To add to rightskewed answer:
While it is true that:
Gencode is an additive set of annotation (the manual one done by Havana and an automated one done by Ensembl),
the annotation (GTF) files are quite similar for a few exceptions involving the X chromosome and Y par and additional remarks in the Gencode file (see more at FAQ - Gencode).
What are the actual ...
Dfam has recently launched a sister resource, Dfam_consensus, whose stated aim is to replace RepBase. From the annoucement:
Dfam_consensus provides an open framework for the community to store both seed alignments (multiple alignments of instances for a given family) and the corresponding consensus sequence model.
Both RepeatMasker and RepeatModeler have ...
Ensembl vs Gencode
The GENCODE annotation is made by merging the Havana manual gene annotation and the Ensembl automated gene annotation. [...] In practical terms, the GENCODE annotation is identical to the Ensembl annotation.
Further, for the GTF file differences:
The only exception is that the genes which are ...
Hail might be an option for you.
It is actively developed by a growing team at the Broad. It is rigorously tested (continuous integration, continuous deployment, bug reports get regression tests, blah blah blah).
It was designed to solve this problem (among others). It can import a variety of formats, including VCF, TSV, UCSC BED, JSON and interval files....
Here's a Perl script that can do this:
## Change this to whatever taxon you are working with
my $taxon = 'taxon:1000';
chomp(my $date = `date +%Y%M%d`);
my (%aspect, %gos);
## Read the GO.terms_and_ids file to get the aspect (sub ontology)
## of each GO term.
open(my $fh, $ARGV) or die "Need a GO....
As Ian explained, these are different transcripts which happen to have the same start and end positions. You have no information on their exonic structure in that file. However, if you look them up at EnsEMBL, you will see:
Transcript Y74C9A.2b.1 :
1 Y74C9A.2b.1.e1 11,499 11,561 - - 63
Intron 1-2 11,562 11,617 56
I am unaware of any "official" or gold-standard UTR annotations in S. cerevisiae.
One option is to use the annotations from the TIF-Seq publication (Pelechano et al. 2013).
The GSE39128_tsedall.txt.gz file contains the major isoforms identified. It would be up to you to computational associate each transcript with a given gene. It is also up to you to ...
You may try to set the correct path through the infernal_dir variable in tRNAscan-SE.conf.src. You might need to do this before the installation process.
Original file starts with:
# tRNAscan-SE 2.0
# Configuration File
# default paths
Try the following:
# tRNAscan-SE 2.0
For pre-existing reliabe TE libraries it is a bit of a mess, because not everybody deposits the species-specific TE libraries to a database like RepBase. And as far as I know DFAM contains only human resources, or am I wrong?
As for de novo generation of species-specific TE libraries (which should be done for any species not already present in eg. RepBase):
N.B., I'm avoiding discussion of "best", since that's more or less impossible to answer.
Your question can actually be divided into two:
What's a good tool for pathways analysis (ideally in R)?
What are good sources of pathway information?
For (1), there are a few different possibilities, but I prefer either roast or camera from limma or goseq (a stand-...
transcript objects cover the co-ordinates from the start of the first exon to the end of the last exon of a transcript (i.e. an isoform). If two different isoforms share the same first and last exons, but have a different set of internal exons, then their transcript entries will be the same, but the set of exon entires associated with each transcript will be ...
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 ...
With a k-mer size of 28 it shouldn't be finding that many matches. And the prokka results are suspicious as well. Maybe you have multiple contigs (none larger than 100kb) in that file? What is the result of
grep ^'>' fasta_file | wc -l
? This would show how many contigs you have in the file.
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.
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 ...
In my opinion, my approach would be to pull out the CDS exons and run bedtools on those.
A Few More Details:
When you pull out the exons, make sure that you assign them all IDs if the don't already have them assigned and record which IDs "belong" to which genes. Now when you get exons that overlap, you know that they are coding and you can ...
If you are still interested, last year miRBase generated new updates. Currently, according to ftp site the last release is 22.1. So, it is not a dead project and for more specific information you should reference the miRBase blog.
In order to get the best set of miRNA annotations, you have to define your target species or even if exists some related ...
To add practical advice to what others have said:
In a practical sense, I think the biggest difference between RefSeq and Ensembl/GENCODE is in the sensitivity/specificity trade off.
Ensembl aims more towards the inclusive end, including a far larger number of transcript variants, many of which are only weakly supported.
RefSeq trades some of this ...
I found the following two files in https://downloads.yeastgenome.org/sequence/S288C_reference/:
According to the README files in the same directory, these are (the README for the 5' file is equivalent):
Information about the SGD_all_ORFs_3prime_UTRs.fsa file.
You don't really need to get around anything, your example can be loaded correctly with read.delim(). The rows with missing values are filled in with blanks. You may, however, prefer the readr package, which handles this a bit more elegantly (it'll tell you which values were missing and not do the annoying string->factor conversion).
While your question is specific to cancerous germline mutations, I'd suggest you look at the COSMIC database of somatic mutations to include in your analysis.
There are other factors to include in this kind of analysis you're suggesting, such as predictive deleterious effects (PolyPhen for example can perform such predictions).
If you have 10M variants/...
You could use RepeatScout, which has defined repeat libraries for a limited number of species (including human, mouse, and rat). If your taxon is not represented, you can also do de novo repeat prediction with RepeatScout to build your own library to feed to RepeatMasker. The RepeatScout publication includes some comparisons with RepBase. Another related ...
There is one very simplistic way I use which might work for what you are doing, it is similar to what terdon proposed.
Take a de-novo microbial genome annotation tool (I have my own, but you could use/modify prokka). Tools like these often first predict gene boundaries (with other tools like prodigal or glimmer) and then try to assign a function to found ...
I have used FEATnotator and I think it can provide all of the columns you would like to see. There are many output files generated, but the consolidated output has the following columns:
snpEff is a great tool for annotating VCF files and you can add custom reference sequences.
Guide to add custom annotation files in snpEff
There are a bunch of pre curated annotation datasets available in ...
You will probably be interested in the following UCSC wiki page, which explains how to go from most of the UCSC tables to GTF/GFF:
The basic gist is that UCSC doesn't store any data internally as GTF or GFF, and so you will need to use our genePredToGtf utility in order to convert from our ...
The GFF3 file format specification doesn't care where annotations are described in the file, although it can make some things easier if the annotations are ordered by the start position:
1 T1 gene 3631 4605 . + . ID=ATNG01010
1 T1 mRNA 3631 4605 . + . ID=ATNG01010.1;Parent=ATNG01010
1 T1 exon 3631 3913 . + ...
You requested a tool similar to PANTHER but in R. First, the PANTHER (Protein ANalysis THrough Evolutionary Relationships) tool does a classification based on evolutionarily related proteins, gene ontologies ( molecular function, and biological process) and pathways.
AFAIK there isn't a tool in R that integrates all these into one, but several packages do ...