Mercurial > repos > artbio > mircounts
changeset 14:c163574c246f draft
"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/mircounts commit 51dc6c56c7d95fc229ffee958354211cd454fd36"
| author | artbio |
|---|---|
| date | Sun, 09 May 2021 17:10:07 +0000 |
| parents | b045c30fb768 |
| children | ffcd42f85b61 |
| files | coverage_plotting.R mircounts.xml test-data/aga.22.gff3 test-data/aga.bam test-data/aga_lattice_dataframe.tab test-data/aga_mir_coverage.pdf test-data/clipped.out.bam test-data/lattice_clipped_dataframe.tab test-data/lattice_unclipped_dataframe.tab test-data/mir_clipped_coverage.pdf test-data/mir_unclipped_coverage.pdf test-data/mouse.19.gff3 test-data/mouse.bam test-data/mouse_lattice_dataframe.tab test-data/mouse_mir_coverage.pdf test-data/translated_dme.22.gff3 test-data/translated_dme.gff3 test-data/unclipped.out.22.bam test-data/unclipped.out.bam |
| diffstat | 19 files changed, 118119 insertions(+), 118115 deletions(-) [+] |
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--- a/coverage_plotting.R Fri Oct 18 19:18:50 2019 -0400 +++ b/coverage_plotting.R Sun May 09 17:10:07 2021 +0000 @@ -1,53 +1,56 @@ #!/usr/bin/env Rscript # Help to be printed -hlp_description = "This script takes a dataframe containing the coverage values for different miRNAs and plots them." -hlp_usage = "Usage : coverage_ploting.R --dataframe [FILE] --type ['relative' or 'absolute'] --output [FILE]" -hlp_dataframe = "--dataframe\tFILE\tThis is a dataframe containing coverage values obtained from mircounts.py" -hlp_type = "--type\t\tSTRING\tType of ploting, either relative or absoute coverage values (default='relative')" -hlp_output = "--output\tFILE\tFile to output the pdf to\n" +hlp_description <- "This script takes a dataframe containing the coverage values for different miRNAs and plots them." +hlp_usage <- "Usage : coverage_ploting.R --dataframe [FILE] --type ['relative' or 'absolute'] --output [FILE]" +hlp_dataframe <- "--dataframe\tFILE\tThis is a dataframe containing coverage values obtained from mircounts.py" +hlp_type <- "--type\t\tSTRING\tType of ploting, either relative or absoute coverage values (default='relative')" +hlp_output <- "--output\tFILE\tFile to output the pdf to\n" -hlp = paste(hlp_description,hlp_usage,hlp_dataframe,hlp_type,hlp_output,sep="\n") -#print(hlp) +hlp <- paste(hlp_description, hlp_usage, hlp_dataframe, hlp_type, hlp_output, sep = "\n") # Setup R error handling to go to stderr -options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) +options(show.error.messages = F, + error = function() { + cat(geterrmessage(), file = stderr()); q("no", 1, F) + } +) library(optparse) library(lattice) # Get arguments option_list <- list( - make_option(c("-d", "--dataframe"), type="character", - help="Dataframe containing coverage values obtained from mircounts.py"), - make_option(c("-t", "--type"), type="character", default="relative", - help="Type of plotting, either relative or absoute coverage values (default='relative')"), - make_option(c("-o", "--output"), type="character", help="File to output the pdf to") + make_option(c("-d", "--dataframe"), type = "character", + help = "Dataframe containing coverage values obtained from mircounts.py"), + make_option(c("-t", "--type"), type = "character", default = "relative", + help = "Type of plotting, either relative or absoute coverage values (default = 'relative')"), + make_option(c("-o", "--output"), type = "character", help = "File to output the pdf to") ) -parser <- OptionParser(usage = "%prog [options] file", option_list=option_list) -args = parse_args(parser) +parser <- OptionParser(usage = "%prog [options] file", option_list = option_list) +args <- parse_args(parser) -if ( !('dataframe' %in% names(args)) || !('output' %in% names(args))) { +if (!("dataframe" %in% names(args)) || !("output" %in% names(args))) { stop("'--dataframe' and '--output' parametters are not optional. Please retry.") } # Plot -coverage = read.delim(args$dataframe, header=T) -if (args$type =="relative") { - graph = xyplot(Norm_count~Norm_offset | Mir_hairpin, data=coverage, col=c("darkblue"), type="l", lwd=1.5, - scales=list(x=list(cex=.5), y=list(cex=.5)), par.strip.text=list(cex=.5), - strip=strip.custom(which.given=1, bg="lightblue"), layout=c(4,15), - as.table=T, xlab="Normalized Counts", ylab="Normalized coordinates", - main="miRNA coverage maps") +coverage <- read.delim(args$dataframe, header = T) +if (args$type == "relative") { + graph <- xyplot(Norm_count ~ Norm_offset | Mir_hairpin, data = coverage, col = c("darkblue"), type = "l", lwd = 1.5, + scales = list(x = list(cex = .5), y = list(cex = .5)), par.strip.text = list(cex = .5), + strip = strip.custom(which.given = 1, bg = "lightblue"), layout = c(4, 15), + as.table = T, xlab = "Normalized Counts", ylab = "Normalized coordinates", + main = "miRNA coverage maps") } else { - graph = xyplot(Count~Offset | Mir_hairpin, data=coverage, col=c("darkblue"), type="l", lwd=1.5, - scales=list(x=list(cex=.5), y=list(cex=.5)), par.strip.text=list(cex=.5), - strip=strip.custom(which.given=1, bg="lightblue"), layout=c(4,15), - as.table=T, xlab="Counts", ylab="Coordinates", - main="miRNA coverage plots") + graph <- xyplot(Count ~ Offset | Mir_hairpin, data = coverage, col = c("darkblue"), type = "l", lwd = 1.5, + scales = list(x = list(cex = .5), y = list(cex = .5)), par.strip.text = list(cex = .5), + strip = strip.custom(which.given = 1, bg = "lightblue"), layout = c(4, 15), + as.table = T, xlab = "Counts", ylab = "Coordinates", + main = "miRNA coverage plots") } # PDF output -pdf(file=args$output, paper="special", height=11.69, width=8.2677) +pdf(file = args$output, paper = "special", height = 11.69, width = 8.2677) plot(graph, newpage = T) dev.off()
--- a/mircounts.xml Fri Oct 18 19:18:50 2019 -0400 +++ b/mircounts.xml Sun May 09 17:10:07 2021 +0000 @@ -1,17 +1,18 @@ -<tool id="mircounts" name="miRcounts" version="1.4.0"> +<tool id="mircounts" name="miRcounts" version="1.5.1"> <description> Counts miRNA alignments from small RNA sequence data</description> <requirements> - <requirement type="package" version="1.2.0">bowtie</requirement> - <requirement type="package" version="1.9=h10a08f8_12">samtools</requirement> - <requirement type="package" version="0.15.3=py27hda2845c_1">pysam</requirement> - <requirement type="package" version="1.6.4=r36h6115d3f_0">r-optparse</requirement> - <requirement type="package" version="0.20_38=r36hcdcec82_1002">r-lattice</requirement> + <requirement type="package" version="1.34=ha1f6473_0">tar</requirement> + <requirement type="package" version="1.3.0=py39h176da8b_2">bowtie</requirement> + <requirement type="package" version="1.12=h9aed4be_1">samtools</requirement> + <requirement type="package" version="0.16.0.1=py39h051187c_3">pysam</requirement> + <requirement type="package" version="1.6.6=r40h6115d3f_1">r-optparse</requirement> + <requirement type="package" version="0.20_44=r40hcfec24a_0">r-lattice</requirement> </requirements> <stdio> <exit_code range="1:" level="warning" description="Tool exception" /> </stdio> <command detect_errors="exit_code"><![CDATA[ - tar -xvzf '$__tool_directory__'/mirbase.tar.gz && + tar -xf '$__tool_directory__'/mirbase.tar.gz && python '$__tool_directory__'/mature_mir_gff_translation.py --gff_path mirbase/${mirbase_version}/genomes/${genomeKey}.gff3 --output $gff3 && ## transcode the mature miR genome coordinates into coordinates relative to the corresponding "miRNA_primary_transcript". @@ -144,12 +145,12 @@ <param name="display" value="relative"/> <param name="output_premir_counts" value="True"/> <param name="output_mir_counts" value="True"/> - <output name="output" file="aga.bam"/> - <output name="gff3" file="aga.22.gff3" lines_diff="22"/> - <output name="pre_mir_count_file" file="aga_pre_mirs_count.22.tab"/> - <output name="mir_count_file" file="aga_mirs_count.22.tab"/> - <output name="latticePDF" file="aga_mir_coverage.pdf"/> - <output name="coverage_dataframe" file="aga_lattice_dataframe.tab"/> + <output name="output" file="aga.bam" compare="sim_size" /> + <output name="gff3" file="aga.22.gff3" lines_diff="22" ftype="gff3" /> + <output name="pre_mir_count_file" file="aga_pre_mirs_count.22.tab" ftype="tabular" /> + <output name="mir_count_file" file="aga_mirs_count.22.tab" ftype="tabular" /> + <output name="latticePDF" file="aga_mir_coverage.pdf" ftype="pdf" /> + <output name="coverage_dataframe" file="aga_lattice_dataframe.tab" ftype="tabular" /> </test> <test> <param name="cutoption" value="yes" /> @@ -165,12 +166,12 @@ <param name="display" value="relative"/> <param name="output_premir_counts" value="True"/> <param name="output_mir_counts" value="True"/> - <output name="output" file="mouse.bam"/> - <output name="gff3" file="mouse.19.gff3" lines_diff="22"/> - <output name="pre_mir_count_file" file="mouse_pre_mirs_count.29.tab"/> - <output name="mir_count_file" file="mouse_mirs_count.29.tab"/> - <output name="latticePDF" file="mouse_mir_coverage.pdf"/> - <output name="coverage_dataframe" file="mouse_lattice_dataframe.tab"/> + <output name="output" file="mouse.bam" compare="sim_size"/> + <output name="gff3" file="mouse.19.gff3" lines_diff="22" ftype="gff3" /> + <output name="pre_mir_count_file" file="mouse_pre_mirs_count.29.tab" ftype="tabular" /> + <output name="mir_count_file" file="mouse_mirs_count.29.tab" ftype="tabular" /> + <output name="latticePDF" file="mouse_mir_coverage.pdf" ftype="pdf" /> + <output name="coverage_dataframe" file="mouse_lattice_dataframe.tab" ftype="tabular" /> </test> <test> <param name="cutoption" value="yes" /> @@ -185,10 +186,10 @@ <param name="plottingOption" value="no"/> <param name="output_premir_counts" value="True"/> <param name="output_mir_counts" value="True"/> - <output name="output" file="unclipped.out.22.bam"/> - <output name="gff3" file="translated_dme.22.gff3" lines_diff="22"/> - <output name="pre_mir_count_file" file="pre_mirs_unclipped_count.22.tab"/> - <output name="mir_count_file" file="mirs_unclipped_count.22.tab"/> + <output name="output" file="unclipped.out.22.bam" compare="sim_size" /> + <output name="gff3" file="translated_dme.22.gff3" lines_diff="22" ftype="gff3" /> + <output name="pre_mir_count_file" file="pre_mirs_unclipped_count.22.tab" ftype="tabular" /> + <output name="mir_count_file" file="mirs_unclipped_count.22.tab" ftype="tabular" /> </test> <test> <param name="cutoption" value="yes" /> @@ -203,10 +204,10 @@ <param name="plottingOption" value="no"/> <param name="output_premir_counts" value="True"/> <param name="output_mir_counts" value="True"/> - <output name="output" file="unclipped.out.bam" /> - <output name="gff3" file="translated_dme.gff3" lines_diff="22"/> - <output name="pre_mir_count_file" file="pre_mirs_unclipped_count.tab"/> - <output name="mir_count_file" file="mirs_unclipped_count.tab"/> + <output name="output" file="unclipped.out.bam" compare="sim_size" /> + <output name="gff3" file="translated_dme.gff3" lines_diff="22" ftype="gff3" /> + <output name="pre_mir_count_file" file="pre_mirs_unclipped_count.tab" ftype="tabular" /> + <output name="mir_count_file" file="mirs_unclipped_count.tab" ftype="tabular" /> </test> <test> <param name="cutoption" value="yes" /> @@ -222,12 +223,12 @@ <param name="display" value="relative"/> <param name="output_premir_counts" value="True"/> <param name="output_mir_counts" value="True"/> - <output name="output" file="unclipped.out.bam"/> - <output name="gff3" file="translated_dme.gff3" lines_diff="22"/> - <output name="pre_mir_count_file" file="pre_mirs_unclipped_count.tab"/> - <output name="mir_count_file" file="mirs_unclipped_count.tab"/> - <output name="latticePDF" file="mir_unclipped_coverage.pdf"/> - <output name="coverage_dataframe" file="lattice_unclipped_dataframe.tab"/> + <output name="output" file="unclipped.out.bam" compare="sim_size" /> + <output name="gff3" file="translated_dme.gff3" lines_diff="22" ftype="gff3" /> + <output name="pre_mir_count_file" file="pre_mirs_unclipped_count.tab" ftype="tabular" /> + <output name="mir_count_file" file="mirs_unclipped_count.tab" ftype="tabular" /> + <output name="latticePDF" file="mir_unclipped_coverage.pdf" ftype="pdf"/> + <output name="coverage_dataframe" file="lattice_unclipped_dataframe.tab" ftype="tabular" /> </test> <test> <param name="cutoption" value="no" /> @@ -239,12 +240,12 @@ <param name="display" value="absolute"/> <param name="output_premir_counts" value="True"/> <param name="output_mir_counts" value="True"/> - <output name="output" file="clipped.out.bam"/> - <output name="gff3" file="translated_dme.gff3" lines_diff="22"/> - <output name="pre_mir_count_file" file="pre_mirs_clipped_count.tab"/> - <output name="mir_count_file" file="mirs_clipped_count.tab"/> - <output name="latticePDF" file="mir_clipped_coverage.pdf"/> - <output name="coverage_dataframe" file="lattice_clipped_dataframe.tab"/> + <output name="output" file="clipped.out.bam" compare="sim_size" /> + <output name="gff3" file="translated_dme.gff3" lines_diff="22" ftype="gff3" /> + <output name="pre_mir_count_file" file="pre_mirs_clipped_count.tab" ftype="tabular" /> + <output name="mir_count_file" file="mirs_clipped_count.tab" ftype="tabular" /> + <output name="latticePDF" file="mir_clipped_coverage.pdf" ftype="pdf" /> + <output name="coverage_dataframe" file="lattice_clipped_dataframe.tab" ftype="tabular" /> </test> </tests> <help>
--- a/test-data/aga.22.gff3 Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/aga.22.gff3 Sun May 09 17:10:07 2021 +0000 @@ -11,7 +11,7 @@ # rather a predicted stem-loop portion that includes the precursor # miRNA. Mature sequences have type "miRNA". # -# generated by mature_mir_gff_translation.py 2019-10-18 20:28:58.142506 +# generated by mature_mir_gff_translation.py 2021-05-09 00:34:02.737449 # aga-bantam . miRNA_primary_transcript 1 111 . + . ID=MI0001599;Alias=MI0001599;Name=aga-bantam aga-bantam . miRNA 70 92 . + . ID=MIMAT0001494;Alias=MIMAT0001494;Name=aga-bantam;Derives_from=MI0001599;Parent_mir_Name=aga-bantam
--- a/test-data/aga_lattice_dataframe.tab Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/aga_lattice_dataframe.tab Sun May 09 17:10:07 2021 +0000 @@ -1,803 +1,803 @@ Mir_hairpin Offset Norm_offset Count Norm_count -aga-bantam 1 0.00900900900901 0.0 0.0 -aga-bantam 2 0.018018018018 0.0 0.0 -aga-bantam 3 0.027027027027 0.0 0.0 -aga-bantam 4 0.036036036036 0.0 0.0 -aga-bantam 5 0.045045045045 0.0 0.0 -aga-bantam 6 0.0540540540541 0.0 0.0 -aga-bantam 7 0.0630630630631 0.0 0.0 -aga-bantam 8 0.0720720720721 0.0 0.0 -aga-bantam 9 0.0810810810811 0.0 0.0 -aga-bantam 10 0.0900900900901 0.0 0.0 -aga-bantam 11 0.0990990990991 0.0 0.0 -aga-bantam 12 0.108108108108 0.0 0.0 -aga-bantam 13 0.117117117117 0.0 0.0 -aga-bantam 14 0.126126126126 0.0 0.0 -aga-bantam 15 0.135135135135 0.0 0.0 -aga-bantam 16 0.144144144144 0.0 0.0 -aga-bantam 17 0.153153153153 0.0 0.0 -aga-bantam 18 0.162162162162 0.0 0.0 -aga-bantam 19 0.171171171171 0.0 0.0 -aga-bantam 20 0.18018018018 0.0 0.0 -aga-bantam 21 0.189189189189 0.0 0.0 -aga-bantam 22 0.198198198198 0.0 0.0 -aga-bantam 23 0.207207207207 0.0 0.0 -aga-bantam 24 0.216216216216 0.0 0.0 -aga-bantam 25 0.225225225225 0.0 0.0 -aga-bantam 26 0.234234234234 0.0 0.0 -aga-bantam 27 0.243243243243 157.0 0.0172888448409 -aga-bantam 28 0.252252252252 177.0 0.0194912454575 -aga-bantam 29 0.261261261261 190.0 0.0209228058584 -aga-bantam 30 0.27027027027 190.0 0.0209228058584 -aga-bantam 31 0.279279279279 190.0 0.0209228058584 -aga-bantam 32 0.288288288288 190.0 0.0209228058584 -aga-bantam 33 0.297297297297 190.0 0.0209228058584 -aga-bantam 34 0.306306306306 190.0 0.0209228058584 -aga-bantam 35 0.315315315315 190.0 0.0209228058584 -aga-bantam 36 0.324324324324 190.0 0.0209228058584 -aga-bantam 37 0.333333333333 190.0 0.0209228058584 -aga-bantam 38 0.342342342342 190.0 0.0209228058584 -aga-bantam 39 0.351351351351 190.0 0.0209228058584 -aga-bantam 40 0.36036036036 190.0 0.0209228058584 -aga-bantam 41 0.369369369369 190.0 0.0209228058584 -aga-bantam 42 0.378378378378 190.0 0.0209228058584 -aga-bantam 43 0.387387387387 190.0 0.0209228058584 -aga-bantam 44 0.396396396396 190.0 0.0209228058584 -aga-bantam 45 0.405405405405 190.0 0.0209228058584 -aga-bantam 46 0.414414414414 190.0 0.0209228058584 -aga-bantam 47 0.423423423423 190.0 0.0209228058584 -aga-bantam 48 0.432432432432 189.0 0.0208126858276 -aga-bantam 49 0.441441441441 180.0 0.01982160555 -aga-bantam 50 0.45045045045 159.0 0.0175090849025 -aga-bantam 51 0.459459459459 0.0 0.0 -aga-bantam 52 0.468468468468 0.0 0.0 -aga-bantam 53 0.477477477477 0.0 0.0 -aga-bantam 54 0.486486486486 0.0 0.0 -aga-bantam 55 0.495495495495 0.0 0.0 -aga-bantam 56 0.504504504505 0.0 0.0 -aga-bantam 57 0.513513513514 0.0 0.0 -aga-bantam 58 0.522522522523 0.0 0.0 -aga-bantam 59 0.531531531532 0.0 0.0 -aga-bantam 60 0.540540540541 0.0 0.0 -aga-bantam 61 0.54954954955 0.0 0.0 -aga-bantam 62 0.558558558559 0.0 0.0 -aga-bantam 63 0.567567567568 0.0 0.0 -aga-bantam 64 0.576576576577 0.0 0.0 -aga-bantam 65 0.585585585586 0.0 0.0 -aga-bantam 66 0.594594594595 0.0 0.0 -aga-bantam 67 0.603603603604 0.0 0.0 -aga-bantam 68 0.612612612613 0.0 0.0 -aga-bantam 69 0.621621621622 11.0 0.00121132033917 -aga-bantam 70 0.630630630631 9079.0 0.999779759938 -aga-bantam 71 0.63963963964 9080.0 0.999889879969 -aga-bantam 72 0.648648648649 9080.0 0.999889879969 -aga-bantam 73 0.657657657658 9080.0 0.999889879969 -aga-bantam 74 0.666666666667 9080.0 0.999889879969 -aga-bantam 75 0.675675675676 9080.0 0.999889879969 -aga-bantam 76 0.684684684685 9080.0 0.999889879969 -aga-bantam 77 0.693693693694 9081.0 1.0 -aga-bantam 78 0.702702702703 9081.0 1.0 -aga-bantam 79 0.711711711712 9081.0 1.0 -aga-bantam 80 0.720720720721 9081.0 1.0 -aga-bantam 81 0.72972972973 9081.0 1.0 -aga-bantam 82 0.738738738739 9081.0 1.0 -aga-bantam 83 0.747747747748 9081.0 1.0 -aga-bantam 84 0.756756756757 9081.0 1.0 -aga-bantam 85 0.765765765766 9078.0 0.999669639907 -aga-bantam 86 0.774774774775 9068.0 0.998568439599 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0.9880952380952381 0.0 0.0 aga-mir-iab-4 84 1.0 0.0 0.0
--- a/test-data/lattice_clipped_dataframe.tab Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/lattice_clipped_dataframe.tab Sun May 09 17:10:07 2021 +0000 @@ -1,330 +1,330 @@ Mir_hairpin Offset Norm_offset Count Norm_count -dme-bantam 1 0.0123456790123 0.0 0.0 -dme-bantam 2 0.0246913580247 0.0 0.0 -dme-bantam 3 0.037037037037 0.0 0.0 -dme-bantam 4 0.0493827160494 0.0 0.0 -dme-bantam 5 0.0617283950617 0.0 0.0 -dme-bantam 6 0.0740740740741 0.0 0.0 -dme-bantam 7 0.0864197530864 0.0 0.0 -dme-bantam 8 0.0987654320988 0.0 0.0 -dme-bantam 9 0.111111111111 0.0 0.0 -dme-bantam 10 0.123456790123 0.0 0.0 -dme-bantam 11 0.135802469136 0.0 0.0 -dme-bantam 12 0.148148148148 0.0 0.0 -dme-bantam 13 0.16049382716 0.0 0.0 -dme-bantam 14 0.172839506173 0.0 0.0 -dme-bantam 15 0.185185185185 0.0 0.0 -dme-bantam 16 0.197530864198 0.0 0.0 -dme-bantam 17 0.20987654321 0.0 0.0 -dme-bantam 18 0.222222222222 0.0 0.0 -dme-bantam 19 0.234567901235 0.0 0.0 -dme-bantam 20 0.246913580247 0.0 0.0 -dme-bantam 21 0.259259259259 0.0 0.0 -dme-bantam 22 0.271604938272 0.0 0.0 -dme-bantam 23 0.283950617284 0.0 0.0 -dme-bantam 24 0.296296296296 0.0 0.0 -dme-bantam 25 0.308641975309 0.0 0.0 -dme-bantam 26 0.320987654321 0.0 0.0 -dme-bantam 27 0.333333333333 0.0 0.0 -dme-bantam 28 0.345679012346 0.0 0.0 -dme-bantam 29 0.358024691358 0.0 0.0 -dme-bantam 30 0.37037037037 0.0 0.0 -dme-bantam 31 0.382716049383 0.0 0.0 -dme-bantam 32 0.395061728395 0.0 0.0 -dme-bantam 33 0.407407407407 0.0 0.0 -dme-bantam 34 0.41975308642 0.0 0.0 -dme-bantam 35 0.432098765432 0.0 0.0 -dme-bantam 36 0.444444444444 0.0 0.0 -dme-bantam 37 0.456790123457 0.0 0.0 -dme-bantam 38 0.469135802469 0.0 0.0 -dme-bantam 39 0.481481481481 0.0 0.0 -dme-bantam 40 0.493827160494 0.0 0.0 -dme-bantam 41 0.506172839506 0.0 0.0 -dme-bantam 42 0.518518518519 0.0 0.0 -dme-bantam 43 0.530864197531 0.0 0.0 -dme-bantam 44 0.543209876543 0.0 0.0 -dme-bantam 45 0.555555555556 0.0 0.0 -dme-bantam 46 0.567901234568 0.0 0.0 -dme-bantam 47 0.58024691358 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-dme-bantam 74 0.913580246914 6.0 0.75 -dme-bantam 75 0.925925925926 1.0 0.125 -dme-bantam 76 0.938271604938 0.0 0.0 -dme-bantam 77 0.950617283951 0.0 0.0 -dme-bantam 78 0.962962962963 0.0 0.0 -dme-bantam 79 0.975308641975 0.0 0.0 -dme-bantam 80 0.987654320988 0.0 0.0 +dme-bantam 1 0.012345679012345678 0.0 0.0 +dme-bantam 2 0.024691358024691357 0.0 0.0 +dme-bantam 3 0.037037037037037035 0.0 0.0 +dme-bantam 4 0.04938271604938271 0.0 0.0 +dme-bantam 5 0.06172839506172839 0.0 0.0 +dme-bantam 6 0.07407407407407407 0.0 0.0 +dme-bantam 7 0.08641975308641975 0.0 0.0 +dme-bantam 8 0.09876543209876543 0.0 0.0 +dme-bantam 9 0.1111111111111111 0.0 0.0 +dme-bantam 10 0.12345679012345678 0.0 0.0 +dme-bantam 11 0.13580246913580246 0.0 0.0 +dme-bantam 12 0.14814814814814814 0.0 0.0 +dme-bantam 13 0.16049382716049382 0.0 0.0 +dme-bantam 14 0.1728395061728395 0.0 0.0 +dme-bantam 15 0.18518518518518517 0.0 0.0 +dme-bantam 16 0.19753086419753085 0.0 0.0 +dme-bantam 17 0.20987654320987653 0.0 0.0 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--- a/test-data/lattice_unclipped_dataframe.tab Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/lattice_unclipped_dataframe.tab Sun May 09 17:10:07 2021 +0000 @@ -1,330 +1,330 @@ Mir_hairpin Offset Norm_offset Count Norm_count -dme-bantam 1 0.0123456790123 0.0 0.0 -dme-bantam 2 0.0246913580247 0.0 0.0 -dme-bantam 3 0.037037037037 0.0 0.0 -dme-bantam 4 0.0493827160494 0.0 0.0 -dme-bantam 5 0.0617283950617 0.0 0.0 -dme-bantam 6 0.0740740740741 0.0 0.0 -dme-bantam 7 0.0864197530864 0.0 0.0 -dme-bantam 8 0.0987654320988 0.0 0.0 -dme-bantam 9 0.111111111111 0.0 0.0 -dme-bantam 10 0.123456790123 0.0 0.0 -dme-bantam 11 0.135802469136 0.0 0.0 -dme-bantam 12 0.148148148148 0.0 0.0 -dme-bantam 13 0.16049382716 0.0 0.0 -dme-bantam 14 0.172839506173 0.0 0.0 -dme-bantam 15 0.185185185185 0.0 0.0 -dme-bantam 16 0.197530864198 0.0 0.0 -dme-bantam 17 0.20987654321 0.0 0.0 -dme-bantam 18 0.222222222222 0.0 0.0 -dme-bantam 19 0.234567901235 0.0 0.0 -dme-bantam 20 0.246913580247 0.0 0.0 -dme-bantam 21 0.259259259259 0.0 0.0 -dme-bantam 22 0.271604938272 0.0 0.0 -dme-bantam 23 0.283950617284 0.0 0.0 -dme-bantam 24 0.296296296296 0.0 0.0 -dme-bantam 25 0.308641975309 0.0 0.0 -dme-bantam 26 0.320987654321 0.0 0.0 -dme-bantam 27 0.333333333333 0.0 0.0 -dme-bantam 28 0.345679012346 0.0 0.0 -dme-bantam 29 0.358024691358 0.0 0.0 -dme-bantam 30 0.37037037037 0.0 0.0 -dme-bantam 31 0.382716049383 0.0 0.0 -dme-bantam 32 0.395061728395 0.0 0.0 -dme-bantam 33 0.407407407407 0.0 0.0 -dme-bantam 34 0.41975308642 0.0 0.0 -dme-bantam 35 0.432098765432 0.0 0.0 -dme-bantam 36 0.444444444444 0.0 0.0 -dme-bantam 37 0.456790123457 0.0 0.0 -dme-bantam 38 0.469135802469 0.0 0.0 -dme-bantam 39 0.481481481481 0.0 0.0 -dme-bantam 40 0.493827160494 0.0 0.0 -dme-bantam 41 0.506172839506 0.0 0.0 -dme-bantam 42 0.518518518519 0.0 0.0 -dme-bantam 43 0.530864197531 0.0 0.0 -dme-bantam 44 0.543209876543 0.0 0.0 -dme-bantam 45 0.555555555556 0.0 0.0 -dme-bantam 46 0.567901234568 0.0 0.0 -dme-bantam 47 0.58024691358 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73 0.901234567901 11.0 1.0 -dme-bantam 74 0.913580246914 9.0 0.818181818182 -dme-bantam 75 0.925925925926 0.0 0.0 -dme-bantam 76 0.938271604938 0.0 0.0 -dme-bantam 77 0.950617283951 0.0 0.0 -dme-bantam 78 0.962962962963 0.0 0.0 -dme-bantam 79 0.975308641975 0.0 0.0 -dme-bantam 80 0.987654320988 0.0 0.0 +dme-bantam 1 0.012345679012345678 0.0 0.0 +dme-bantam 2 0.024691358024691357 0.0 0.0 +dme-bantam 3 0.037037037037037035 0.0 0.0 +dme-bantam 4 0.04938271604938271 0.0 0.0 +dme-bantam 5 0.06172839506172839 0.0 0.0 +dme-bantam 6 0.07407407407407407 0.0 0.0 +dme-bantam 7 0.08641975308641975 0.0 0.0 +dme-bantam 8 0.09876543209876543 0.0 0.0 +dme-bantam 9 0.1111111111111111 0.0 0.0 +dme-bantam 10 0.12345679012345678 0.0 0.0 +dme-bantam 11 0.13580246913580246 0.0 0.0 +dme-bantam 12 0.14814814814814814 0.0 0.0 +dme-bantam 13 0.16049382716049382 0.0 0.0 +dme-bantam 14 0.1728395061728395 0.0 0.0 +dme-bantam 15 0.18518518518518517 0.0 0.0 +dme-bantam 16 0.19753086419753085 0.0 0.0 +dme-bantam 17 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-dme-mir-iab-8 56 0.823529411765 0.0 0 -dme-mir-iab-8 57 0.838235294118 0.0 0 -dme-mir-iab-8 58 0.852941176471 0.0 0 -dme-mir-iab-8 59 0.867647058824 0.0 0 -dme-mir-iab-8 60 0.882352941176 0.0 0 -dme-mir-iab-8 61 0.897058823529 0.0 0 -dme-mir-iab-8 62 0.911764705882 0.0 0 -dme-mir-iab-8 63 0.926470588235 0.0 0 -dme-mir-iab-8 64 0.941176470588 0.0 0 -dme-mir-iab-8 65 0.955882352941 0.0 0 -dme-mir-iab-8 66 0.970588235294 0.0 0 -dme-mir-iab-8 67 0.985294117647 0.0 0 +dme-mir-iab-8 52 0.7647058823529411 0.0 0 +dme-mir-iab-8 53 0.7794117647058824 0.0 0 +dme-mir-iab-8 54 0.7941176470588235 0.0 0 +dme-mir-iab-8 55 0.8088235294117647 0.0 0 +dme-mir-iab-8 56 0.8235294117647058 0.0 0 +dme-mir-iab-8 57 0.8382352941176471 0.0 0 +dme-mir-iab-8 58 0.8529411764705882 0.0 0 +dme-mir-iab-8 59 0.8676470588235294 0.0 0 +dme-mir-iab-8 60 0.8823529411764706 0.0 0 +dme-mir-iab-8 61 0.8970588235294118 0.0 0 +dme-mir-iab-8 62 0.9117647058823529 0.0 0 +dme-mir-iab-8 63 0.9264705882352942 0.0 0 +dme-mir-iab-8 64 0.9411764705882353 0.0 0 +dme-mir-iab-8 65 0.9558823529411765 0.0 0 +dme-mir-iab-8 66 0.9705882352941176 0.0 0 +dme-mir-iab-8 67 0.9852941176470589 0.0 0 dme-mir-iab-8 68 1.0 0.0 0
--- a/test-data/mouse.19.gff3 Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/mouse.19.gff3 Sun May 09 17:10:07 2021 +0000 @@ -11,7 +11,7 @@ # rather a predicted stem-loop portion that includes the precursor # miRNA. Mature sequences have type "miRNA". # -# generated by mature_mir_gff_translation.py 2019-10-18 20:30:06.659497 +# generated by mature_mir_gff_translation.py 2021-05-09 00:34:28.737636 # mmu-let-7a-1 . miRNA_primary_transcript 1 94 . - . ID=MI0000556;accession_number=MI0000556;Name=mmu-let-7a-1 mmu-let-7a-5p . miRNA 61 82 . - . ID=MIMAT0000521;accession_number=MIMAT0000521;Name=mmu-let-7a-5p;derives_from=MI0000556;Parent_mir_Name=mmu-let-7a-1
--- a/test-data/mouse_lattice_dataframe.tab Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/mouse_lattice_dataframe.tab Sun May 09 17:10:07 2021 +0000 @@ -1,1240 +1,1240 @@ Mir_hairpin Offset Norm_offset Count Norm_count -mmu-let-7a-1 1 0.0106382978723 0.0 0.0 -mmu-let-7a-1 2 0.0212765957447 0.0 0.0 -mmu-let-7a-1 3 0.031914893617 0.0 0.0 -mmu-let-7a-1 4 0.0425531914894 0.0 0.0 -mmu-let-7a-1 5 0.0531914893617 0.0 0.0 -mmu-let-7a-1 6 0.063829787234 0.0 0.0 -mmu-let-7a-1 7 0.0744680851064 0.0 0.0 -mmu-let-7a-1 8 0.0851063829787 0.0 0.0 -mmu-let-7a-1 9 0.0957446808511 0.0 0.0 -mmu-let-7a-1 10 0.106382978723 0.0 0.0 -mmu-let-7a-1 11 0.117021276596 0.0 0.0 -mmu-let-7a-1 12 0.127659574468 0.0 0.0 -mmu-let-7a-1 13 0.13829787234 8.0 1.0 -mmu-let-7a-1 14 0.148936170213 8.0 1.0 -mmu-let-7a-1 15 0.159574468085 8.0 1.0 -mmu-let-7a-1 16 0.170212765957 8.0 1.0 -mmu-let-7a-1 17 0.18085106383 8.0 1.0 -mmu-let-7a-1 18 0.191489361702 8.0 1.0 -mmu-let-7a-1 19 0.202127659574 8.0 1.0 -mmu-let-7a-1 20 0.212765957447 8.0 1.0 -mmu-let-7a-1 21 0.223404255319 8.0 1.0 -mmu-let-7a-1 22 0.234042553191 8.0 1.0 -mmu-let-7a-1 23 0.244680851064 8.0 1.0 -mmu-let-7a-1 24 0.255319148936 8.0 1.0 -mmu-let-7a-1 25 0.265957446809 8.0 1.0 -mmu-let-7a-1 26 0.276595744681 8.0 1.0 -mmu-let-7a-1 27 0.287234042553 8.0 1.0 -mmu-let-7a-1 28 0.297872340426 8.0 1.0 -mmu-let-7a-1 29 0.308510638298 8.0 1.0 -mmu-let-7a-1 30 0.31914893617 8.0 1.0 -mmu-let-7a-1 31 0.329787234043 8.0 1.0 -mmu-let-7a-1 32 0.340425531915 8.0 1.0 -mmu-let-7a-1 33 0.351063829787 8.0 1.0 -mmu-let-7a-1 34 0.36170212766 7.0 0.875 -mmu-let-7a-1 35 0.372340425532 1.0 0.125 -mmu-let-7a-1 36 0.382978723404 0.0 0.0 -mmu-let-7a-1 37 0.393617021277 0.0 0.0 -mmu-let-7a-1 38 0.404255319149 0.0 0.0 -mmu-let-7a-1 39 0.414893617021 0.0 0.0 -mmu-let-7a-1 40 0.425531914894 0.0 0.0 -mmu-let-7a-1 41 0.436170212766 0.0 0.0 -mmu-let-7a-1 42 0.446808510638 0.0 0.0 -mmu-let-7a-1 43 0.457446808511 0.0 0.0 -mmu-let-7a-1 44 0.468085106383 0.0 0.0 -mmu-let-7a-1 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--- a/test-data/translated_dme.22.gff3 Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/translated_dme.22.gff3 Sun May 09 17:10:07 2021 +0000 @@ -11,7 +11,7 @@ # rather a predicted stem-loop portion that includes the precursor # miRNA. Mature sequences have type "miRNA". # -# generated by mature_mir_gff_translation.py 2019-10-18 20:31:17.146495 +# generated by mature_mir_gff_translation.py 2021-05-09 00:35:07.306527 # dme-bantam . miRNA_primary_transcript 1 81 . + . ID=MI0000387;Alias=MI0000387;Name=dme-bantam dme-bantam-5p . miRNA 15 37 . + . ID=MIMAT0020823;Alias=MIMAT0020823;Name=dme-bantam-5p;Derives_from=MI0000387;Parent_mir_Name=dme-bantam
--- a/test-data/translated_dme.gff3 Fri Oct 18 19:18:50 2019 -0400 +++ b/test-data/translated_dme.gff3 Sun May 09 17:10:07 2021 +0000 @@ -10,7 +10,7 @@ # rather a predicted stem-loop portion that includes the precursor # miRNA. Mature sequences have type "miRNA". # -# generated by mature_mir_gff_translation.py 2019-10-18 20:33:13.877649 +# generated by mature_mir_gff_translation.py 2021-05-09 00:36:13.649865 # dme-bantam . miRNA_primary_transcript 1 81 . + . ID=MI0000387;Alias=MI0000387;Name=dme-bantam dme-bantam-5p . miRNA 15 37 . + . ID=MIMAT0020823;Alias=MIMAT0020823;Name=dme-bantam-5p;Derives_from=MI0000387;Parent_mir_Name=dme-bantam