User: tiago211287
tiago211287 • 1.1k
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Posts by tiago211287
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... You can try to convert your retired IDs using the [ID Conversion tool][1] for GRCh37. Or maybe this [Biostar][2] link can help you to access the GRCh37 using biomaRt.
This Ensembl [page][3] contains information about converting between both assemblies.
[1]: http://grch37.ensembl.org/Homo_sapien ...
written 11 months ago by
tiago211287 • 1.1k
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... Can you please share a sample of the remaining id's?
...
written 11 months ago by
tiago211287 • 1.1k
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... You need a column with your regular gene_id_versions called **ensembl_gene_id_version**. And Another column with the edited **ensembl_gene_id** without versions. created with gsub.
my_ids$ensembl_gene_id <- gsub("\\..*","", my_ids$ensembl_gene_id_version)
Only then you can merge, using:
...
written 11 months ago by
tiago211287 • 1.1k
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... The numbers after the dot are the gene version. It might be that you have very old gene versions.
You may try to use R to get your information. Removing the Ensembl gene version.
Like this:
#Get gene names annotation
source("http://bioconductor.org/biocLite.R")
BiocInstaller::biocLite ...
written 11 months ago by
tiago211287 • 1.1k
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... Maybe Adobe Illustrator? ...
written 11 months ago by
tiago211287 • 1.1k
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... Maybe Helpful?
https://www.nature.com/articles/s41467-018-08023-x ...
written 11 months ago by
tiago211287 • 1.1k
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Comment:
C: ATAC-seq DE analysis
... Your link does not work anymore. Could you update it? Thanks! ...
written 14 months ago by
tiago211287 • 1.1k
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1
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Comment:
C: bam file merge
... In http://www.htslib.org/doc/samtools-1.2.html,
it says,
> -h is specified the @SQ headers of input files will be merged into
> the specified header, otherwise they will be merged into a composite
> header created from the input headers. If in the process of merging
> @SQ lines for coor ...
written 19 months ago by
tiago211287 • 1.1k
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Comment:
C: Filtering rRNA from RNAseq data
... You can normally align the reads against genome + annotation (gencode), using STAR for example which can count the number of reads/feature. Then just check the annotated rRNA gene count percentage. You would need "good" samples as a control of what is normal/bad. ...
written 19 months ago by
tiago211287 • 1.1k
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... Have you already called your peaks? You can Use MACS2 for peak calling. To only then, choose strong peak signals. I would also use deeptools to access Chip-Seq Sequencing quality and statistics. ...
written 19 months ago by
tiago211287 • 1.1k
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