Hello,

I'm following the "Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses [version 2; referees: 3 approved]" to analyse my 454 sequence data. I followed DADA2 pipeline tutorial (https://benjjneb.github.io/dada2/tutorial.html) and fixed some commands according to the Q&A (https://benjjneb.github.io/dada2/faq.html).

After assigning taxonomy, I constructed phylogenetic tree using phangorn R package, and started prevalence filtering. But the abundance graph is not like Figure 3. I found that phangorn is not good for the large taxa (5238 taxa in my data), so I also tried RAxML to construct the tree (benjjneb/dada2#88).

But the result is same as above. How can I make a abudance graph like figure 3?
Let me know if I'm being unclear or you need more info.

Thank you,

Chaewon

Hello,
I am new in this field. I am writing to enquire about a problem that I got while I was following each step of workflow and I am stuck in the step of constructing NJ-tree:

treeNJ <- NJ(dm)

Error in nj(x) : cannot allocate memory block of size 134217728 Tb

could someone advise me how to solve this, please?

Hello,

I had a question regarding how you identified the outliers by their sample IDs from Figure 8? Also, why did you choose the 12th column for the rel_abund data frame?

Also, when I tried to install Shiny-phyloseq because I read that I can detect outliers using the app, I encountered an issue. I've attempted to install it both automatically and manually. The error was produced using the manual method.

In DESeq2, after applying getVarianceStabilizedData, all counts = 0 are transformed to negative values. These correspond to 2/3 of the values in the matrix. I am worried that this will interfere with downstream Hierarchical FDR analysis.

1. Is it "normal" to get negative normalized values for counts = 0?
2. Can I proceed with Hierarchical multiple testing with structSSI using this transformed counts?
3. Could I just go with log transformed counts?
4. Should I apply other transformation?

Thanks Susan for publishing this helpful guide.

This section of the workflow is throwing an error:

library(phyloseq)
ps <- phyloseq(otu_table(seqtabNoC, taxa_are_rows=FALSE), 
               sample_data(samdf), 
               tax_table(taxTab),phy_tree(fitGTR$tree))
ps <- prune_samples(sample_names(ps) != "Mock", ps) # Remove mock sample
ps

The error is:

Error in validObject(.Object) : invalid class “phyloseq” object: Component sample names do not match. Try sample_names()

I'm pretty sure I've gotten through the sample workflow without this error. Can someone tell me exactly which row or column names are supposed to be matching but are not?

These are the rownames of my variables:

> rownames(otu_table(seqtabNoC, taxa_are_rows=FALSE))
 [1] "F3D0"   "F3D1"   "F3D141" "F3D142" "F3D143" "F3D144" "F3D145" "F3D146" "F3D147" "F3D148" "F3D149" "F3D150"
[13] "F3D2"   "F3D3"   "F3D5"   "F3D6"   "F3D7"   "F3D8"   "F3D9"  
> rownames(sample_data(samdf))
 [1] "NA"    "NA.1"  "NA.2"  "NA.3"  "NA.4"  "NA.5"  "NA.6"  "NA.7"  "NA.8"  "NA.9"  "NA.10" "NA.11" "NA.12"
[14] "NA.13" "NA.14" "NA.15" "NA.16" "NA.17" "NA.18"
> rownames(phy_tree(fitGTR$tree))
NULL

what codes comes after bioinformatics R, the top of the file says this is part 1 of 3?

Hi,

I want to apply this workflow to my data analysis. But I can not understand how the following code was transformed. May I know more about it?
PCoA on the ranks
abund <- otu_table(pslog)
abund_ranks <- t(apply(abund, 1, rank))
I found the result is not the order. The import data are logtransformed raw data. the apply function transform it into ranks based on the row. However, the final tranformed data is not started from 1, how could that be?

And then the fountion abund_ranks <- abund_ranks - 329 set minus 329, why is 329 set here, and how to set the number properly?

Thanks in advance.

Hi, this is my first time posting a question here in the github forums. I would like to state that I'm relatively new in using R and in particular using it to analyze DNA sequences.

I was trying to generate a phylogenetic tree for my phyloseq object, employing the following script:
treeNJ <- NJ(dm)
fit = pml(treeNJ, data=phangAlign)
fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))

Everthing was running OK, until the final line of:
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))

This is were I recieved the following error message:
"Error in if (((ll1 - ll)/ll < control$eps) && rounds > 2) opti <- FALSE :
missing value where TRUE/FALSE needed
In addition: There were 50 or more warnings (use warnings() to see the first 50)
#The error messages are:
1: In optimize(f = fn, interval = c(0.1, 500), lower = 0.1, ... :
NA/Inf replaced by maximum positive value"

What does it mean and what can I do about it? I've been stuck on this issue for a couples of days now and haven't been able to solve it. Can anyone help me out with this? Other users have stated similar issues, but their solutions haven't worked for me.

I'm using a virtual machine with the following specs:

sessionInfo():
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
RAM: 125 GB
HDD: 180GB
Matrix products: default

locale:
[1] LC_COLLATE=Spanish_Chile.1252
[2] LC_CTYPE=Spanish_Chile.1252
[3] LC_MONETARY=Spanish_Chile.1252
[4] LC_NUMERIC=C
[5] LC_TIME=Spanish_Chile.1252

attached base packages:
[1] grid stats4 parallel stats graphics
[6] grDevices utils datasets methods base

other attached packages:
[1] knitr_1.29 SummarizedExperiment_1.18.1
[3] DelayedArray_0.14.0 matrixStats_0.56.0
[5] Biobase_2.48.0 GenomicRanges_1.40.0
[7] GenomeInfoDb_1.24.2 BiocManager_1.30.10
[9] phangorn_2.5.5 ape_5.4
[11] Rcpp_1.0.5 xtable_1.8-4
[13] gridExtra_2.3 plyr_1.8.6
[15] XLConnect_1.0.1 Biostrings_2.56.0
[17] XVector_0.28.0 IRanges_2.22.2
[19] S4Vectors_0.26.1 BiocGenerics_0.34.0

loaded via a namespace (and not attached):
[1] lattice_0.20-41 foreach_1.5.0
[3] zlibbioc_1.34.0 rstudioapi_0.11
[5] data.table_1.12.8 Matrix_1.2-18
[7] BiocParallel_1.22.0 stringr_1.4.0
[9] igraph_1.2.5 RCurl_1.98-1.2
[11] bit_1.1-15.2 tinytex_0.24
[13] compiler_4.0.2 xfun_0.15
[15] pkgconfig_2.0.3 biomformat_1.16.0
[17] GenomeInfoDbData_1.2.3 quadprog_1.5-8
[19] codetools_0.2-16 XML_3.99-0.4
[21] crayon_1.3.4 MASS_7.3-51.6
[23] bitops_1.0-6 nlme_3.1-148
[25] jsonlite_1.7.0 gtable_0.3.0
[27] DBI_1.1.0 magrittr_1.5
[29] stringi_1.4.6 reshape2_1.4.4
[31] fastmatch_1.1-0 Rhdf5lib_1.10.0
[33] iterators_1.0.12 tools_4.0.2
[35] ade4_1.7-15 bit64_0.9-7
[37] rhdf5_2.32.2 cluster_2.1.0
[39] rJava_0.9-13

Thanks,

Hi,

I'm following the "Workflow for Microbiome Data Analysis" to analyse some 16S and ITS datasets. I'm unsure about the effects that prevalence filtering could have in downstream analysis I'm planning to do (a differential abundance analysis with DESeq2, for example).

This is how I'm performing prevalence filtering right now:

ps_16s_relative <- transform_sample_counts(ps_16s, function(x) x/sum(x))

prevalence_16s <- apply(X = otu_table(ps_16s_relative),
                        MARGIN = 1,
                        FUN = function(x) sum(x >= 0.0001))

prevalence_df_16s <- data.frame(prevalence = prevalence_16s,
                                relative_prevalence = prevalence_16s/nsamples(ps_16s_relative),
                                total_abundance = taxa_sums(ps_16s_relative),
                                tax_table(ps_16s_relative))

ps_16s <- prune_taxa(rownames(prevalence_df_16s)[(prevalence_df_16s$relative_prevalence >= 0.05)], ps_16s)

And these are the numbers I'm getting (with the 16S data):

'Number of ASVs before the prevalence filtering: 28582'
'Number of phyla before the prevalence filtering: 40'

'Number of ASVs after the prevalence filtering: 5762'
'Number of phyla after the prevalence filtering: 23'

I'm felling that even though the filtered data may be a better representation of the microbial community, I'm losing too much data.

Is the prevalence filtered data recommended for downstream analysis?

Hi, all,

I am following the Workflow for Microbiome Data Analysis dealing with ITS1 NGS data of fungal communities. We adopted the notion of ASV in our analysis, rather than OTU. However, this brings a problem, that lots of ASVs still remain after taxon agglomeration at species level.

Considering that some ASVs with similar sequences which could belong to one same fungal species, and too many ASVs will cause troubles in the following analysis and interpretations, we wanted to reduce the number of ASVs.

What I’m doing now is to identify all ASVs based on UNITE reference and at bootstrap=75, and select the ASVs which failed being identified at species level. The selected ASVs are clustered into OTU at 97% using “kmer” package in R (truncate all sequences due to their minimum length when necessary, as kmer::otu only deal with sequences with same lengths). Then to identify OTUs again. Finally, the ASV-table where ASVs with species level taxonomical assignments, and the OTU-table with re-identified OTUs were combined for the following tax_glom.

This methods indeed dramatically reduces the numbers of “operational taxa” in the final dataset, and the ASVs with similar sequences were merge into one OTU. But I’m not sure whether this method would be acceptable by other researchers, since I haven’t seen analogous method in other’s publication. Is there anybody would like to share some comments on this method?

Thanks!

I am trying to download the raw data using

miseq_path <- file.path("data", "MiSeq_SOP")
file_path <- file.path("data", "filtered")

if(!file_test("-d", miseq_path)) {

• dir.create(miseq_path)
• download.file("http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar",

•             destfile = file.path(miseq_path, "StabilityNoMetaG.tar"))
  

• system(paste0("tar -xvf ", file.path(miseq_path, "StabilityNoMetaG.tar"),

•             " -C ", miseq_path, "/"))
  

• }

but get this error

downloaded 0 bytes

Error in download.file("http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar", :
cannot download all files
In addition: Warning messages:
1: In dir.create(miseq_path) :
cannot create dir 'data/MiSeq_SOP', reason 'No such file or directory'
2: In download.file("http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar", :
URL http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar: cannot open destfile 'data/MiSeq_SOP/StabilityNoMetaG.tar', reason 'No such file or directory'
3: In download.file("http://www.mothur.org/MiSeqDevelopmentData/StabilityNoMetaG.tar", :
downloaded length 0 != reported length 884254720

can anyone guide me as to the cause of the error

I am trying to plot NMDS plot from microbiome data in phyloseq and I used below command, It is working perfectly, but here I used raw otu count for NMDS plot and the rank-threshold transformation is recommended as input FOR NMDS ordinate command.

Mymensingh.NMDS.ORDINATE <- ordinate(Mymensingh_all, "NMDS", "bray")

plot_ordinattion(Mymensingh_all, ordination = Mymensingh.NMDS.ORDINATE, color="Region") + theme_minimal() + geom_point(size=5)

I have tried to do rank-threshold transformation as mention in the below publication but I am getting error, I will be thankful for your time and help.

https://f1000research.com/articles/5-1492

Error

ntaxa(mymensingh_all)
[1] 44481

abund <- otu_table(mymensingh_all)

abund_ranks <- t(apply(abund, 1, rank))
abund_ranks <- abund_ranks - 20000
abund_ranks[abund_ranks < 1] <- 1

prev.ordNMDS_rank <- ordinate(abund_ranks, "NMDS", "bray")

Error in ordinate(abund_ranks, "NMDS", "bray") : Expected a phyloseq object or otu_table object.

The link to the data returns this message:
"The requested URL /MiSeqDevelopmentData/StabilityNoMetaG.tar was not found on this server."

is there another way to get this data to run the workflow?

Thanks.

Hello,
I am new in this field and having trouble in analysing my data. Can anyone please suggest me what exactly is wrong in creating tree. As m getting this error after 20th days :

fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,

rearrangement = "stochastic", control = pml.control(trace = 0))

Error in optim(par = lbf, fn = fn, gr = NULL, method = "Nelder-Mead", :
function cannot be evaluated at initial parameters
In addition: There were 48 warnings (use warnings() to see them)

warnings()
Warning messages:
1: In optimize(f = fn, interval = c(0.1, 500), lower = 0.1, ... :
NA/Inf replaced by maximum positive value
2: In optimize(f = fn, interval = c(0.1, 500), lower = 0.1, ... :
NA/Inf replaced by maximum positive value

Information about:
A) Sequencing Library:
We outsourced our samples where we amplified V3-V4 region using Illumina miseq platform using nexetra library preparation kit.
B) Size of the ASV table:
are you talking about this?
otu_table() OTU Table: [ 24226 taxa and 72 samples ]
sample_data() Sample Data: [ 72 samples by 19 sample variables ]
tax_table() Taxonomy Table: [ 24226 taxa by 6 taxonomic ranks ]

I am having trouble in making tree. Its getting killed.
Please suggest.

Hi there,

I'm trying to get a scale bar for my phlyogenetic tree.

I’m using the instructions found in this tutorial: https://f1000research.com/articles/5-1492/v2

After I process my sequences and run the alignment, my script is as follows:

phang.align <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phang.align)
treeNJ <- NJ(dm)
fit = pml(treeNJ, data=phang.align)
negative edges length changed to 0!
fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE, rearrangement = "stochastic", control = pml.control (trace = 0))

I combine this into my phyloseq object, then I filter and prune my data set to give me the new phyloseq object: test15_2

I plot this tree using:

plot_tree(test15_2, color="Species", ladderize="left", label.tips=labeltips)

But I’m unsure of how to add the scale bar in here?

Any help would be appreciated.

Cheers,

When I try to run the code:

for(i in seq_along(fnFs)) {
   fastqPairedFilter(c(fnFs[[i]], fnRs[[i]]),
                    c(fnFs[[i]], fnRs[[i]]),
                    trimLeft=10, truncLen=c(245, 160),
                    maxN=0, maxEE=2, truncQ=2,
                    compress=TRUE)
}

on my PC, I always get the error

Warning in file.remove(fout[[1]]) :
  cannot remove file 'C:/Users/Martina/Documents/Martina Stats Summer/StabilityNoMetaG/F3D0_S188_L001_R1_001.fastq.gz', reason 'Permission denied'
Error in fastqPairedFilter(c(fnFs[[i]], fnRs[[i]]), c(fnFs[[i]], fnRs[[i]]),  : 
  Failed to overwrite file:C:/Users/Martina/Documents/Martina Stats Summer/StabilityNoMetaG/F3D0_S188_L001_R1_001.fastq.gz

Consequently, it's not allowing me to trim and filter the sequences for future steps.

I am trying to download "MiSeqDevelopmentData"
from http://www.mothur.org, however, the website is currently down. Is there any other place to download this dataset? Thank you in advance.

I am applying your workflow to analyze my metagenomics data. Thank you for publishing this versatile and powerful guide.
I want to determine potential taxa associated to the rhizosphere of a plant. For this purpose I am following your recommendations on the Hierarchical multiple testing with structSSI. I also believe this is probably the best approach since it profits from the topology of the phylogenetic tree to carry out hypothesis testing across all depths in the tree, thus maximizing the information to be mined from the data (compared to other methods which that do not consider hierarchy such as implemented in DESeq2).

I would much appreciate your opinion when setting alpha value in the function hFDR.adjust. The algorithm stops whenever p-value from a node (the probability of finding differences in that node) falls above alpha, so shallower nodes/tips get no adjusted p-values (“NA”; the hypothesis is not rejected). I noticed you set in your example an alpha=.75. If I set this same value I only get 3 nodes evaluated because the p-values for my deepest nodes is very high. To overcome this and get adjusted p-values for all nodes/tips in the tree I set alpha to 1. To my understanding this will decrease the power of my analysis because the number of hypothesis evaluated increases.
So:

1. Setting an alpha=1 is equivalent to doing non-hierarchical Benjamini-Hochberg procedure (such as implemented in DeSeq2)? would it be OK to interpret ajusted p-values using this alpha?
2. Is there trade-off between power (alpha set) and significant results? After checking the ASVs with most significant values their assigned taxonomy is very coherent with the environments of the metagenomic samples.

Thanks again

Hi, I reanalysed my Illumina miseq data and found some discrepancies between the old and my new results. I will post my code and the R output here in the hope of receiving some feedback on the accuracy of my coding... I would very much appreciate it if someone could tell me if what I did is correct. Thank you very much.

#This section demonstrates the “full stack” of amplicon bioinformatics: construction of the sample-by-sequence
#feature table from the raw reads, assignment of taxonomy, and creation of a phylogenetic tree relating the sample sequences.
#First we load the necessary packages.
library("knitr")
library("BiocStyle")
.cran_packages <- c("ggplot2", "gridExtra")
.bioc_packages <- c("dada2", "phyloseq", "DECIPHER", "phangorn")
.inst <- .cran_packages %in% installed.packages()
if(any(!.inst)) {

• install.packages(.cran_packages[!.inst])
• }

.inst <- .bioc_packages %in% installed.packages()
if(any(!.inst)) {

• source("http://bioconductor.org/biocLite.R")
• biocLite(.bioc_packages[!.inst], ask = F)
• }

Load packages into session, and print package version

sapply(c(.cran_packages, .bioc_packages), require, character.only = TRUE)
ggplot2 gridExtra dada2 phyloseq DECIPHER phangorn
TRUE TRUE TRUE TRUE TRUE TRUE
set.seed(100)
getwd()
[1] "/home/aut/AG_Loibner/Daten/Seq_data_mcra/Map9/Reads_retrimmed/Original_reads"
setwd("/home/aut/AG_Loibner/Daten/Seq_data_mcra/Map9/Reads_retrimmed/Original_reads/")
getwd()
[1] "/home/aut/AG_Loibner/Daten/Seq_data_mcra/Map9/Reads_retrimmed/Original_reads"
miseq_path <- "./MCRA_Lehen.fq/" # CHANGE to the directory containing the fastq files after unzipping.
list.files(miseq_path)
[1] "M1-T1_R1.fastq" "M1-T1_R2.fastq" "M2-T1_R1.fastq" "M2-T1_R2.fastq" "M4-T2_R1.fastq" "M4-T2_R2.fastq" "M5-T2_R1.fastq" "M5-T2_R2.fastq"
[9] "M6-T2_R1.fastq" "M6-T2_R2.fastq"

#Inspect read quality, sort reads, Trim and filter

Sort ensures forward/reverse reads are in same order

fnFs <- sort(list.files(miseq_path, pattern="_R1.fastq"))
fnRs <- sort(list.files(miseq_path, pattern="_R2.fastq"))

Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq

sampleNames <- sapply(strsplit(fnFs, "_"), [, 1)
fnFs <- file.path(miseq_path, fnFs)
fnRs <- file.path(miseq_path, fnRs)
fnFs[1:3]
[1] "./MCRA_Lehen.fq//M1-T1_R1.fastq" "./MCRA_Lehen.fq//M2-T1_R1.fastq" "./MCRA_Lehen.fq//M4-T2_R1.fastq"
fnRs[1:3]
[1] "./MCRA_Lehen.fq//M1-T1_R2.fastq" "./MCRA_Lehen.fq//M2-T1_R2.fastq" "./MCRA_Lehen.fq//M4-T2_R2.fastq"
#Most Illumina sequencing data shows a trend of decreasing average quality towards the end of sequencing reads.

#Plot quality of the first two forward reads:
plotQualityProfile(fnFs[1:5])
plotQualityProfile(fnRs[1:5])
#pdf("qualityProfile.pdf")
toPlot<-c()
for (i in 1:length(fnFs)){

• toPlot<-c(toPlot,fnFs[i],fnRs[i])
• }

head(toPlot)
[1] "./MCRA_Lehen.fq//M1-T1_R1.fastq" "./MCRA_Lehen.fq//M1-T1_R2.fastq" "./MCRA_Lehen.fq//M2-T1_R1.fastq" "./MCRA_Lehen.fq//M2-T1_R2.fastq"
[5] "./MCRA_Lehen.fq//M4-T2_R1.fastq" "./MCRA_Lehen.fq//M4-T2_R2.fastq"
plotQualityProfile(fnFs[1:length(fnFs)])
plotQualityProfile(fnFs[1:length(fnRs)])

ggsave("qualityPlot.svg",

•    plotQualityProfile(toPlot)+
  

•      geom_hline(yintercept=30)+
  

•      geom_hline(yintercept=20)+
  

•      geom_vline(xintercept=100)+
  

•      geom_vline(xintercept=245)
  

• )
  Saving 8.72 x 7 in image

#Save plot, add horizontal line
#Based on the quality analysis we will trimm at position 245 for the forward strand and 100 for the reverse strand

Place filtered files in filtered/ subdirectory

filt_path<-file.path(".","filtered")
if(!file_test("-d", filt_path)) dir.create(filt_path)
filtFs <- file.path(filt_path, basename(fnFs))
filtRs <- file.path(filt_path, basename(fnRs))

out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,

•                  trimLeft=c(58,64), maxN=0, maxEE=c(2,6), minQ=2,
  

•                  compress=FALSE, verbose=T, multithread=TRUE) # trimLeft used to remove forward and reverse primer sequences
  

print(out)
reads.in reads.out
M1-T1_R1.fastq 14367 5012
M2-T1_R1.fastq 20036 4193
M4-T2_R1.fastq 19142 2519
M5-T2_R1.fastq 16947 1839
M6-T2_R1.fastq 18387 4534

derepFs <- derepFastq(filtFs)
Not all sequences were the same length.
Not all sequences were the same length.
Not all sequences were the same length.
Not all sequences were the same length.
Not all sequences were the same length.
derepRs <- derepFastq(filtRs)
Not all sequences were the same length.
Not all sequences were the same length.
Not all sequences were the same length.
Not all sequences were the same length.
Not all sequences were the same length.
sam.names <- sapply(strsplit(basename(filtFs), "_"), [, 1)
names(derepFs) <- sam.names
names(derepRs) <- sam.names

ddF<-dada(derepFs[1:5], err=NULL, selfConsist=TRUE)
Initializing error rates to maximum possible estimate.
Sample 1 - 5012 reads in 2340 unique sequences.
Sample 2 - 4193 reads in 2066 unique sequences.
Sample 3 - 2519 reads in 1255 unique sequences.
Sample 4 - 1839 reads in 1118 unique sequences.
Sample 5 - 4534 reads in 1791 unique sequences.
selfConsist step 2
selfConsist step 3
selfConsist step 4
Convergence after 4 rounds.
ddR<-dada(derepRs[1:5], err=NULL, selfConsist=TRUE)
Initializing error rates to maximum possible estimate.
Sample 1 - 5012 reads in 3096 unique sequences.
Sample 2 - 4193 reads in 2622 unique sequences.
Sample 3 - 2519 reads in 1607 unique sequences.
Sample 4 - 1839 reads in 1292 unique sequences.
Sample 5 - 4534 reads in 2558 unique sequences.
selfConsist step 2
selfConsist step 3
selfConsist step 4
selfConsist step 5
Convergence after 5 rounds.

dadaFs <- dada(derepFs, err=ddF[[1]]$err_out, pool=TRUE)
5 samples were pooled: 18097 reads in 7175 unique sequences.
dadaRs <- dada(derepRs, err=ddR[[1]]$err_out, pool=TRUE)
5 samples were pooled: 18097 reads in 9851 unique sequences.

mergers <- mergePairs(dadaFs, derepFs, dadaRs, derepRs)

################################################################################

Mapping

################################################################################
seqtab.all <- makeSequenceTable(mergers[!grepl("Mock", names(mergers))])
The sequences being tabled vary in length.
seqtab <- removeBimeraDenovo(seqtab.all)
As of the 1.4 release, the default method changed to consensus (from pooled).
seqtab.nochim <- removeBimeraDenovo(seqtab, method="consensus", multithread=TRUE, verbose=TRUE)
Identified 0 bimeras out of 32 input sequences.
#Here we need to set the taxonomy to the mcrA fasta file
#We have to pass a file formatted in the following way to assignTaxonomy
#>Level1;Level2;Level3;Level4;Level5;Level6;
#ACCTAGAAAGTCGTAGATCGAAGTTGAAGCATCGCCCGATGATCGTCTGAAGCTGTAGCATGAGTCGATTTTCACATTCAGGGATACCATAGGATAC
#>Level1;Level2;Level3;Level4;Level5;
#CGCTAGAAAGTCGTAGAAGGCTCGGAGGTTTGAAGCATCGCCCGATGGGATCTCGTTGCTGTAGCATGAGTACGGACATTCAGGGATCATAGGATAC
#We got the list of genes from FunGen, selecting the 10000 best genes (from 10484)
#Yang, Sizhong; Liebner, Susanne; Alawi, Mashal; Ebenhöh, Oliver; Wagner, Dirk (2014): Supplement to: Taxonomic database and cutoff value for processing mcrA gene 454 pyrosequencing data by MOTHUR. Deutsches GeoForschungsZentrum GFZ. http://doi.org/10.5880/GFZ.4.5.2014.001
#Sequenzdatenbank umformatieren von >Accno. Seq to >taxonomy +Seq
#getwd()
ref_fasta <- "/home/aut/AG_Loibner/Daten/Seq_data_mcra/ref_db_mcra/db_mcra_genus.fasta"
taxtab <- assignTaxonomy(seqtab, refFasta = ref_fasta, tryRC=T, multithread=T)
#taxtabCath <- assignTaxonomy(seqtab, refFasta = ref_fastaCath,tryRC=T,multithread=T)
#Add species. Mapping must 100%
#taxtab<-addSpecies(taxtab,"./mcrASequenceDBAssignSpecies.fa",verbose=T)
help("addSpecies")
#Trick to add species without 100% mapping
#taxtabWithSpec <- assignTaxonomy(seqtab, refFasta="./mcrASequenceDBAssignTaxonomyWithSpecies.fa",tryRC=T,multithread=T,verbose=T)
colnames(taxtab) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus")
#Prepare phyloseq elements
library(DECIPHER)
#To construct a phylogenetic tree:
#seqs <- getSequences(seqtab)
#names(seqs) <- seqs # This propagates to the tip labels of the tree
#alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA)

##Construct phylogenetic tree
#phang.align <- phyDat(as(alignment, "matrix"), type="DNA")
#dm <- dist.ml(phang.align)
#treeNJ <- NJ(dm) # Note, tip order != sequence order
#fit = pml(treeNJ, data=phang.align)

negative edges length changed to 0!

#fitGTR <- update(fit, k=4, inv=0.2)
#fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,

rearrangement = "stochastic", control = pml.control(trace = 0))

#detach("package:phangorn", unload=TRUE)
#Combine data into a phyloseq object
mimarks_path <- "./SampleInformation.csv"
samdf<-read.csv(mimarks_path, header=TRUE)
Error in file(file, "rt") : cannot open the connection
In addition: Warning message:
In file(file, "rt") :
cannot open file './SampleInformation.csv': No such file or directory
samdf$SampleID<-samdf$sample_id

taxa.print <- taxtab # Removing sequence rownames for display only
rownames(taxa.print) <- NULL
#head(taxa.print)
#head(seqtab.nochim)
samdf <- read.table("/home/aut/AG_Loibner/Daten/Seq_data_mcra/meta.data.txt", header = TRUE)
print(samdf)
SampleID Type Depth.m. Site Month Year
1 M1-T1 Reservoir 1200 LEH-002 November 2016
2 M2-T1 Reservoir 1200 LEH-002 November 2016
3 M4-T2 Reservoir 1350 LEH-002 November 2016
4 M5-T2 Reservoir 1350 LEH-002 November 2016
5 M6-T2 Reservoir 1350 LEH-002 November 2016
rownames(samdf) <- rownames(seqtab.nochim)
ps <- phyloseq(tax_table(taxtab), sample_data(samdf),

•            otu_table(seqtab, taxa_are_rows = FALSE))
  

library("phyloseq")
packageVersion("phyloseq")
[1] ‘1.22.3’

#remotes::install_github("cpauvert/psadd")
library("psadd")
plot_krona(ps,variable="SampleID",output="test2plot")
Writing test2plot.html...
Warning message:
In dir.create(output) : 'test2plot' already exists

ps2 = filter_taxa(ps, function(x) mean(x) > 0.1, TRUE)
ps2
phyloseq-class experiment-level object
otu_table() OTU Table: [ 32 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 6 sample variables ]
tax_table() Taxonomy Table: [ 32 taxa by 6 taxonomic ranks ]
plot_krona(ps2,variable="SampleID",output="ps2plot")
Writing ps2plot.html...
Warning message:
In dir.create(output) : 'ps2plot' already exists
ps3 = transform_sample_counts(ps2, function(x) x / sum(x) )
ps3
phyloseq-class experiment-level object
otu_table() OTU Table: [ 32 taxa and 5 samples ]
sample_data() Sample Data: [ 5 samples by 6 sample variables ]
tax_table() Taxonomy Table: [ 32 taxa by 6 taxonomic ranks ]

Also, could we replace this inelegant programming which is wasteful in space:
plyr::ddply(prevdf, "Phylum", function(df1){mean(df1$Prevalence)})
plyr::ddply(prevdf, "Phylum", function(df1){sum(df1$Prevalence)})

with something that produces just one output with the two columns
(pipe challenge maybe here?)

Dear community,

I am having memory issues while running the "Bioconductor workflow for microbiome data analysis: from raw reads to community analyses" and need some orientation. I am using this workflow for more then 2 years and this is the first time I am having issues with this amazing workflow. Let me put it in context.

I am working with upper respiratory tract samples. Libraries were performed following Illumina's protocol (16S v3v4), but sequenced with MiSeq V2 kit (we know that MiSeq V3 kit is recommended and will be using it from now on, but we have also performed successful analyses with V2 kit before). Since we had problems with MiSeq, we have a batch effect on these samples, but I believe this is not the problem. I believe we had a good sequencing quality also (attached - samples with low quality were removed - M49, M66...).
RevQualityPlot.pdf
FwdQualityPlot.pdf

The filter and trim was perfomed as follows:

for(i in seq_along(fnFs)) {
fastqPairedFilter(c(fnFs[[i]], fnRs[[i]]),
c(fnFs[[i]], fnRs[[i]]),
trimLeft=c(27,31), truncLen=c(240, 240),
maxN=0, maxEE=2, truncQ=2,
compress=TRUE)
}

I performed a pooled inference of sequences which resulted in around 11 million sequences and more then 1 million unique sequences.

dadaFs <- dada(derepFs, err=ddF[[1]]$err_out, pool=TRUE)
dadaRs <- dada(derepRs, err=ddR[[1]]$err_out, pool=TRUE)

After merging, chimera removal and trimming of non-target length sequences, these resulted in which I think is a plausible number of ASVs.

mergers <- mergePairs(dadaFs, derepFs, dadaRs, derepRs)
seqtab.all <- makeSequenceTable(mergers[!grepl("Mock", names(mergers))])
seqtab2 <- removeBimeraDenovo(seqtab.all)
table(nchar(getSequences(seqtab2)))

If I am not wrong, my expected amplicon size is ~402. The majority of my sequences length were between 398-409 (402 and 407 had most of them), so I removed the sequences outside this range.

seqtab2 <- seqtab[,nchar(colnames(seqtab)) %in% seq(398,408)]

Reads through the pipeline:

Reads through the pipeline
input filtered denoisedF denoisedR merged nonchim
M03 213895 177214 176972 176990 174471 146119
M04 214250 177991 176814 177140 167873 63085
M05 111659 76484 76335 76409 75549 72923
M06 205858 144111 142696 143278 133717 66378
M07 221406 183339 182223 182781 175147 83000
M08 202433 171376 170365 170763 162915 100385
M10 260685 212201 211421 211882 206629 157737
M11 168531 120209 119097 119297 111884 51200
M13 225351 186372 185161 185649 177682 95916
M14 238618 193832 192852 193165 186893 121542
M15 255374 176133 175008 175574 167765 69808
M16 210136 139221 138490 138794 132123 50854
M17 173373 123043 122929 122965 121961 110472
M18 234219 161497 159773 159856 149661 90075
M20 205971 137406 136095 136592 129638 66840
M21 169008 105744 105091 104999 100811 72806
M22 173211 128590 127405 127632 119163 50332
M23 222669 176956 175712 175855 168136 121634
M24 253965 189089 187971 188081 179780 109824
M25 271688 197873 196825 196877 190451 110410
M26 214020 176747 175353 175750 165288 66787
M27 169323 121260 120503 120702 115835 77525
M28 98143 73598 73447 73461 72256 45415
M29 168769 114365 113325 113765 106913 57167
M30 79706 47161 46705 46747 43990 32545
M31 216586 174373 173449 173069 166518 115607
M32 245302 198261 195913 196865 180968 71785
M33 252679 174899 174184 174269 169890 114857
M34 165374 115868 115657 115624 113552 90644
M35 295397 241362 239965 240552 231041 117235
M36 179980 123456 122385 122763 116195 64997
M37 137855 88581 87982 88225 84790 55937
M38 221521 173301 171426 171445 157802 71829
M39 237677 165944 164886 164375 154693 68683
M40 138247 107340 106856 106980 103382 59637
M41 74104 57649 56946 57051 53156 22797
M42 250248 216338 215940 215957 212792 126272
M43 201777 165061 162960 163791 150705 76924
M44 194097 159804 158934 159243 153752 76524
M45 154146 115990 114709 115034 107032 67916
M46 98062 74028 73436 73698 69333 34707
M47 151414 114086 113620 113802 110812 68390
M50 160701 124570 124090 124157 121332 93739
M51 172661 125215 124349 124694 118074 78879
M52 171285 110213 109614 109786 104073 68427
M53 78156 57138 56807 56909 54413 30587
M54 207224 157448 157096 157168 155455 124792
M55 117487 89900 89137 89387 84053 39041
M56 150359 115705 115185 115323 112669 68167
M57 185730 147264 146206 146635 140884 83492
M58 168137 130837 130102 130269 124604 61054
M59 128055 103151 102778 102957 101100 60711
M60 115132 85510 84374 84719 76580 41714
M61 268359 219507 218663 218897 211526 120186
M62 152527 112127 111656 111649 107430 66822
M63 189507 143665 140768 141921 126686 74779
M64 164891 126167 125433 125563 120433 71083
M65 214833 135956 135166 135227 129453 93481
M66 67953 19342 19144 19203 17511 12326
M67 24965 19296 19093 19165 18226 14620
M68 368955 283223 281144 281786 268906 175704
M69 221485 167142 165543 165323 149716 70501
M70 237922 166857 165619 165967 155037 67710
M72 247257 198857 197425 198209 192654 140753
M73 241340 195173 194223 194673 188389 123507
M74 259993 199604 198181 198941 191798 124866
M75 447637 365839 361253 362044 336607 139179
M76 92208 59597 59080 59212 55080 30423
M77 261440 205708 203914 204524 191137 98587
M78 269973 222774 222089 222217 217919 164074
M80 232728 185294 182677 183727 169525 92942
M81 221378 177994 175379 176259 161605 88817
M82 87115 37924 37767 37810 36643 23878
M83 258835 193636 193044 193131 189052 139140
M84 100648 71908 71607 71642 69494 30952
M85 402056 309207 307966 308214 298330 186767
M86 72130 25457 25401 25424 25135 20839
M87 228186 180601 179640 179982 173012 90041
M88 135730 58092 57940 57960 56081 31948

Then I performed Assign Taxonomy with eHOMD database formatted for dada2 available here. https://github.com/fconstancias/metabaRpipe/tree/master/databases

Then the problem started. I tried to perform sequence alignment as follows:
seqs <- getSequences(seqtab)
names(seqs) <- seqs # This propagates to the tip labels of the tree
mult <- msa(seqs, method="ClustalW", type="dna", order="input")

This resulted in bad alloc error.

Then I changed to DECIPHER alignment, as follows:

seqs <- getSequences(seqtab)
names(seqs) <- seqs # This propagates to the tip labels of the tree
alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA)

This ran perfectly and completed in less then 24 hours. I don't know how to check for alignment quality, but I believe that the problem is here (suggestions on how to check alignment quality?).

After this, I performed the following:

phang.align <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phang.align)

This also completed and the resulting 'dm' object was extremelly large (> 18gb). Until now, the objects had reasonable sizes. Comparing with previous analysis, executed with MSA, this is the first time I had this size of 'dm' object. But it is also possible that this is a feature of this specific data.. I don't know..

Then, when I tried to execute:

treeNJ <- NJ(dm)

I got the bad alloc error again..

Then I decided to revisit the alignment... trimmed the data a little more (398-408), which made MSA alignment executable wth our memory capacity. But the MSA alignment is running for 5 days by now, and I don't know how much time it will take to finish... I have a close deadline and can't wait for this anymore.

I tried to use the DECIPHER alignment to build the tree on RAxML and got the following error:

The RAxML analysis 73345 has terminated : OUT_OF_MEMORY

You can access the result at: https://raxml-ng.vital-it.ch/#/result/73345/code/cdo1GZi6VrB4.

The cause of the analysis failure might be described in the log below:

slurmstepd: error: couldn't chdir to /var/vhosts/[vital-it.ch/raxml-ng/htdocs/api](http://vital-it.ch/raxml-ng/htdocs/api)': No such file or directory: going to /tmp instead slurmstepd: error: couldn't chdir to /var/vhosts/vital-it.ch/raxml-ng/htdocs/api': No such file or directory: going to /tmp instead
slurmstepd: error: Detected 1 oom-kill event(s) in StepId=57947.0 cgroup. Some of your processes may have been killed by the cgroup out-of-memory handler.
srun: error: cpt04: task 0: Out Of Memory
scp: /scratch/local/weekly/raxml/job_73345/sequenceAlignment.fa: Permission denied

Thank you for using the RAxML analysis service provided by SIB.

I think the problem is with DECIPHER alignment or with, maybe, with the FASTA I exported from RStudio. I used the following code to generate fasta:

writeXStringSet(alignment, 'alignment.fa')

My questions are:

Do you detect any preprocessing failures that could be compromising this analysis?
The 'dm' object is really to large or this is a common size for this object? If this is a common size for this object, any suggestions on how to build a phylogenetic tree with this (64gb RAM memory)?
If this is not a common size for 'dm' object, any suggestion on how to solve the DECIPHER alignment problem (if this is really an alignment problem)?

Feel free to ask for any additional information you need.
Hope you can help me with this because I'm getting a little desperate here...

Best wishes.

Hi-

Within DESeq you normally apply some sort of contrast with the results function i.e. (from: https://www.bioconductor.org/packages/release/bioc/vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html):
ddsTEST <- phyloseq_to_deseq2(ps.glom, ~ SeasonLoc)
ddsTEST = estimateSizeFactors(ddsTEST, geoMeans=geoMeans)
ddsTEST <- DESeq(ddsTEST, fitType="parametric")
resT = results(ddsTEST, contrast=c("SeasonLoc", "PermDry", "PermWet"))

Which then will give you log2fold changes and adjusted p-values between your defined groups (i.e. "PermDry" compared to "PermWet" in this example).

I understand with the hierarchical multiple testing it's accounting for higher level structure in the data based on phylogeny, but I don't see how I define which groups the procedure compares. In the workflow, which age bins is it finding have different Lachnospiraceae abundance as there are 3 bins (0-100, 100-200, 200-400)? Is there any sort of directionality you can get from the output (increasing/decreasing abundance)?

Let me know if I'm being unclear or you need more info.

Thanks for any help,
Ben

I have got the following Error messages:

> ddF <- dada(derepFs[1:40], err=NULL, selfConsist=TRUE)
Error in dada(derepFs[1:40], err = NULL, selfConsist = TRUE) : 
  The derep argument must be a derep-class object, list of derep-class objects, or a character vector of fastq filenames.

Enclosed are updated R code and log file.
Rcode.txt
log.txt

I am trying to download "MiSeqDevelopmentData"
from http://www.mothur.org, however, the website is currently down. Is there any other place to download this dataset? Thank you in advance.

Thanks very much for your paper on the workflow
I am trying to find bootstrap in my tree so that I can have it in my tree graph but I haven't been successful yet,
your advice is appreciated about which of the below 24 list is considered the bootstrap?
I created the tree based on the paper as follows:

seqs <- getSequences(seqtab)
names(seqs) <- seqs # This propagates to the tip labels of the tree
alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA)
phang.align <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phang.align)
treeNJ <- NJ(dm) # Note, tip order != sequence order
fit = pml(treeNJ, data=phang.align)

negative edges length changed to 0!

fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
                      rearrangement = "stochastic", control = pml.control(trace = 0))
detach("package:phangorn", unload=TRUE)

a quick example of the tree I viewed using plot_tree

here the structure of the final tree created before merging it into the phyloseq object
dismiss the $data because it takes a space out of the 24 list shown below
I don't know which one of these is considered the bootstrap, if this is to be figured out, I think bootstrap would be easily integrated into the tree plot as I need to do that.

str(fitGTR)
List of 24
$ logLik : num -751575
$ inv : num 0.175
$ k : num 4
$ shape : num 0.968
$ Q : num [1:6] 0.905 2.009 1.01 0.873 2.279 ...
$ bf : num [1:4] 0.27 0.224 0.221 0.285
$ rate : num 1
$ siteLik : num [1:1296, 1] -1.5 -1.31 -1.51 -1.26 -1.27 ...
$ weight : int [1:1296] 1 2 3 3 1 2 1 1 1 1 ...
$ g : num [1:4] 0.158 0.566 1.205 2.919
$ w : num [1:4] 0.206 0.206 0.206 0.206
$ eig :List of 3
..$ values : num [1:4] -1.612 -1.46 -0.945 0
..$ vectors: num [1:4, 1:4] -0.0221 -0.6764 -0.0368 0.7353 0.7141 ...
..$ inv : num [1:4, 1:4] -0.0207 0.6445 0.4995 0.5031 -0.7649 ...
..- attr(, "class")= chr "eigen"
$ data :List of 6579
..$ TAGGGAATATTGCACAATGGAGGAAACTCTGATGCAGCGACGTCGCGTGAGGGAAGAAGGTTTTCGGATTGTAAACCTCTGTCTTTGGTGAAGAAAATGACGGTAACCAAAGAGGAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGTAGGCGGGGGAATAAGTTGAATGTTAAAACTATCGGCTCAACCGATAGCAGCGTTCAAAACTATTTCTCTTGAGTGGAGTAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTTTACTGGGCTCTAACTGACGCTGAGGCTCGAAAGCGTGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTAAATTAATACTTTGCTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAAGGTTGATGCCTAATACGCATCAGCTGTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGGATAAGTTAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTAAAACTGTCCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGGGGAGGAAGGGAGTAAAGTTAATACCTTTGCTCATTGACGTTACCCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGAGGAATATTGGTCAATGGGCGAGAGCCTGAACCAGCCAAGTCGCGTGAAGGAAGACTGTCCTAAGGATTGTAAACTTCTTTTATACGGGAATAACGGGCGATACGAGTATTGCATTGAATGTACCGTAAGAATAAGCATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATGCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGTTGTTCGGTAAGTCAGCGGTGAAACCTGAGCGCTCAACGTTCAGCCTGCCGTTGAAACTGCCGGGCTTGAGTTCAGCGGCGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATATCACGAGGAACTCCGATTGCGAAGGCAGCTTGCCATACTGCGACTGACACTGAAGCACGAAGGCGTGGGTATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCGCAATGGGCGAAAGCCTGACGGAGCAACGCCGCGTGAGTGATGAAGGTCTTCGGATCGTAAAACTCTGTTATTAGGGAAGAACAAATGTGTAAGTAACTATGCACGTCTTGACGGTACCTAATCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCGCGCGTAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGGAAAACTTGAGTGCAGAAGAGGAAAGTGGAATTCCATGTGTAGCGGTGAAATGCGCAGAGATATGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGTCTGTAACTGACGCTGATGTGCGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCGGCAATGGACGAAAGTCTGACCGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAGCTCTGTTGTAAGTCAAGAACGTGTGTGAGAGTGGAAAGTTCACACAGTGACGGTAGCTTACCAGAAAGGGACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAGCGCAGGCGGTCAGGAAAGTCTGGAGTAAAAGGCTATGGCTCAACCATAGTGTGCTCTGGAAACTGTCTGACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTCACTGACGCTGAGGCTCGAAAGCGTGGGTAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGGCGAAAGCCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGGTTTCGGCTCGTAAAACTCTGTTGTTAAAGAAGAACGTATCTGATAGTAACTGATCAGGTAGTGACGGTATTTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGGGAGTGCAGGCGGTTATTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGAAGGGCATCGGAAACTGGATAACTTGAGTACAGAAGAGGGTAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTACCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTAACCTAATACGTTATTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTTGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTGGCAAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTCCGCAATGGGCGAAAGCCTGACGGAGCAATGCCGCGTGGAGGTAGAGGCCCCTGGGTCATGAACTTCTTTTCCCGGAGAAGAAAAAATGACGGTATCCGGGGAATAAGCATCGGCTAACTCTGTGCCAGCAGCCGCGGTAAGACAGAGGATGCAAGCGTTATCCGGAATGATTGGGCGTAAAGCGTCTGTAGGTGGCTTTTTAAGTTCGCTGTCAAATACCAGGGCTCAACCCTGGACAGGTGGTGAAAACTACTAAGCTAGAGTACGGTAGGGGCAGAGGGAATTTCCGGTGGAGCGATGAAATGCGTAGAGATCGGAAGGAACACCAACGGCGAAAGCACTCTGCTGGGCCGACACTGACACTGAGAGACGAAAGCTAGGGGAGCGAATG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAAGGTTGGTAGCTAATAACTGCCAACTGTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGGATAAGTTAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTAAAACTGTCCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATATTGGGCAATGGAGGCAACTCTGACCCAGCCATGCCGCGTGCAGGAAGAAGGCGTTATGCGTTGTAAACTGCTTTTATATAGGAAGAAATAGTCCTTGCGAGGAAAGTTGACGGTACTATATGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTGTCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTATTAAGTCAGTGGTGAAAGACGGTCGCTCAACGATTGCAGTGCCATTGATACTGGTAGACTTGAGTGGGATTGAGGTAGCTGGAATGGATAGTGTAGCGGTGAAATGCATAGATATTATCCAGAACACCAATTGCGTAGGCAAGTTACTAAGTCTCAACTGACGCTGAGGCACGAAAGTGTGGGTATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGACGAAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGGTTTCGGCTCGTAAAACTCTGTTGTTAAAGAAGAACATATCTGAGAGTAACTGTTCAGGTATTGACGGTATTTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGTATGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGAGGAATATTGGTCAATCTGCGAAAGCGGGAACCAGCAACGCCGCGTGAAGGAAGAAGGCCCTCGGGTTGTAAACTTCTTTAGGGGAAGACGAGAAAGGACGGTATTCCCAAAATAAGCAACGGCAAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTTGCGAGCGTTATCCGGATTTACTGGGCGTAAAGCGCTTGTAGGCGGTAATATAAGTTGGGCGTGAAACCTCTGGGCTTAACCCGGAGCATGCGCACAATACTGTAATACTAGAGGGTGTCAGAGGAAAACGGAATTCCCGGTGTAGTAGTGAAATGCGTAGATATCGGGAGGAACATCAGTGGCGAAGGCGGTTTTCTGGGACATTACTGACGCTGAGAAGCGACAGCTAGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTGGACAATGGGCGAAAGCCCGATCCAGCAATATCGCGTGAGTGAAGAAGGGCAATGCCGCTTGTAAAGCTCTTTCGTCGAGTGCGCGATCATGACAGGACTCGAGGAAGAAGCCCCGGCTAACTCCGTGCCAGCAGCCGCGGTAAGACGGGGGGGGCAAGTGTTCTTCGGAATGACTGGGCGTAAAGGGCACGTAGGCGGTGAATCGGGTTGAAAGTGAAAGTCGCCAAAAAGTGGCGGAATGCTCTCGAAACCAATTCACTTGAGTGAGACAGAGGAGAGTGGAATTTCGTGTGTAGGGGTGAAATCCGTAGATCTACGAAGGAACGCCAAAAGCGAAGGCAGCTCTCTGGGTCCCTACCGACGCTGGGGTGCGAAAGCATGGGGAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCGGCAATGGACGCAAGTCTGACCGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGTTAGAGAAGAACAAGGATGAGAGTGGAAAGTTCATCCCTTGACGGTATCTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCCCCGGCTTAACCGGGGAGGGTCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGAAAGCGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTAGACAATGGGGGAAACCCTGATCTAGCCATGCCGCGTGATCGATGAAGGCCTTAGGGTTGTAAAGATCTTTCAGATGGGAAGATAATGACGGTACCATCAGAAGAAGCCCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGGGCTAGCGTTATTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGATTGGATAGTTGGAGGTGAAATCCCAGGGCTCAACCTTGGAACTGCCTTCAAAACTTCCAGTCTTGAGTTCGAGAGAGGTGAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTCACTGGCTCGATACTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAGTGATGACGGTCTTCGGATTGTAAAGCTCTGTCTTCAGGGACGATAATGACGGTACCTGAGGAGGAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGTGGATATTTAAGTGGGATGTGAAATACTCGGGCTTAACCTGGGTGCTGCATTCCAAACTGGATATCTAGAGTGCAGGAGAGGAAAGTAGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAATACCAGTGGCGAAGGCGACTTTCTGGACTGTAACTGACACTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGGCGCAAGCCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGGTTTCGGCTCGTAAAACTCTGTTGTTAAAGAAGAACGTATCTGAGAGTAACTGTTCAGATAGTGACGGTATTTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGGGAGTGCAGGCGGTTATTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGAAGGGCATCGGAAACTGGATAACTTGAGTGCAGAAGAGGGTAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTACCTAGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TCGAGAATAATTCACAATGGGGGAAACCCTGATGGTGCAACGCCGCGTGGAGGATGAAGGTCTTCGGATTGTAAACTCCTGTCATCCGGGAGTAAGACCTGGCGGTGAATAGCCGACAGGGTTGATAGTACCGGAAGAGGAAGGGACGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGTCCCAAGCGTTGTTCGGAATCACTGGGCGTAAAGGGTGCGTAGGCGGTTTGGTAAGTCAGATGTGAAATCCCGGGGCTCAACCCCGGAACTGCATCCGATACTGCCAGACTAGAGGACTGGAGAGGTGACTGGAATTCTCGGTGTAGCAGTGAAATGCGTAGAGATCGAGAGGAACACTCGTGGCGAAGGCGAGTCACTGGACAGTATCTGACGCTGAGGCACGAAGGCCAGGGTAGCGAAAG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGAAGAATCTCAAATATAAAATATATTTTGATTTGTATAATACTTTTTTGGTTACTACATGATTCCATATGTGTTATTTCATAGTTTTGTTGTCTTCACCATTATTCTACAATGTAGAAAATAGTAAAAAATAAAAAACCCATGAATTAGTAGTGTCCAAAACTTTAACTGGACTGTATATATATTTAAAATAATAAAATAAAAATTATGCCGACAATTACATCAATAGAAGCCGCAATGTTGAAGCTGATAACAAAAATAAAAGAAGAATATCCTGTTTCACTTGATTTAAAAACCAAGCATACCCCTTCTCCTCATCACCAAAGCTTTATTAAAATATCAAGTTTGGCAGCAGCAAAACAGTGAGTGGAAATCCCCTTTTATCCATATACAGTTGAAGTCGGAAGTTTACCGTCGTGGGACCCG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGAAGGCTTTCGGGTCGTAAAACTCTGTTGTTAGGGAAGAACAAGTGCTAGTTGAATAAGCTGGCACCTTGACGGTACCTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCGCGCGCAGGTGGTTTCTTAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGAAAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGAGATATGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGTCTGTAACTGACACTGAGGCGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTAAATTAATACTTTGCTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGTCGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTGCGCAATGGGCGAAAGCCTGACGCAGCAACGCCGCGTGCGGGATGAAGGCCTTCGGGTTGTAAACCGCTTTCAGCAGGGACGAAAATGACGGTACCTGCAGAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGAGCTCGTAGGCGGTTCCGTAAGTCGAGTGTGAAAAATCTGGGCTCAACCCAGTGGAGCACTCGATACTGCGGTGACTAGAGTACGGTAGAGGAGTGTGGAATTCCTGGTGTAGCGGTGAAATGCGCAGATATCAGGAGGAACACCAACGGCGAAGGCAGCACTCTGGGCCGGTACTGACGCTGAGGAGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTTACCTAATACGTGATTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTAATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTAAATTAATACTTTGCTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATATTGGGCAATGGACGGAAGTCTGACCCAGCCATGCCGCGTGCAGGAAGAAGGCGCTCAGCGTTGTAAACTGCTTTTGATGGGGAAGAAAGTGCGGGATGCGTCCTGTTTTGCCGGTACCCATCGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTGTCCGGATTTATTGGGTTTAAAGGGTGCGTAGGTGGTTGAATAAGTCTGGTTTGAAAGTCAGTCGCTTAACGATTGAGGGTGGCTGGATACTGTTCAACTTGAATAATCTGGAGGTAGGCGGAACGGGTTGTGTAGCGGTGAAATGCATAGATATGACCCAGAACACCGATTGCGAAGGCAGCCTACTACGGATTGATTGACACTGAGGCACGAGAGCATGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAGGGTTCAGTGTTAATAGCACTGTTCATTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGCGCTTAACGTGGGAACTGCATTTGAAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGAGGAATATTGGACAATGGGTGAGAGCCTGATCCAGCCATCCCGCGTGAAGGACGACGGCCCTATGGGTTGTAAACTTCTTTTGTATAGGGATAAACCTACTCTCGTGAGAGTAGCTGAAGGTACTATACGAATAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTCCGTAGGCGGATCTGTAAGTCAGTGGTGAAATCTCACAGCTTAACTGTGAAACTGCCATTGATACTGCAGGTCTTGAGTGTTGTTGAAGTAGCTGGAATAAGTAGTGTAGCGGTGAAATGCATAGATATTACTTAGAACACCAATTGCGAAGGCAGGTTACTAAGCAACAACTGACGCTGATGGACGAAAGCGTGGGGAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGCAGGAAGAAGGCCTTCGGGTTGTAAACTGCTTTAGTCTAGGAAAAAGGGGTGCGAGGTTAATACCCTCGTGCTTTGATGGTACTGGAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTCTTGTAAGACAGTCGTGAAATCCCTGGGCTTAACCTAGGAACTGCGATTGTGACTGCAAGGCTAGAGTGTGTCAGAGGGGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAACACCGATGGCGAAGGCAGCCCCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ CATGGCCTTGTCTTAGCTGTTCTGAGGTGCCAGCTTGGCCCTCAGTGGTCTGAACAGAAGTGGACACCAGAGAAGCTCTGTGCAAGGAGTTGTGCCTAATGGAGGCCATGTCAACAGACACGTCAGCCAACATTTTAACACAATTAGAAAGCTCTTTCTGCAAGTGGTCTACAGTGTTAACCAGAGGAGAAAAAACTGAATTAACGTCCTTGAGAACCTCAGTGTGATGATGTTGCAAGCGCCACTGGAGAGTGGCAGTGAGTTGGGTTTGTTGGTAGTCAAACTGCCTGTCCATGGCACCAGTAACATCCCGTCTTCCACTCTCACTGAGCTCTGTCAGTGTGTGGGTTAGAGTGCTCAGTGAGTTTCTGAATTCCATGGCACTCTGTTGTTGCTCTTCACTCAGACATTTGAATTTATGGT : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGACGAAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGGTTTCGGCTCGTAAAGCTCTGTTGTTAAAGAAGAACGTGGGTAAGAGTAACTGTTTACCCAGTGACGGTATTTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTAATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATTGGAAACTGGGAAACTTGAGTGCAGAAGAGGATAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTATCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGTAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCGCAATGGGCGAAAGCTTGACGGAGCAACGCCGCGTGAGTGATGAAGGTCTTCGGATCGTAAAACTCTGTTATTAGGGAAGAACAAATGTGTAAGTAACTATGCACGTCTTGACGGTACCTAATCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCGCGCGTAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGGAAAACTTGAGTGCAGAAGAGGAAAGTGGAATTCCATGTGTAGCGGTGAAATGCGCAGAGATATGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGTCTGTAACTGACGCTGATGTGCGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGTTGTAGATTAATACTCTGCAATTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTTGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACTGACTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTAATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGTGGGAAGAAGGCCTTCGGGTTGTAAACCACTTTTGTACGGAACGAAACGGTCTGCTTTAATACAGTGGGCTAATGACGGTACCGTAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATATAAGACAGTTGTGAAATCCCCGGGCTCAACCTGGGAATTGCATCTGTGACTGTATAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGGCGCAAGCCTGATGGAGCAACGCCGCGTGTGTGATGAAGGCTTTCGGGTCGTAAAGCACTGTTGTATGGGAAGAAATGCTAAAATAGGGAATGATTTTAGTTTGACGGTACCATACCAGAAAGGGACGGCTAAATACGTGCCAGCAGCCGCGGTAATACGTATGTCCCGAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGACGGTTGATTAAGTCTGATGTGAAAGCCCGGAGCTCAACTCCGGAATGGCATTGGAAACTGGTTAACTTGAGTGTTGTAGAGGTAAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACAACAACTGACGTTGAGGCTCGAAAGTGTGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAAGGTTGATGCCTAATACGCATCAGCTGTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGGATAAGTTAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTAAAACTGTCCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATATCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGAAAGCCTGATCCAGCCATACCGCGTGCGGGAAGAAGGCCTTCGGGTTGTAAACCGCTTTTGTCAGGGAAGAAATGCCTCGGGTTAATACCCTGGGGTGATGACGGTACCTGAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTATAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGTGACTGTAGAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCGGCAATGGACGGAAGTCTGACCGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAGCTCTGTTGTTAGAGAAGAACGTTGGTAGGAGTGGAAAATCTACCAAGTGACGGTAACTAACCAGAAAGGGACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTCTTTAAGTCTGAAGTTAAAGGCAGTGGCTTAACCATTGTACGCTTTGGAAACTGGAGGACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATATTGCACAATGGAGGAAACTCTGATGCAGCGACGTCGCGTGAGGGAAGAAGGTTTTCGGATTGTAAACCTCTGTCTTTGGTGAAGAAAATGACGGTAACCAAAGAGGAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGTAGGCGGGGGAATAAGTTGAATCTTAAAACTATCGGCTCAACCGATAGCAGCGTTCAAAACTATTTCTCTTGAGTGGAGTAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTTTACTGGGCTCTAACTGACGCTGAGGCTCGAAAGCGTGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCGACGCCGCGTGCGGGATGAAGGCCTTCGGGTTGTAAACCGCTTTTGATTGGGAGCAAGCGAGAGTGAGTGTACCTTTCGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTTTGTCGCGTCTGGTGTGAAAGTCCATCGCTTAACGGTGGATCTGCGCCGGGTACGGGCAGGCTAGAGTGCGACAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGTCGTCACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTAGACAATGGGCGCAAGCCTGATCTAGCCATGCCGCGTGATCGATGAAGGCCTTAGGGTTGTAAAGATCTTTCAGTGGGGAAGATAATGACTGTACCCACAGAAGAAGCCCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGGGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGACTGGAAAGTCAGAGGTGAAATCCCAGGGCTCAACCTTGGAACTGCCTTTGAAACTCCCGGTCTTGAGGTCGAGAGAGGTGAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTCACTGGCTCGATACTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTCCGCAATGGGCGAAAGCCTGACGGAGCAATACCGCGTGAGGGATGACGGCCTATGGGTTGTAAACCTCTTTTTTCAGGGAGGAATCAATGACGTGTACCTGAGGAATAAGCATCGGCTAACTCCGTGCCAGCAGCCGCGGTAAGACGGAGGATGCAAGTGTTATCCGGAATCACTGGGCGTAAAGCGTCTGTAGGTGGTCAAATAAGTCAACTGTTAAATCTTGAGGCTCAACCTCAAAATCGCAGTCGAAACTATTCGACTAGAGTATAGTAGGGGTAAAGGGAATTTCCAGTGGAGCGGTGAAATGCGTAGAGATTGGAAAGAACACCGATGGCGAAGGCACTTTACTGGGCTATTACTAACACTGAGAGACGAAAGCTAGGGTAGCAAATG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGAGCAGTAAATTAATACTTTGCTGTTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCGTTAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGTCGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTCCGCAATGGGCGAAAGCCTGACGGAGCAATGCCGCGTGGAGGTGGAAGGCCTACGGGTCGTCAACTTCTTTTCTCGGAGAAGAAACAATGACGGTATCTGAGGAATAAGCATCGGCTAACTCTGTGCCAGCAGCCGCGGTAAGACAGAGGATGCAAGCGTTATCCGGAATGATTGGGCGTAAAGCGTCTGTAGGTGGCTTTTCAAGTCCGCCGTCAAATCCCAGGGCTCAACCCTGGACAGGCGGTGGAAACTACCAAGCTGGAGTACGGTAGGGGCAGAGGGAATTTCCGGTGGAGCGGTGAAATGCATTGAGATCGGAAAGAACACCAACGGCGAAAGCACTCTGCTGGGCCGACACTGACACTGAGAGACGAAAGCTAGGGGAGCAAATG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGGAAGCCTGATGCAGCAACGCCGCGTGCGGGATGACGGCCTTCGGGTTGTAAACCGCTTTCGCCTGTGACGAAGCGTGAGTGACGGTAATGGGTAAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTGATACGTAGGGTGCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGCTCGTAGGTGGTTGATCGCGTCGGAAGTGTAATCTTGGGGCTTAACCCTGAGCGTGCTTTCGATACGGGTTGACTTGAGGAAGGTAGGGGAGAATGGAATTCCTGGTGGAGCGGTGGAATGCGCAGATATCAGGAGGAACACCAGTGGCGAAGGCGGTTCTCTGGGCCTTTCCTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTCCGCAATGGGCGAAAGCCTGACGGAGCAATGCCGCGTGAAGGTAGAAGGCCTACGGGTCATGAACTTCTTTTCCCGGAGAAGAAGCAATGACGGTATCCGGGGAATAAGCATCGGCTAACTCTGTGCCAGCAGCCGCGGTAAGACAGAGGATGCAAGCGTTATCCGGAATGATTGGGCGTAAAGCGTCTGTAGGTGGCTTTTTAAGTTCGCCGTCAAATCCCAGGGCTCAACCCTGGACAGGCGGTGGAAACTACCAAGCTGGAGTACGGTAGGGGCAGAGGGAATTTCCGGTGGAGCGGTGAAATGCGTAGAGATCGGAAAGAACACCAACGGCGAAAGCACTCTGCTGGGCCGACACTGACACTGAGAGACGAAAGCTAGGGGAGCGAATG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TTGGGAATCTTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGCGTGGACGATGAAGGCCTTAGGGTTGTAAAGTCCTTTTTTGGGGAAAGACTTAGGACGGTACCCCAAGAATAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAAGACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGAGCGCGTAGGCGGCTCGTTAAGTGTGAAGTGAAATCTCCAGTGCTCAACACGGAAACTGCTTTACATACTGGCGAGCTTGAGGAAAGCAGAGGTAACTGGAATTCCTGGTGTAGCGGTGAAATGCGTTGATATCAGGAGGAACACCCATGGCGAAGGCAGGTTACTGGGCTTTATCTGACGCTGAGGCGCGAAAGCGTGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTACCCGGGATGATAATGACAGTACCGGGAGAATAAGCTCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTATTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGCTTTGTAAGTAAGAGGTGAAAGCCCAGAGCTCAACTCTGGAATTGCCTTTTAGACTGCATCGCTTGAATCATGGAGAGGTCAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGGCTGACTGGACATGTATTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCGCAATGGGCGAAAGCCTGACGGAGCAACGCCGCGTGAGTGATGAAGGTCTTCGGATCGTAAAACTCTGTTATTAGGGAAGAACATATGTGTAAGTAACTGTGCACATCTTGACGGTACCTAATCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCGCGCGTAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGGAAAACTTGAGTGCAGAAGAGGAAAGTGGAATTCCATGTGTAGCGGTGAAATGCGCAGAGATATGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGTCTGTAACTGACGCTGATGTGCGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGAGTGATGAAGGCCCTAGGGTTGTAAAGCTCTTTTACCCGGGATGATAATGACAGTACCGGGAGAATAAGCTCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCGCGTAGGCGGCTTTTTAAGTCAGAGGTGAAAGCCCGGGGCTCAACCCCGGAATTGCCTTTGAAACTGGGAAGCTAGAATCTTGGAGAGGTCAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGACTGACTGGACAAGTATTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTAGACAATGGGCGCAAGCCTGATCTAGCCATGCCGCGTGATCGATGAAGGCCTTAGGGTTGTAAAGATCTTTCAGGTGGGAAGATAATGACGGTACCACCAGAAGAAGCCCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGGGCTAGCGTTATTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGATCGGAAAGTCAGAGGTGAAATCCCAGGGCTCAACCCTGGAACTGCCTTTGAAACTCCCGATCTTGAGGTCGAGAGAGGTGAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTCACTGGCTCGATACTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCGGCAATGGACGAAAGTCTGACCGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGGTAGAGAAGAACGTTGGTGAGAGTGGAAAGCTCATCAAGTGACGGTAACTACCCAGAAAGGGACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGTGGTTTATTAAGTCTGGTGTAAAAGGCAGTGGCTCAACCATTGTATGCATTGGAAACTGGTAGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGCCTGTAACTGACACTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGGGGAACCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGCCTTATGGTTGTAAAGCACTTTAAGCGAGGAGGAGGCTCCTGTAGTTAATACCTACAGTGAGTGGACGTTACTCGCAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCGAGCGTTAATCGGATTTACTGGGCGTAAAGCGTGCGTAGGCGGCTTTTTAAGTCGGATGTGAAATCCCCGAGCTTAACTTGGGAATTGCATTCGATACTGGGAAGCTAGAGTATGGGAGAGGATGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGATGGCGAAGGCAGCCATCTGGCCTAATACTGACGCTGAGGTACGAAAGCATGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTGGACAATGGGCGAAAGCCCGATCCAGCAATATCGCGTGAGTGAAGAAGGGCAATGCCGCTTGTAAAGCTCTTTCGTCGAGTGCGCGATCATGACAGGACTCGAGGAAGAAGCCCCGGCTAACTCCGTGCCAGCAGCCGCGGTAAGACGGGGGGGGCAAGTGTTCTTCGGAATGACTGGGCGTAAAGGGCACGTAGGCGGTGAATCGGGTTGAAAGTTAAAGTCGCCAAAAACTGGTGGAATGCTCTCGAAACCAATTCACTTGAGTGAGACAGAGGAGAGTGGAATTTCGTGTGTAGGGGTGAAATCCGCAGATCTACGAAGGAACGCCAAAAGCGAAGGCAGCTCTCTGGGTCCCTACCGACGCTGGAGTGCGAAAGCATGGGGAGCGAACG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTACCAGGGATGATAATGACAGTACCTGGAGAATAAGCTCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAGGGGAGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGTGCGTAGGCGGTTACTCAAGTCAGAGGTGAAAGCCCGGGGCTCAACCCCGGAACGGCCTTTGAAACTAGGTAGCTAGAATCTTGGAGAGGTTAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGACTAACTGGACAAGCATTGACGCTGAGGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGGCGCAAGCCTGATGGAGCAACACCGCGTGAGTGAAGAAGGGTTTCGGCTCGTAAAGCTCTGTTGTTAAAGAAGAACACGTATGAGAGTAACTGTTCATACGTTGACGGTATTTAACCAGAAAGTCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGAGAGTGCAGGCGGTTTTCTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGAAGTGCATCGGAAACTGGATAACTTGAGTGCAGAAGAGGGTAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTACCTGGTCTGCAACTGACGCTGAGACTCGAAAGCATGGGTAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGGGGAACCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGCCTTATGGTTGTAAAGCACTTTAAGCGAGGAGGAGGCTCTCTTGGTTAATACCCAAGATGAGTGGACGTTACTCGCAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCGAGCGTTAATCGGATTTACTGGGCGTAAAGCGTGCGTAGGCGGCTTTTTAAGTCGGATGTGAAATCCCCGAGCTTAACTTGGGAATTGCATTCGATACTGGGAAGCTAGAGTATGGGAGAGGATGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGATGGCGAAGGCAGCCATCTGGCCTAATACTGACGCTGAGGTACGAAAGCATGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAGGGCAGTGTGTTAATAGCACATTGCATTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGCGCTTAACGTGGGAACTGCATTTGAAACTGGCAAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAAGGAATATTGGTCAATGGAGGCAACTCTGAACCAGCCATGCCGCGTGCAGGAAGACAGCCCTCTGGGTCGTAAACTGCTTTTATTCGGGAATAAACCTACTTACGTGTAAGTAGCTGAATGTACCGAAGGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCCTATTAAGTCAGGGGTGAAAGACGGTAGCTCAACTATCGCAGTGCCCTTGATACTGATGGGCTTGAATACACTAGAGGTAGGCGGAATGTGACAAGTAGCGGTGAAATGCATAGATATGTCACAGAACACCGATTGCGAAGGCAGCTTACTATGGTGTTATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAAGGAATATTGGTCAATGGAGGCAACTCTGAACCAGCCATGCCGCGTGCAGGAAGACAGCCCTCTGGGTCGTAAACTGCTTTTATTCGGGAATAAACCTACTTACGTGTAAGTAGCTGAATGTACCGAAGGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCCTATTAAGTCAGGGGTGAAAGACGGTAGCTCAACTATCGCAGTGCCCTTGATACTGATGGGCTTGAATACACTAGAGGTAGGCGGAATGTGACAAGTAGCGGTGAAATGCATAGATATGTCACAGAACACCGATTGCGAAGGCAGCTTACTATGGTGTCATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGACGCAAGTCTGATCCAGCAATGCCGCGTGCAGGACGAAGGCCTTCGGGTTGTAAACTGCTTTTGTACGGAACGAAACGGTCTTCTTTAATACAGAGGGCTAATGACGGTACCGTAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATATAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGACCTGCATTTGAGACTGTATAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTAAGTTAATACCTTGCTGTTTTGACGTTACCAACAGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCAGCAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTACTGAGCTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGACGAAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGTTGGAGAAGAACGTATTTGATAGTAACTGATCAGGTAGTGACGGTATCCAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGTTGTAGATTAATACTCTGCAATTTTGACGTTACCGACAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCGTTAAGTTGGATGTGAAAGCCCCGGGCTCAACCTGGGAACTGCATTCAAAACTGACGAGCTAGAGTATGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCATACCGCGTGGGTGAAGAAGGCCTTCGGGTTGTAAAGCCCTTTTGTTGGGAAAGAAATCCAGCTGGTTAATACCCGGTTGGGATGACGGTACCCAAAGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGTGCAAGCGTTACTCGGAATTACTGGGCGTAAAGCGTGCGTAGGTGGTCGTTTAAGTCCGTTGTGAAAGCCCTGGGCTCAACCTGGGAACTGCAGTGGATACTGGACGACTAGAGTGTGGTAGAGGGTAGCGGAATTCCTGGTGTAGCAGTGAAATGCGTAGAGATCAGGAGGAACATCCATGGCGAAGGCAGCTACCTGGACCAACACTGACACTGAGGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGACGAAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGTTGGAGAAGAACACGTTTGAGAGTAACTGTTCAGACGTTGACGGTATCCAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGAAGTGCATCGGAAACTGGGGAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGTAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGACGAAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGTTGGAGAAGAACACGTTTGAGAGTAACTGTTCAGACGTTGACGGTATCCAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGTAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCATGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTGTCCGGGACGATAATGACGGTACCGGAAGAATAAGCCCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGCTCGGAATCACTGGGCGTAAAGGGCGCGTAGGCGGCCATTCAAGTCGGGGGTGAAAGCCTGTGGCTCAACCACAGAATTGCCTTCGATACTGTTTGGCTTGAGTTTGGTAGAGGTTGGTGGAACTGCGAGTGTAGAGGTGAAATTCGTAGATATTCGCAAGAACACCAGTGGCGAAGGCGGCCAACTGGACCAACACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAAGGAATATTGGTCAATGGAGGCAACTCTGAACCAGCCATGCCGCGTGCAGGAAGACGGCCCTCTGGGTTGTAAACTGCTTTTATTCGGGAATAAACCTATCTACGTGTAGATAGCTGAATGTACCGAAGGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGATCCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCCTATTAAGTCAGGGGTGAAAGACGGTAGCTCAACTATCGCAGTGCCCTTGATACTGATGGGCTTGAATGAACTAGAGGTAGGCGGAATGTGACAAGTAGCGGTGAAATGCATAGATATGTCACAGAACACCGATTGCGAAGGCAGCTTACTATGGTTTTATTGACGCTGAGGCACGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGAAGAATCTCAAATATAAAATATATTTTGATTTGTATAATACTTTTTTGGTTACTACATGATTCCATATGTGTTATTTCATAGTTTTGTTGTCTTCACCATTATTCTACAATGTAGAAAATAGTAAAAAAATAAAAAACCCATGAATTAGTAGTGTCCAAAACTTTAACTGGACTGTATATATATTTAAAATAATAAAATAAAAATTATGCCGACAATTACATCAATAGAAGCCGCAATGTTGAAGCTGATAACAAAAATAAAAGAAGAATATCCTGTTTCACTTGATTTAAAAACCAAGCATACCCCTTCTCCTCATCACCAAAGCTTTATTAAAATATCAAGTTTGGCAGCAGCAAAACAGTGAGTGGAAATCCCCTTTTATCCATATACAGTTGAAGTCGGAAGTTTACCGTCGTGGGACCCG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCGGCAATGGACGGAAGTCTGACCGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAGCTCTGTTGTAAGAGAAGAACGAGTGTGAGAGTGGAAAGTTCACACTGTGACGGTATCTTACCAGAAAGGGACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTAGATAAGTCTGAAGTTAAAGGCTGTGGCTTAACCATAGTACGCTTTGGAAACTGTTTAACTTGAGTGCAAGAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGCTTGTAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGCAAGCCTGATCCAGCAATGCCGCGTGCAGGATGAAGGCCTTCGGGTTGTAAACTGCTTTTGTACGGAACGAAACGGCTCTTTCTAATAAAGGGAGCTAATGACGGTACCGTAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGACCTGCATTTGTGACTGCATAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGTGGGAAGAAGGCCTTCGGGTTGTAAACCACTTTTGTACGGAACGAAACGGTTCGCTTTAATACAGTGGGCTAATGACGGTACCGTAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATATAAGACAGTTGTGAAATCCCCGGGCTCAACCTGGGAATTGCATCTGTGACTGTATAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGAAGGTCTTCGGATTGTAAAGCTCTGTTAATCGGGACGAAAGAGCCTAGTGTGAATAATGCTAGGAAGTGACGGTACCGGAATAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATCTGCCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTACAGATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGGAGCGAACG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCAATTCCGCGTGGGTGAAGAAGGTCTTCGGATTGTAAAGCCCTTTCGACAGGGACGATGATGACGGTACCTGTAGAAGAAGCCCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGCTCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGCACAACTAGTCAGGCGTGAAATTCCTGGGCTTAACCTGGGGGCTGCGTTTGATACGGTTGGGCTAGAGGATGGAAGAGGCTCGTGGAATTCCCAGTGTAGAGGTGAAATTCGTAGATATTGGGAAGAACACCGGTGGCGAAGGCGGCGAGCTGGTCCATTACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGAAAGCCTGATCCAGCCATACCGCGTGCGGGAAGAAGGCCTTCGGGTTGTAAACCGCTTTTGTCAGGGAAGAAATACCTCGGGCTAATACCCTGGGGTGATGACGGTACCTGAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTATAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGTGACTGTAGAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGACGCAAGTCTGATGGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAGCTCTGTTGTTGGTGAAGAAGGATAGAGGCAGTAACTGGTCTTTATTTGACGGTAATCAACCAGAAAGTCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAATGATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAACTGCATCGGAAACTGTCATTCTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGTAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ GCGGTCGACGTCACCGGCTTTCTAGCTATCGCCGCTCAATTTTTCATTGTTCCATTTGTTTTGTCTTGTTCCCTGCACACCTGGTTTACATTCCCCAATCACACTGCATGTATTTATTCCTCTGTTCCCCCACATGTCTTTGTGTGAAATTGTTTTGTGTTACATGTTTGAGGTTACGCGCCAGCCTGGTGTTTATGATCCATGTTATTGCACGTAGTATGTTTATTTGATATCATTAGCTTTTGTGACTGTGTTTTGTGCTCTGCACTTTTGCTATTGTGGCTGGATGTTTTGACGCAGTTGCGTCCGTCTGCTGTTTTCTCCTGCCTCAATAAAGTGTGCGCCTGTTCGCAAATCTCGGCTCTCCTGCGCCTGACTTCACTACCAGTACGTGCCCAACATCCTGACAGAATTTCACACCACCCATGGAGTCAGCA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGGAAGCCTGATGCAGCGACGCCGCGTGAGGGATGACGGCCTTCGGGTTGTAAACCTCTTTCAGCACGGAAGAAGCGAGAGTGACGGTACGTGCAGAAGAAGCGCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTTTGTCGCGTCTGCTGTGAAAGCCCGGGGCTTAACCCCGGGTGTGCAGTGGGTACGGGCAGACTTGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGAAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTTACTGACGCTGAGGAGCGAAAGCATGGGGAGCGAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAAGGAATATTGCGCAATGGACGAAAGTCTGACGCAGCGACGCCGCGTGGGGGATGAAGGTCTTCGGATTGTAAACCCCTTTCGGGAGGGAAGATGGAGTGGGTAACCACTTGGACGGTACCTCCAGAAGCAGCCACGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGTGGCAAGCGTTGTTCGGATTCACTGGGCGTACAGGGAGCGTAGGCGGTTGGGTAAGCCCTCCGTGAAATCTCCGGGCCTAACCCGGAAAGTGCAGAGGGGACTGCTCAGCTAGAGGATGGGAGAGGAGCGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAAGGCCGGTGGCGAAGGCGGCGCTCTGGAACATTTCTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGGAAGCCTGATGCAGCGACGCCGCGTGAGGGATGAAGGCCTTCGGGTTGTAAACCTCTTTCAGCAGGGACGAAGCGTGAGTGACGGTACCTGCAGAAGAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCGAGCGTTGTCCGGAATTACTGGGCGTAAAGAGTTCGTAGGCGGTTTGTCGCGTCGTTTGTGAAAACCCGGGGCTCAACTTCGGGCTTGCAGGCGATACGGGCAGACTTGAGTGTTTCAGGGGAGACTGGAATTCCTGGTGTAGCGGTGAAATGCGCAGATATCAGGAGGAACACCGGTGGCGAAGGCGGGTCTCTGGGAAACAACTGACGCTGAGGAACGAAAGCGTGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGGGGAGGAAGGTGTTGTGGTTAATAACCACAGCAATTGACGTTACCCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAAGGTTGGCGCCTAATACGTGTCAACTGTGACGTTACTCGCAGAAGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGGATAAGTTAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTAAAACTGTCCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ CTGGTCCCAGTTCCTCCCGTCTCTCTCGATGACCTTCTTCAGCATCTGCTTTAGGGTCTTATTGAAGAATTTGACTAGCACGTCCGTTTGTGGGATGGTACACACTGGTCCTCACCTGTTCAAATCGTAAAAGATTGCATTACACAAGACATGAATGTGGTGCCCTGGTCGTCAGGATTTCCCACGGCAATACCCACCCAGCTGAATAAATGGAAGAGCTCCTGTGTGGTACCTTTCGCTGCCGCTGTGTGTAATGGGATTGCCTCCGGATACGGCTGGCATATTCCACGATTACCAGGATGTATTGATGTCCCCTCGGGGTCTTCACCAACGGACCCACCAGAACCAAAGGGTTTCTATAATGTGCCCTCGGGGCTGTGATCTGGCACTC : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATATTGGTCAATGGGGGCAACCCTGAACCAGCCATGCCGCGTGCAGGAATAAGGCCCTATGGGTCGTAAACTGCTTTTGAACGGGAAGAAATCTATTTACGTGTAAATAGTTGACGGTACCGTTAGAATAAGGATCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGATCCAAGCGTTGTCCGGATTTACTGGGTTTAAAGGGTGCGTAGGCGGACTTATAAGTCAGTGGTGAAAGCCTTCAGCTTAACTGAAGAACTGCCATTGAAACTGTAAGTCTCGAGTTCGGTTGAGGTCATTGGAATGTAACATGTAGCGGTGAAATGCATAGATATGTTACAGAACACCGATTGCGAAGGCAGATGACTAAACCGAAACTGACGCTGAGGCACGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGTTAAAGAAGAACAAGGATGAGAGTAACTGCTCATCCCCTGACGGTATTTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTCTTTAAGTCTGATGTGAAAGCCCCCGGCTCAACCGGGGAGGGTCATTGGAAACTGGAGAACTTGAGTGCAGAAGAGGAGAGTGGAATTCCACGTGTAGCGGTGAAATGCGTAGATATGTGGAGGAACACCAGTGGCGAAGGCGACTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCGGCAATGGACGCAAGTCTGACCGAGCAACGCCGCGTGAGTGAAGAAGGTTTTCGGATCGTAAAACTCTGTTGTTAGAAAAGAACAAGGATGAGAGTGGAAAGTTCATCCCTTGACGGTATCTAACCAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCCCCGGCTTAACCGGGGAGGGTCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGAAAGCGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATCTTCCACAATGGGCGAAAGCCTGATGGAGCAACGCCGCGTGTGTGATGAAGGCTTTCGGGTCGTAAAGCACTGTTGTATGGGAAGAACAGCTAGAATAGGGAATGATTTTAGTTTGACGGTACCATACCAGAAAGGGACGGCTAAATACGTGCCAGCAGCCGCGGTAATACGTATGTCCCGAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGACGGTTGATTAAGTCTGATGTGAAAGCCCGGAGCTCAACTCCGGAATGGCATTGGAAACTGGTTAACTTGAGTGCAGTAGAGGTAAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAACTGACGTTGAGGCTCGAAAGTGTGGGTAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGGGAAACCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGCCTTTTGGTTGTAAAGCACTTTAAGCGAGGAGGAGGCTACCCTGACTAATATTCAGGAGTAGTGGACGTTACTCGCAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGCGGTTTATTAAGTCGGATGTGAAATCCCTGAGCTTAACTTAGGAATTGCATTCGATACTGGTAAGCTAGAGTATGGGAGAGGAAGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGATGGCGAAGGCAGCCTTCTGGCCTAATACTGACGCTGAGGTGCGAAAGCATGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TAGGGAATATTGGGCAATGGATGCAAGTCTGACCCAGCCATGCCGCGTGCAGGAAGAAGGTCCTCTGGATTGTAAACTGCTTTTGTCGGGGAAGAATAGGCATCTTGCGAGGTGTTGTGACGGTACCCGATGAATAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGTTTAAAGGGTGCGTAGGTGGTTTGCTAAGTCAGTGGTGAAAGCTGGTTGCTCAACAATCAAGTTGCCATTGATACTGTCAGACTTGAGAGAAGTGGAGGCTCATGGAATGGATGGTGTAGCGGTGAAATGCATAGATATCATCCAGAACGTCGATTGCGAAGGCAGTGGGCTGTACTTTTTCTGACACTGAGGCACGAAAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAGTGATGAAGGCCTTCGGGTTGTAAAGCTCTGTCTTTGGGGACGATAATGACGGTACCCAAGGAGGAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGGGATGTGAAATACCCGGGCTCAACTTGGGTGCTGCATTCCAAACTGGAAGTCTAGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGCAAGCCTGATCCAGCCATACCGCGTGGGTGAAGAAGGCCTTCGGGTTGTAAAGCCCTTTTGTTGGGAAAGAAATCCAGCTGGCTAATACCCGGTTGGGATGACGGTACCCAAAGAATAAGCACCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGTGCAAGCGTTACTCGGAATTACTGGGCGTAAAGCGTGCGTAGGTGGTCGTTTAAGTCCGTTGTGAAAGCCCTGGGCTCAACCTGGGAACTGCAGTGGATACTGGACGACTAGAGTGTGGTAGAGGGTAGCGGAATTCCTGGTGTAGCAGTGAAATGCGTAGAGATCAGGAGGAACATCCATGGCGAAGGCAGCTACCTGGACCAACACTGACACTGAGGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCAATGCCGCGTGAGTGATGAAGGCCTTAGGGTTGTAAAGCTCTTTTACCAGGGATGATAATGACAGTACCTGGAGAATAAGCTCCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGAGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGTAGGCGGCTACTCAAGTCAGAGGTGAAAGCCCGGGGCTCAACCCCGGAACTGCCTTTGAAACTAGGTAGCTAGAATCTTGGAGAGGTCAGTGGAATTCCGAGTGTAGAGGTGAAATTCGTAGATATTCGGAAGAACACCAGTGGCGAAGGCGACTGACTGGACAAGTATTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTGGACAATGGGCGCAAGCCTGATCCAGCCATGCCGCGTGCGGGAAGAAGGCCTTCGGGTTGTAAACCGCTTTTGTCAGGGAAGAAATCCTTTGAACTAATACTTCGGAGGGATGACGGTACCTGAAGAATAAGCACCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTATGCAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGTGACTGCATAGCTAGAGTACGGTAGAGGGGGATGGAATTCCGCGTGTAGCAGTGAAATGCGTAGATATGCGGAGGAACACCGATGGCGAAGGCAATCCCCTGGACCTGTACTGACGCTCATGCACGAAAGCGTGGGGAGCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ GTGCACGCGCCTAAGCCCATTGTCCCTTGATCGGCCACTTGAGAAAGGCGATAATGTGTTTCAGCCTGGGGCTGGAATGACGACATTCAGGTTTTTCCCGGGCTCTGAGAGCCTATTGGAGCTGTGGGAAGTGTCACGTTACCGCAGAGATCCTAAATTTTGGAAAGAGATGTCAAAGAAAGACAATAAATGGTCAGACAGGCCACTTCCTGTAAAGGAATCTCTCAGGTTTTGAAAAGCCAATAATTATATGCATATTCTAGTTACTGGGCAGGAGTAGTAACCAGATTAAATCGGGTACGTTTTTTATCCAGCCGTGTCAATACTGCCCCCTAGCCCTAACAGGTTAAAGAATAGCCAGGCATCTTTTACTAATAGAATGACGTCAATATCC : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTTTCCGCAATGGGCGAAAGCCTGACGGAGCAATACCGCGTGAGGGAAGAATGCCTATGGGTTGTAAACCTCTTTTTTCAGGGAGGAATAAAATGACGTGTACCTGAAGAATAAGCATCGGCTAACTCCGTGCCAGCAGCCGCGGTAAGACGGAGGATGCAAGTGTTATCCGGAATCACTGGGCGTAAAGCGTCTGTAGGTGGTTTAATAAGTCAACTGTTAAATCTTGATGGCTCAACTTCAAAATCGCAGTCGAAACTATTAGACTAGAGTATAGTAGAGGTAAAGGGAATTTCCAGTGGAGCGGTGAAATGCGTAGATATTGGAAAGAACACCGATGGCGAAAGCACTTTACTGGGCTATTACTAACACTCAGAGACGAAAGCTAGGGTAGCAAATG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TGGGGAATTGTTCGCAATGGGCGCAAGCCTGACGACGCAACGCCGCGTGGAGGATGAAGGTCTTCGGATTGTAAACTCCTGTTGATCGGGACGAAAAGCCTCAACCTAATACGTTGGGGACTGACGGTACCGGTTGAGGAAGCCACGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGTGGCAAGCGTTGTTCGGAATTACTGGGCGTAAAGGGCGCGTAGGCGGCCTTCTAAGTCGGACGTGAAAGCCCCAGGCTTAACCTGGGAACTGCGTCCGATACTGGGAGGCTTGGATTCGGGAGAGGGATGTGGAATTCCAGGTGTAGCGGTGAAATGCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCATCCTGGACCGAGATCGACGCTGAGGCGCGAAAGCTAGGGGAGCAAACG : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
..$ TTGGGAATCTTGGACAATGGGGGAAACCCTGATCCAGCCATGCCGCGTGAGTGATGAAGGCCTTCGGGTTGTAAAACTCTTTCGCGCACGACGATAATGACGGTAGTGCGAGAAGAAGCTCCGGCTAACTTCGTGCCAGCAGCCGCGGTAATACGAAGGGGGCTAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCGCGCAGGCGGTCTTCCAAGTCAGTGGTGAAAGCCCGGAGCTCAACTCCGGAACTGCCATTGAAACTGTGAGACTTGAGTATGAGAGAGGTGAGTGGAATTCCCAGTGTAGAGGTGAAATTCGTAGATATTGGGAAGAACACCGGTGGCGAAGGCGGCTCACTGGCTCATTACTGACGCTCAGGCGCGACAGCGTGGGGATCAAACA : int [1:1296] 18 18 18 18 18 18 18 18 18 18 ...
.. [list output truncated]
..- attr(, "weight")= int [1:1296] 1 2 3 3 1 2 1 1 1 1 ...
..- attr(, "nr")= int 1296
..- attr(, "nc")= num 4
..- attr(, "index")= int [1:1563] 1 2 3 2 4 3 4 3 4 5 ...
..- attr(, "levels")= chr [1:4] "a" "c" "g" "t"
..- attr(, "allLevels")= chr [1:18] "a" "c" "g" "t" ...
..- attr(, "type")= chr "DNA"
..- attr(, "contrast")= num [1:18, 1:4] 1 0 0 0 0 1 1 1 0 0 ...
.. ..- attr(, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:4] "a" "c" "g" "t"
..- attr(, "class")= chr "phyDat"
$ model : chr "GTR"
$ INV :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. ..@ i : int [1:201] 1 5 53 87 117 127 130 186 211 214 ...
.. ..@ p : int [1:5] 0 53 103 145 201
.. ..@ Dim : int [1:2] 1296 4
.. ..@ Dimnames:List of 2
.. .. ..$ : NULL
.. .. ..$ : NULL
.. ..@ x : num [1:201] 1 1 1 1 1 1 1 1 1 1 ...
.. ..@ factors : list()
$ ll.0 : num [1:1296, 1] 0.0392 0.0472 0.0387 0.0498 0.0498 ...
..- attr(, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : NULL
$ tree :List of 4
..$ edge : int [1:13155, 1:2] 7250 7250 11522 11522 10189 10189 8542 8542 7624 7624 ...
..$ edge.length: num [1:13155] 7.42e-02 1.90e-01 1.00e-08 3.83e-03 1.32e-01 ...
..$ tip.label : chr [1:6579] "TAGGGAATATTGCACAATGGAGGAAACTCTGATGCAGCGACGTCGCGTGAGGGAAGAAGGTTTTCGGATTGTAAACCTCTGTCTTTGGTGAAGAAAATGACGGTAACCAAA"| truncated "TGGGGAATATTGGACAATGGGCGAAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGTCTTCGGATTGTAAAGCACTTTAAGTTGGGAGGAAGGGCAGTAAATTAATA"| truncated "TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGCGAGGAGGAAAGGTTGATGCCTAATA"| truncated "TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGGGGAGGAAGGGAGTAAAGTTAATA"| truncated ...
..$ Nnode : int 6577
..- attr(, "class")= chr "phylo"
..- attr(, "order")= chr "postorder"
$ lv : num [1:1296, 1:4] 0.224 0.27 0.221 0.285 0.284 ...
$ call : language pml(tree = treeNJ, data = phang.align, k = 4, inv = 0.174896258961264, bf = c(0.269859436060646, 0.22404764639921| truncated ...
$ df : num 13165
$ wMix : num 0
$ llMix : num 0
$ Mkv : logi FALSE
$ site.rate: chr "gamma"

• attr(*, "class")= chr "pml"

> sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS/LAPACK: /home/r01mt19/.conda/envs/updatedR/lib/libopenblasp-r0.3.18.so

locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8 LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] microbiome_1.14.0 ggplot2_3.3.5 ape_5.5 data.table_1.14.2 magrittr_2.0.1 phyloseq_1.36.0

loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 lattice_0.20-45 tidyr_1.1.4 Biostrings_2.60.2 assertthat_0.2.1
[6] digest_0.6.28 foreach_1.5.1 utf8_1.2.2 R6_2.5.1 GenomeInfoDb_1.28.4
[11] plyr_1.8.6 stats4_4.1.0 pillar_1.6.4 zlibbioc_1.38.0 rlang_0.4.12
[16] rstudioapi_0.13 vegan_2.5-7 S4Vectors_0.30.2 Matrix_1.3-2 labeling_0.4.2
[21] splines_4.1.0 Rtsne_0.15 stringr_1.4.0 igraph_1.2.7 RCurl_1.98-1.5
[26] munsell_0.5.0 compiler_4.1.0 pkgconfig_2.0.3 BiocGenerics_0.38.0 multtest_2.48.0
[31] mgcv_1.8-38 biomformat_1.20.0 tidyselect_1.1.1 tibble_3.1.6 GenomeInfoDbData_1.2.6
[36] IRanges_2.26.0 codetools_0.2-18 fansi_0.5.0 permute_0.9-5 crayon_1.4.2
[41] dplyr_1.0.7 withr_2.4.2 MASS_7.3-54 bitops_1.0-7 rhdf5filters_1.4.0
[46] grid_4.1.0 nlme_3.1-153 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.1
[51] DBI_1.1.1 scales_1.1.1 stringi_1.7.5 farver_2.1.0 XVector_0.32.0
[56] reshape2_1.4.4 ellipsis_0.3.2 generics_0.1.1 vctrs_0.3.8 Rhdf5lib_1.14.2
[61] iterators_1.0.13 tools_4.1.0 ade4_1.7-18 Biobase_2.52.0 glue_1.5.0
[66] purrr_0.3.4 parallel_4.1.0 survival_3.2-13 yaml_2.2.1 colorspace_2.0-2
[71] rhdf5_2.36.0 cluster_2.1.2

Hello!
I am new to coding and I am using the 'Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses'. I began with the DADA2 pipeline and am using that for other analyses. I am hoping that I can add some of these analyses as well. I am trying to go through the section under creating and plotting graphs. In order to work it out on my computer (windows 10) before I use my own data just to make sure that I can do it correctly. I am using the sample data ps and running thorough the code. I am currently stuck on this part of it. I am able to successfully got through the first 5 lines but as soon as I get to the V(net)... portion I get an error message (seen below). I am not sure what it means and how to move forward.

library("phyloseqGraphTest")
library("igraph")
library("ggnetwork")
net <- make_network(ps, max.dist=0.35)
sampledata <- data.frame(sample_data(ps))
V(net)$id <- sampledata[names(V(net)), "host_subject_id"]
V(net)$litter <- sampledata[names(V(net)), "family_relationship"]

Error in V<-(tmp, value = 1:4) : invalid indexing

Thanks

Hi,
I have a question that how long does it take to construct the phen tree.
I mean that run the optim.pml command took me more than 3 days and not done so far

fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))

I found some trick on internet but not sure if it can be apply with the dada2 workflow which help me to complete the construct tree

fitGTR <- optim.pml(fitGTR, rearrangement = "stochastic", ratchet.par = list(iter = 5L, maxit = 5L, prop = 1/3))

data can be download here and the code i run below

(https://drive.google.com/file/d/0B5ombffhUkLFNGQwLWpBblJpdGM/view?usp=sharing)
###Remove chimeras
seqtab.nochim <- removeBimeraDenovo(seqtab, method="consensus", multithread=TRUE, verbose=TRUE)
dim(seqtab.nochim)
sum(seqtab.nochim)/sum(seqtab)
seqs <- getSequences(seqtab.nochim)
names(seqs) <- seqs # This propagates to the tip labels of the tree
alignment <- AlignSeqs(DNAStringSet(seqs), anchor=NA)
save(seqs, ps,taxa, file = "dada.phylo.Rdata")
length(seqtab.nochim)
dim(seqtab.nochim)
library(phangorn)
phang.align <- phyDat(as(alignment, "matrix"), type="DNA")
dim(alignment)
dm <- dist.ml(phang.align)
compact.align <- unique(phang.align)
length(compact.align) # ~5000
length(phang.align)
treeNJ <- NJ(dm) # Note, tip order != sequence order
fit = pml(treeNJ, data=phang.align)

negative edges length changed to 0!

fitGTR <- update(fit, k=4, inv=0.2)

this one did not complete

fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
rearrangement = "stochastic", control = pml.control(trace = 0))

this one complete on a day

fitGTR <- optim.pml(fitGTR, rearrangement = "stochastic", ratchet.par = list(iter = 5L, maxit = 5L, prop = 1/3))

Hello,

I'm having an issue with the hierarchical testing portion. I've done everything until the "preprocessing" portion. I skipped down to the hierarchical testing part, and I kept receiving this error:
Error in estimateSizeFactorsForMatrix(counts(object), locfunc = locfunc, : every gene contains at least one zero, cannot compute log geometric means

I'm not sure what this means, but when I checked the ps_dds variable, I have this outcome apart from the paper's result:

Hello everyone, I have run into a problem when I use the F1000_workflow to analyze my own data.

When I run the commandline,

phangAlign <- phyDat(as(alignment, "matrix"), type="DNA")
dm <- dist.ml(phangAlign)
treeNJ <- NJ(dm) # Note, tip order != sequence order

fit = pml(treeNJ, data=phangAlign)
fitGTR <- update(fit, k=4, inv=0.2)
fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
        rearrangement = "stochastic", control = pml.control(trace = 0))
detach("package:phangorn", unload=TRUE)

The whole process become extremely slow and showed error message like

"NA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive valueNA/Inf replaced by maximum positive value"

I follow solutions as suggested in the post, but it seems that all my packages are uptodate. ape 5.1, Phangorn 2.4.0

Grateful to anyone who might have an idea.

Lichen

Hi, I have been a question about the pipeline used in the F1000 paper compared to the "official"pipeline included in the dada2 webpage, concerning the error rate.

In the F1000 manuscrip the code used for learning errors is:

ddF <- dada(derepFs[1:40], err=NULL, selfConsist=TRUE)
ddR <- dada(derepRs[1:40], err=NULL, selfConsist=TRUE)
dadaFs <- dada(derepFs, err=ddF[[1]]$err_out, pool=TRUE)
dadaRs <- dada(derepRs, err=ddR[[1]]$err_out, pool=TRUE)

In the dada2 "official" site it is

errF <- learnErrors(filtFs, multithread=TRUE)
errR <- learnErrors(filtRs, multithread=TRUE)
dadaFs <- dada(derepFs, err=errF,multithread=TRUE)
dadaRs <- dada(derepRs, err=errR, multithread=TRUE)

In the F1000 manuscript is it only using sample 1 to estimate the overall errors? Is it enough?

Thank you very much for you answer,

Best regards,

Cristina

When I run

knit("PartIIphyloseq.rnw")

I get:

Warning messages: 1: In is.na(e1) | is.na(e2) : longer object length is not a multiple of shorter object length 2: In!=.default(Phylum, c("", "uncharacterized")) : longer object length is not a multiple of shorter object length

My sessionInfo():

R version 3.3.1 (2016-06-21)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.2 (El Capitan)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] gridExtra_2.2.1 ggplot2_2.1.0 phyloseq_1.17.2 BiocStyle_2.0.2
[5] knitr_1.13.1

loaded via a namespace (and not attached):
[1] Rcpp_0.12.6 formatR_1.4 plyr_1.8.4
[4] highr_0.6 XVector_0.12.0 iterators_1.0.8
[7] tools_3.3.1 zlibbioc_1.18.0 digest_0.6.9
[10] jsonlite_1.0 evaluate_0.9 nlme_3.1-128
[13] rhdf5_2.16.0 gtable_0.2.0 lattice_0.20-33
[16] mgcv_1.8-12 Matrix_1.2-6 foreach_1.4.3
[19] igraph_1.0.1 parallel_3.3.1 stringr_1.0.0
[22] cluster_2.0.4 Biostrings_2.40.2 S4Vectors_0.10.2
[25] IRanges_2.6.1 stats4_3.3.1 ade4_1.7-4
[28] multtest_2.28.0 grid_3.3.1 Biobase_2.32.0
[31] data.table_1.9.6 survival_2.39-5 reshape2_1.4.1
[34] magrittr_1.5 MASS_7.3-45 splines_3.3.1
[37] scales_0.4.0 codetools_0.2-14 BiocGenerics_0.18.0
[40] biomformat_1.0.2 permute_0.9-0 ape_3.5
[43] colorspace_1.2-6 labeling_0.3 stringi_1.1.1
[46] munsell_0.4.3 vegan_2.4-0 chron_2.3-47

The walkthrough states:

"The last bit of information needed is the sample data contained in a .csv file."

But then just sets the path and reads it in and there I cannot find where the file would be created and what information needs to be in it.

Thanks
Dan

Hello,

Going through the latest version of the paper and file, what are the mock samples used for? I'm referring to the Mock_S280 and Mock2_S366.

Thanks!
-Nina

Hello!
I was having a problem with this code a week or so ago and now I have a new and different problem. I am using the 'Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses'. I began with the DADA2 pipeline. I am trying to go through the section under creating and plotting graphs.
I was able to get it working with the sample data from the paper. Now I am trying to get it to work with my own data. I am able to get a network but so far I am not able to tell which node belongs to which subject.
This is the table of data that I am working with:

This is the code that I am using:

net <- make_network(ts, max.dist= 1) #was only able to get 4 points until I increased the max.dist. Then I was able to access all of the data. Once I get it working I will adjust this
net
sampledata <- data.frame(sample_data(ts))
V(net)$id <- sampledata[names(V(net)), "Subject"] # $__ means a vertex attribute.
colrs <- c("gray50", "tomato", "gold", "red", "blue", "green", "pink", "teal", "orange", "yellow", "purple", "scarlet", "grey", "magenta") #this enabled me to both add more colors as well as increase the number of labels in the legend. the only thing is that the labels are labeled with the colors listed and do not appear to correspond with those colors (so if we change the names listed then the legend would change.)
V(net)$color <- colrs[V(net)$id]
V(net)$subject <- sampledata[names(V(net)), "Subject"]
ggplot(net, aes(colour, x = x, y = y, xend = xend, yend = yend), layout = "fruchtermanreingold") +
geom_edges(color = "darkgray") +
geom_nodes(aes(colour = color), size = 3, vertex.label<- V(net)$Subject) +
scale_shape_identity() + # not entirely sure what this does as the graph looks the same either way.
theme(axis.text = element_blank(), axis.title = element_blank(),
legend.key.height = unit(1.5,"line")) +
guides(col = guide_legend(override.aes = list(size = 4)))

This is the resulting graph:

The ideal graph would have the subject id in place of all of the color names. I don't think that I can type them out since I am still unsure of which node belongs to which id. I assume that I would have to have the program go into the data file and pull out the subject id and then input it into the legend but I am not sure how to do that.

I am also having another small problem. each time I run the code, the graph changes even if I had not changed the code. see the graphs above and below to see the changes. Because of this I am not sure if the graph that I am getting is a correct representation of the data.

Thank you :)

Currently color only maps to one level.

hello!
when i try to run

mult <- msa(seqs, method="ClustalW", type="dna", order="input")

it report error

use default substitution matrix
Error in convertAlnRows(result$msa, type) : There is an invalid aln file!

Can you help me slove this probolem?please

Hello

I created the tree using the workflow mentioned in https://f1000research.com/articles/5-1492
So could you please let me what is the format of the tree in that case?
Is there is a function or something to know the format? if it is NEWICK, or etc

Thanks very much

I am running through just the first 10 forward and reverse sequences to make the computation easier and be able to try work out what each bit is doing.

At the step:

seqtab.all <- makeSequenceTable(mergers[!grepl("Mock", names(mergers))])

It returns nothing as names(mergers) returns NULL. mergers is a large list of each of the combined forward and reverse sequences (of length ten in my test case).

I have just removed the [!grepl("Mock", names(mergers))] and it seems to work fine but just checking something hasn't gone wrong along the line.

What is the expected output of names(mergers)?

Cheers
Dan

PS this tutorial is awesome