search for: gene5

Hi, Try this: lines1<- readLines(textConnection("gene1 or1|1234 or3|56 or4|793 gene4 or2|347 gene5 or3|23 or7|123456789")) lines2<-readLines(textConnection(">or1|1234 ATCGGATTCAGG >or2|347 GAACCTATCGGGGGGGGAATTTATATATTTTA >or3|56 ATCGGAGATATAACCAATC >or3|23 AAAATTAACAAGAGAATAGACAAAAAAA >or4|793 ATCTCTCTCCTCTCTCTCTAAAAA >or7|123456789 ACGTGTGTACCCCC"))? lin...

Hey guys, can anyone help? i have a sample table: >table <- structure(c(4, 7, 0.2, 3, .1, 7, 222, 3, 10, 5, 11, 8, 8, 10, 7), .Dim = c(5L, 3L), .Dimnames = list(c("gene1", "gene2", "gene3", "gene4", "gene5"), c("codon1", "codon2", "codon3"))) >table codon1 codon2 codon3 gene1 4.0 7 11 gene2 7.0 222 8 gene3 0.2 3 8 gene4 3.0 10 10 gene5 0.1 5 7 i want to plot column 1 versus column 3. Does an...

Hello all! I have the following problem with the %in% command: 1) I have a data frame that consists of functions (rows) and genes (columns). The whole has been loaded with the "read.delim" command because of gene-duplications between the different rows. 2) Now, there is another data frame that contains all the genes (only the genes and without duplicates) from all the functions of

...es A range(gene) can be seem as an observation has three columns chromosome, start and end, like that seqnames start end width strand gene1 chr1 1 5 5 + gene2 chr1 10 15 6 + gene3 chr1 12 17 6 + gene4 chr1 20 25 6 + gene5 chr1 30 40 11 + I just wondering is there an efficient way to find overlapped, upstream and downstream genes for each gene in the granges For example, assuming all_genes_gr is a ~50000 genes genomic range, the result I want like belows: gene_nameupstream_genedownstream_geneoverla...

Hey guys, if i do a correspondance analysis, e.g.: table <- structure(c(4, 7, 0.2, 3, .1, 7, 222, 3, 10, 5, 11, 8, 8, 10, 7), .Dim = c(5L, 3L), .Dimnames = list(c("gene1", "gene2", "gene3", "gene4", "gene5"), c("codon1", "codon2", "codon3"))) Library(ca) plot(ca(table)) is there a way that i can see the "second principal axis" of this analysis? Aoife [[alternative HTML version deleted]]

...es A range(gene) can be seem as an observation has three columns chromosome, start and end, like that seqnames start end width strand gene1 chr1 1 5 5 + gene2 chr1 10 15 6 + gene3 chr1 12 17 6 + gene4 chr1 20 25 6 + gene5 chr1 30 40 11 + I just wondering is there an efficient way to find *overlapped, upstream and downstream genes for each gene in the granges* For example, assuming all_genes_gr is a ~50000 genes genomic range, the result I want like belows: gene_name upstream_gene downstream_gene ov...

I have 2 files containing data analysed by 2 different methods. I would like to find out which genes appear in both analyses. Can someone show me how to do this? _________________________________________________________________ [[trailing spam removed]] [[alternative HTML version deleted]]

...he significant value. GeneID treatment control treatment control treatment control Gene1 2.1 1 2 2.2 1.1 0.7 2.7 Gene2 1.5 1.4 1.7 2.2 1.3 1.2 Gene3 1.4 1.7 1.8 2.7 1.6 1.5 Gene4 2.2 2.4 2.1 2.3 2.1 1.9 Gene5 2.6 3.4 2.1 1.3 2.6 2.9 Objective: find genes who are differentially epxressed. -- View this message in context: http://www.nabble.com/Statistical-Questions%3A-finding-differentially-expressed-genes-tp15873163p15873163.html Sent from the R help mailing list archive at Nabbl...

...>GeneID treatment control treatment control treatment control >Gene1 2.1 1 2 2.2 1.1 0.7 2.7 >Gene2 1.5 1.4 1.7 2.2 1.3 1.2 >Gene3 1.4 1.7 1.8 2.7 1.6 1.5 >Gene4 2.2 2.4 2.1 2.3 2.1 1.9 >Gene5 2.6 3.4 2.1 1.3 2.6 2.9 >Objective: find genes who are differentially epxressed. I'm not sure what you are asking, but to find whether one of your genes is significantly expressed is relatively straightforward in R or excel, and you have already outlined the proced...

....14 0.07 -0.58 -0.56 -0.25 -0.17 1.02 0.98 0.18 0.28 0.23 0.37 gene2 NA NA NA NA NA NA NA NA NA NA NA NA gene3 0.00 0.28 -0.01 0.29 0.14 NA 0.23 NA 0.08 0.00 -0.47 -0.57 gene4 -0.58 -1.22 -0.43 -0.23 NA -0.36 0.30 0.28 0.30 0.41 0.33 -0.08 gene5 -1.51 -1.36 -1.64 -1.89 -1.32 -0.38 -0.14 -0.32 0.39 0.58 0.19 -0.40 gene6 -0.50 -0.60 -0.42 0.41 0.32 NA NA NA -0.69 0.29 0.12 0.11 > md.pattern(y) V2 V3 V4 V5 V10 V11 V12 V13 V14 V15 V16 V6 V8 V7 V9 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1...