Bioinformatics
Phylogenetics
Phylogenetic analyses of the bioinformatically recovered
transcripts were performed to allow reconstruction of the
molecular evolutionary history of each toxin type. Toxin
sequences were identified by comparison of the translated
DNA sequences with previously characterised toxins using a
BLAST search (Altschul et al. 1997) of the UniProtKB
protein database. Molecular phylogenetic analyses of toxin
transcripts were conducted using the translated amino acid
sequences. Comparative sequences from physiological gene
homologues identified from non-venom tissues were included
in each dataset as outgroup sequences. To minimize
confusion, all sequences obtained in this study are referred
by their Genbank accession numbers (http://www.ncbi.nlm.
nih.gov/sites/entrez?db=Nucleotide) and sequences from
previous studies are labelled with their UniProtKB accession
numbers (http://www.expasy.org/cgi-bin/sprot-search-ful).
Resultant sequence sets were aligned using CLC Mainbench.
When presented as sequence alignments, the leader sequence
(as identified through use of SignalP) is shown in lowercase
and cysteines are highlighted in black. [ and indicate
incomplete N/50 or C/30 ends, respectively. Datasets were
analysed using Bayesian inference implemented onMrBayes,
version 3.0b4 (Ronquist and Huelsenbeck 2003). Two different
run conditions were used to test for congruence: lset
rates = invgamma with prset aamodelpr = fixed (WAG)
and lset rates = gamma with prset aamodelpr = mixed. The
analysis was performed by running a minimum of 1 9 107
generations in four chains, and saving every 100th tree. The
log-likelihood score of each saved tree was plotted against
the number of generations to establish the point at which the
log-likelihood scores reached their asymptote, and the posterior
probabilities for clades established by constructing a
majority-rule consensus tree for all trees generated after
completion of the burn-in phase. Trees shown are invgamma
with WAG, which are identical in topology to gamma with
mixed.
BioinformaticsPhylogeneticsPhylogenetic analyses of the bioinformatically recoveredtranscripts were performed to allow reconstruction of themolecular evolutionary history of each toxin type. Toxinsequences were identified by comparison of the translatedDNA sequences with previously characterised toxins using aBLAST search (Altschul et al. 1997) of the UniProtKBprotein database. Molecular phylogenetic analyses of toxintranscripts were conducted using the translated amino acidsequences. Comparative sequences from physiological genehomologues identified from non-venom tissues were includedin each dataset as outgroup sequences. To minimizeconfusion, all sequences obtained in this study are referredby their Genbank accession numbers (http://www.ncbi.nlm.nih.gov/sites/entrez?db=Nucleotide) and sequences fromprevious studies are labelled with their UniProtKB accessionnumbers (http://www.expasy.org/cgi-bin/sprot-search-ful).Resultant sequence sets were aligned using CLC Mainbench.When presented as sequence alignments, the leader sequence(as identified through use of SignalP) is shown in lowercaseand cysteines are highlighted in black. [ and indicateincomplete N/50 or C/30 ends, respectively. Datasets wereanalysed using Bayesian inference implemented onMrBayes,version 3.0b4 (Ronquist and Huelsenbeck 2003). Two differentrun conditions were used to test for congruence: lsetrates = invgamma with prset aamodelpr = fixed (WAG)and lset rates = gamma with prset aamodelpr = mixed. The
analysis was performed by running a minimum of 1 9 107
generations in four chains, and saving every 100th tree. The
log-likelihood score of each saved tree was plotted against
the number of generations to establish the point at which the
log-likelihood scores reached their asymptote, and the posterior
probabilities for clades established by constructing a
majority-rule consensus tree for all trees generated after
completion of the burn-in phase. Trees shown are invgamma
with WAG, which are identical in topology to gamma with
mixed.
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