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CVTree Online User's Manual

Zhao XU and Bailin HAO



CVTree is an alignment-free tool for phylogeny study based on whole genome sequences, which was firsted introduced as a web server in the 2004 NAR web server issue Qi et al. (2004a). The new features of this CVTree update include:

  1. the inbuilt database has been enlarged and is now updated monthly from the NCBI FTP site Sayers et al. (2009).
  2. Users may upload sequences of their own and carry out phylogenetic study together with genomes selected from the inbuilt database.
  3. Many kinds of tree files are provided to facilitate comparison with taxonomy. Some tree files are directly uploadable to MEGA Tamura et al. (2007) or the Interactive Tree Of Life (iTOL) project Letunic and Bork (2007) in order to display the results in different ways.
  4. The efficiency of CVTree has been significantly enhanced to meet the requirement of treating thousands of genomes in a single run.

Furthermore, we have added 82 fungal genomes into our genome data sets and more Eukaryote genomes are being collected. This makes the new CVTree server more suitable for the Assembling the Tree of Life project as an independent source of information in addition to the SSU rRNA based or few-gene based phylogeny.

How to cite CVTree:
Zhao Xu, Bailin Hao, ``CVTree update: a newly designed phylogenetic study platform using composition vectors and whole genomes'', Nucleic Acids Res. published online on April 26, 2009. doi:10.1093/nar/gkp278.

Web Interface

Getting started

First user please click on `Create a new project' button [Fig 1-\vbox{\kern3pt\textcircled{{1}}}]. As the button name suggests, you will get a project space for your own. In this project space you can select inbuilt species that meet your specific interest to built phylogenetic trees as well as upload your own genome data to find out their possible phylogenetic position. You may also download some sequences for further study.

If you have already created a project, you can input the project number and press `Reload project' button [Fig 1-\vbox{\kern3pt\textcircled{{3}}}] to get back to the previous project. Please note that a project will be discarded if not in use for 2 days in order to save disk space.

If you want to try our server, there is an Example project with pre-loaded data. Just click `Example project' [Fig 1-\vbox{\kern3pt\textcircled{{2}}}] and then click `All parameters are fine, run project!' button to see the result page appearing online.

Figure 1: First page

Setting project parameters

Once you have created a project, you will see the project page. In this page user can select the K-mer length K in the CV method. We suggest K=5 or 6 for Prokaryotes and K=7 for Eukaryotes.

In the project page, you have to decide whether to use whole gnome FASTA nucleotide coding regions file (*.ffn) or FASTA amino acid file (*.faa) to construct phylogenetic trees. We have carried out a series of study on phylogenetic trees constructed by using protein sequences GAO (2003); Gao et al. (2007); Gao and Qi (2007); Qi et al. (2004b) while the DNA phylogeny has not been fully explored yet.

User can download inbuilt species for their own further study by clicking the `Download selected genomes' button [Fig 2-\vbox{\kern3pt\textcircled{{1}}}]. Note that each time user can only be allowed to choose no more than 900 species to download due to disk space limitation.

Figure 2: Selected genomes

Uploading sequences

User can upload their own sequences into the project space. The sequences should be in FASTA format. During uploading, all files will be regarded as Protein/DNA sequences according to the ``Sequence Type'' chosen on the top of CVTree Project Page. Extension name of each file will be wrapped to .faa or .ffn for Protein or DNA respectivly. A bunch of sequences can be compressed in one file for uploading. The following compressed files are acceptable: GZIP(.tar.gz, .gz), BZIP2(.tar.bz2, bz2), TAR(.tar), RAR( .rar) and ZIP(.zip).

Please note that a single file to be uploading should not be greater than 20MB, and the total size of uploaded files in one project space should not be larger than 100MB.

Here are some example files:

Table: Compressed file examples
Example file Size(Bytes)
test1.faa.gz 335,185
test2.rar 1,243,263 1,276,008
test4.tar.bz2 1,121,911
test5.tar.gz 1,269,450
test6.tgz 1,269,450
test7.tar 2,478,080

Choosing inbuilt species

At the bottom of the project page, user finds a `see details' button, By clicking this button, you will see the inbuilt species page. In this page, there are several ways to select species that you are interested in.

Here are some more examples:

As the neighbor joining program produces an unrooted tree, choosing an outgroup may help to output the tree in a more convenient fashion. This is done by clicking the `out-group' radio button in the first column of the species list. If not chosen, an outgroup will be selected at random by the neighbor joining program. The outgroup information is shown in the project page.

Result page

Output files

Example ascii tree

If we selected some inbuilt species and run the project, an ascii tree will be displayed like this (it is the NJtree.txt):
  15 Populations

Neighbor-Joining/UPGMA method version 3.67

 Neighbor-joining method

 Negative branch lengths allowed

  ! +---Staphylococcus_aureus_aureus_MRSA252
  ! !
  ! !     +Staphylococcus_aureus_JH1
  ! !   +-2
  ! !   ! +Staphylococcus_aureus_JH9
  6-7 +-4
  ! ! ! !   +Staphylococcus_aureus_Mu3
  ! ! ! ! +-1
  ! ! ! +-3 +Staphylococcus_aureus_Mu50
  ! ! !   !
  ! ! !   +Staphylococcus_aureus_N315
  ! +-8
  !   !   +Staphylococcus_aureus_MW2
  !   ! +-5
  !   ! ! +Staphylococcus_aureus_aureus_MSSA476
  !   ! !
  !   +-9     +Staphylococcus_aureus_COL
  !     !  +-12
  !     !  !  !  +Staphylococcus_aureus_USA300
  !     !  !  +-10
  !     +-13     +Staphylococcus_aureus_USA300_TCH1516
  !        !
  !        !  +Staphylococcus_aureus_NCTC_8325
  !        +-11
  !           +Staphylococcus_aureus_Newman

Inbuilt Genome Data Sets

In the CVTree web server, the inbuilt genome data sets consist of two major parts: a monthly updated prokaryote genome set from NCBI Sayers et al. (2009) and a manually collected fungi genome set from FGI (Fungal Genome Initiative), JGI (DOE Joint Genome Institute), RFCG and other sources. By the end of May 2009, there are total 972 organism, including 824 Bacteria, 62 Archaea, 82 Fungi and 4 more Eukaryotes. The later ones were used as outgroup species in our previous study Gao et al. (2007).

A user can either study phylogenetic relationship within the inbuilt species or append the CVTree with their own sequences.

Prokaryote Genomes

There are two available sets of prokaryote complete genomes. Those in GenBank Benson et al. (2009) are the original data submitted by their authors. Those at the National Center for Biotechnological Information (NCBI) are reference genomes curated by NCBI staff. Since the latter represents the approach of one and the same group using the same set of tools, it may provide a more consistent background for comparison. Therefore, we use all the translated amino acid sequences (the .faa files with NC_ accession numbers) from NCBI. This part of data is automatically updated monthly.

Fungi Genomes

We have collected 82 Fungi genomes from different sources, see the following table for detailed information.

Species Strain (Sub)Phylum Source
Aspergillus clavatus NRRL1 Ascomycota BROAD-FGI
Aspergillus flavus NRRL3357 Ascomycota BROAD-FGI
Aspergillus fumigatus Af293 Ascomycota BROAD-FGI
Aspergillus nidulans FGSCA4 Ascomycota BROAD-FGI
Aspergillus niger ATCC1015 Ascomycota BROAD-FGI
Aspergillus oryzae RIB40 Ascomycota BROAD-FGI
Aspergillus terreus NIH2624 Ascomycota BROAD-FGI
Botrytis cinerea B05.10 Ascomycota BROAD-FGI
Candida albicans WO-1 Ascomycota BROAD-FGI
Candida albicans SC5314 Ascomycota BROAD-FGI
Candida glabrata CBS138 Ascomycota NCBI
Candida guilliermondii ATCC6260 Ascomycota BROAD-FGI
Candida lusitaniae ATCC42720 Ascomycota BROAD-FGI
Candida parapsilosis isolate 317 Ascomycota BROAD-FGI
Candida tropicalis MYA-3404 Ascomycota BROAD-FGI
Chaetomium globosum CBS148.51 Ascomycota BROAD-FGI
Coccidioides immitis RS Ascomycota BROAD-FGI
Coccidioides immitis h538.4 Ascomycota BROAD-FGI
Coccidioides immitis RMSCC2394 Ascomycota BROAD-FGI
Coccidioides immitis RMSCC3703 Ascomycota BROAD-FGI
Coccidioides posadasii Silveira Ascomycota BROAD-FGI
Coccidioides posadasii RMSCC3488 Ascomycota BROAD-FGI
Cochliobolus heterostrophus C5 Ascomycota JGI
Paracoccidioides brasiliensis Pb01 Ascomycota BROAD-FGI
Paracoccidioides brasiliensis Pb03 Ascomycota BROAD-FGI
Paracoccidioides brasiliensis Pb18 Ascomycota BROAD-FGI
Debaryomyces hansenii CBS767 Ascomycota BROAD-FGI
Eremothecium gossypii$ ^{a}$ ATCC10895 Ascomycota NCBI
Fusarium graminearum PH-1 Ascomycota BROAD-FGI
Fusarium oxysporum f.sp.lycopersici Ascomycota BROAD-FGI
Fusarium verticillioides 7600 Ascomycota BROAD-FGI
Histoplasma capsulatum$ ^{b}$ WU24(NAm1) Ascomycota BROAD-FGI
Kluyveromyces lactis NRRLY-1140 Ascomycota BROAD-FGI
Kluyveromyces waltii NCYC 2644 Ascomycota RFCG
Lodderomyces elongisporus NRRLYB-4239 Ascomycota BROAD-FGI
Magnaporthe grisea 70-15 Ascomycota BROAD-FGI
Mycosphaerella fijiensis CIRAD86 Ascomycota JGI
Mycosphaerella graminicola IPO323 Ascomycota JGI
Nectria haematococca$ ^{c}$ MPVI Ascomycota JGI
Neosartorya fischeri NRRL181 Ascomycota BROAD-FGI
Neurospora crassa OR74A Ascomycota BROAD-FGI
Pyrenophora tritici-repentis Pt-1C-BFP Ascomycota BROAD-FGI
Pichia stipitis CBS6054 Ascomycota JGI
Podospora anserina DSM980 Ascomycota RFCG
Saccharomyces cerevisiae S288C Ascomycota NCBI
Saccharomyces cerevisiae rm11-1a Ascomycota BROAD-FGI
Saccharomyces cerevisiae YJM789 Ascomycota RFCG
Saccharomyces paradoxus NRRLY-17217 Ascomycota RFCG
Saccharomyces mikatae IFO1815 Ascomycota RFCG
Saccharomyces kudriavzevii IFO1802 Ascomycota RFCG
Saccharomyces bayanus MCYC623 Ascomycota RFCG
Saccharomyces castellii NRRLY-12630 Ascomycota RFCG
Saccharomyces kluyveri NRRL Y-12651 Ascomycota RFCG
Schizosaccharomyces japonicus yFS275 Ascomycota BROAD-FGI
Schizosaccharomyces octosporus yFS286 Ascomycota FGR
Schizosaccharomyces pombe 972h- Ascomycota BROAD-FGI
Sclerotinia sclerotiorum 1980 Ascomycota BROAD-FGI
Stagonospora nodorum SN15 Ascomycota BROAD-FGI
Trichoderma atroviride IMI202040 Ascomycota JGI
Trichoderma reesei QM6a Ascomycota JGI
Trichoderma virens Gv29-8 Ascomycota JGI
Uncinocarpus reesii 1704 Ascomycota BROAD-FGI
Verticillium dahliae VdLs.17 Ascomycota BROAD-FGI
Verticillium albo-atrum VaMs.102 Ascomycota BROAD-FGI
Yarrowia lipolytica CLIB122 Ascomycota NCBI
Coprinus cinereus Okayama7#130 Basidiomycota BROAD-FGI
Cryptococcus neoformans serotypeA,strainH99 Basidiomycota BROAD-FGI
Cryptococcus neoformans serotypeD,strainJEC21 Basidiomycota NCBI
Cryptococcus gattii serotypeB,strainWM276 Basidiomycota RFCG
Cryptococcus gattii serotypeB/C,strainR265 Basidiomycota RFCG
Laccaria bicolor S238N-H82 Basidiomycota JGI
Malassezia globosa CBS7966 Basidiomycota FGR
Phanerochaete chrysosporium RP-78 Basidiomycota JGI
Postia placenta Basidiomycota JGI
Puccinia graminis f.sp.tritici Basidiomycota BROAD-FGI
Sporobolomyces roseus Basidiomycota JGI
Ustilago maydis 521 Basidiomycota BROAD-FGI
Batrachochytrium dendrobatidis JAM81 Chytridiomycota JGI
Batrachochytrium dendrobatidis JEL423 Chytridiomycota BROAD-FGI
Rhizopus oryzae RA99-880 Mucoromycotina BROAD-FGI
Phycomyces blakesleeanus Mucoromycotina JGI
Encephalitozoon cuniculi GB-M1 Microsporidia NCBI
Footnotes of Table 1:
$ ^{a}$ synonym: Ashbya gossypii
$ ^{b}$ teleomorph: Ajellomyces capsulata
$ ^{c}$ anamorph: Fusarium solani

Eukaryote Genomes

Currently we only provide 4 more Eukaryote genomes. User can use these genomes as outgroup species in their phylogeny study. They are Caenorhabditis elegans, Arabidopsis thaliana, Plasmodium falciparum and Drosophila melanogaster.


Frequency or Probability of Appearance of K-Strings

Comparison of $ G+C$ content or amino acid composition has long been a standard practice in analyzing biological sequences. By extending single nucleotide or single amino acid counting to longer strings one increases ˇ°resolution powerˇ± of the analysis, takes into account short-term correlations in the sequences, and enhances species-specificity of some sequence features. Among early work along this line we mention the use of dinulceotide relative abundance as a genomic signature Karlin and Burge (1995). Given a DNA or amino acid sequence of length $ L$ , we count the number of appearance of (overlapping) strings of a fixed length $ K$ in the sequence. The counting may be performed for a complete genome or for a collection of translated amino acid sequences. There are in total $ N$ possible types of such strings: $ N=4^K$ for DNA and $ N=20^K$ for amino acid sequences.

For concreteness consider the case of one protein sequence of length $ L$ . Denote the frequency of appearance of the $ K$ -string $ a_1a_2
\cdots a_K$ by $ f(a_1a_2 \cdots a_K)$ , where each $ a_i$ is one of the 20 amino acid single-letter symbols. This frequency divided by the total number $ (L-K+1)$ of $ K$ -strings in the given protein sequence may be taken as the probability $ p(a_1a_2 \cdots a_K)$ of appearance of the string $ a_1a_2
\cdots a_K$ in the protein:

$\displaystyle p(a_1a_2 \cdots a_K)=\frac{f(a_1a_2 \cdots a_K)}{(L-K+1)}$ (1)

The collection of such frequencies or probabilities reflects both the result of random mutations and selective evolution in terms of $ K$ -strings as ˇ°building blocksˇ±.

Subtraction of Random Background

Mutations happen in a more or less random manner at the molecular level, while selections shape the direction of evolution. Neutral mutations lead to some randomness in the $ K$ -string composition. In order to highlight the selective diversification of sequence composition one must subtract a random background from the simple counting results. This is done as follows.

Suppose we have done direct counting for all strings of length $ (K-1)$ and $ (K-2)$ . The probability of appearance of $ K$ -strings is predicted by using a Markov assumption:

$\displaystyle p^0(a_1a_2 \cdots a_K) = \frac{p(a_1a_2 \cdots a_{K-1})p(a_2a_3\cdots a_K)}{p(a_2a_3 \cdots a_{K-1})}$ (2)

The superscript 0 on $ p^0$ indicates the fact that it is a predicted quantity. We note that the denominator comes from the frequency of $ (K-2)$ -strings. This kind of Markov prediction has been used in biological sequence analysis Brendel et al. (1986). It can be justified by virtue of a maximal entropy principle with appropriate constraints Hu and Wang (2001).

Composition Vectors and Distance Matrix

It is the difference between the actual counting result $ p$ and the predicted value $ p^0$ that really reflects the shaping role of selective evolution. Therefore, we collect

$\displaystyle a_i(a_1a_2 \cdots a_K) = \begin{cases}\frac{p(a_1a_2 \cdots a_K) ...
...\cdots a_K)} & \text{when $p^0 \neq 0$}\\ 0 & \text{when $p^0 = 0$} \end{cases}$ (3)

for all possible strings $ a_1a_2
\cdots a_K$ as components to form a composition vector for a species. To further simplify the notations, we write $ a_i$ for the $ i$ -th component corresponding to the string type $ i$ , where $ i$ runs from 1 to $ N=20^K$ . Putting these components in a fixed lexicographic order, we obtain a composition vector for the species $ A$ :

$\displaystyle A=(a_1,a_2,\cdots,a_N)$    

Likewise, for the species $ B$ we have a composition vector

$\displaystyle B=(b_1,b_2,\cdots,b_N)$    

In principle there are three different ways to construct the composition vectors. First, one may use the whole genome sequence. Second, one may just collect the coding sequences in the genome. Third, one makes use of the translated amino acid sequences from the coding segments of DNA. As mutation rates are higher and more variable in non-coding segments and protein sequences change at a more or less constant rate, one expects that the third choice is the best and the second is better than the first. We tried all three choices and the requirement of consistency served as a criterion. By consistency we mean the topology of the trees constructed with growing $ K$ should converge. This is best realized with phylogenetic relations obtained from protein sequences. Therefore, in what follows we concentrate on results based on amino acid sequences.

The correlation $ C(A,B)$ between any two species $ A$ and $ B$ is calculated as the cosine function of the angle between the two representative vectors in the $ N$ -dimensional space of composition vectors:

$\displaystyle C(A,B)=\frac{\sum_{i=1}^Na_i \times b_i}{(\sum_{i=1}^Na_i^2 \times \sum_{i=1}^Nb_i^2)^{\frac{1}{2}}}$ (4)

The distance $ D(A,B)$ between the two species is defined as

$\displaystyle D(A,B)=\frac{1-C(A,B)}{2}$ (5)

Since $ C(A,B)$ may vary between -1 and 1, the distance is normalized to the interval $ (0,1)$ . The collection of distances for all species pairs comprises a distance matrix.

Tree Construction

The emphasis of the CVTree approach is to provide a new way to infer evolutionary distances between species from the whole genome data without doing sequence alignment. Once a distance matrix has been calculated it is straightforward to construct phylogenetic trees by following the standard procedures. We use the neighbor-joining method Saitou and Nei (1987) in the PHYLIP package for all $ K \geq 3$ trees. The Fitch method is not feasible when the number of species gets large. We did not use such algorithm as the maximal likelihood since it is not based on distance matrices alone. The final phylogenetic trees are drawn using the NEIGHBOR software in the PHYLIP package.

Source code availability

The source code of the latest stand-alone CVTree program has been tested on CentOS 4.3 x86_64 Linux with GCC 4.2.2, which can be downloaded form:

Development history and acknowledgements

The CVTree approach was first announced in 2002 at C.N. Yang's 80th Birthday Conference Hao et al. (2003) and applied to coronaviruses GAO (2003) and prokaryotes Qi et al. (2004b). Stand-alone CVTree programs were written from scratch by Qi, Gao and Sun independently at different times. The first CVTree web server was built by Ji Qi and Hong Luo in 2004. The CVTree update was constructed by Zhao Xu in 2007 and tested by many users since then.

The CVTree project has been supported by National Basic Research Program of China (The 973 Program No. 2007CB814800) and Shanghai Leading Academic Discipline Project (Project No. B111).


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CVTree Online User's Manual

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