search for: transcriptom

Hi All I am wondering if people based on their experience could share what methods one could use to compare two gff/gtf files. The reason why I want to do so is that we have constructed a RNA-Seq based transcriptome and would like to compare it with reference transcriptome we had from in-silico approaches. Ideally we are looking to find out 1. new genes we see 2. transcripts where the start/end side is changed (upstream/downstream) 3. possible gene fusions may be because in-silico approaches were not corre...

...ld help to monitor immunosuppression and stratify patients in clinical trials. Within this structure, a position funded for 2 years (post-doctoral or equivalent) in bioinformatics and biostatistics is open for recruitment. Job Description Bioinformatics to analyze high-throughput datasets from transcriptomic such as microarrays, RNA-seq. Biostatistics and epidemiological analyses addressing the issue of healthcare-associated infections and biomarker evaluation in case control or cohort studies : multivariate analyses, survival studies, etc ... The candidate will work as part of a multidisciplinary...

I am attempting to join several dataframes that summarize sampling effort for different samples into one large data.frame/table. I have looked at the merge command, but have not been clever enough to figure out how to get it to do what I want. A simplified example of what I am trying to do: The dataframes I have look like this (they were generated using the table command) species1.effort

*The Jenner Institute is seeking to appoint an outstanding Bioinformatician to run a Transcriptomics Core Facility at the Jenner Institute and to identify, develop and integrate bioinformatics resources into the research programmes of the institute. You will be based at the Jenner Institute in the Old Road Campus Research Building, Headington, Oxford and working in conjunction with Gilean M...

...width / height ratio. Here is a typical example : dev.new() heatmap(matrix(rnorm(1200), nrow=10)) I have a non square matrix to plot with heatmap, as there are many more columns than rows, row labels are larger than necessary but column labels can't be read (this is quite common in transcriptomics, where there are many more genes than samples studied). With a classical plot, this could be fixed using a non square device area : dev.new(width=10, height=5) heatmap(matrix(rnorm(1200), nrow=10)) But as can be seen, heatmap don't make profit of it and leave empty room on each s...

*The Jenner Institute is seeking to appoint an outstanding Bioinformatician to run a Transcriptomics Core Facility at the Jenner Institute and to identify, develop and integrate bioinformatics resources into the research programmes of the institute. You will be based at the Jenner Institute in the Old Road Campus Research Building, Headington, Oxford and working in conjunction with Gilean M...

...is not available. So i had contaced the Biomart people but they said that the Biomart is working fine. below is the complete R code i am usign with the corresponding error, library(TCGAbiolinks) query.exp <- GDCquery(project = "TCGA-LUAD", data.category = "Transcriptome Profiling", data.type = "Gene Expression Quantification", workflow.type = "HTSeq - Counts", experimental.strategy = "RNA-Seq") query_load <- GDCdownload(query.exp) luad.exp <- GDCprepare(qu...

...pain Publication Date: April 2009 Number of Pages: 368 Emphasizing the search for patterns within and between biological sequences, trees, and graphs, this book shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data. It implements the algorithms in Perl and R, two widely used scripting languages in computational biology. The book supplies a comprehensive view of the whole field of combinatorial pattern matching from a computational biology perspective. Along wit...

...methodologies: regularized CCA and sparse PLS to unravel relationships between two heterogeneous data sets of size (nxp) and (nxq) where the p and q variables are measured on the same samples or individuals n. These data may come from high throughput technologies, such as omics data (e.g. transcriptomics, metabolomics or proteomics data) that require an integrative or joint analysis. However, mixOmics can also be applied to any other large data sets where p+q>>n. rCCA is a regularized version of CCA to deal with the large number of variables. sPLS allows variable selection in a one...