Swaap and Swaap PH

Swaap 1.0.2

Swaap 1.0.2 was created to be an easy-to-use integrated package for the analysis of multiple alignments and bacterial genome sequences. Its primary purpose is for sliding window analysis, to analyze trends in substitutions and similarity across multiple alignments, or trends in nucleotide usage patterns across bacterial genomes. Most functions available in Swaap can be performed for entire genomes or multiple alignments, for portions of each, or with a sliding window approach. Swaap has rudimentary plotting functions, however, it is designed to work with Microsoft Excel to analyze and plot resulting data.

Multiple alignment functions available in Swaap:

Nucleotide and amino acid identity
Hydropathy index
Evolutionary distances (4 methods available)
Synonymous and nonsynonymous substitutions (3 methods available)
Transitions and transversions (4 methods available)
G + C content
Codon usage and relative synonymous codon usage (RSCU)
Pattern density (Markov chain and Zero-Order Markov available)
Find sequences in alignments
Translate nucleotide sequences
Reverse, complement, and reverse-complement sequences
Save sequences in different formats (3 formats available)
Remove sequences from alignments
Remove insertions and deletions from alignments
Perform analysis by codon position
Degenerate primer design

Bacterial genome functions available in Swaap:

G + C content
Find ORFs
Pattern density (Markov chain and Zero-Order Markov available)
GC and AT skew
Oligonucleotide word frequencies (dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide) using 3 different methods (Markov Chain, Zero-Order Markov, and codon usage)
Oligonucleotide difference index (oligonucleotides across sliding window)
Removal of horizontal DNA (based on GC content, or tetranucleotide difference index) in genomes
Updating of help file currently in progress

Swaap was used in our recent publications:

Pride, D.T., and M.J. Blaser (2002). Concerted evolution between duplicated genetic elements in

Helicobacter pylori. J Mol Biol. 316: 629-42.

Pride, D.T., and M.J. Blaser (2002). Identification of horizontally acquired genetic elements in

Helicobacter pylori and other prokaryotes using oligonucleotide difference analysis.

Genome Letters 1: 2-15.

Ghose, C., G.I. Perez-Perez, M.G. Dominguez-Bella, D.T. Pride, C. Bravi, and M.J. Blaser (2002).

East Asian Genotypes of Helicobacter pylori strains in Amerindians provides evidence for its

ancient human carriage. PNAS 99: 15107-11

Pride, D.T., R.J. Meinersmann, T.M. Wassenaar, and M.J. Blaser (2003). Evolutionary implications

of microbial genome tetranucleotide frequency biases. Genome Research 13: 145-58.

Pride, D.T., T.M. Wassenaar, C. Ghose, and M. J. Blaser (2006). Evidence of host-virus co-evolution

in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genomics 7: 8.

Pride, D.T., and C. Ghose (2005). Core genomic signature analysis in prokaryotes: what happens to

phylogenetic signal when the horizontal DNA is gone? In Progress.

Swaap is available for 32- bit Windows platforms only.

Download updated version Swaap version 1.0.2 (4.4mb zip file)

Tips for getting started:

If having problems with MSF file formats, convert multiple alignments to GDE format using ClustalX
All bacterial genomes must begin with the word 'ORIGIN'
Save genomes in Swaap Genome Format to avert converting the same genome multiple times
Swaap does not interpret amino acid alignments, but does translate nucleotide alignments
Swaap can only interpret multiple alignments with up to 100 different sequences

Swaap Phylogeny Helper (Swaap PH)

Swaap PH was created to analyze differences in patterns of nucleotide usage between coding and noncoding regions of microbial genomes, as well as to create distances between different microbes based on nucleotide usage differences. The distances can be used as the basis for microbial phylogeny, using Phylip95 or Paup 4.0. Swaap PH is designed to work in conjunction with Swaap for genome analysis. Swaap PH also is capable of creating artificial bacterial genomes (pseudogenomes) based on several different criteria. These pseudogenomes can be analyzed for patterns of nucleotide usage using Swaap, and compared with actual genome data. The output of each analysis is designed to be used with Microsoft Excel.

Bacterial genome functions available in Swaap PH for coding regions:

G + C content analysis
Analysis of G + C content deviation by codon position
Analysis of G + C content deviation in reference to other genes
Codon usage and RSCU analysis
Codon usage and RSCU deviation analysis
Oligonucleotide word deviation analysis (dinucleotide, trinucleotide, and tetranucleotide)
Oligonucleotide word deviation analysis in reference to other genes
Oligonucleotide word frequencies (dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide) using 2 different methods (Markov Chain, Zero-Order Markov)
Tetranucleotide frequencies based on codon usage
Sliding window analysis of transcription direction

Bacterial genome functions available in Swaap PH for noncoding regions:

Find noncoding regions (based on genbank annotation)
G + C content
Oligonucleotide word frequencies (dinucleotide, trinucleotide, tetranucleotide, pentanucleotide, and hexanucleotide) using 3 different methods (Markov Chain, Zero-Order Markov)

Bacterial genome functions available in Swaap PH for pseudogenome creation:

Pseudogenome based on G + C content
Pseudogenome based on codon usage
Pseudogenome based on codon usage, retaining intergenic regions
Pseudogenome based on dinucleotide usage
Pseudogenome based on di-amino acid usage

Bacterial genome functions available in Swaap PH for microbial phylogeny:

Assemble files for distance matrix creation (dinucleotides, trinucleotides, tetranucleotides, pentanucleotides, and hexanucleotides)
Distance matrix based on sum of differences in oligonucleotide frequencies (bootstrapping available)
Distance matrix based on character states of oligonucleotide frequencies (2 character states - overrepresented and underrepresented)
Extensive distance matrix based on character states of oligonucleotide frequencies (multiple character states based on the extent of over- and underpresentation of oligonucleotides)
Distance matrix analysis after removal of aberrantly represented oligonucleotides

Swaap PH was used in our recent publications:

Pride, D.T., and M.J. Blaser (2002). Identification of horizontally acquired genetic elements in

Helicobacter pylori and other prokaryotes using oligonucleotide difference analysis.

Genome Letters 1: 2-15.

Pride, D.T., R.J. Meinersmann, T.M. Wassenaar, and M.J. Blaser (2003). Evolutionary implications

of microbial genome tetranucleotide frequency biases. Genome Research 13: 145-58.

Pride, D.T., T.M. Wassenaar, C. Ghose, and M. J. Blaser (2006). Evidence of host-virus co-evolution

in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genomics 7: 8.

Pride, D.T., and C. Ghose (2005). Core genomic signature analysis in prokaryotes: what happens to

phylogenetic signal when the horizontal DNA is gone? In Progress.

Swaap PH is available for 32- bit Windows platforms only.

Download updated Swaap version 1.0.2 (4.5mb zip file)

Tips for getting started:

All bacterial genome coding region files must begin with the word 'ORIGIN'
Finding noncoding regions requires genomes in Swaap genome format (requires use of Swaap)
Save data files, and open using Microsoft Excel
Creation of di-amino acid genomes requires analysis of di-amino acids
Distance files are created based on oligonucleotides requires use of the 'Input files' function under the 'Phylogeny' menu. Files to be input are those saved files from analysis of oligonucleotides. These files also can be created manually. See help file for details.
Swaap PH only interprets genomes with up to 8000 ORFs

Disclaimer:

This software is supplied as-is, with no warranty of any kind expressed or implied. I have made efforts to make it reliable, and to avoid errors, however but will not be liable for its use or misuse. The user is solely responsible for the validity of any results generated. Specifically, the author, Vanderbilt University, New York University, Massachusetts General Hospital, Harvard University, and The Bacteria Museum are not liable for any damage or loss of data resulting from use of this software, even if it is due to negligence on the part of the author of this software.

This software and this document are the responsibility of David T. Pride, MD PhD. The views expressed herein do not necessarily represent the views of The Virtual Museum of Bacteria.

Page last modified: 26 October 2006