srsilva Notas

Hi there, I've been using mitobim1.8 to reconstruct mitochondrial genomes from a bird using a related species. It seems to have worked; however I don't quite understand the output. In my last iteration file, there are a number of padded and unpadded fasta files... named, for example: sample-reference-out- AllStrains.padded.fasta sample-reference-out- AllStrains.unpadded.fasta sample-reference-out-sample. padded.fasta sample-reference-out-sample. unpadded.fasta sample-reference-out- reference.padded.fasta sample-reference-out- reference.unpadded.fasta I understand the difference between the unpadded and padded, but not AllStrains vs sample vs reference. Could you please explain this to me? I think I've worked out that the AllStrains is being used as the backbone for the next iteration. However this confuses me, as it also appears that in the info folder from mira, in the assembly.txt file all the tags relate to the sample version. Also, I was wondering if you have

https://evosite3d.blogspot.com.br/2011/09/identifying-positive-selection-in.html

http://openrefine.org/ Planilhas - merge de planilhas - importação de colunas - (...)

Column headers: qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore  1.  qseqid  query (e.g., gene) sequence id  2.  sseqid  subject (e.g., reference genome) sequence id  3.  pident  percentage of identical matches  4.  length  alignment length  5.  mismatch  number of mismatches  6.  gapopen  number of gap openings  7.  qstart  start of alignment in query  8.  qend  end of alignment in query  9.  sstart  start of alignment in subject  10.  send  end of alignment in subject  11.  evalue   expect value  12.  bitscore   bit score

cat Uame-Purple-merged.list.txt | cut -f 2,3 | sed 's,|,\t,g' | cut -f 2,4 | nsort -u | cut -f1 | nsort | uniq -c

setwd ('/data/project/flowers/orthologous/transcriptional_factors') lista1 <- read.delim(file='TFs.list', header=F, stringsAsFactors=F) lista2 <- read.delim(file='PlantTFDB-allxUame-Purple_assm_id60_moresensitive_cov60', header=F, stringsAsFactors=F) colnames(lista1) <- c('ID', 'Desc') colnames(lista2) <- c('Transcript', 'ID', 'pident', 'length', 'mismatch', 'gapopen', 'qstart', 'qend', 'sstart', 'send', 'evalue', 'bitscore') head (lista1) head (lista2) merged_list <-  merge(x=lista1, y=lista2, by.x='ID', by.y='ID') head (merged_list) dim(merged_list) library(xlsx) #write.xlsx(merged_list, 'Uame-Purple-merged.list.xls') write.table(merged_list, file='Uame-Purple-merged.list.txt', row.names=F, col.names=T, quote=F, sep="\t")

retirado de : http://www.peter.unmack.net/molecular/programs/paup.command.blocks.html Common PAUP analysis commands By Peter Unmack I usually paste the blocks below to the end of my nexus files, open the command line version (which runs faster than the GUI) and execute the file (I usually copy the datafile to my paup directory, or copy paup to where my datafile is).   Stuff highlighted in yellow is what usually gets changed by the user depending upon their dataset and needs. Parsimony Analysis begin paup; set autoclose=yes; set criterion=parsimony; set root=outgroup; set storebrlens=yes; set increase=auto; outgroup fish1 fish2 etc ; hsearch addseq=random nreps=1000 swap=tbr hold=1; savetrees file= datasetname .cb.pa.tree.nex format=altnex brlens=yes; pscores /tl ci ri rc; end; Parsimony Bootstrap Analysis begin paup; set autoclose=yes; set criterion=parsimony; set root=outgroup; set storebrlens=yes; set increase=auto; outgroup