similar to: visualizing nucleotide sequence properties

Hi, I am very new to R and wanted to know if there is a package that, given very long nucleotide sequences, searches and identifies short (7-10nt) motifs.. I would like to look for enrichment of certain motifs in genomic sequences. I tried using MEME (not an R package, I know), but the online version only allows sequences up to MAX 60000 nucleotides, and that's too short for my needs..

Hi there, I am looking for R-packages that can help me visualize properties on nucleotide sequences. I want to display sequences in the 1-100K base range as lines and plot features above and below those lines. Any ideas would be welcome. Thanks, Bernd

Dear R-helpers: thank your for your attention. i am a newer to R and i am doing some protein category classification based on the amino acid sequence.while i have some questions urgently. 1. any packages for analysis amino acid sequence 2. given two sequences "AAA" and "BBB",how can i combine them into "AAABBB" 3. based on "AAABBB",how can i get some

Hi Function sequence() repeatedly concatenates its output, and this is slow. It is possible to improve on the performance of sequence by defining myseq <- function(x){unlist(sapply(x,function(i){1:i}))} The following session compares the performance of myseq(), and sequence(), at least on my G5: > identical(sequence(1:50),myseq(1:50)) [1] TRUE > system.time(ignore <-

Hi Function sequence() repeatedly concatenates its output, and this is slow. It is possible to improve on the performance of sequence by defining myseq <- function(x){unlist(sapply(x,function(i){1:i}))} The following session compares the performance of myseq(), and sequence(), at least on my G5: > identical(sequence(1:50),myseq(1:50)) [1] TRUE > system.time(ignore <-

Hi, I have encountered this problem quite a few times and thought I would ask. Let's say that I have two endpoints, a and b, which are integers. If a <= b, I would like to get a:b, but if a > b, then numeric(0), for example: myseq(3,5) => 3:5 myseq(3,3) => 3 myseq(3,2) => numeric(0) The operator : just gives decreasing sequences in the latter case, and I could not coax seq

All, Recently I ran across a URL documenting published research using R: http://www.cgd.ucar.edu/ccr/ammann/millennium/CODES_MBH.html A note on the site indicates that the code is being revised. The code and data are provided, so that one could reproduce the results without having to buy a proprietary software program. In poking around the R website it is clear that a lot of thought has gone

Dear Mag. Ferri Leberl, I'm using something like: ----------------------- tex.tex --------------------------- \documentclass{article} \usepackage{graphicx} \usepackage{fancyvrb} \newcommand{\Rlogo}{\protect\includegraphics[height=1.8ex,keepaspectratio]{Rlogo.pdf}} \newcommand{\myinput}[1] {\begin{scriptsize} \VerbatimInput[frame=single,label=#1]{#1} \end{scriptsize}} \title{The R logo,

Dear Mag. Ferri Leberl, I'm using something like: ----------------------- tex.tex --------------------------- \documentclass{article} \usepackage{graphicx} \usepackage{fancyvrb} \newcommand{\Rlogo}{\protect\includegraphics[height=1.8ex,keepaspectratio]{Rlogo.pdf}} \newcommand{\myinput}[1] {\begin{scriptsize} \VerbatimInput[frame=single,label=#1]{#1} \end{scriptsize}} \title{The R logo,

Dear all, I'm trying to produce two dotcharts side-by-side within a Sweave document. When I'm compiling this example: \documentclass{article} \begin{document} <<fig=T,width=8,height=4>>= par(mfrow = c(1, 2), cex = 0.7) for(i in 1:2) dotchart(1:10) @ <<fig=T,width=8,height=4>>= par(mfrow = c(1, 2), cex = 0.7) for(i in 1:2) hist(1:10) @

Hi. Suppose I have a vector that I partition into disjoint, contiguous subvectors. For example, let v = c(1,4,2,6,7,5), partition it into three subvectors, v1 = v[1:3], v2 = v[4], v3 = v[5:6]. I want to find the maximum element of each subvector. In this example, max(v1) is 4, max(v2) is 6, max(v3) is 7. If I knew that the successive subvector maxima would never decrease, as in the example,

Last Friday, I noticed that it is difficult to work with regression models in which there are factors. It is easier to do the old fashioned thing of coding up "dummy" variables with 0-1 values. The predict function's newdata argument is not suited to insertion of hypothetical values for the factor, whereas it has no trouble with numeric variables. For example, if one uses a

Dear R-help, After making a typo (reminiscent of FORTRAN 77, I guess) I found the following: > identical(all.equal(2^(-10:10), 2**(-10:10)), TRUE) [1] TRUE I have tried to find the documentation about the ** operator but I was unsuccesful this way: > sessionInfo() Version 2.3.1 (2006-06-01) powerpc-apple-darwin8.6.0 attached base packages: [1] "methods" "stats"

> Dear useRs, > > by a circular diagram representation I mean what you will get by entering > this at your R promt: > > pie(1:5) > > Nice to have R as a lingua franca :-) > > The folowing quote is from page 360 in this very interesting paper: > > @article{SpenceI2005, > title = {No Humble Pie: The Origins and Usage of a Statistical Chart}, >

Hallo everyone, excuse me if this is not a genuine R question but I do not know where to ask else. Referring to e.g. https://stat.ethz.ch/pipermail/r-help/2004-December/062114.html I wonder if these measurements of 3000 criminals (raw data) are available anywhere. At least I didn't find them in the R datasets package or by means of Google. What I did find was a table of frequencies of

Dear R-help, I would like to include the documentation of an R function in an *.rnw document processed by Sweave. Because I'm sharing my *.rnw files with colleagues under Linux and Windows (I'm on Mac OS X), I would like a pure R solution. The naive approach doesn't work, because Sweaving this *.rnw file: -------- tmp.rnw -------- \documentclass{article} \begin{document}

Dear all, I'm confused by the time serie parameters in data(lh) : sueoka:~ lobry$ R --vanilla --quiet > tsp(lh) [1] 1 48 1 because documentation says: QUOTE A regular time series giving the luteinizing hormone in blood samples at 10 mins intervals from a human female, 48 samples. UNQUOTE So that I would expect the time serie to end at 480 minutes or 8 hours. Shouldn't we have

Dear All, I am using the plotrix library to plot some matrices. I have a problem: some of my data are outliers, hence using a linear color scale does not work very well (you would see too many cells having a similar, indistinguishable color). See the code snipped at the end of the email. Plotting the logarithm of the data gets the job done, but my problem is that I would like to write in every

Dear All, I'am looking for examples showing that correlation does not imply causality, the targeted audience consists of undergraduate students (their first year at the university but in the BioMathStat track). All practicals are under R. I was able to extract this from R datasets: ### begin data(sunspots) data(lynx) spots <- window(sunspots, freq = 1, start = 1880, end = 1900) lnx <-

Dear list, I'm trying to understand the graphical parameters by a systematic exploration of the par() function (if you are interested by the result it's here http://pbil.univ-lyon1.fr/R/fichestd/tdr75.pdf, the comments are all in french but it's pure R code under Sweave). I have a problem with par(tmag) illustrated by the following code: