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### Running command:
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###   /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data AneuFinder
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* checking for file ‘AneuFinder/DESCRIPTION’ ... OK
* preparing ‘AneuFinder’:
* checking DESCRIPTION meta-information ... OK
* cleaning src
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘AneuFinder.Rnw’ using knitr
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect,
    is.unsorted, lapply, mapply, match, mget, order, paste, pmax,
    pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which.max,
    which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    I, expand.grid, unname

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: ggplot2
Loading required package: cowplot
Loading required package: AneuFinderData

Please visit https://github.com/ataudt/aneufinder for the latest bugfixes and features.

Aneufinder             package:AneuFinder              R Documentation

_W_r_a_p_p_e_r _f_u_n_c_t_i_o_n _f_o_r _t_h_e '_A_n_e_u_F_i_n_d_e_r' _p_a_c_k_a_g_e

_D_e_s_c_r_i_p_t_i_o_n:

     This function is an easy-to-use wrapper to bin the data, find
     copy-number-variations, locate breakpoints, plot genomewide
     heatmaps, distributions, profiles and karyograms.

_U_s_a_g_e:

     Aneufinder(inputfolder, outputfolder, configfile = NULL, numCPU = 1,
       reuse.existing.files = TRUE, binsizes = 1e+06, stepsizes = binsizes,
       variable.width.reference = NULL, reads.per.bin = NULL,
       pairedEndReads = FALSE, assembly = NULL, chromosomes = NULL,
       remove.duplicate.reads = TRUE, min.mapq = 10, blacklist = NULL,
       use.bamsignals = FALSE, reads.store = FALSE, correction.method = NULL,
       GC.BSgenome = NULL, method = c("edivisive"), strandseq = FALSE,
       R = 10, sig.lvl = 0.1, eps = 0.01, max.time = 60, max.iter = 5000,
       num.trials = 15, states = c("zero-inflation", paste0(0:10, "-somy")),
       confint = NULL, refine.breakpoints = FALSE, hotspot.bandwidth = NULL,
       hotspot.pval = 0.05, cluster.plots = TRUE)
     
_A_r_g_u_m_e_n_t_s:

inputfolder: Folder with either BAM or BED files.

outputfolder: Folder to output the results. If it does not exist it
          will be created.

configfile: A file specifying the parameters of this function (without
          'inputfolder', 'outputfolder' and 'configfile'). Having the
          parameters in a file can be handy if many samples with the
          same parameter settings are to be run. If a 'configfile' is
          specified, it will take priority over the command line
          parameters.

  numCPU: The numbers of CPUs that are used. Should not be more than
          available on your machine.

reuse.existing.files: A logical indicating whether or not existing
          files in 'outputfolder' should be reused.

binsizes: An integer vector with bin sizes. If more than one value is
          given, output files will be produced for each bin size.

stepsizes: A vector of step sizes the same length as 'binsizes'. Only
          used for 'method="HMM"'.

variable.width.reference: A BAM file that is used as reference to
          produce variable width bins. See 'variableWidthBins' for
          details.

reads.per.bin: Approximate number of desired reads per bin. The bin
          size will be selected accordingly. Output files are produced
          for each value.

pairedEndReads: Set to 'TRUE' if you have paired-end reads in your BAM
          files (not implemented for BED files).

assembly: Please see 'fetchExtendedChromInfoFromUCSC' for available
          assemblies. Only necessary when importing BED files. BAM
          files are handled automatically. Alternatively a data.frame
          with columns 'chromosome' and 'length'.

chromosomes: If only a subset of the chromosomes should be imported,
          specify them here.

remove.duplicate.reads: A logical indicating whether or not duplicate
          reads should be removed.

min.mapq: Minimum mapping quality when importing from BAM files. Set
          'min.mapq=NA' to keep all reads.

blacklist: A 'GRanges-class' or a bed(.gz) file with blacklisted
          regions. Reads falling into those regions will be discarded.

use.bamsignals: If 'TRUE' the 'bamsignals' package will be used for
          binning. This gives a tremendous performance increase for the
          binning step. 'reads.store' and 'calc.complexity' will be set
          to 'FALSE' in this case.

reads.store: Set 'reads.store=TRUE' to store read fragments as RData in
          folder 'data' and as BED files in 'BROWSERFILES/data'. This
          option will force 'use.bamsignals=FALSE'.

correction.method: Correction methods to be used for the binned read
          counts. Currently only ''GC''.

GC.BSgenome: A 'BSgenome' object which contains the DNA sequence that
          is used for the GC correction.

  method: Any combination of 'c('HMM','dnacopy','edivisive')'. Option
          'method='HMM'' uses a Hidden Markov Model as described in
          doi:10.1186/s13059-016-0971-7 to call copy numbers. Option
          ''dnacopy'' uses 'segment' from the 'DNAcopy' package to call
          copy numbers similarly to the method proposed in
          doi:10.1038/nmeth.3578, which gives more robust but less
          sensitive results compared to the HMM. Option ''edivisive''
          (DEFAULT) works like option ''dnacopy'' but uses the
          'e.divisive' function from the 'ecp' package for
          segmentation.

strandseq: A logical indicating whether the data comes from Strand-seq
          experiments. If 'TRUE', both strands carry information and
          are treated separately.

       R: method-edivisive: The maximum number of random permutations
          to use in each iteration of the permutation test (see
          'e.divisive'). Increase this value to increase accuracy on
          the cost of speed.

 sig.lvl: method-edivisive: The level at which to sequentially test if
          a proposed change point is statistically significant (see
          'e.divisive'). Increase this value to find more breakpoints.

     eps: method-HMM: Convergence threshold for the Baum-Welch
          algorithm.

max.time: method-HMM: The maximum running time in seconds for the
          Baum-Welch algorithm. If this time is reached, the Baum-Welch
          will terminate after the current iteration finishes. Set
          'max.time = -1' for no limit.

max.iter: method-HMM: The maximum number of iterations for the
          Baum-Welch algorithm. Set 'max.iter = -1' for no limit.

num.trials: method-HMM: The number of trials to find a fit where state
          'most.frequent.state' is most frequent. Each time, the HMM is
          seeded with different random initial values.

  states: method-HMM: A subset or all of
          'c("zero-inflation","0-somy","1-somy","2-somy","3-somy","4-somy",...)'.
          This vector defines the states that are used in the Hidden
          Markov Model. The order of the entries must not be changed.

 confint: Desired confidence interval for breakpoints. Set
          'confint=NULL' to disable confidence interval estimation.
          Confidence interval estimation will force 'reads.store=TRUE'.

refine.breakpoints: A logical indicating whether breakpoints from the
          HMM should be refined with read-level information.
          'refine.breakpoints=TRUE' will force 'reads.store=TRUE'.

hotspot.bandwidth: A vector the same length as 'binsizes' with
          bandwidths for breakpoint hotspot detection (see 'hotspotter'
          for further details). If 'NULL', the bandwidth will be chosen
          automatically as the average distance between reads.

hotspot.pval: P-value for breakpoint hotspot detection (see
          'hotspotter' for further details). Set 'hotspot.pval = NULL'
          to skip hotspot detection.

cluster.plots: A logical indicating whether plots should be clustered
          by similarity.

_V_a_l_u_e:

     'NULL'

_A_u_t_h_o_r(_s):

     Aaron Taudt

_E_x_a_m_p_l_e_s:

     ## Not run:
     
     ## The following call produces plots and genome browser files for all BAM files in "my-data-folder"
     Aneufinder(inputfolder="my-data-folder", outputfolder="my-output-folder")
     ## End(Not run)
     

Reading file hg19_diploid.bam.bed.gz ... 5.48s
Fetching chromosome lengths from UCSC ... 0.7s
Subsetting chromosomes ... 0.11s
Filtering reads ... 0.3s
Subsetting specified chromosomes ... 0.02s
Calculating complexity ... 0.93s
Removing duplicate reads ... 2.16s
Calculating coverage ... 0.77s
Making fixed-width bins for bin size 1e+05 ... 0.11s
Splitting into strands ... 0.02s
Counting overlaps for binsize_1e+05 ... 0.5s
Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Writing to file /tmp/RtmpRsoRZr/file565f58b23c75.bed.gz ... 0.09s
Quitting from lines 156-174 (AneuFinder.Rnw) 
Error: processing vignette 'AneuFinder.Rnw' failed with diagnostics:
there is no package called 'BSgenome.Hsapiens.UCSC.hg19'
--- failed re-building ‘AneuFinder.Rnw’

SUMMARY: processing the following file failed:
  ‘AneuFinder.Rnw’

Error: Vignette re-building failed.
Execution halted