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13

There's rarely a good reason to use a hard-masked genome (sometimes for blast, but that's it). For that reason, we use soft-masked genomes, which only have the benefit of showing roughly where repeats are (we never make use of this for our *-seq experiments, but it's there in case we ever want to). For primary vs. toplevel, very few aligners can properly ...


11

You can use the standard annotation package for humans (you can also use biomaRt, but it can be more confusing, see below): library("org.Hs.eg.db") # remember to install it if you don't have it already symbols <- mapIds(org.Hs.eg.db, keys = ensemblsIDS, keytype = "ENSEMBL", column="SYMBOL") The ensemblsIDS should be a ...


9

Whenever you are wondering about things like this, just look up the identifier on the Ensembl web page. If you look up ENSMUSG00000083840, you will see: This identifier is not in the current EnsEMBL database In this case, it doesn't seem to have been replaced by anything and it wasn't a protein coding gene, so it looks like it was simply a wrong ...


9

getSequence has only been enabled for the main Ensembl (vertebrates) biomaRt. This is all at the Bioconductor end. You'll need to use a getBM instead, for example: os_genes <- getBM(attributes =c("ensembl_transcript_id", "peptide"), filters = "ensembl_transcript_id", values = osj_id, mart = osj)


9

If your question is: can probeset IDs from different platforms be mapped to one another in a similar way as mapping probesets to genes, then the answer is: Yes. BioMart allows you to map almost anything that has an ID to anything else that has an ID. You can use BioMart either via the web interface or programatically. A brief guide to using the web ...


8

Generally, you should use the soft-masked or unmasked primary assembly. Cross-species whole-genome aligners, especially older ones, do need to know soft-masked regions; otherwise they can be impractically slow for mammalian genomes. Modern read aligners are designed to work with repeats efficiently and therefore they don't need to see the soft mask. For ...


8

You need to specify the number without the version. Instead of "ENSMUST00000178862.1" just "ENSMUST00000178862": You can do this with one more line: g <- gsub("\\..*", "", rownames(txi.kallisto$counts)) (hgnc_symbols <- getBM(attributes = c("hgnc_symbol", "chromosome_name", "ensembl_transcript_id"), filters = "ensembl_transcript_id", values = g, mart ...


7

If it's 3' incomplete that means the evidence used to create it was a fragment. Here's the evidence used to construct BRCA1-214 ENST00000477152.5, a 3' incomplete. You can see that there's a full length cDNA from EMBL, AK307553.1, which was used to create this model. The sequence was mapped against the genome sequence to create the transcript model. When ...


7

Conversion using R: library(biomaRt) mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl")) genes <- getBM( filters="ensembl_gene_id", attributes=c("ensembl_gene_id", "entrezgene"), values=ensembl.genes, mart=mart) Where ensembl.genes is a vector of Ensembl gene IDs.


7

Instead of biomaRt, it is also possible to use the mapping databases built into Bioconductor itself, and map from probe to gene, and then from gene to probe in the second. Some R code to convert between hgu133 and hgu95 using the same probe ID provided in another: library(hgu133plus2.db) library(hgu95av2.db) query_probe <- "210519_s_at" hgu133_ensembl &...


6

I don't believe this is possible using biomaRt, nor using AnnotationHub. I have two suggestions, neither of them very satisfactory. First, you can specify an Ensembl archive for biomaRt, for example: mart72.hs <- useMart("ENSEMBL_MART_ENSEMBL", "hsapiens_gene_ensembl", host = "jun2013.archive.ensembl.org") Of course, that ...


6

The ID you need is the NCBI gene ID, which is the same as the EntrezGene ID.


6

It looks like you were using an old annotation. The problematic IDs you posted existed in the GRCh37 annotations, but don't in the most recent GRCh38 annotation. For that reason they were excluded. The IDs that have - as symbols don't have associated symbols, but are present in the database. To use an archived version in biomart: mart = useDataset("...


6

There’s no 1:1 mapping between the IDs of different database schemas. As a consequence, some Ensembl gene IDs map to multiple MGI symbols, or to none (and vice versa). Therefore, you can’t assume that the number of result rows will be equal to the number of query IDs.


6

If you print G_list, you'll see this half way down: 1 ENSMUSG00000060550 15018 H2-Q7 Mus musculus histocompatibility 2, Q region locus 9 (H2-Q9), mRNA. [Source:RefSeq mRNA;Acc:NM_001201460] 2 ENSMUSG00000060550 110558 H2-Q7 Mus musculus histocompatibility 2, Q region locus 9 (H2-Q9), mRNA. [Source:RefSeq mRNA;Acc:NM_001201460] ...


5

I tried several R packages (mygene, org.Hs.eg.db, biomaRt, EnsDb.Hsapiens.v79) to convert Ensembl.gene to gene.symbol, and found that the EnsDb.Hsapiens.v79 package / gene database provides the best conversion quality (in terms of being able to convert most of Ensembl.gene to gene.symbol). # Install the package if you have not installed by running this ...


4

BiomaRt cannot do that I'm afraid. There is an Ensembl REST API endpoint that will get you the genomic location of protein coordinates. It needs an Ensembl peptide ID an input though, so you could use the xref endpoint first to get that. There's a bit of sample code in R on those pages, you could use that to script together if you've got a list of these.


4

The genes that are missed are probably not official mgi symbols. You might wanna look them up at mgi: cd45 -> Ptprc cd11b -> Itgam p2ry12 -> P2ry12 (?) (CAPS sensitive?) glast -> Slc1a3 s100a -> S100a1 pecam -> Pecam1 (?) I suspect biomaRt is not the problem here, the only better way I can come up with is to download the ...


4

Sorry, this was my mistake in the last question. To search down the ontology, rather than just for the specific association with a term, biomaRt needs a different filter: 'go_parent_term'. Try: gene <- getBM(attributes = c('external_gene_name'), filters = 'go_parent_term', values = 'GO:0030098', mart = ensembl)


4

Which tools/aligners take into account softmasked repeat regions? If you're doing whole genome - whole genome alignment (rather than read alignment) then using the softmasked genome is definitely best. Tools suitable for such large scale alignments task tend to skip marked repeats completely in their initial steps to prevent the build up of bogus short ...


4

If you look at the original data at Ensembl you'll notice that most of these are labeled "CDS 3' incomplete" and have a TSL (transcript support level) of 1, which is as low as it goes. It seems likely that that is simply an incomplete annotation. I'm not surprised that there are a bunch of these for BRCA1, since there was a long time when there were a LOT of ...


3

it's not clear to me that it would support both versions of genome builds. You can specify what version of the database you are querying. I'd start with biomart on the ensembl website, see if you can get it to do what you want, then work on getting biomaRt to do what you want.


3

This is the code to get a look-up table to convert between Ensembl ID and HGNC: ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl") theBM <- getBM(attributes=c('ensembl_gene_id','hgnc_symbol'), filters = c('ensembl_gene_id'), values = gsub("\\..*", "", charg), ...


2

I used gProfileR where there are no problematic IDs with that. It converted ENSEMBL IDs to Gene_symbols and made my work easier with GO analysis too. There is an R package and also API.


2

TOPLEVEL These files contains all sequence regions flagged as toplevel in an Ensembl schema. This includes chromsomes, regions not assembled into chromosomes and N padded haplotype/patch regions. E.g: I used the soft masked assemblies for genome annotation pipelines like MAKER, also toplevel unmasked ones for RNA-seq, ChipSeq analysis PRIMARY ASSEMBLY ...


2

Other answers explain why there might not be one to one mapping between the probes. The AbsID database does conversion based on mapping the probe sequences to a genome build, and then determines mappings based on overlapping genome alignment coordinates. This is really useful if you want to be sure that two probes are actually likely measuring the same ...


2

You could also save your "biomart_query_result" object after the first time it is generated. Then you can re-use it without having to depend on the biomaRt API functionality.


2

It turned out that I needed to reinstall OSX to upgrade my R version and biomaRt install.


2

rsIDs change with the dbSNP builds, not with the reference genome builds. rsIDs do not depend on a reference genome. They point to a specified locus regardless of the differences in genomic assemblies. rsIDs numbersare assigned by dbSNP database maintainers. The numbers may be deleted from database (e.g. when a submitter withdraw data) and may change on ...


1

Short answer, we get our annotations from GOA, which has the three listed. We're in touch with GOA to see why they differ from MGI.


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