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 ...
I'll follow up to the great answer from Kamil S Jaron:
Regarding predicting what the variant ("mutation" is a very loaded term) will do, there are a variety of tools. Chief among these are annovar and VEP. The general idea behind these is to classify the variants according to their overlap with genes, which codons they change (if any), how big that change ...
Yes, of course. Exons are not limited to the protein coding regions. Many UTRs are in exons. In fact, you even have various cases of UTRs being multiple exons, and being spliced.
What is strange in your file is not so much that you have exons beyond the stop codon, but that you also have them marked as CDS (coding sequence). That isn't possible, no. While ...
Gene names are not consistent across species. Most of the time genes are automatically annotated and the algorithms try to give names that are similar to their matches in other organisms, but not always. It's also more likely that genes will be automatically named/annotated based on their sequence, and manually named/annotated based on their function.
Part 1 : how to detect mutations
The keywords you are searching for are "variant calling". Basically you have to map sequencing reads to a reference genome (or gene) and then estimate for each position of the genome if the observed difference of mapped reads and the reference is more likely a sequencing error or a mutation (in genomic glossary - variant).
There are multiple ways to do this, and multiple protein interaction databases besides the ones you mentioned, such as BioGRID or IntAct. Interaction databases are different in how interactions are defined, sometimes it can be experimental evidence of interaction, sometimes coexpression, orthology-based predictions, etc.
There is no single solution to your ...
The first column contains Ensembl gene identifiers, and the suffix is a version number that can be used to track changes to the gene annotations over time. From the Ensembl Stable IDs documentation:
Ensembl annotation uses a system of stable IDs that have prefixes based on the species scientific name plus the feature type, followed by a series of digits ...
I tend to use Ensembl Biomart for such queries since there are APIs for various programming languages, e.g. biomaRt, and, maybe more interestingly, via a REST API (although it’s a pretty terrible one).
To translate identifiers from different databases, proceed as follows:
Choose database “Ensembl genes”
Choose dataset your desired oganism
Go on “Filters” › ...
International Mouse Phenotyping Consortium is building a database of phenotypes and knock-outs of mouse. I believe that this database will be fairly complete (20000 knock-outs), but these are knock-outs, not SNPs... There are several mouse GWAS studies, but I am not aware of a database that would pull all the results together.
Arabidopsis big GWAS project ...
If you're comfortable doing a little programming, check out mygene.info (web services for gene annotations of all sorts). ID translation is specifically one of the use cases addressed in the bioconductor client (see the vignette), and there is a python client as well available through pypi. The documentation for mygene can be found here.
I wonder if there is a simpler solution recently? (and hopefully, I can solve it within the scope of python. )
A simpler solution, I don't know... but this is at least one Python solution using Biopython's ELink method via NCBI's Entrez E-utils.
The Biopython library is flexible enough (they have an in-depth tutorial worth reading) to modify the code below ...
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 ...
As others have mentioned in their answer, bioMart is usually the best place to go for this infomation because it draws its data directly from the Ensembl database.
However, you will find you do not get a full translation of Ensembl IDs to Entrez IDs even there. The reason for this is there simply does not exist a simple 1-to-1 mapping of Entrez IDs to ...
Conversion using R:
mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
genes <- getBM(
Where ensembl.genes is a vector of Ensembl gene IDs.
I can show you a simple way in R, using biomaRt. Let's say you have two snp ids you want to exmine.
SNPids <- c("rs431905509", "rs431905511")
# To show which marts are available
# You need the SNP mart
mart <- useMart("ENSEMBL_MART_SNP")
# Find homo sapiens
# This will be the dataset we want to ...
Here you can find some example R code to compute the gene length given a GTF file (it computes GC content too, which you don't need). This uses one of a number of ways of computing gene length, in this case the length of the "union gene model". In this method, the non-duplicated exons for each gene are simply summed up ("non-duplicated" in that no genomic ...
Description of how to use biomaRt here. Filter by your list of gene IDs. Get the descriptions etc as attributes. Eg:
> ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl")
> IDs <- c("BRCA2","BRAF")
genedesc <- getBM(attributes=c('external_gene_name','description'), filters = 'external_gene_name', values ...
There is no golden/standard way to define a gene signature but they are completely different from a gene set enrichment analysis (GSEA). I will start with how to obtain a gene signature:
Usually this kind of signatures are defined by comparing a group against another ie doing a differential gene expression analysis. One then selects the most up or down ...
You seem to refer to the GSEA provided by the Broad institute, (there are other GSEA algorithms).
1) You can provide whatever you wish, but if you want to know if those gene sets in which side of the ordered list are they, then provide all the list (of genes) you have.
2) GSEA analyize if the order of a given list distributes in certain way the elements ...
Use the following R package for Gene Set Enrichment analysis of RNA-seq data: seqGSEA
There is another R package (fgsea) recently published called "Fast Gene Set Enrichment Analysis" by Alexey Sergushichev.
Some of this information (at least some domains, active sites, etc) is available from UniProt.
If you want to download their whole database, you can search without specifying any terms and then click the Download button.
Methylation levels have high local correlation, so Fisher's method would be problematic. Having said that, you have no reason to use Fisher's method after a paired t-test. A paired t-test will give you a single p-value per gene, which is what you want. Do be sure to only include CpG with some minimal coverage in both group.
Perhaps this small test script will help demonstrate some of the principles:
# define the current line
my $line = "foo\tbar\tbaz\n";
Many interaction databases now work with PSI format files. Most of the main databases can do this and the EBI has set up PSICQUIC View, a very useful page where you can query multiple databases at once.
Note that it is very important to limit the results according to the detection method. There is a lot of noise in protein interaction databases. Depending ...
I know that the GWAS association p-value threshold is 1e-8
This may be a common threshold of statistical significance that is used, but it's definitely not an absolute value. It's a hack to try to work around many issues with GWAS associated with testing millions of SNPs. Unfortunately, the most relevant issue in GWAS (for spurious significance) is ...
What you are looking for is SNP annotation. If you have the chromosome:position reference and alternate alleles for your SNPs of interest, it can be as simple as uploading them to the variant effect predictor.
This will give you the predicted protein change and novelty of the variant with respect to known ...
Since this is a microarray (U133A), what you're seeing is that each probe is associated with one or more transcripts. The general strategy when dealing with microarrays is to use RMA (or fRMA) to summarize to probesets and then collapseRows to summarize to gene-level information. Since this is all typically done in R, you can find some further discussion on ...