roblanf

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roblanf

``` r
library(ape)

# make 1K trees to ensure we get some that seem odd...
trees = lapply(rep(4, 1000), rtree, rooted = FALSE)

# test monophyly of all six possible pairs
m1 = unlist(lapply(trees, is.monophyletic, tips = c("t1", "t2")))
m2 = unlist(lapply(trees, is.monophyletic, tips = c("t1", "t3")))
m3 = unlist(lapply(trees, is.monophyletic, tips = c("t1", "t4")))

roblanf

 ID  gCF gDF1 gDF2 gN   sCF  sDF1  sDF2     sN bootstrap branchlength
327 1.15 3.45    0 87 37.34 29.98 32.68 350.19       100   0.00304084

     ID   gCF  gDF1  gDF2 gN   sCF  sDF1  sDF2     sN bootstrap branchlength       gEF_p        sEF_p
14  249 78.57  5.36  5.36 56 56.14 17.86 26.00 383.86       100   0.02134550 1.000000000 1.605600e-02
33  268 62.07  8.62 13.79 58 39.02 27.36 33.62 624.87       100   0.02370450 0.405380556 4.506223e-02
34  269 87.50  0.00  0.00 56 69.10 13.40 17.50 771.13       100   0.10990200 1.000000000 4.051930e-02
36  271 67.35  0.00  8.16 49 46.61 25.97 27.42 426.92       100   0.02632530 0.045500264 6.818018e-01
39  274 34.33 17.91 17.91 67 39.51 23.62 36.87 258.95       100   0.00694057 1.000000000 6.116524e-03

roblanf

library(viridis)
library(ggplot2)
library(dplyr)
library(ggrepel)
library(GGally)
library(entropy)

# read the data
d = read.delim("concord.cf.stat", header = T, comment.char=‘#')
               

roblanf

# infer the concatentated tree with 1000 ultrafast bootstraps and an edge-linked fully-partitioned model (-p is the same as -spp from version 1.7 onwards) 
iqtree -s alignment.nex -p alignment.nex --prefix concat -bb 1000 -nt AUTO

# infer the 88 single-locus trees
iqtree -s alignment.nex -S alignment.nex --prefix loci -nt 50

# calculate concordance factors
iqtree -t concat.treefile --gcf loci.treefile -s alignment.nex --scf 100 --prefix concord

roblanf

library(viridis)
library(ggplot2)
library(dplyr)
library(ggrepel)
library(GGally)
library(entropy)

# read the data
d = read.delim("concord.cf.stat", header = T, skip = 15)

roblanf

#NEXUS
begin DATA;
	dimensions ntax=235 nchar=137324;
	format datatype=nucleotide missing=? gap=-;
	matrix
	Acanthisitta_chloris_genome NNCTCTCAGGATCCAGACTTCACACAGGTT--AGAAACTACT-C-T-GCCTTCTGGATCC-T-TGCT----------------TT-ATGCCC---T-GTCCTG-TTTTATTGTGACAGTTGCCTG--------------TTTTT-CACAGAA--------------AC-TAAG-----------GCCTAGTGA---------CTCCTTATCCTTTGG--TTCGGGCA--ACACAGCTGTC-TTATCTTAAGGCCCAGTGGAATGA--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------G-TCAGCTGGCTC-CTCAGGTGTGA--------AAAC------------TTGGG-CATAG------GTATAGATATGCTCT-TAATTGACCTC-TAGCTG------------GTG--

roblanf

echo "copying source pics"
mkdir source_pics
cp ~/pikrellcam/media/timelapse/* source_pics
cd source_pics

echo "renaming"
mkdir renamed
counter=1
ls -1tr *.jpg | while read filename; do cp $filename renamed/$(printf %05d $counter)_$filename; ((cou\
nter++)); done

roblanf

# build the map for R9.5 flowcell, as a long-form dataframe that translates
# channels into rows and columns on the flowcell. Good for plotting in R.
p1 = data.frame(channel=33:64, row=rep(1:4, each=8), col=rep(1:8, 4))
p2 = data.frame(channel=481:512, row=rep(5:8, each=8), col=rep(1:8, 4))
p3 = data.frame(channel=417:448, row=rep(9:12, each=8), col=rep(1:8, 4))
p4 = data.frame(channel=353:384, row=rep(13:16, each=8), col=rep(1:8, 4))
p5 = data.frame(channel=289:320, row=rep(17:20, each=8), col=rep(1:8, 4))
p6 = data.frame(channel=225:256, row=rep(21:24, each=8), col=rep(1:8, 4))
p7 = data.frame(channel=161:192, row=rep(25:28, each=8), col=rep(1:8, 4))
p8 = data.frame(channel=97:128, row=rep(29:32, each=8), col=rep(1:8, 4))

roblanf