# Tag Info

4

pyensembl should do the job: >>> from pyensembl import EnsemblRelease >>> data = EnsemblRelease(76) >>> data.transcript_by_id("ENST00000506751") Transcript(..., contig='5', start=140861224, end=140863521, strand='+', genome='GRCh38')

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Devon will probably add a more precise answer, until then I just link his Tweet from last week: https://twitter.com/dpryan79/status/1394714988720308226 Edit: 31.5.21 Rollout started yesterday, should take a week to complete: https://twitter.com/dpryan79/status/1399273322601459712

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This answer does not rely on STRINGdb package. STRING provides supplementary identifier mapping files here: https://string-db.org/mapping_files/. It looks like you are interested in human STRING name one - https://string-db.org/mapping_files/STRING_display_names/human.name_2_string.tsv.gz. You could read it as a separate lookup.

2

I started the roll-out on the 30th in the evening. There's not much of a delay for the October Bioconductor release, but for the summer release we first have to build the newest R version and then rebuild all conda CRAN packages. It's probably not widely known, but Bioconductor packages are only compatible with a single R version. For example, the ones being ...

2

As I answered on the linked question, the right way to declare packages from repositories recognized by R is to just declare normally on the Imports, Depends, Enhances and LinkingTo. Bioconductor is an accepted repository so it doesn't need to be added on Additional_repository field. BiocViews is a required field from Bioconductor. As CRAN accepts other ...

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The class of your object which the rma function returns is an ExpressionSet which is a standardized data structure in Bioconductor/R from the Biobase package. It stores both expression (a numeric matrix) data as well as column- and row annotation data. You have to use the getter function exprs() to access the expression data slot, so exprs(data2) which is ...

1

DE results do not change from one run to another. The code given in your question will give identical results each time you run it.

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If you have an existing understanding of a fold change that you would consider to be biologically relevant, it will be better to use the lfcThreshold for comparisons. Working out that fold change is the hard bit. When using the Wald test, the p-value is associated specifically with that threshold, rather than the difference from zero. This is explained in ...

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That package was deprecated in Bioconductor version 3.13 after several attempts to contact the package author/maintainer. Your options include reverting back to Bioconductor version 3.12 or using one of the other options for GO/pathway enrichment analyses, e.g. topGO, enrichR, or clusterProfiler.

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rlang 0.4.10 is the current version at CRAN: https://cran.r-project.org/web/packages/rlang/index.html Try to install it via install.packages, not via GitHub directly.

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I don't know if it's too late, but I met same question and got an answer. You can use add_proteins_description(). It works in same way with map(). If you put a data frame having STRING_id column, it adds preferred_name, protein_size and annotation. You may find your answer on preferred_name column. require(STRINGdb) string_db <- STRINGdb$new() string_db$...

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This is late but I am putting it here for future reference. You can use the vc_nonsyn argument in the read.maf function to manually list the variant classifications you want to plot later. It will look like this: df.maf <- read.maf(maf = df, vc_nonSyn = c("Frame_Shift_Del","Missense_Mutation","Nonstop_Mutation","Silent&...

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Recently I have done something similar for my work, I am using sangerseqR which is s Bioconductor package in Python Environment using rpy2 import rpy2.robjects as r from rpy2.robjects.packages import importr utils = importr('utils') utils.install_packages('sangerseqR', repos="https://git.bioconductor.org/packages/sangerseqR") ...

1

As you answered yourself @Death Metal, voom will by default not perform additional normalization. However in virtually all cases you would want to do some kind of normalization at least to correct for differences in sequenced reads between the samples. This is why in the manual (page 71 right at the top) the calcNormFactors function from edgeR is used. This ...

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