native protein structures (Casari-Sippl, 1992)

CORJ870101

NNEIG index (Cornette et al., 1987)

CORJ870102

SWEIG index (Cornette et al., 1987)

CORJ870103

PRIFT index (Cornette et al., 1987)

CORJ870104

PRILS index (Cornette et al., 1987)

CORJ870105

ALTFT index (Cornette et al., 1987)

CORJ870106

ALTLS index (Cornette et al., 1987)

CORJ870107

TOTFT index (Cornette et al., 1987)

CORJ870108

TOTLS index (Cornette et al., 1987)

MIYS990101

Relative partition energies derived by the Bethe approximation (Miyazawa-Jernigan, 1999)

MIYS990102

Optimized relative partition energies - method A (Miyazawa-Jernigan, 1999)

MIYS990103

Optimized relative partition energies - method B (Miyazawa-Jernigan, 1999)

MIYS990104

Optimized relative partition energies - method C (Miyazawa-Jernigan, 1999)

MIYS990105

Optimized relative partition energies - method D (Miyazawa-Jernigan, 1999)

ENGD860101

Hydrophobicity index (Engelman et al., 1986)

FASG890101

Hydrophobicity index (Fasman, 1989)

K6.5

Values of Wc in proteins from class Beta, cutoff 6 A, separation 5 (Wozniak, 2014)

K8.5

Values of Wc in proteins from class Beta, cutoff 8 A, separation 5 (Wozniak, 2014)

K12.5

Values of Wc in proteins from class Beta, cutoff 12 A, separation 5 (Wozniak, 2014)

K6.15

Values of Wc in proteins from class Beta, cutoff 6 A, separation 15 (Wozniak, 2014)

K8.15

Values of Wc in proteins from class Beta, cutoff 8 A, separation 15 (Wozniak, 2014)

K12.15

Values of Wc in proteins from class Beta, cutoff 12 A, separation 15 (Wozniak, 2014)

Source

AAIndex database.

References

Kawashima, S. and Kanehisa, M. (2000) AAindex: amino acid index database. Nucleic Acids Res., 28:374.

Wozniak, P. and Kotulska M. (2014) Characteristics of protein residue-residue contacts and their application in contact prediction. 20(11):2497

Examples

data(aaprop)

Add 1-grams

Description

Builds (n+1)-grams from n-grams.

Usage

add_1grams(ngram, u, seq_length)

Arguments

Details

n-grams are built by pasting every possible unigram in the every possible free position. The total length of n-gram (n plus total distance between elements of the n-gram) is limited by the length of an origin sequence, because the n-gram cannot be longer than an origin sequence.

Value

vector of n-grams (where n is equal to the n of the input plus one).

See Also

Reverse function: gap_ngrams.

Examples

add_1grams("1_2.3.4_3.0", 1L:4, 8)

add_1grams("a.a_1", c("a", "b", "c"), 4)

Coerce feature_test object to a data frame

Description

Coerce results of test_features function to a data.frame.

Usage

## S3 method for class 'feature_test'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  stringsAsFactors = FALSE,
  ...
)

Arguments

Value

a data frame with four columns: names of n-gram, p-values, occurrences in positive and negative sequences.

Binarize

Description

Binarizes a matrix.

Usage

binarize(x)

Arguments

Value

a matrix or simple_triplet_matrix (depending on the input).

Calculate value of criterion

Description

Computes a chosen statistical criterion for each feature versus target vector.

Usage

calc_criterion(target, features, criterion_function)

Arguments

Details

The permutation test implemented in biogram uses several criterions to filter important features. Each can be used by test_features by specifying the criterion parameter.

Value

a integer vector of length equal to the number of features containing computed information gain values.

Note

Both target and features must be binary, i.e. contain only 0 and 1 values.

See Also

test_features.

Examples

tar <- sample(0L:1, 100, replace = TRUE)
feats <- matrix(sample(0L:1, 400, replace = TRUE), ncol = 4)

# Information Gain
calc_criterion(tar, feats, calc_ig)

# hi-squared-based measure
calc_criterion(tar, feats, calc_cs)

# Kullback-Leibler divergence
calc_criterion(tar, feats, calc_kl)

Calculate Chi-squared-based measure

Description

Computes Chi-squared-based measure between features and target vector.

Usage

calc_cs(feature, target, len_target, pos_target)

Arguments

Value

A numeric vector of length 1 representing computed Chi-square values.

Note

Both target and features must be binary, i.e. contain only 0 and 1 values.

The function was designed to be as fast as possible subroutine of calc_criterion and might be cumbersome if directly called by a user.

See Also

test_features.

chisq.test - Pearson's chi-squared test for count data.

Examples

tar <- sample(0L:1, 100, replace = TRUE)
feat <- sample(0L:1, 100, replace = TRUE)
calc_cs(feat, tar, 100, sum(tar))

Calculate encoding distance

Description

Computes the encoding distance between two encodings.

Usage

calc_ed(a, b, prop = NULL, measure)

Arguments

Value

an encoding distance.

See Also

calc_si: compute the similarity index of two encodings. encoding2df: converts an encoding to a data frame. validate_encoding: validate a structure of an encoding.

Examples

# calculate encoding distance between two encodings of amino acids
aa1 = list(`1` = c("g", "a", "p", "v", "m", "l", "i"), 
           `2` = c("k", "h"), 
           `3` = c("d", "e"), 
           `4` = c("f", "r", "w", "y", "s", "t", "c", "n", "q"))

aa2 = list(`1` = c("g", "a", "p", "v", "m", "l", "q"), 
           `2` = c("k", "h", "d", "e", "i"), 
           `3` = c("f", "r", "w", "y", "s", "t", "c", "n"))
calc_ed(aa1, aa2, measure = "pi") 
    
# the encoding distance between two identical encodings is 0
calc_ed(aa1, aa1, measure = "pi") 

Calculate IG for single feature

Description

Computes information gain of single feature and target vector.

Usage

calc_ig(feature, target, len_target, pos_target)

Arguments

Details

The information gain term is used here (improperly) as a synonym of mutual information. It is defined as:

IG(X; Y) = \sum_{y \in Y} \sum_{x \in X} p(x, y) \log \left(\frac{p(x, y)}{p(x) p(y)} \right)

In biogram package information gain is computed using following relationship: IG = E(S) - E(S|F)

Value

A numeric vector of length 1 representing information gain in nats.

Note

During calculations 0 \log 0 = 0. For a justification see References.

The function was designed to be afast subroutine of calc_criterion and might be cumbersome if directly called by a user.

References

Cover TM, Thomas JA Elements of Information Theory, 2nd Edition Wiley, 2006.

Examples

tar <- sample(0L:1, 100, replace = TRUE)
feat <- sample(0L:1, 100, replace = TRUE)
calc_ig(feat, tar, 100, sum(tar))

Calculate KL divergence of features

Description

Computes Kullback-Leibler divergence between features and target vector.

Usage

calc_kl(feature, target, len_target, pos_target)

Arguments

Value

A numeric vector of length 1 representing Kullback-Leibler divergence value.

Note

Both target and features must be binary, i.e. contain only 0 and 1 values.

The function was designed to be as fast as possible subroutine of calc_criterion and might be cumbersome if directly called by a user.

References

Kullback S, Leibler RA On information and sufficiency. Annals of Mathematical Statistics 22 (1):79-86, 1951.

See Also

test_features. Kullback-Leibler divergence is calculated using KL.plugin.

Examples

tar <- sample(0L:1, 100, replace = TRUE)
feat <- sample(0L:1, 100, replace = TRUE)
calc_kl(feat, tar, 100, sum(tar))

Calculate partition index

Description

Computes the encoding distance between two encodings.

Usage

calc_pi(a, b)

Arguments

Details

The encoding distance between a and b is defined as the minimum number of amino acids that have to be moved between subgroups of encoding to make a identical to b (order of subgroups in the encoding and amino acids in a group is unimportant).

If the parameter prop is supplied, the encoding distance is normalized by the factor equal to the sum of distances for each group in a and the closest group in b. The position of a group is defined as the mean value of properties of amino acids or nucleotides belonging the group.

See the package vignette for more details.

Value

an encoding distance.

See Also

calc_si: compute the similarity index of two encodings. encoding2df: converts an encoding to a data frame. validate_encoding: validate a structure of an encoding.

Examples

# calculate encoding distance between two encodings of amino acids
aa1 = list(`1` = c("g", "a", "p", "v", "m", "l", "i"), 
           `2` = c("k", "h"), 
           `3` = c("d", "e"), 
           `4` = c("f", "r", "w", "y", "s", "t", "c", "n", "q"))

aa2 = list(`1` = c("g", "a", "p", "v", "m", "l", "q"), 
           `2` = c("k", "h", "d", "e", "i"), 
           `3` = c("f", "r", "w", "y", "s", "t", "c", "n"))
calc_pi(aa1, aa2) 
    
# the encoding distance between two identical encodings is 0
calc_pi(aa1, aa1) 
 

Compute similarity index

Description

Computes similarity index between two encodings.

Usage

calc_si(a, b)

Arguments

Details

Briefly, the similarity index is a fraction of elements that have the same pairing in both encodings. Pairing is a binary variable, that has value 1 if two elements are in the same group and 0 if not. For more details, see references.

Value

the value of similarity index.

References

Stephenson, J.D., and Freeland, S.J. (2013). Unearthing the Root of Amino Acid Similarity. J Mol Evol 77, 159-169.

See Also

calc_ed: calculate the encoding distance between two encodings.

Examples

# example from Stephenson & Freeland, 2013 (Fig. 6)
enc1 <- list(`1` = "A",
             `2` = c("F", "E"),
             `3` = c("C", "D", "G"))

enc2 <- list(`1` = c("A", "G"),
             `2` = c("C", "D", "E", "F"))

enc3 <- list(`1` = c("D", "G"),
             `2` = c("E", "F"),
             `3` = c("A", "C"))
             
calc_si(enc1, enc2)
calc_si(enc2, enc3)
calc_si(enc1, enc3)

Check chosen criterion

Description

Checks if the criterion is viable or matches it to the list of implemented criterions.

Usage

check_criterion(input_criterion, criterion_names = c("ig", "kl", "cs"))

Arguments

Value

a list of three:

• criterion name,

• its function,

• nice name for outputs.

See Also

Calculate the value of criterion: calc_criterion.

Clustering of sequences based on regular expression

Description

Clusters sequences hierarchically with regular expressions. At each step we minimize number of degrees of freedom for all regular expressions needed to describe the data

Usage

cluster_reg_exp(ngrams)

Arguments

Details

Regular expression is a list of the length equal to the length of the input sequences. Each element of the list represents a position in the sequence and contains amino acid, that are likely to occure on this position.

Value

List of four

• "regExps"regular expression in best clustering

• "seqClustering"clustering of sequences in best clustering

• "allRegExps"all regular expressions.

• "allIndices"all clusterings

Examples

data(human_cleave)
#cluster_reg_exp is computationally expensive

results <- cluster_reg_exp(human_cleave[1L:10, 1L:4])

Code n-grams

Description

Code human-friendly representation of n-grams into a biogram format.

Usage

code_ngrams(decoded_ngrams)

Arguments

Value

a character vector of n-grams.

See Also

Inverse function: decode_ngrams.

Examples

code_ngrams(c("11_2", "1__12", "222"))
code_ngrams(c("aaa_b", "d__aa", "abd"))

Construct and filter n-grams

Description

Builds and selects important n-grams stepwise.

Usage

construct_ngrams(
  target,
  seq,
  u,
  n_max,
  conf_level = 0.95,
  gap = TRUE,
  use_heuristics = TRUE
)

Arguments

Details

construct_ngrams starts by extracting unigrams from the sequences, pasting them together in all combination and choosing from them significant features (with p-value below conf_level). The chosen n-grams are further extended to the specified by n_max size by pasting unigrams at both ends.

The gap parameter determines if construct_ngrams performs the feature selection on exact n-grams (gap equal to FALSE) or on all features in the Hamming distance 1 from the n-gram (gap equal to TRUE).

Value

a vector of n-grams.

See Also

Feature filtering method: test_features.

Examples

# to make the example faster, we run construct_ngrams() on the 
# subset of data
deg_seqs <- degenerate(human_cleave[c(1L:100, 801L:900), 1L:9],
list(`1` = c(1, 6, 8, 10, 11, 18),
     `2` = c(2, 13, 14, 16, 17),
     `3` = c(5, 19, 20),
     `4` = c(7, 9, 12, 15),
     '5' = c(3, 4)))
bigrams <- construct_ngrams(human_cleave[c(1L:100, 801L:900), "tar"], deg_seqs, 1L:5, 2)

Detect and count multiple n-grams in sequences

Description

A convinient wrapper around count_ngrams for counting multiple values of n and d.

Usage

count_multigrams(
  ns,
  ds = rep(0, length(ns)),
  seq,
  u,
  pos = FALSE,
  scale = FALSE,
  threshold = 0
)

Arguments

Details

ns vector and ds vector must have equal length. Elements of ds vector are used as equivalents of d parameter for respective values of ns. For example, if ns is c(4, 4, 4), the ds must be a list of length 3. Each element of the ds list must have length 3 or 1, as appropriate for a d parameter in count_ngrams function.

Value

An integer matrix with named columns. The naming conventions are the same as in count_ngrams.

Examples

seqs <- matrix(sample(1L:4, 600, replace = TRUE), ncol = 50)
count_multigrams(c(3, 1), list(c(1, 0), 0), seqs, 1L:4, pos = TRUE)
# if ds parameter is not present, n-grams are calculated for distance 0
count_multigrams(c(3, 1), seq = seqs, u = 1L:4)

# calculate three times n-gram with the same length, but different distances between
# elements
count_multigrams(c(4, 4, 4), list(c(2, 0, 1), c(2, 1, 0), c(0, 1, 2)), 
                 seqs, 1L:4, pos = TRUE)

Count n-grams in sequences

Description

Counts all n-grams or position-specific n-grams present in the input sequence(s).

Usage

count_ngrams(seq, n, u, d = 0, pos = FALSE, scale = FALSE, threshold = 0)

Arguments

Details

A distance vector should be always n - 1 in length. For example when n = 3, d = c(1,2) means A_A__A. For n = 4, d = c(2,0,1) means A__AA_A. If vector d has length 1, it is recycled to length n - 1.

n-gram names follow a specific convention and have three parts for position-specific n-grams and two parts otherwise. The parts are separated by _. The . symbol is used to separate elements within a part. The general naming scheme is POSITION_NGRAM_DISTANCE. The optional POSITION part of the name indicates the actual position of the n-gram in the sequence(s) and will be present only if pos = TRUE. This part is always a single integer. The NGRAM part of the name is a sequence of elements in the n-gram. For example, 4.2.2 indicates the n-gram 422 (e.g. TCC). The DISTANCE part of the name is a vector of distance(s). For example, 0.0 indicates zero distances (continuous n-grams), while 1.2 represents distances for the n-gram A_A__A.

Examples of n-gram names:

• 46_4.4.4_0.1 : trigram 44_4 on position 46

• 12_2.1_2 : bigram 2__1 on position 12

• 8_1.1.1_0.0 : continuous trigram 111 on position 8

• 1.1.1_0.0 : continuous trigram 111 without position information

Value

a simple_triplet_matrix where columns represent n-grams and rows sequences. See Details for specifics of the naming convention.

Note

By default, the counted n-gram data is stored in a memory-saving format. To convert an object to a 'classical' matrix use the as.matrix function. See examples for further information.

See Also

Create vector of possible n-grams: create_ngrams.

Extract n-grams from sequence(s): seq2ngrams.

Get indices of n-grams: get_ngrams_ind.

Count n-grams for multiple values of n: count_multigrams.

Count only specified n-grams: count_specified.

Examples

# count trigrams without position information for nucleotides
count_ngrams(sample(1L:4, 50, replace = TRUE), 3, 1L:4, pos = FALSE)
# count position-specific trigrams from multiple nucleotide sequences
seqs <- matrix(sample(1L:4, 600, replace = TRUE), ncol = 50)
ngrams <- count_ngrams(seqs, 3, 1L:4, pos = TRUE)
# output results of the n-gram counting to screen
as.matrix(ngrams)

Count specified n-grams

Description

Counts specified n-grams in the input sequence(s).

Usage

count_specified(seq, ngrams)

Arguments

Details

count_specified counts only selected n-grams declared by user in the ngrams parameter. Declared n-grams must be written using the biogram notation.

Value

A simple_triplet_matrix where columns represent n-grams and rows sequences.

See Also

Count all possible n-grams: count_ngrams.

Examples

seqs <- matrix(c(1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 3, 4, 1, 2, 2, 4), nrow = 2)
count_specified(seqs, ngrams = c("1.1.1_0.0", "2.2.2_0.0", "1.1.2_0.0"))

seqs <- matrix(sample(1L:5, 200, replace = TRUE), nrow = 20)
count_specified(seqs, ngrams = c("2_4.2_0", "2_1.4_0", "3_1.3_0",
                                 "2_4.2_1", "2_1.4_1", "3_1.3_1",
                                 "2_4.2_2", "2_1.4_2", "3_1.3_2"))

Count total number of n-grams

Description

Computes total number of n-grams that can be extracted from sequences.

Usage

count_total(seq, n, d)

Arguments

Details

The maximum number of possible n-grams is limited by their length and the distance between elements of the n-gram.

Value

An integer rperesenting the total number of n-grams.

Note

A format of d vector is discussed in Details of count_ngrams. The maximum

Examples

seqs <- matrix(sample(1L:4, 600, replace = TRUE), ncol = 50)
# make several sequences shorter by replacing them partially with NA
seqs[8L:11, 46L:50] <- NA
seqs[1L, 31L:50] <- NA
count_total(seqs, 3, c(1, 0))

Create encoding

Description

Reduces an alphabet using physicochemical properties.

Usage

create_encoding(prop, len)

Arguments

Details

The encoding is a list of groups to which elements of an alphabet should be reduced. All elements of the alphabet (all amino acids or all nucleotides) should appear in the encoding.

Value

An encoding.

See Also

calc_ed: calculate the encoding distance between two encodings. encoding2df: converts an encoding to a data frame. validate_encoding: validate a structure of an encoding.

Examples

enc1 = list(`1` = c("a", "t"), 
            `2` = c("g", "c"))
encoding2df(enc1)

Create feature according to given contingency matrix

Description

Creates a matrix of features and target based on the values from contingency matrix.

Usage

create_feature_target(n11, n01, n10, n00)

Arguments

Value

a matrix of 2 columns and n11+n10+n01+n00 rows. Columns represent target and feature vectors, respectively.

Examples

# equivalent of 
#         target
# feature 10 375
#        15 600
target_feature <- create_feature_target(10, 375, 15, 600)

Get all possible n-Grams

Description

Creates the vector of all possible n_grams (for given n).

Usage

create_ngrams(n, u, possible_grams = NULL)

Arguments

Details

See Details section of count_ngrams for more information about n-grams naming convention. The possible information about distance must be added by hand (see examples).

Value

a character vector. Elements of n-gram are separated by dot.

Note

Input data must be a matrix or data frame of numeric elements.

Examples

# bigrams for standard aminoacids
create_ngrams(2, 1L:20)
# bigrams for standard aminoacids with positions, 10 amino acid long sequence, so 
# only 9 bigrams can be located in sequence
create_ngrams(2, 1L:20, 9)
# bigrams for DNA with positions, 10 nucleotide long sequence, distance 1, so only 
# 8 bigrams in sequence
# paste0 adds information about distance at the end of n-gram
paste0(create_ngrams(2, 1L:4, 8), "_0")

criterion_distribution class

Description

A result of distr_crit function.

Details

An object of class criterion_distribution is a numeric matrix.

Data

1st column:

possible values of criterion.

2nd column:

probability density function.

3rd column:

cumulative distribution function.

Attributes

plot_data

A matrix with values of the criterion and their probabilities.

nice_name

'Nice' name of the criterion.

Categorize tested features

Description

Categorizes results of test_features function into groups based on their significance.

Usage

## S3 method for class 'feature_test'
cut(x, split = "significances", breaks = c(0, 1e-04, 0.01, 0.05, 1), ...)

Arguments

Value

the value of function depends on the split parameter. The function returns a named list of length equal to the length of significances (when split equals "significances") or frequencies (when split equals "positives" or "negatives") minus one. Each elements of the list contains names of the n-grams belonging to the given significance or frequency group.

Decode n-grams

Description

Transforms a vector of n-grams into a human-friendly form.

Usage

decode_ngrams(ngrams)

Arguments

Value

a character vector of length equal to the number of n-grams.

Note

Decoded n-grams lose the position information.

See Also

Validate n-gram structure: is_ngram.

Inverse function: code_ngrams.

Examples

decode_ngrams(c("2_1.1.2_0.1", "3_1.1.2_2.0", "3_2.2.2_0.0"))

Degenerate protein sequence

Description

'Degenerates' amino acid or nucleic sequence by aggregating elements to bigger groups.

Usage

degenerate(seq, element_groups)

Arguments

Value

A character vector or matrix (if input is a matrix) containing aggregated elements.

Note

Characters not present in the element_groups will be converted to NA with a warning.

See Also

l2n to easily convert information stored in biological sequences from letters to numbers. calc_ed to calculate distance between encodings.

Examples

sample_seq <- c(1, 3, 1, 3, 4, 4, 3, 1, 2)
table(sample_seq)

# aggregate sequence to purins and pyrimidines
deg_seq <- degenerate(sample_seq, list(w = c(1, 4), s = c(2, 3)))
table(deg_seq)

Degenerate n-grams

Description

'Degenerates' n-grams by aggregating amino acid or nucleotide elements into bigger groups.

Usage

degenerate_ngrams(x, element_groups, binarize = FALSE)

Arguments

Value

A character vector or matrix (if input is a matrix) containing degenerated n-grams.

Compute criterion distribution

Description

Computes criterion distribution under null hypothesis for all contingency tables possible for a feature and a target.

Usage

distr_crit(target, feature, criterion = "ig", iter_limit = 200)

Arguments

Details

both target and feature vectors may contain only 0 and 1.

Value

An object of class criterion_distribution.

See Also

calc_criterion.

Examples

target_feature <- create_feature_target(10, 375, 15, 600) 
distr_crit(target = target_feature[,1], feature = target_feature[,2])

Convert encoding to data frame

Description

Converts an encoding to a data frame.

Usage

encoding2df(x, sort = FALSE)

Arguments

Details

The encoding is a list of groups to which elements of an alphabet should be reduced. All elements of the alphabet (all amino acids or all nucleotides) should appear in the encoding.

Value

data frame with two columns. First column represents an index of a group in the supplied encoding and the second column contains all elements of the encoding.

See Also

calc_ed: calculate the encoding distance between two encodings. encoding2df: converts an encoding to a data frame. validate_encoding: validate a structure of an encoding.

Examples

create_encoding(aaprop[1L:5, ], 5)

2d cross-tabulation

Description

Quickly cross-tabulates two binary vectors.

Usage

fast_crosstable(target, len_target, pos_target, feature)

Arguments

Details

Input looks odd, but the function was build to be fast subroutine of calc_ig, which works on many features but only one target.

Value

a vector of length four:

1. target +, feature+

2. target +, feature-

3. target -, feature+

4. target -, feature-

Note

Binary vector means a numeric vector with 0 or 1.

Examples

tar <- sample(0L:1, 100, replace = TRUE)
feat <- sample(0L:1, 100, replace = TRUE)
fast_crosstable(tar, length(tar), sum(tar),  feat)

feature_test class

Description

A result of test_features function.

Details

An object of the feature_test class is a numeric vector of p-values. Additional attributes characterizes futher the details of test which returned these p-values.

Attributes

criterion

the criterion used in permutation test.

adjust

the name of p-value adjusting method.

times

the number of permutations. If QuiPT was chosen NA.

occ

frequency of features splitted in subset based on the value of target.

See Also

Methods:

• as.data.frame.feature_test

• cut.feature_test

• print.feature_test

• summary.feature_test

Convert encoding from full to simple format

Description

Converts an encoding from the full format to the simple format.

Usage

full2simple(x)

Arguments

Examples

aa1 = list(`1` = c("g", "a", "p", "v", "m", "l", "i"), 
           `2` = c("k", "h"), 
           `3` = c("d", "e"), 
           `4` = c("f", "r", "w", "y", "s", "t", "c", "n", "q"))
full2simple(aa1)

Gap n-grams

Description

Introduces gaps in the n-grams.

Usage

gap_ngrams(ngrams)

Arguments

Details

A single element of the input n-gram at a time will be replaced by a gap. For example, introducing gaps in n-gram 2_1.1.2_0.1 will results in three n-grams: 3_1.2_1 (where the 2_1_0 unigram was replaced by a gap), 2_1.2_2 and 2_1.1_0.

Value

A character vector of (n-1)-grams with introduced gaps.

See Also

Reverse function: add_1grams.

Examples

gap_ngrams(c("2_1.1.2_0.1", "3_1.1.2_0.0", "3_2.2.2_0.0"))
gap_ngrams(c("1.1.2_0.1", "1.1.2_0.0", "2.2.2_0.0"))

Generate sequence

Description

Generate a sequences using an alphabet of unigrams and set of rules.

Usage

generate_sequence(alphabet, regions)

Arguments

Generate single region

Description

Generate a region using an alphabet of unigrams and considering provided set of rules.

Usage

generate_single_region(alphabet, reg_len, prop_ranges, exactness)

Arguments

Examples

props1 <- list(P1 = c(0, 0.5), 
               P2 = c(0.2, 0.4),
               P3 = c(0.5, 1),
               P4 = c(0, 0))

props2 <- list(P1 = c(0.5, 1), 
               P2 = c(0.4, 1),
               P3 = c(0, 0.5),
               P4 = c(1, 1))


alph <- generate_unigrams(c(replicate(8, props1, simplify = FALSE),
                            replicate(12, props2, simplify = FALSE)),
                          unigram_names = letters[1L:20])

rules1 <- list(P1 = c(0.5, 1), 
               P2 = c(0.4, 1),
               P3 = c(0, 0.5),
               P4 = c(1, 1))

generate_single_region(alph, 10, rules1, 0.9)

Generate single unigram

Description

Assign randomly generated properties to a single unigram.

Usage

generate_single_unigram(unigram_ranges)

Arguments

See Also

generate_single_unigram is a helper function for generate_unigrams.

Examples

generate_single_unigram(list(P1 = c(0, 0.5), 
                             P2 = c(0.2, 0.4),
                             P3 = c(0.5, 1),
                             P4 = c(0, 0)))

Generate unigrams

Description

Generates an alphabet of unigrams based on given list of properties.

Usage

generate_unigrams(unigram_list, unigram_names = NULL, prop_names = NULL)

Arguments

Details

Unigram parameters are represented as a list of intervals, where each interval corresponds to a different property. The function generate unigrams randomly choosing values of properties from given intervals using uniform distribution. All lists of ranges should have the same length, which equils to describing each unigram using the same properties.

Examples

props1 <- list(P1 = c(0, 0.5), 
               P2 = c(0.2, 0.4),
               P3 = c(0.5, 1),
               P4 = c(0, 0))

props2 <- list(P1 = c(0.5, 1), 
               P2 = c(0.4, 1),
               P3 = c(0, 0.5),
               P4 = c(1, 1))


alph <- generate_unigrams(c(replicate(8, props1, simplify = FALSE),
                          replicate(12, props2, simplify = FALSE)),
                          unigram_names = letters[1L:20])
                          

Get indices of n-grams

Description

Computes list of n-gram elements positions in sequence.

Usage

get_ngrams_ind(len_seq, n, d)

Arguments

Details

A format of d vector is discussed in Details of count_ngrams.

Value

A list with number of elements equal to n. Every element is a vector containing locations of given n-gram letter. For example, first element of list contain indices of first letter of all n-grams. The attribute d of output contains distances between letter used to compute locations (see Details).

Examples

# positions trigrams in sequence of length 10
get_ngrams_ind(10, 9, 0)

Human signal peptides cleavage sites

Description

A set of 648 cleavage sites and 648 parts of mature proteins shortly after cleavage sites derived from human proteome.

Format

A data frame with 1296 observations on the following 10 variables. Columns from P1 to P9 describes positions in an extracted peptide. tar is a target vector. It has value 1 if a peptide is a cleavage site and 0 if not.

Details

Each peptide in the data set is nine amino acid residues long. In case of cleavage sites, the clevage is located between fifth and sixth peptide. The non-cleavage sites are parts of mature proteins starting five positions after cleavage site.

Note

Amino acid residues were recoded as integers.

Source

UniProt

Examples

data(human_cleave)
table(human_cleave[, 1])

Validate n-gram

Description

Checks if the character string may be used as an n-gram and its notation follows specific convention of biogram package.

Usage

is_ngram(x)

Arguments

Value

TRUE if n-gram's notation is correct, FALSE if not.

Examples

print(is_ngram("1_1.1.1_0.0"))
print(is_ngram("not_ngram"))

Convert letters to numbers

Description

Converts biological sequence from letter to number notation.

Usage

l2n(seq, seq_type)

Arguments

Value

a numeric vector or matrix containing converted elements.

See Also

l2n is a wrapper around degenerate.

Inverse function: n2l.

Examples

sample_seq <- c("a", "d", "d", "g", "a", "g", "n", "a", "l")
l2n(sample_seq, "prot")

Convert list of sequences to matrix

Description

Converts list of sequences to matrix.

Usage

list2matrix(seq_list)

Arguments

Value

A matrix with the number of rows equal to the number of sequences and the number of columns equal to the length of the longest sequence.

Note

Since matrix must have specified number of columns, ends of shorter sequences are completed with NAs.

Examples

list2matrix(list(s1 = c("c", "g", "g", "t"),
                 s2 = c("g", "t", "c", "t", "t", "g"),
                 s3 = c("a", "a", "t")))

Convert numbers to letters

Description

Converts biological sequence from number to letter notation.

Usage

n2l(seq, seq_type)

Arguments

Value

a character vector or matrix containing converted elements.

See Also

n2l is a wrapper around degenerate.

Inverse function: l2n.

Examples

sample_seq <- c(1, 3, 3, 6, 1, 6, 12, 1, 10)
n2l(sample_seq, "prot")

n-grams to data frame

Description

Tranforms a vector of n-grams into a data frame.

Usage

ngrams2df(ngrams)

Arguments

Value

a data.frame with 2 (in case of n-grams without known position) or three columns (n-grams with position information).

See Also

Decode n-grams: decode_ngrams.

Examples

ngrams2df(c("2_1.1.2_0.0", "3_1.1.2_0.0", "3_2.2.2_0.0", "2_1.1_0"))

Plot criterion distribution

Description

Plots results of distr_crit function.

Usage

## S3 method for class 'criterion_distribution'
plot(x, ...)

Arguments

Value

nothing.

Examples

target_feature <- create_feature_target(10, 375, 15, 600) 
example_result <- distr_crit(target = target_feature[,1], 
                             feature = target_feature[,2])
plot(example_result)

# a ggplot2 plot
library(ggplot2)
ggplot_distr <- function(x) {
b <- data.frame(cbind(x=as.numeric(rownames(attr(x, "plot_data"))), 
                      attr(x, "plot_data")))
d1 <- cbind(b[,c(1,2)], attr(x, "nice_name"))
d2 <- cbind(b[,c(1,3)], "Probability")
colnames(d1) <- c("x", "y", "panel")
colnames(d2) <- c("x", "y", "panel")
d <- rbind(d1, d2)
p <- ggplot(data = d, mapping = aes(x = x, y = y)) + 
  facet_grid(panel~., scale="free") + 
  geom_freqpoly(data= d2, aes(color=y), stat = "identity") + 
  scale_fill_brewer(palette = "Set1") + 
  geom_point(data=d1, aes(size=y), stat = "identity") + 
  guides(color = "none") + 
  guides(size = "none") + 
  xlab("Number of cases with feature=1 and target=1") + ylab("")
p
}
ggplot_distr(example_result)

Position n-grams

Description

Tranforms a vector of positioned n-grams into a list of positions filled with n-grams that start on them.

Usage

position_ngrams(ngrams, df = FALSE, unigrams_output = TRUE)

Arguments

Value

if df is FALSE, returns a list of length equal to the number of unique n-gram starts present in n-grams. Each element of the list contains n-grams that start on this position. If df is FALSE, returns a data frame where first column contains n-grams and the second column represent their start positions.

See Also

Transform n-gram name to human-friendly form: decode_ngrams.

Validate n-gram structure: is_ngram.

Examples

# position data in the list format
position_ngrams(c("2_1.1.2_0.1", "3_1.1.2_0.0", "3_2.2.2_0.0"))
# position data in the data frame format
position_ngrams(c("2_1.1.2_0.1", "3_1.1.2_0.0", "3_2.2.2_0.0"), df = TRUE)

Print tested features

Description

Prints results of test_features function.

Usage

## S3 method for class 'feature_test'
print(x, ...)

Arguments

Value

nothing.

Read FASTA files

Description

A lightweight tool to read nucleic or amino-acid sequences from a file in FASTA format.

Usage

read_fasta(file)

Arguments

Value

a list of sequences.

See Also

read.fasta: heavier function for processing FASTA files.

Examples

## Not run: 
  read_fasta("https://www.uniprot.org/uniprot/P28307.fasta")
  
## End(Not run)

Regenerate n-grams

Description

'Regenerates' amino acid or nucleic sequence written in a simplified alphabet by converting groups to regular expression.

Usage

regenerate(x, element_groups)

Arguments

Value

A character string representing a POSIX regular expression.

Note

Gaps (_) will be converted to any possible character from the alphabet (nucleotides or amino acids).

See Also

degenerate to easily convert information stored in biological sequences from letters to numbers. calc_ed to calculate distance between simplified alphabets.

Examples

regenerate("ssw", list(w = c(1, 4), s = c(2, 3)))

regional_param class

Description

List of rules defining the region.

Details

An object of the regional_param class is a list consisting of all rules necessary to properly build a region.

Attributes

reg_len

the number of unigrams inside the region. Might be 0

prop_ranges

required intervals of properties of unigrams in the region

exactness

a numeric value between 0 and 1 defining how stricly unigrams are kept within prop_ranges. If 1, only unigrams within prop_ranges are inside the region. if 0.9, there is 10 unigrams that are not in the prop_ranges will be inside the region.

See Also

generate_sequence

Extract n-grams from sequence

Description

Extracts vector of n-grams present in sequence(s).

Usage

seq2ngrams(seq, n, u, d = 0, pos = FALSE)

Arguments

Details

A format of d vector is discussed in Details of count_ngrams.

Value

A character matrix of n-grams, where every row corresponds to a different sequence.

Examples

# trigrams from multiple sequences
seqs <- matrix(sample(1L:4, 600, replace = TRUE), ncol = 50)
seq2ngrams(seqs, 3, 1L:4)

Convert encoding from simple to full format

Description

Converts an encoding from the simple format to the full format.

Usage

simple2full(x)

Arguments

Details

The encoding should be named. Each name should correspond to a different amino acid or nucleotide.

Examples

aa1 = structure(c("1", "4", "3", "3", "4", "1", "2", "1", "2", "1", 
                  "1", "4", "1", "4", "4", "4", "4", "1", "4", "4"), 
                .Names = c("a", "c", "d", "e", "f", "g", "h", "i", 
                           "k", "l", "m", "n", "p", "q", 
                           "r", "s", "t", "v", "w", "y"))
simple2full(aa1)

Summarize tested features

Description

Summarizes results of test_features function.

Usage

## S3 method for class 'feature_test'
summary(object, conf_level = 0.95, ...)

Arguments

Value

nothing.

Tabulate n-grams

Description

Builds a contingency table of the n-gram counts versus their class labels.

Usage

table_ngrams(seq, ngrams, target)

Arguments

Value

a data frame with the number of columns equal to the length of the target plus 1. The first column contains names of the n-grams. Further columns represents counts of n-grams for respective value of the target.

Examples

seqs_pos <- matrix(sample(c("a", "c", "g", "t"), 100, replace = TRUE, 
            prob = c(0.2, 0.4, 0.35, 0.05)), ncol = 5)
seqs_neg <- matrix(sample(c("a", "c", "g", "t"), 100, replace = TRUE), 
            ncol = 5)
tab <- table_ngrams(seq = rbind(seqs_pos, seqs_neg), 
                    ngrams = c("1_c.t_0", "1_g.g_0", "2_t.c_0", "2_g.g_0", "3_c.c_0", "3_g.c_0"), 
                    target = c(rep(1, 20), rep(0, 20)))
# see the results
print(tab)
# easily plot the results using ggplot2

Permutation test for feature selection

Description

Performs a feature selection on positioned n-gram data using a Fisher's permutation test.

Usage

test_features(
  target,
  features,
  criterion = "ig",
  adjust = "BH",
  threshold = 1,
  quick = TRUE,
  times = 1e+05
)

Arguments

Details

Since the procedure involves multiple testing, it is advisable to use one of the avaible p-value adjustment methods. Such methods can be used directly by specifying the adjust parameter.

Available criterions:

ig

Information Gain: calc_ig.

kl

Kullback-Leibler divergence: calc_kl.

cs

Chi-squared-based measure: calc_cs.

Value

an object of class feature_test.

Note

Both target and features must be binary, i.e. contain only 0 and 1 values.

Features occuring too often and too rarely are considered not informative and may be removed using the threshold parameter.

References

Radivojac P, Obradovic Z, Dunker AK, Vucetic S, Feature selection filters based on the permutation test in Machine Learning: ECML 2004, 15th European Conference on Machine Learning, Springer, 2004.

See Also

binarize - binarizes input data.

calc_criterion - computes selected criterion.

distr_crit - distribution of criterion used in QuiPT.

summary.feature_test - summary of results.

cut.feature_test - aggregates test results in groups based on feature's p-value.

Examples

# significant feature
tar_feat1 <- create_feature_target(10, 390, 0, 600) 
# significant feature
tar_feat2 <- create_feature_target(9, 391, 1, 599)
# insignificant feature
tar_feat3 <- create_feature_target(198, 202, 300, 300)
test_res <- test_features(tar_feat1[, 1], cbind(tar_feat1[, 2], tar_feat2[, 2], 
                          tar_feat3[, 2]))
summary(test_res)
cut(test_res)

# real data example
# we will analyze only a subsample of a dataset to make analysis quicker
ids <- c(1L:100, 701L:800)
deg_seqs <- degenerate(human_cleave[ids, 1L:9], 
                       list(`a` = c(1, 6, 8, 10, 11, 18), 
                            `b` = c(2, 5, 13, 14, 16, 17, 19, 20), 
                            `c` = c(3, 4, 7, 9, 12, 15)))

# positioned n-grams example
bigrams_pos <- count_ngrams(deg_seqs, 2, letters[1L:3], pos = TRUE)
test_features(human_cleave[ids, 10], bigrams_pos)

# unpositioned n-grams example, binarization required
bigrams_notpos <- count_ngrams(deg_seqs, 2, letters[1L:3], pos = TRUE)
test_features(human_cleave[ids, 10], binarize(bigrams_notpos))

Validate encoding

Description

Checks the structure of an encoding.

Usage

validate_encoding(x, u)

Arguments

Details

The encoding is a list of groups to which elements of an alphabet should be reduced. All elements of the alphabet (all amino acids or all nucleotides) should appear in the encoding.

Value

TRUE if the x is a correctly reduced u, FALSE in any other cases.

See Also

calc_ed: calculate the encoding distance between two encodings. encoding2df: converts an encoding to a data frame.

Examples

enc1 = list(`1` = c("a", "t"), 
            `2` = c("g", "c"))
# see if enc1 is the correctly reduced nucleotide (DNA) alphabet
validate_encoding(enc1, c("a", "c", "g", "t"))

# enc1 is not the RNA alphabet, so the results is FALSE
validate_encoding(enc1, c("a", "c", "g", "u"))

# validate_encoding works also on other notations
enc2 = list(a = c(1, 4),
            b = c(2, 3))
validate_encoding(enc2, 1L:4)

Write encodings to a file

Description

Saves a list of encodings (or a single encoding to the file).

Usage

write_encoding(x, file = "")

Arguments

Examples

aa1 = list(`1` = c("g", "a", "p", "v", "m", "l", "i"), 
           `2` = c("k", "h"), 
           `3` = c("d", "e"), 
           `4` = c("f", "r", "w", "y", "s", "t", "c", "n", "q"))
write_encoding(aa1)

Write FASTA files

Description

A lightweight tool to read nucleic or amino-acid sequences from a file in FASTA format.

Usage

write_fasta(seq, file, nchar = 80)

Arguments

See Also

write.fasta: heavier function for writing FASTA files.