BACKGROUND
in  <dig>  hebert et al  <cit>  proposed to use a 648-bp region from the 5'-end of the cytochrome c oxidase subunit  <dig>  gene as a dna barcode for identifying all metazoan species.
the final goal of dna barcoding is to identify all eukaryotic species  <cit> .
recently, dna barcoding has been tested on several groups of organisms including birds  <cit> , fishes  <cit>  and amphibians  <cit>  with promising results.
ratnasingham and hebert  <cit>  estimated that the barcode of life data system  would eventually generate  <dig> million records  twice the current size of the genbank database, and that enterprise-scale software would be needed to handle such a large dataset.
the goal of bold is to generate a "taxon id tree" based on a neighbour-joining  algorithm for every query sequence for easy identification.
as in other traditional methods of tree construction, sequence alignment has to be performed before construction of the k2p/nj-tree .
in bold, alignment is executed based on the hidden markov models  <cit> .
due to the high computational burden involved, bold has unfortunately been unable to incorporate all data records in constructing the k2p/nj tree.
the short-term solution is to divide the large barcode dataset into several "sub-projects" with a size limit of  <dig>  specimens each for analysis  <cit> .
however, as an estimated  <dig>  additional barcode records will be entered in the database each year  <cit> , the limit of  <dig>  specimens for each sub-project will be quickly saturated because closely related taxa  should not be divided into subsets but preferably analyzed together.
the long-term solution is therefore to develop more efficient analytical methods as alternatives or supplements for handling such a large dataset.
coi has several claims to be a suitable dna barcode marker, including ease in amplification across a wide variety of organisms and provision of enough information to enable organisms to be identified to the species level.
but it also has its drawbacks, including inherent risks due to maternal inheritance , the presence of pseudogenes , and its inconsistent evolutionary rate among lineages  <cit> .
these disadvantages continue to disappoint biologists hoping to rely on single gene as the sole marker for taxonomic identification  <cit> .
the feasibility of using alternative and additional genes as dna barcodes has been explored  <cit> .
ribosomal rna  genes and their internal transcribed spacer   <cit>  show promise as dna barcode markers for distinguishing different organisms and, as coi is highly conserved in plants, barcode markers such as the trnh-psba spacer and rbcl gene  <cit>  have also been proposed.
yet because of the high frequency of insertions/deletions in the rrna and the other non-protein-coding markers, sequence alignment is a critical step in the analysis.
this, in turn, requires a large amount of additional computational power during the alignment step, further pushing the analytical power of bold system toward its limit.
the process of sequence alignment is not only very costly in computational power, but has also yet to be standardised.
a lack of standard protocols and inconsistencies between the aligned datasets of different laboratories represent a drawback  <cit>  to the incorporation of any non-protein-barcoding markers in bold.
including markers such as rrna in bold would mean that alignment will be executed automatically and any problems in alignment cannot be adjusted manually.
the problems associated with the alignment procedure effectively limit the use of dna barcoding  <cit> .
our previous study  <cit>  demonstrated that the composition vector  method  <cit>  was an effective and efficient tree construction method for analyzing rrna datasets, thereby facilitating the use of these genes as dna barcodes.
we believe that the cv approach can also sidestep the problems associated with sequence alignment in analyzing large datasets of both coi and non-protein-coding barcode markers, the latter of which usually involve sequence length variation even among closely related taxa.
in the present study, we analyzed large dna barcode datasets, each with more than  <dig> sequences  with the cv method.
sequences from three published dna barcode datasets available from genbank, namely birds  <cit> , fishes  <cit>  and nematodes  <cit> , were analyzed.
a 16s rrna dataset of decapod crustaceans containing  <dig> genbank sequences and  <dig> sequences generated in our laboratory was also analyzed.
these datasets were chosen because they included the most common genetic markers that have been proposed as dna barcodes, including coi, 16s rrna and nuclear ssu rrna genes, and contained the largest number of dna sequences, ranging from  <dig> to  <dig>  so far assembled.
the aim of our study was to evaluate how well and how fast the cv method could handle these large barcode datasets without sequence alignment.
RESULTS
bird dataset
the coi dataset of  <dig> north american bird species  <cit>  included  <dig> dna sequences from  <dig> genera,  <dig> families and  <dig> orders .
in the k2p/nj tree , species from  <dig> orders could be properly grouped, except that falco peregrinus, f. columbarius, f. sparverius and cathartes aura from the order falconiformes and grus canadensis from the order gruiformes did not cluster with members of the same order.
at the family level, species from  <dig> families were grouped respectively.
all species could be correctly grouped and identified at the genus/species level .
in the cv/nj tree , other than species from the above two orders, members of two other orders, passeriformes and ciconiiformes, could not be grouped together.
in the former, two taxa  were clustered with members in the order columbiformes, while one species  from ciconiiformes was nested within the gruiformes species.
at the family level, species from  <dig> families could be grouped correctly in our cv/nj tree .
compared with the k2p/nj tree, species from two additional families failed to cluster together.
one species , oenanthe oenanthe, from the family turdidae and three species , melospiza georgiana, m. lincolnii and m. melodia, from the emberizidae could not be assigned to their respective families.
as in the k2p/nj method, all species could be grouped and identified at the genus/species level in the cv/nj tree .
the cv method matched the overall grouping effectiveness with the k2p analysis in this dataset.
k
fish dataset
the coi dataset of  <dig> australian marine fish species  <cit>  contained  <dig> coi sequences with an average length of  <dig> bp from  <dig> classes,  <dig> orders,  <dig> families and  <dig> genera .
in the k2p/nj tree ,  <dig>   <dig>   <dig> and  <dig> species of the orders perciformes , scorpaeniformes '' rajiformes  and lamniformes  could not be grouped with members of the same order, respectively.
these former orders also failed to be grouped accordingly in the cv/nj tree  but all species from the order lamniformes could be successfully grouped together.
yet the cv/nj tree failed to assign callorhinchus milii, a species of chimaeriformes , to the clade of this order.
at the family level , four out of the  <dig> families could not be grouped correctly in the k2p/nj tree.
<dig>   <dig>   <dig> and  <dig> species of the families dasyatididae , percichthyidae , sciaenidae  and scorpidae  could not be grouped with members from the same family, respectively.
in the cv/nj tree , members from six additional families failed to be grouped properly.
<dig>   <dig>   <dig>   <dig>   <dig> and  <dig> species of the families myliobatidae , platycephalidae , serranidae , scombridae , scorpaenidae  and triglidae  could not be grouped with members from the same family.
as with the k2p/nj tree, all  <dig> species could be correctly discriminated and identified at the genus/species level with the cv/nj method .
the cv method matched the resolving power of the k2p method in terms of its effectiveness in identifying species.
nematode dataset
holterman et al  <cit>  used a small-subunit  rdna tree to reconstruct the phylogenetic relationships in the phylum nematoda.
the dataset of nearly full-length ssu rdna sequences  contained  <dig> ingroup taxa and ten outgroup taxa .
based on three phylogenetic reconstruction methods, including bayesian inference , maximum parsimony  and nj, the species could be separated into  <dig> major clades.
the cv/nj tree  was also able to generate a similar tree topology with the  <dig> clades, and successfully assigned  <dig>  taxa into their respective clades , except two species in clade  <dig> , two species from clade  <dig> , four species from clade  <dig> , five species from clade  <dig>  and one species from clade  <dig> .
the four species from clades  <dig> and  <dig> were placed in the basal position in the cv/nj tree while the five species from clade  <dig> formed a sister group to the group comprising clades  <dig> and  <dig>  the five species in clade  <dig> were clustered with brevibucca sp.
from clade  <dig>  holterman et al  <cit> , using traditional methods, were not able to suggest a phylogenetic position of the family choanolaimidae.
with the cv method, changing the k value would alter the position of choanolaimidae.
for instance, with the k value of  <dig> choanolaimidae was assigned into clade  <dig>  when the k value dropped below  <dig>  choanolaimidae became a sister group to the group comprising clades  <dig> and  <dig>
decapod dataset
the 16s rrna dataset of decapod crustaceans contained  <dig> sequences  from eight infraorders,  <dig> superfamilies,  <dig> families and  <dig> genera .
the average sequence length was  <dig> bp.
both the k2p/nj and cv/nj trees showed similar topologies, and the relationships among the taxa were also highly similar between the two trees.
at the infraorder level, neither tree could group the  <dig> anomura species together.
in the k2p/nj tree , species from the infraorder anomura were separated into three major groups, while only two were evident in the cv/nj tree .
for the infraorder astacidea, the cv/nj tree  separated the  <dig> species into two clades.
although the k2p/nj tree was able to group all these species into a single clade, the clade also contained species from two other infraorders, thalassinidea and palinura.
neither tree was able to assign infraorders thalassinidea  and palinura  as reciprocal monophyletic groups.
by contrast, all the  <dig> species from the infraorder brachyura could be successfully grouped in the k2p/nj tree, while  <dig> of the species could not be grouped with other members of this infraorder by the cv/nj method.
in fact, the brachyura clade in the cv/nj tree also contained a small number of species from the infraorder thalassinidea.
by using the k2p/nj method, all  <dig> species from infraorder caridea could be successfully grouped as a single clade, while three species could not be grouped into the caridea clade in the cv/nj tree.
at the family level, both the k2p and cv methods showed a high resolving power in assigning taxa to their respective families.
with the k2p/nj method, only members from six families, viz.
ocypodidae, oplophoridae, palaemonidae, palinuridae, penaeidae and potamonautidae, could not be grouped respectively, out of a total of  <dig> families.
interestingly, while species from penaeidae could be grouped together in the cv/nj tree, members from two other families, atyidae and gecarcinidae, failed to cluster together in this tree.
the overall grouping effectiveness of the k2p and cv methods was comparable .
processing time
all the data analyses in this study were preformed on a  <dig>  ghz notebook computer.
with the k2p/nj method, the alignment step using clustal w  <cit>  alone took more than five hours to complete for the bird dataset, and required more than  <dig> hours to complete for the other three datasets .
when the other two faster algorithms mafft  <cit>  and muscle  <cit>  were used, the processing time was reduced by  <dig>  and  <dig>  folds respectively, so that between one and two hours were needed for aligning the bird dataset.
by contrast, with the cv method it took less than three minutes to analyze this bird dataset, and the analysis of the other three datasets was completed in five minutes .
thus, while the cv/nj method matched the resolving power of the k2p/nj method in generating a highly similar tree, it took less than 6% of the time needed in sequence alignment on the same computer.
discussion
qi et al  <cit>  first introduced the cv method to analyze the phylogeny of prokaryotes based on the complete genomes, and this approach has subsequently been applied to analyze the chloroplast genomes  <cit> .
all these studies were based on protein sequences, and a procedure to subtract the random background from the frequencies of oligopeptide strings was used before computation of the cvs in order to diminish the influence of random neutral mutations at the molecular level and to highlight the shaping role of selective evolution.
however, in adopting the cv method in analyzing short rdna sequences for barcoding, chu et al  <cit>  have shown that a subtraction procedure for random background does not further enhance the reliability of the groupings.
in the present study, we focus on how well the cv method could handle large dna datasets, by comparing this method with the k2p/nj method in analyzing four datasets including both rrna and protein-coding genes.
although in each of the four datasets tested the topologies between the cv/nj and k2p/nj trees were not identical, this does not mean that one method obtained better results than the other.
the rationale for generating the k2p/nj trees in dna barcoding studies or in bold is that the k2p/nj tree construction is relatively simple  <cit> , so that a query sequence can be rapidly identified to the species level.
however, the k2p/nj tree was not intended to reflect the phylogenetic relationships among the taxa analyzed  <cit> .
thus any detailed comparison in tree topologies between the cv and the k2p methods is meaningless.
in fact, both the cv and k2p methods performed equally well in identifying and discriminating taxa at the genus/species level in the datasets tested .
the dataset of nematodes  <cit>  was used to resolve the phylogenetic relationships among the species, and three different approaches  were applied.
however, the classification of nematodes based on the molecular approach is never an easy task.
the phylogenetic position of the family choanolaimidae, for example, could not be determined based on any of the three approaches.
two species, trichinella spiralis and trichuris muris, could only be assigned into clade  <dig> based on the bi tree but not on the mp and nj trees  <cit> .
the problem might be due to the inadequacy of the ssu rdna sequences in resolving the phylogeny of this group.
the evolutionary rates of this gene may also differ among different nematode clades  <cit> .
on the other hand, floyd et al  <cit>  proposed to use the number of ssu rdna sequence differences to define "molecular operational taxonomic units" rather than adopting the classical species concepts in nematode biodiversity studies.
it is not surprising to find inconsistent tree topologies based on different analytical methods, including the cv method, from this nematode dataset.
in fact, the cv method could generate a tree topology similar to those generated by the three phylogenetic reconstruction methods, by clustering all of the taxa into  <dig> main clades .
besides its impressive resolving power, the main advantage of the cv/nj method over the k2p/nj algorithm is its speed.
in every dataset analyzed in this study, the cv/nj method could generate trees in less than five minutes on a  <dig>  ghz notebook computer.
the time needed is at least 15-fold more using the k2p/nj method, which requires sequence alignment.
given its high analytical speed, the cv method could profitably serve as a quick barcoding identification tool capable of matching a query sequence against a pre-installed reference dataset from bold on a notebook computer for field workers who may not have internet access.
the cv method not only saves time by omitting the alignment step, but also avoids the introduction of any human errors during the alignment process.
where no universal alignment parameters can be defined, every gap representing insertion/deletion that is assigned to a dna sequence should be checked by eye carefully, and even this step might be subjective  <cit> .
manual checking becomes a very tedious and laborious procedure when dealing with a large dataset generated by multiple alignments, but is a necessary step for verifying the reliability of the dataset and its suitability for further analysis.
the cv method does away with this rate-limiting, tedious step in the tree construction procedure for dna barcoding.
CONCLUSIONS
the cv method was first demonstrated to facilitate the use of rdna datasets for barcoding purposes since no sequence alignment was necessary  <cit> .
in the present study, we further demonstrated the power of the cv method in analyzing large dna barcode datasets, regardless of the type of gene markers used.
in all cases tested, the cv/nj method achieved tree topologies similar to those based on the traditional methods which involve sequence alignment, with compatible grouping effectiveness.
furthermore, when the cv method was used, the computational time was at least 15-fold shorter than that based on the k2p method.
besides its effective resolving power and very fast speed of analysis, the cv/nj method can routinely generate reliable and reproducible trees by eliminating human errors in the multiple alignment process.
to conclude, we propose that the cv/nj method can be used as an effective and efficient tree construction algorithm in analyzing dna barcode datasets.

 in hebert et al proposed to use a 648-bp region from the 5'-end of the cytochrome c oxidase subunit gene as a dna barcode for identifying all metazoan species. the final goal of dna barcoding is to identify all eukaryotic species. recently, dna barcoding has been tested on several groups of organisms including birds, fishes and amphibians with promising results. ratnasingham and hebert estimated that the barcode of life data system would eventually generate million records twice the current size of the genbank database, and that enterprise-scale software would be needed to handle such a large dataset. the goal of bold is to generate a "taxon id tree" based on a neighbour-joining algorithm for every query sequence for easy identification. as in other traditional methods of tree construction, sequence alignment has to be performed before construction of the k2p/nj-tree. in bold, alignment is executed based on the hidden markov models. due to the high computational burden involved, bold has unfortunately been unable to incorporate all data records in constructing the k2p/nj tree. the short-term solution is to divide the large barcode dataset into several "sub-projects" with a size limit of specimens each for analysis. however, as an estimated additional barcode records will be entered in the database each year, the limit of specimens for each sub-project will be quickly saturated because closely related taxa should not be divided into subsets but preferably analyzed together. the long-term solution is therefore to develop more efficient analytical methods as alternatives or supplements for handling such a large dataset. coi has several claims to be a suitable dna barcode marker, including ease in amplification across a wide variety of organisms and provision of enough information to enable organisms to be identified to the species level. but it also has its drawbacks, including inherent risks due to maternal inheritance, the presence of pseudogenes, and its inconsistent evolutionary rate among lineages. these disadvantages continue to disappoint biologists hoping to rely on single gene as the sole marker for taxonomic identification. the feasibility of using alternative and additional genes as dna barcodes has been explored. ribosomal rna genes and their internal transcribed spacer show promise as dna barcode markers for distinguishing different organisms and, as coi is highly conserved in plants, barcode markers such as the trnh-psba spacer and rbcl gene have also been proposed. yet because of the high frequency of insertions/deletions in the rrna and the other non-protein-coding markers, sequence alignment is a critical step in the analysis. this, in turn, requires a large amount of additional computational power during the alignment step, further pushing the analytical power of bold system toward its limit. the process of sequence alignment is not only very costly in computational power, but has also yet to be standardised. a lack of standard protocols and inconsistencies between the aligned datasets of different laboratories represent a drawback to the incorporation of any non-protein-barcoding markers in bold. including markers such as rrna in bold would mean that alignment will be executed automatically and any problems in alignment cannot be adjusted manually. the problems associated with the alignment procedure effectively limit the use of dna barcoding. our previous study demonstrated that the composition vector method was an effective and efficient tree construction method for analyzing rrna datasets, thereby facilitating the use of these genes as dna barcodes. we believe that the cv approach can also sidestep the problems associated with sequence alignment in analyzing large datasets of both coi and non-protein-coding barcode markers, the latter of which usually involve sequence length variation even among closely related taxa. in the present study, we analyzed large dna barcode datasets, each with more than sequences with the cv method. sequences from three published dna barcode datasets available from genbank, namely birds, fishes and nematodes, were analyzed. a 16s rrna dataset of decapod crustaceans containing genbank sequences and sequences generated in our laboratory was also analyzed. these datasets were chosen because they included the most common genetic markers that have been proposed as dna barcodes, including coi, 16s rrna and nuclear ssu rrna genes, and contained the largest number of dna sequences, ranging from to so far assembled. the aim of our study was to evaluate how well and how fast the cv method could handle these large barcode datasets without sequence alignment.  bird dataset the coi dataset of north american bird species included dna sequences from genera, families and orders. in the k2p/nj tree, species from orders could be properly grouped, except that falco peregrinus, f. columbarius, f. sparverius and cathartes aura from the order falconiformes and grus canadensis from the order gruiformes did not cluster with members of the same order. at the family level, species from families were grouped respectively. all species could be correctly grouped and identified at the genus/species level. in the cv/nj tree, other than species from the above two orders, members of two other orders, passeriformes and ciconiiformes, could not be grouped together. in the former, two taxa were clustered with members in the order columbiformes, while one species from ciconiiformes was nested within the gruiformes species. at the family level, species from families could be grouped correctly in our cv/nj tree. compared with the k2p/nj tree, species from two additional families failed to cluster together. one species, oenanthe oenanthe, from the family turdidae and three species, melospiza georgiana, m. lincolnii and m. melodia, from the emberizidae could not be assigned to their respective families. as in the k2p/nj method, all species could be grouped and identified at the genus/species level in the cv/nj tree. the cv method matched the overall grouping effectiveness with the k2p analysis in this dataset. k fish dataset the coi dataset of australian marine fish species contained coi sequences with an average length of bp from classes, orders, families and genera. in the k2p/nj tree, and species of the orders perciformes, scorpaeniformes'' rajiformes and lamniformes could not be grouped with members of the same order, respectively. these former orders also failed to be grouped accordingly in the cv/nj tree but all species from the order lamniformes could be successfully grouped together. yet the cv/nj tree failed to assign callorhinchus milii, a species of chimaeriformes, to the clade of this order. at the family level, four out of the families could not be grouped correctly in the k2p/nj tree. and species of the families dasyatididae, percichthyidae, sciaenidae and scorpidae could not be grouped with members from the same family, respectively. in the cv/nj tree, members from six additional families failed to be grouped properly. and species of the families myliobatidae, platycephalidae, serranidae, scombridae, scorpaenidae and triglidae could not be grouped with members from the same family. as with the k2p/nj tree, all species could be correctly discriminated and identified at the genus/species level with the cv/nj method. the cv method matched the resolving power of the k2p method in terms of its effectiveness in identifying species. nematode dataset holterman et al used a small-subunit rdna tree to reconstruct the phylogenetic relationships in the phylum nematoda. the dataset of nearly full-length ssu rdna sequences contained ingroup taxa and ten outgroup taxa. based on three phylogenetic reconstruction methods, including bayesian inference, maximum parsimony and nj, the species could be separated into major clades. the cv/nj tree was also able to generate a similar tree topology with the clades, and successfully assigned taxa into their respective clades, except two species in clade, two species from clade, four species from clade, five species from clade and one species from clade. the four species from clades and were placed in the basal position in the cv/nj tree while the five species from clade formed a sister group to the group comprising clades and the five species in clade were clustered with brevibucca sp. from clade holterman et al, using traditional methods, were not able to suggest a phylogenetic position of the family choanolaimidae. with the cv method, changing the k value would alter the position of choanolaimidae. for instance, with the k value of choanolaimidae was assigned into clade when the k value dropped below choanolaimidae became a sister group to the group comprising clades and decapod dataset the 16s rrna dataset of decapod crustaceans contained sequences from eight infraorders, superfamilies, families and genera. the average sequence length was bp. both the k2p/nj and cv/nj trees showed similar topologies, and the relationships among the taxa were also highly similar between the two trees. at the infraorder level, neither tree could group the anomura species together. in the k2p/nj tree, species from the infraorder anomura were separated into three major groups, while only two were evident in the cv/nj tree. for the infraorder astacidea, the cv/nj tree separated the species into two clades. although the k2p/nj tree was able to group all these species into a single clade, the clade also contained species from two other infraorders, thalassinidea and palinura. neither tree was able to assign infraorders thalassinidea and palinura as reciprocal monophyletic groups. by contrast, all the species from the infraorder brachyura could be successfully grouped in the k2p/nj tree, while of the species could not be grouped with other members of this infraorder by the cv/nj method. in fact, the brachyura clade in the cv/nj tree also contained a small number of species from the infraorder thalassinidea. by using the k2p/nj method, all species from infraorder caridea could be successfully grouped as a single clade, while three species could not be grouped into the caridea clade in the cv/nj tree. at the family level, both the k2p and cv methods showed a high resolving power in assigning taxa to their respective families. with the k2p/nj method, only members from six families, viz. ocypodidae, oplophoridae, palaemonidae, palinuridae, penaeidae and potamonautidae, could not be grouped respectively, out of a total of families. interestingly, while species from penaeidae could be grouped together in the cv/nj tree, members from two other families, atyidae and gecarcinidae, failed to cluster together in this tree. the overall grouping effectiveness of the k2p and cv methods was comparable. processing time all the data analyses in this study were preformed on a ghz notebook computer. with the k2p/nj method, the alignment step using clustal w alone took more than five hours to complete for the bird dataset, and required more than hours to complete for the other three datasets. when the other two faster algorithms mafft and muscle were used, the processing time was reduced by and folds respectively, so that between one and two hours were needed for aligning the bird dataset. by contrast, with the cv method it took less than three minutes to analyze this bird dataset, and the analysis of the other three datasets was completed in five minutes. thus, while the cv/nj method matched the resolving power of the k2p/nj method in generating a highly similar tree, it took less than 6% of the time needed in sequence alignment on the same computer. discussion qi et al first introduced the cv method to analyze the phylogeny of prokaryotes based on the complete genomes, and this approach has subsequently been applied to analyze the chloroplast genomes. all these studies were based on protein sequences, and a procedure to subtract the random background from the frequencies of oligopeptide strings was used before computation of the cvs in order to diminish the influence of random neutral mutations at the molecular level and to highlight the shaping role of selective evolution. however, in adopting the cv method in analyzing short rdna sequences for barcoding, chu et al have shown that a subtraction procedure for random background does not further enhance the reliability of the groupings. in the present study, we focus on how well the cv method could handle large dna datasets, by comparing this method with the k2p/nj method in analyzing four datasets including both rrna and protein-coding genes. although in each of the four datasets tested the topologies between the cv/nj and k2p/nj trees were not identical, this does not mean that one method obtained better results than the other. the rationale for generating the k2p/nj trees in dna barcoding studies or in bold is that the k2p/nj tree construction is relatively simple, so that a query sequence can be rapidly identified to the species level. however, the k2p/nj tree was not intended to reflect the phylogenetic relationships among the taxa analyzed. thus any detailed comparison in tree topologies between the cv and the k2p methods is meaningless. in fact, both the cv and k2p methods performed equally well in identifying and discriminating taxa at the genus/species level in the datasets tested. the dataset of nematodes was used to resolve the phylogenetic relationships among the species, and three different approaches were applied. however, the classification of nematodes based on the molecular approach is never an easy task. the phylogenetic position of the family choanolaimidae, for example, could not be determined based on any of the three approaches. two species, trichinella spiralis and trichuris muris, could only be assigned into clade based on the bi tree but not on the mp and nj trees. the problem might be due to the inadequacy of the ssu rdna sequences in resolving the phylogeny of this group. the evolutionary rates of this gene may also differ among different nematode clades. on the other hand, floyd et al proposed to use the number of ssu rdna sequence differences to define "molecular operational taxonomic units" rather than adopting the classical species concepts in nematode biodiversity studies. it is not surprising to find inconsistent tree topologies based on different analytical methods, including the cv method, from this nematode dataset. in fact, the cv method could generate a tree topology similar to those generated by the three phylogenetic reconstruction methods, by clustering all of the taxa into main clades. besides its impressive resolving power, the main advantage of the cv/nj method over the k2p/nj algorithm is its speed. in every dataset analyzed in this study, the cv/nj method could generate trees in less than five minutes on a ghz notebook computer. the time needed is at least 15-fold more using the k2p/nj method, which requires sequence alignment. given its high analytical speed, the cv method could profitably serve as a quick barcoding identification tool capable of matching a query sequence against a pre-installed reference dataset from bold on a notebook computer for field workers who may not have internet access. the cv method not only saves time by omitting the alignment step, but also avoids the introduction of any human errors during the alignment process. where no universal alignment parameters can be defined, every gap representing insertion/deletion that is assigned to a dna sequence should be checked by eye carefully, and even this step might be subjective. manual checking becomes a very tedious and laborious procedure when dealing with a large dataset generated by multiple alignments, but is a necessary step for verifying the reliability of the dataset and its suitability for further analysis. the cv method does away with this rate-limiting, tedious step in the tree construction procedure for dna barcoding.  the cv method was first demonstrated to facilitate the use of rdna datasets for barcoding purposes since no sequence alignment was necessary. in the present study, we further demonstrated the power of the cv method in analyzing large dna barcode datasets, regardless of the type of gene markers used. in all cases tested, the cv/nj method achieved tree topologies similar to those based on the traditional methods which involve sequence alignment, with compatible grouping effectiveness. furthermore, when the cv method was used, the computational time was at least 15-fold shorter than that based on the k2p method. besides its effective resolving power and very fast speed of analysis, the cv/nj method can routinely generate reliable and reproducible trees by eliminating human errors in the multiple alignment process. to conclude, we propose that the cv/nj method can be used as an effective and efficient tree construction algorithm in analyzing dna barcode datasets. 
sequence alignment is the rate-limiting step in constructing profile trees for dna barcoding purposes. we recently demonstrated the feasibility of using unaligned rrna sequences as barcodes based on a composition vector approach without sequence alignment. here, we further explored the grouping effectiveness of the cv method in large dna barcode datasets from a variety of organisms, including birds, fishes, nematodes and crustaceans. our results indicate that the grouping of taxa at the genus/species levels based on the cv/nj approach is invariably consistent with the trees generated by traditional approaches, although in some cases the clustering among higher groups might differ. furthermore, the cv method is always much faster than the k2p method routinely used in constructing profile trees for dna barcoding. the cv method performs well in grouping effectiveness of dna barcode sequences, as compared to k2p analysis of aligned sequences. we conclude that the cv method is a fast and reliable method for analyzing large datasets for dna barcoding purposes. it was also able to reduce the time required for analysis by over 15-fold, making it a far superior method for analyzing large datasets. for instance, the alignment of coi sequences from fishes took more than ten hours to complete, while the whole tree construction process using the cv/nj method required no more than five minutes on the same computer.
 sequence alignment is the rate-limiting step in constructing profile trees for dna barcoding purposes. we recently demonstrated the feasibility of using unaligned rrna sequences as barcodes based on a composition vector approach without sequence alignment. here, we further explored the grouping effectiveness of the cv method in large dna barcode datasets from a variety of organisms, including birds, fishes, nematodes and crustaceans.  our results indicate that the grouping of taxa at the genus/species levels based on the cv/nj approach is invariably consistent with the trees generated by traditional approaches, although in some cases the clustering among higher groups might differ. furthermore, the cv method is always much faster than the k2p method routinely used in constructing profile trees for dna barcoding. for instance, the alignment of coi sequences from fishes took more than ten hours to complete, while the whole tree construction process using the cv/nj method required no more than five minutes on the same computer.  the cv method performs well in grouping effectiveness of dna barcode sequences, as compared to k2p analysis of aligned sequences. it was also able to reduce the time required for analysis by over 15-fold, making it a far superior method for analyzing large datasets. we conclude that the cv method is a fast and reliable method for analyzing large datasets for dna barcoding purposes. 