Nucleotide Sequence Analysis

Calculate and interactively explore sequence statistics; calculate sequence properties; analyze motifs; design primers; find restriction enzymes

Perform in-depth analyses of nucleotide sequences to get better understanding of sequence features and functions. You can compute sequence statistics, such as codon frequency and density of nucleotides, and interactively explore the sequences in the Sequence Viewer app. Calculate complementary strands and sequence properties of DNA oligonucleotides. Use various sequence utility functions to convert DNA to RNA sequences and vice versa, find nucleotide codes, and calculate consensus sequences.

Apps

Sequence Viewer

Visualize and interactively explore biological sequences

Functions

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Compute Sequence Statistics

`basecount`	Count nucleotides in sequence
`codonbias`	Calculate codon frequency for each amino acid coded for in nucleotide sequence
`codoncount`	Count codons in nucleotide sequence
`dimercount`	Count dimers in nucleotide sequence
`ntdensity`	Plot density of nucleotides along sequence
`seqwordcount`	Count number of occurrences of word in sequence

Convert Sequences

`nt2aa`	Convert nucleotide sequence to amino acid sequence
`aa2nt`	Convert amino acid sequence to nucleotide sequence
`nt2int`	Convert nucleotide sequence from letter to integer representation
`int2nt`	Convert nucleotide sequence from integer to letter representation
`dna2rna`	Convert DNA sequence to RNA sequence
`rna2dna`	Convert RNA sequence to DNA sequence
`revgeneticcode`	Return reverse mapping (amino acid to nucleotide codon) for genetic code
`seqcomplement`	Complement of nucleotide sequence
`seqrcomplement`	Calculate reverse complementary strand of nucleotide sequence
`seqreverse`	Calculate reverse strand of nucleotide sequence
`baselookup`	Find nucleotide codes, integers, names, and complements
`geneticcode`	Return nucleotide codon to amino acid mapping for genetic code
`oligoprop`	Calculate sequence properties of DNA oligonucleotide
`seq2regexp`	Convert sequence with ambiguous characters to regular expression
`seqinsertgaps`	Insert gaps into nucleotide or amino acid sequence

Analyze Sequence Features

`cpgisland`	Locate CpG islands in DNA sequence
`joinseq`	Join two sequences to produce shortest supersequence
`palindromes`	Find palindromes in sequence
`randseq`	Generate random sequence from finite alphabet
`seqmatch`	Find matches for every character vector in library
`seqviewer`	Visualize and interactively explore biological sequences
`seqdisp`	Format long sequence output for easy viewing
`seqshoworfs`	Display open reading frames in sequence
`seqprofile`	Calculate sequence profile from set of multiply aligned sequences
`featureparse`	Parse features from GenBank, GenPept, or EMBL data
`featureview`	Draw linear or circular map of features from GenBank structure

Visualize Sequences

`seqconsensus`	Calculate consensus sequence
`seqdotplot`	Create dot plot of two sequences
`seqlogo`	Display sequence logo for nucleotide or amino acid sequences

Find Restriction Enzymes

`rebasecuts`	Find restriction enzymes that cut nucleotide sequence
`restrict`	Split nucleotide sequence at restriction site

Topics

Exploring a Nucleotide Sequence Using Command Line
Starting with a DNA sequence, calculate statistics for the nucleotide content.
Exploring a Nucleotide Sequence Using the Sequence Viewer App
Use a graphical interface for the sequence functions.
Sequence Utilities and Statistics
You can manipulate and analyze your sequences to gain a deeper understanding of the physical, chemical, and biological characteristics of your data.

Featured Examples

Analyzing Synonymous and Nonsynonymous Substitution Rates

How the analysis of synonymous and nonsynonymous mutations at the nucleotide level can suggest patterns of molecular adaptation in the genome of HIV-1. This example is based on the discussion of natural selection at the molecular level presented in Chapter 6 of "Introduction to Computational Genomics. A Case Studies Approach" [1].

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Exploring Primer Design

Use the Bioinformatics Toolbox™ to find potential primers that can be used for automated DNA sequencing.

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Identifying Over-Represented Regulatory Motifs

Illustrates a simple approach to searching for potential regulatory motifs in a set of co-expressed genomic sequences by identifying significantly over-represented ungapped words of fixed length. The discussion is based on the case study presented in Chapter 10 of "Introduction to Computational Genomics. A Case Studies Approach" [1].

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