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    • Project History
    • How does cogent3 relate to PyCogent?
    • Projects using cogent3
    • Data Files Used in the Documentation
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  • GitHub
  • Image Gallery
  • Installation
  • User Guides
  • Community
  • License
  • Project History
  • How does cogent3 relate to PyCogent?
  • Projects using cogent3
  • Data Files Used in the Documentation
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Section Navigation

  • apps
    • Overview of apps
      • Overview of using apps
      • Getting help on an app
      • Getting an app
      • Displaying installed apps
      • Data stores – collections of data records
      • Tracking records that could not be processed
    • App Cookbook
      • Loading data
        • Loading aligned sequence data
        • Loading unaligned sequence data
        • Loading tabular data
        • Loading JSON serialised objects
      • Sequence Alignment
        • Using a nucleotide model
        • Using a codon model
        • Using a protein model
      • Filtering data
        • Removing degenerate characters
        • Removing highly gapped positions
        • Remove problem sequences from an alignment
        • Remove duplicated sequences from an alignment
        • Filter sequence collections and alignments by length
        • Sample an alignment to a fixed length
        • Remove trailing stop codons from sequences in an alignment
        • Sample nucleotides from a given codon position
        • Select named sequences from a collection
        • Select n sequences from a collection
        • Concatenating alignments
      • Writing Data
        • Writing sequences and sequence alignments
        • Writing tabular data
        • Writing JSON Serialised Objects
        • Writing a database to file
    • Tutorials
      • Modelling sequence evolution
        • Apply a non-stationary nucleotide model to an alignment with 3 sequences
        • Apply a non-stationary nucleotide model to an alignment with a tree
        • Specifying a non-stationary model with time-heterogeneous parameters
        • Extracting maximum likelihood estimates from a model_result
        • Reconstructing ancestral states
        • Applying a discrete-time, non-stationary nucleotide model
        • Applying a time-reversible nucleotide model
        • Applying GNC, a non-stationary codon model
        • Applying a time-reversible codon model
      • Testing hypotheses about sequence evolution
        • Testing a hypothesis – non-stationary or time-reversible
      • Testing for natural selection
        • natsel_neutral – a test for selective neutrality
        • natsel_timehet – a test of branch heterogeneity
        • natsel_sitehet – a test of site heterogeneity
        • natsel_zhang – a branch-site test
    • Custom Apps
      • Why write apps?
  • Cookbook
    • Manipulating biological data
      • Loading a sequence from a file
      • Sequences
      • Protein sequences
      • Sequence Collections and Alignments
      • Features
      • Annotation Databases
      • Molecular types
      • Alphabets
      • Available genetic codes
      • Using genetic codes
      • Trees
    • Tabular data
    • Building alignments
    • Genetic distance calculation
    • Building phylogenies
    • Evolutionary Analysis Using Likelihood
    • Useful Utilities
  • Tutorials
    • Map protein alignment gaps to DNA alignment gaps
    • Manipulation of Tree Node Objects
    • Estimating periodic signals
    • The simplest script
    • Performing a relative rate test
    • A test of the neutral theory
    • Allowing substitution model parameters to differ between branches
    • Using codon models
    • Use an empirical protein substitution model
    • Likelihood analysis of multiple loci
    • Simulate an alignment
    • Performing a parametric bootstrap
    • Analysis of rate heterogeneity
    • Evaluate process heterogeneity using a Hidden Markov Model
    • Inferring Amino Acid frequencies with a nonstationary model
    • Calculate pairwise distances between sequences
    • Make a neighbor joining tree
    • Make a UPGMA cluster
    • Phylogenetic reconstruction by least squares
    • Parallel computations
  • API
    • load_seq
    • load_aligned_seqs
    • load_unaligned_seqs
    • load_delimited
    • load_table
    • load_tree
    • load_annotations
    • open_data_store
    • open
    • make_seq
    • make_aligned_seqs
    • make_unaligned_seqs
    • make_table
    • make_tree
    • get_code
    • get_moltype
    • get_model
    • available_codes
    • available_moltypes
    • available_models
    • available_apps
    • alignment
      • SequenceCollection
      • Alignment
      • ArrayAlignment
    • annotation
      • Feature
    • annotation_db
      • BasicAnnotationDb
      • GffAnnotationDb
      • GenbankAnnotationDb
    • sequence
      • DnaSequence
      • RnaSequence
      • ProteinSequence
    • genetic_code
      • GeneticCode
    • moltype
      • MolType
    • alphabet
      • Alphabet
    • table
      • Table
      • Columns
      • FilteringParser
    • tree
      • PhyloNode
    • new_alignment
      • SeqsData
      • SeqDataView
      • SequenceCollection
      • AlignedSeqsData
      • AlignedDataView
      • Aligned
      • Alignment
    • new_sequence
      • DnaSequence
      • RnaSequence
      • ProteinSequence
      • SeqView
      • SliceRecord
    • new_genetic_code
      • GeneticCode
    • new_moltype
      • MolType
    • new_alphabet
      • CharAlphabet
      • KmerAlphabet
    • define_app
    • register_datastore_reader
    • DataMember
    • DataStoreDirectory
    • ReadOnlyDataStoreZipped
    • DataStoreSqlite
    • deserialise_object
    • register_deserialiser
  • User Guides
  • apps
  • Custom Apps

Custom Apps#

You can write your own apps.

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natsel_zhang – a branch-site test

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Why write apps?

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