Calculation of Kmer Frequency Matrix from DNAbin for Both Reference and Query Sequences

Description

Calculation of kmer frequency matrices from DNAbin for both reference and query sequences.

Usage

DNAbin2kmerFreqMatrix(ref, que, kmer = kmer)

Arguments

Value

kmer frequency matrices for both ref and que sequences, but only based on kmers found in ref!!! new kmers in que will be ignored.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA. zhangab2008(at)mail.cnu.edu.cn

References

zhangab2008(at)mail.cnu.edu.cn

Examples

data(TibetanMoth) 
ref<-as.DNAbin(as.character(TibetanMoth[1:50,]))
que<-as.DNAbin(as.character(TibetanMoth[51:60,]))
out<-DNAbin2kmerFreqMatrix(ref,que,kmer=3)
out

Fuzzy Membership Function Value

Description

Calculation fuzzy membership function value given a distance from query to a potenial species, maximal intraspecific variation of the potential species theta1, and minimal interspecific distance (here, the distance between the potential species and its nearest neighbor theta2) (fuzzy-set based method, Zhang et al. 2012), different definition of distances could also be used.

Usage

FMF(xtheta12)

Arguments

Value

a numeric between 0 and 1.

Note

different definitions of distances could also be used.

Author(s)

Ai-bing ZHANG, Zhi-yong SHI. CNU, Beijing, CHINA, contact at zhangab2008(at)mail.cnu.edu.cn

References

Zhang, A. B., C. Muster, H.B. Liang, C.D. Zhu, R. Crozier, P. Wan, J. Feng, R. D. Ward.(2012). A fuzzy-set-theory-based approach to analyse species membership in DNA barcoding. Molecular Ecology, 21(8):1848-63.

Examples

xtheta12<-c(0.6289163,0.1465522,0.6379375) 
FMF.out<-FMF(xtheta12)
FMF.out

Calculate Intraspecific and Interspecific Variation

Description

Calculation intraspecific variation (sd) of the potential species theta1, and mean interspecific distance (here, the mean distance between the potential species and its nearest neighbor theta2) (fuzzy-set based method,slightly modified from Zhang et al. 2012). The calculation was done for all species in the reference dataset.

Usage

FMFtheta12(ref)

Arguments

Value

a data frame containing intraspecific (sd, theta1) and interspefic variation (mean) of all species, and their corresponding nearest neighbor (NN).

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA, contact at zhangab2008 (at) mail.cnu.edu.cn.

References

Zhang, A. B., C. Muster, H.B. Liang, C.D. Zhu, R. Crozier, P. Wan, J. Feng, R. D. Ward.(2012). A fuzzy-set-theory-based approach to analyse species membership in DNA barcoding. Molecular Ecology, 21(8):1848-63.

Examples

data(TibetanMoth) 
ref<-as.DNAbin(as.character(TibetanMoth[1:50,]))
FMF.theta12<-FMFtheta12(ref)
FMF.theta12

Extracts Lables of Samples

Description

Extract sequence names from different objects of DNAbin, including generated from fasta2DNAbin() (package:adegenet), and read.dna() (package:ape).

Usage

NAMES(seqs)

Arguments

Value

a character string array/vector.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA.

References

zhangab2008(at)mail.cnu.edu.cn

Examples

data(TibetanMoth) 
seqNames<-NAMES(TibetanMoth)
seqNames

TDR2 Species Membership Value

Description

To calculate TDR value for a set of queries and one potential species. Its value is in the range of [0,1], 0 indicates extremly weak species membership, values close 1 indicating strong species membership.

Usage

TDR2(oneSpe, que, boot, boot2)

Arguments

Value

a numeric vector represents TDR values for each query against the species

Note

oneSpe and que should be the same in sequence length, i.e., they should be aligned in prior. It's strongly recommended that oneSpe should have large enough sample size,e.g., 20.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA, contact at zhangab2008(at)mail.cnu.edu.cn

References

Jin Q, L,J.He, A.B. Zhang* (2012). A Simple 2D Non-Parametric Resampling Statistical Approach to Assess Confidence in Species Identification in DNA Barcoding-An Alternative to Likelihood and Bayesian Approaches. PLoS ONE 7(12): e50831. doi:10.1371/ journal. pone. 0050831. http://dx.plos.org/ 10.1371/ journal. pone. 0050831.

Examples

data(TibetanMoth)  
sampleSpeNames<-NAMES(TibetanMoth)
Spp<-gsub(".+,","",sampleSpeNames)
oneSpe<-TibetanMoth[grep("Macdunnoughia_crassisigna", Spp, value = FALSE,fixed = TRUE),] 
oneSpe<-as.DNAbin(as.character(oneSpe[1:5,]))
que<-TibetanMoth[grep("Agrotis_justa", Spp, value = FALSE,fixed = TRUE),] 
que2<-oneSpe[1:2,]
out<-TDR2(oneSpe,que, boot=10,boot2=10) ### true false identification

Tibetan Moth

Description

COI DNA barcodes of Tibetan Moth in China.

Usage

data("TibetanMoth")

Format

The format is: 'DNAbin' raw [1:319, 1:630] a t a a ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:319] "LS0909030M,Noctuidae_Himalaea_unica" "LZ0827026M,Noctuidae_Amphipyra_pyramidea" "ML0829010M,Noctuidae_Auchmis_saga" "BM0830055M,Noctuidae_Auchmis_saga" ... ..$ : NULL

Details

COI DNA barcodes of Tibetan Moth (Noctuidae) in China.

Source

http://dx.plos.org/10.1371/journal.pone.0064428.

References

Q. Jin, H.L. Han, X.M. Hu, X.H. Li,C.D. Zhu,S. Y. W. Ho, R. D. Ward, A.B. Zhang* . (2013).Quantifying Species Diversity with a DNA Barcoding-Based Method: Tibetan Moth Species (Noctuidae) on the Qinghai-Tibetan Plateau. PloS One 8: e644.

Examples

data(TibetanMoth)
TibetanMoth

Barcodes Evaluation

Description

Evaluate two barcodes using species identification success rate criteria.

Usage

barcodes.eval(barcode1, barcode2, kmer1 = kmer1, kmer2 = kmer2)

Arguments

Value

a list containing p_value of prop.test(), and so on.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA.

References

zhangab2008 (at) mail. cnu. edu. cn.

See Also

prop.test()

Examples

data(TibetanMoth) 
barcode1<-as.DNAbin(as.character(TibetanMoth[1:30,]))
barcode2<-barcode1
b.eval<-barcodes.eval(barcode1,barcode2,kmer1=1,kmer2=3)
b.eval

Barcoding Gap Calculation

Description

Calculation of DNA barcoding gap. Besides K2P distance, raw distance and euclidean could also be used for calculation DNA barcoding gap.

Usage

barcoding.gap(ref, dist = dist)

Arguments

Value

a list indicates the summary statistics of interspecific and intraspecific genetic distance, such as k2P distance.

Note

the current version of the function can only be used for protein-coding barcodes, such as, COI. The futuren version may incorporate calculation for non-coding barcodes,for instance, ITS1, ITS2.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA, contact at zhangab2008(at)mail.cnu.edu.cn

References

Meyer, Christopher P., and Gustav Paulay. (2005). ”DNA barcoding: error rates based on comprehensive sampling.”.PLoS biology 3.12: e422.

F.Jiang, Q. Jin, L. Liang, A.B. Zhang,and Z.H. Li.(2014). Existence of Species Complex Largely Reduced Barcoding Success for Invasive Species of Tephritidae: A Case Study in Bactrocera spp. Mol Ecol Resour. 14(6):1114-1128 DOI: 10.1111/1755-0998.12259.

Examples

data(TibetanMoth)
TibetanMoth<-as.DNAbin(as.character(TibetanMoth[1:20,]))
b.gap<-barcoding.gap(ref=TibetanMoth,dist="K80")
b.gap

Species Identification using Protein-coding Barcodes

Description

Species identification using protein-coding barcodes with different methods,including BP-based method (Zhang et al. 2008), fuzzy-set based method (Zhang et al. 2012), Bayesian-based method (Jin et al. 2013).

Usage

barcoding.spe.identify(ref, que, method = "bpNewTraining")

Arguments

Value

a list containing model parameters used, species identification success rates using references, query sequences, species inferred, and corresponding confidence levels (bp probability for BP-based method / FMF values for fuzzy set theory based method / posterior probability for Bayesian method) when available.

Note

functions fasta2DNAbin() from package:adegenet and read.dna() from package:ape were used to obtain DNAbin object in our package. The former is used to read large aligned coding DNA barcodes, the latter unaligned ones. ref and que should be aligned with identical sequence length. We provided a pipeline to perform fast sequences alignment for reference and query sequences. Windows users could contact zhangab2008(at)mail.cnu.edu.cn for an exec version of the package. For very large DNA dataset, read.fas() package:phyloch is strongly suggested instead of fasta2DNAbin() since the latter is very slow.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA. zhangab2008(at)mail.cnu.edu.cn

References

Zhang, A. B., M. D. Hao, C. Q. Yang, and Z. Y. Shi. (2017). BarcodingR: an integrated R package for species identification using DNA barcodes. Methods Ecol Evol. 8(5):627-634. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12682.

Jin,Q., H.L. Han, X.M. Hu, X.H. Li,C.D. Zhu,S. Y. W. Ho, R. D. Ward, A.B. Zhang . (2013). Quantifying Species Diversity with a DNA Barcoding-Based Method: Tibetan Moth Species (Noctuidae) on the Qinghai-Tibetan Plateau. PloS One 8: e644. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064428.

Zhang, A. B., C. Muster, H.B. Liang, C.D. Zhu, R. Crozier, P. Wan, J. Feng, R. D. Ward.(2012). A fuzzy-set-theory-based approach to analyse species membership in DNA barcoding. Molecular Ecology, 21(8):1848-63. https://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2011.05235.x

Zhang, A. B., D. S. Sikes, C. Muster, S. Q. Li. (2008). Inferring Species Membership using DNA sequences with Back-propagation Neural Networks. Systematic Biology, 57(2):202-215. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12682

Examples

data(TibetanMoth)
ref<-as.DNAbin(as.character(TibetanMoth[1:5,]))
que<-as.DNAbin(as.character(TibetanMoth[50:55,]))
bsi<-barcoding.spe.identify(ref, que, method = "fuzzyId")
bsi
bsi<-barcoding.spe.identify(ref, que, method = "bpNewTraining")
bsi
bsi<-barcoding.spe.identify(ref, que, method = "Bayesian")
bsi

Species Identification Based on Fuzzy-set Method and kmer

Description

Species identification based on fuzzy-set method (Zhang et al. 2012)and kmer.

Usage

barcoding.spe.identify2(ref, que, kmer = kmer, optimization = TRUE)

Arguments

Value

a list indicating the identified species.

Note

read.dna() from package ape was used to obtain DNAbin object for unaligned non-coding barcodes.

Author(s)

Ai-bing ZHANG, Cai-qing YANG, Meng-di HAO, CNU, Beijing, CHINA, contact at zhangab2008 (at) mail. cnu. edu. cn.

References

Zhang, A. B., M. D. Hao, C. Q. Yang, and Z. Y. Shi. (2017). BarcodingR: an integrated R package for species identification using DNA barcodes. Methods Ecol Evol. 8(5):627-634. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12682.

Jin,Q., H.L. Han, X.M. Hu, X.H. Li,C.D. Zhu,S. Y. W. Ho, R. D. Ward, A.B. Zhang . (2013). Quantifying Species Diversity with a DNA Barcoding-Based Method: Tibetan Moth Species (Noctuidae) on the Qinghai-Tibetan Plateau. PloS One 8: e644. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064428.

Zhang, A. B., C. Muster, H.B. Liang, C.D. Zhu, R. Crozier, P. Wan, J. Feng, R. D. Ward.(2012). A fuzzy-set-theory-based approach to analyse species membership in DNA barcoding. Molecular Ecology, 21(8):1848-63. https://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2011.05235.x

Zhang, A. B., D. S. Sikes, C. Muster, S. Q. Li. (2008). Inferring Species Membership using DNA sequences with Back-propagation Neural Networks. Systematic Biology, 57(2):202-215. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12682

Examples

data(pineMothITS2) 
ref<-pineMothITS2
que<-ref
spe.id<-barcoding.spe.identify2(ref,que, kmer = 1, optimization = FALSE)
spe.id
 

Bp Barcoding Species Identify using Kmer

Description

Species identification using BP-based method for both protein-coding barcodes, for instance, COI, and non-coding barcodes, such as, ITS, using kmer statistics.

Usage

bbsik(ref, que, kmer = kmer, UseBuiltModel = FALSE, lr = 5e-05, maxit = 1e+06)

Arguments

Value

a list containing model parameters used, species identification success rates using references, query sequences, species inferred, and corresponding confidence levels (bp probability for BP-based method).

Author(s)

Ai-bing ZHANG, Meng-di HAO, Cai-qing YANG, CNU, Beijing, CHINA. zhangab2008 (at) mail. cnu. edu.cn

References

Zhang, A. B., D. S. Sikes, C. Muster, S. Q. Li. (2008). Inferring Species Membership using DNA sequences with Back-propagation Neural Networks. Systematic Biology, 57(2):202-215. https://academic.oup.com/sysbio/article/57/2/202/1622290

Examples

data(TibetanMoth) 
ref<-as.DNAbin(as.character(TibetanMoth[1:50,]))
que<-as.DNAbin(as.character(TibetanMoth[51:60,]))
out<-bbsik(ref, que, kmer = 1, UseBuiltModel = FALSE)
out
out$convergence
out$success.rates.ref

data(pineMothITS2) 
ref<-pineMothITS2
que<-pineMothITS2
out<-bbsik(ref, que, kmer = 1, UseBuiltModel = FALSE)
out
out$convergence
out$success.rates.ref

Character to Integer Vector

Description

Conversion from a character vector to an integer vector.

Usage

char2NumVector(c)

Arguments

Value

an integer vector.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA. zhangab2008 (at) mail. cnu. edu.cn.

References

zhangab2008 (at) mail. cnu. edu. cn.

Examples

c<-c("a","a","b")
num<-char2NumVector(c)
num

Comparision between two Delimitations

Description

Comparision between two delimitations of a group of samples, for instance, traditionally morphological delimitation and molecular delimitation (MOTU).

Usage

compare2delimitations(deli1, deli2)

Arguments

Value

a list containing the adjusted Rand index comparing the two partitions (a scalar). This index has zero expected value in the case of random partition, and it is bounded above by 1 in the case of perfect agreement between two partitions; the numbers of matches, splits,merges, and corresponding percentage.

Note

This is for the same set of samples with two partitions/delimitations.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA.

References

L. Hubert and P. Arabie (1985) Comparing Partitions, Journal of the Classification 2:193-218.

Examples

deli1<-c(1,1,1,1,1,1)
deli2<-c(1,1,2,1,1,3)
out<-compare2delimitations(deli1,deli2)
out

Consensus Identification

Description

Make consensus for identifications from two or more methods, usually for a set of query sequences.

Usage

consensus.identify(identifiedBy2orMore)

Arguments

Value

a data frame with consensus.identification, and corresponding votes.

Note

Suitable for case where a set of queries were identified by more than two methods.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA, contact at zhangab2008(at)mail.cnu.edu.cn

Examples

queIDs<-c("q1","q2","q3")

bp<-c("sp1","sp1","sp1")
bpk<-c("sp1","sp1","sp2")
bayes<-c("sp2","sp1","sp3")
fuzzyID<-c("sp1","sp1","sp2")
identifiedBy2orMore<-data.frame(bp=bp,bpk=bpk,bayes=bayes,fuzzyID=fuzzyID)
rownames(identifiedBy2orMore)<-queIDs<-c("q1","q2","q3")
ccs<-consensus.identify(identifiedBy2orMore)

Digitize DNAbin

Description

Digitize an object of DNAbin.

Usage

digitize.DNA(seqs)

Arguments

Value

a numeric matrix of DNA sequences digitized.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA.

References

zhangab2008(at)mail.cnu.edu.cn

Examples

data(TibetanMoth)
digitized.DNA<-digitize.DNA(seqs=TibetanMoth)
digitized.DNA

Optimize kmer Length

Description

Optimize kmer length by trying kmers which length is in the range from 1 to max.kmer. The optimal kmer will have maximal species identification success rate.

Usage

optimize.kmer(ref, max.kmer = max.kmer)

Arguments

Value

a numeric indicating the optimal kmer in the range examined.

Author(s)

Ai-bing ZHANG, Cai-qing YANG, Meng-di HAO, CNU, Beijing, CHINA.

References

zhangab2008 (at) mail. cnu. edu. cn/zhangab2008 (at) gmail.com.

Examples

data(TibetanMoth) 
ref<-TibetanMoth[1:10,]
optimial.kmer<-optimize.kmer(ref,max.kmer=5)

pine Moth COI

Description

COI DNA barcodes of Pine Moth in China.

Usage

data("pineMothCOI")

Format

The format is: 'DNAbin' raw [1:140, 1:652] a t a a ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:140] "A111,Lasiocampidae_Dendrolimus_punctatus" "A22,Lasiocampidae_Dendrolimus_punctatus" "A23,Lasiocampidae_Dendrolimus_punctatus" "A27,Lasiocampidae_Dendrolimus_punctatus" ... ..$ : NULL

Details

COI DNA barcodes of Pine Moth (Lasiocampidae) in China.

Source

http://dx.plos.org/10.1371/journal.pone.0064428.

References

Dai Q-Y, Gao Q, Wu C-S, Chesters D, Zhu C-D, and A.B. Zhang*. (2012) Phylogenetic Reconstruction and DNA Barcoding for Closely Related Pine Moth Species (Dendrolimus) in China with Multiple Gene Markers. PLoS ONE 7(4): e32544.

Examples

data(pineMothCOI)
pineMothCOI

pine Moth ITS1

Description

ITS1 sequences of seven closely related pine moths species sampled through China.

Usage

data("pineMothITS1")

Format

The format is: List of 69 $ A43,Lasiocampidae_ Dendrolimus_ punctatus: raw [1:698] 48 48 48 28 ... $ A54,Lasiocampidae_ Dendrolimus_ punctatus: raw [1:696] 48 18 28 18 ... - attr(*, "class")= chr "DNAbin"

Details

ITS1 used as DNA barcodes for closely related rine moth species (Dendrolimus) in China.

Source

http://dx.plos.org/ 10.1371/ journal.pone.0064428.

References

Dai Q-Y, Gao Q, Wu C-S, Chesters D, Zhu C-D, and A.B. Zhang*. (2012) Phylogenetic Reconstruction and DNA Barcoding for Closely Related Pine Moth Species (Dendrolimus) in China with Multiple Gene Markers. PLoS ONE 7(4): e32544.

Examples

data(pineMothITS1)
pineMothITS1

pine Moth ITS2

Description

ITS2 sequences of seven closely related pine moths species sampled in China.

Usage

data("pineMothITS2")

Format

The format is: List of 97 $ A22,Lasiocampidae_ Dendrolimus_ punctatus: raw [1:568] 48 48 48 28 ... $ A23,Lasiocampidae_ Dendrolimus_ punctatus: raw [1:574] 48 18 28 18 ... $ A29,Lasiocampidae_ Dendrolimus_ punctatus: raw [1:569] 88 48 48 48 ... $ A52,Lasiocampidae_ Dendrolimus_ punctatus: raw [1:570] 48 18 28 18 ... - attr(*, "class")= chr "DNAbin"

Details

ITS2 used as DNA barcodes for closely related rine moth species (Dendrolimus) in China.

Source

http://dx.plos.org /10.1371/ journal.pone.0064428.

References

Dai Q-Y, Gao Q, Wu C-S, Chesters D, Zhu C-D, and A.B. Zhang*. (2012) Phylogenetic Reconstruction and DNA Barcoding for Closely Related Pine Moth Species (Dendrolimus) in China with Multiple Gene Markers. PLoS ONE 7(4): e32544.

Examples

data(pineMothITS2)
pineMothITS2

Sample Random Datasets from References (DNAbin)

Description

Randomly sample reference data at different levels of taxon.

Usage

sample.ref(ref, sample.porp = 0.5, sample.level = "full")

Arguments

Value

a list containing the selected samples and the samples left, in DNAbin format stored in a matrix or a list.

Note

the ref must contain information on taxonomy, in format like, ">LS0909030M,Noctuidae_Himalaea_unica", i.e., "seqID,family_genus_species", or ">LS0909030M,Himalaea_unica"; in case there is only one sample/individual for a taxon level, this sample will be retained in ref.selected.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA.

References

zhangab2008(at)mail.cnu.edu.cn;

Examples

data(TibetanMoth) 
data(pineMothITS2)
ref<-TibetanMoth
ref2<-pineMothITS2
out<-sample.ref(ref,sample.porp=0.5,sample.level="full")
out
out2<-sample.ref(ref2,sample.porp=0.5,sample.level="full")
out2

Save Identifications

Description

Output identified results to an outfile in temporty directory (found by tempdir() function).

Usage

save.ids(outfile = "identified.txt", ids)

Arguments

Value

no value returned,but an output file.

Author(s)

Ai-bing ZHANG, PhD. CNU, Beijing, CHINA.

References

zhangab2008(at)mail.cnu.edu.cn

See Also

barcoding.spe.identify()

Examples

data(TibetanMoth) 
ref<-as.DNAbin(as.character(TibetanMoth[1:50,]))
que<-as.DNAbin(as.character(TibetanMoth[50:60,]))
bsi<-barcoding.spe.identify(ref, que, method = "fuzzyId")
bsi
save.ids(outfile="identified.txt",bsi)

Summarize Reference Data

Description

Summarize taxon information, sequence statistics,barcodes numbers per species for reference dataset.

Usage

summarize.ref(ref, taxonStat = TRUE, seqStat = TRUE, barcodeStat = TRUE)

Arguments

Value

a list containing taxon statistics, sequence statistics, population parameters,barcoding statistics ()

Author(s)

Ai-bing ZHANG, Meng-di HAO, CNU, Beijing, CHINA.

References

zhangab2008(at)mail.cnu.edu.cn./zhangab2008(at)gmail.com.

Examples

data(TibetanMoth)
s.r<-summarize.ref(TibetanMoth,taxonStat=TRUE,seqStat=TRUE,barcodeStat=TRUE)
s.r