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Sequence Composition - Protein
Software for Sequence Composition -
Protein
- backtranseq - Back translate a protein
sequence
(part of the EMBOSS package)
- charge - Create a protein charge plot
(part of the EMBOSS package)
- checktrans - Report stop codons and ORF
statistics of a protein
(part of the EMBOSS package)
- compseq - Count composition of dimer,
trimer and other words in a sequence
(part of the EMBOSS package)
- emowse - Identify proteins by mass
spectrometry
(part of the EMBOSS package)
- freak - Create a residue or base
frequency table or plot
(part of the EMBOSS package)
- iep - Calculate the isoelectric point
of a protein
(part of the EMBOSS package)
- mwcontam - Show molecular weights that
match across a set of files
(part of the EMBOSS package)
- mwfilter - Filter noisy molecular
weights from mass spectrometry output
(part of the EMBOSS package)
- octanol - Display protein hydropathy
(part of the EMBOSS package)
- pepinfo - Plot simple amino acid
properties in parallel
(part of the EMBOSS package)
- pepstats - Create a report of simple
protein sequence information
(part of the EMBOSS package)
- pepwindow - Display protein hydropathy
(part of the EMBOSS package)
- pepwindowall - Display protein
hydropathy for a set of sequences
(part of the EMBOSS package)
- Phobius - Predict transmembrane
topology and signal peptides from the amino acid sequence
of a protein
- ProP - Predict arginine and lysine
propeptide cleavage sites in eukaryotic protein sequences using an
ensemble of neural networks
- PSORT - Predict protein sorting signals
and localization sites in amino acid sequences
- SAPS (Statistical Analysis of Protein
Sequences) - evaluate a wide variety of protein sequence properties
using statistical criteria
- SecretomeP - Predict non-classical
(not signal peptide triggered) protein secretion
- SignalP - Predict the presence and
location of signal peptide cleavage sites in amino acid sequences
from different organisms based on neural networks and Hidden Markov
Models
- TMHMM - Predict transmembrane helices
based on a Hidden Markov Model
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