| kmer.frac.curve | R Documentation |
kmer.frac.curve predicts the expected fraction of k-mers observed at
least r times in a high-throughput sequencing experiment given the
amount of sequencing
kmer.frac.curve(n, k, read.len, seq, r=2, mt=20)
n |
A two-column matrix. The first column is the frequency j = 1,2,…; and the second column is N_j, the number of k-mers observed exactly j times in the initial experiment. The first column must be sorted in an ascending order. |
k |
The number of nucleotides in a k-mer. |
read.len |
The average length of a read. |
seq |
The amount of nucleotides sequenced.. |
r |
A positive integer. Default is 1. |
mt |
An positive integer constraining possible rational function approximations. Default is 20. |
kmer.frac.curve is mainly designed for metagenomics to evaluate how
saturated a metagenomic data is.
kmer.frac.curve is the fast version of kmer.frac.curve.bootstrap.
The function does not provide the confidence interval. To obtain the
confidence interval along with the estimates, one should use the function
kmer.frac.curve.bootstrap.
A two-column matrix. The first column is the amount of sequencing in an experiment. The second column is the estimate of the fraction of k-mers observed at least r times in the experiment.
Chao Deng
Deng, C and Smith, AD (2016). Estimating the number of species to attain sufficient representation in a random sample. arXiv preprint arXiv:1607.02804
## load library library(preseqR) ## import data data(SRR061157_k31) ## the fraction of 31-mers represented at least 10 times in an experiment when ## sequencing 1M, 10M, 100M, 1G, 10G, 100G, 1T nucleotides kmer.frac.curve(n=SRR061157_k31, k=31, read.len=100, seq=10^(6:12), r=10, mt=20)
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