There area few different influenza virus database resources:
The Influenza Research Database (IRD) (a.k.a FluDB - based upon URL)
A NIAID Bioinformatics Resource Center or BRC which highly curates the data brought in and integrates it with numerous other relevant data types
The NCBI Influenza Virus Resource
A sub-project of the NCBI with data curated ...
As Pierre mentioned, NCBI is a good resource for this kind of transformation.
You can still use taxize to perform the conversion:
species <- c('Helianthus annuus', 'Mycobacterium bovis', 'Rattus rattus', 'XX', 'Mus musculus')
uids <- get_uid(species)
# keep only uids which you have in the database
uids.found <- as.uid(uids[!is....
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 ...
use the NCBI taxon dump under ftp://ftp.ncbi.nih.gov/pub/taxonomy
in taxdmp.zip you'll find all the names for a given NCBI taxon
$ grep -w ^9606 names.dmp
9606 | Homo sapiens | | scientific name |
9606 | Homo sapiens Linnaeus, 1758 | | authority |
9606 | human | | genbank common name |
9606 | man | |...
The Global Alliance for Genomics and Health has been working on the issue of representing sequencing data and metadata for storage and sharing for quite some time, though with mixed results. They do offer a model and API for storing NGS data in their GitHub repository, but it can be a bit of a pain to get a high-level view. I am not sure if any better ...
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” › ...
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 believe you're looking for env_nr? It's listed as such, under Metagenomic proteins in the blastp webpage. It appears that the word within brackets should be supplied alongside the -db parameter. A quick test with a dummy amino acid fasta file does turn up a result to a valid NCBI protein accession.
Edit: I followed up on this with a little more digging ...
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 ...
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):
You mention Biopython, which contains tests: https://github.com/biopython/biopython/tree/master/Tests.
Some of the tests consist in reading files present in the folders listed in the above link. These files could be a starting point for a database of test files. Whenever one comes across a test case not covered with these files, one could construct a new ...
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 ...
No, there are no standardized three-letter abbreviations for species names. Three-letter abbreviations with roman letters is 26^3 combinations, which is only 17,576 possible combinations. As a result there are not enough three-letter abbreviations for all living species.
All three-letter abbreviations are either specific to a database or perhaps are shared ...
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 ...
It should be possible to get this information from the ChEBI database, see the exported tables. You could download the ontologies (in OWL / OBO), parse them using some ad-hoc parser or using a dedicated library (e.g., Pronto), build a directed acyclic graph based on the is_a relationship, and extract the antibiotic subtree.
You could also use ...
Using SeqIO.index rather than SeqIO.parse lets you read all the records into a dict, from which you can then extract the raw entry:
from Bio import SeqIO
record_dict = SeqIO.index('Input.txt', 'swiss')
for key in record_dict:
Now you should be able to apply your test for a transmembrane protein to each entry, ...
I am the developer of Uberon and I would be happy to help you with what you need, either from Uberon, or from the FMA.
You mentioned you need something simpler than FMA. There are a variety of tools for creating custom subsets. Additionally, some ontologies provide ready-made subsets for particular purposes.
For example, we make a subset called 'basic.obo' ...
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.
These are not. The closest to "standard" is the 5-character abbreviation by Swissprot. It names 25,886 species and has been used for decades. It is also easy to remember for some common species such as HUMAN, RAT, MOUSE and HORSE. I think databases should stop inventing new species abbreviations.
R supports logistic regression, which would seem to be the most efficient method for tackling this question. Assuming the "Chemo" variable is the type of chemo the code would be something like:
glm( (response_to_chemo == "yes") ~ BMI + Chemo + Predictor + DJANGO + gender,
EDIT: Corrected a typo (added a missing double quote)
Not that I am aware. It is best to go with format specifications when coding.
Also it may be good to look at the example files that come together with various tools performing file conversions and handling. E.g.
Trimmomatic comes with few .fa files (http://www.usadellab.org/cms/?page=trimmomatic)
Samtools with a toy.sam (https://github.com/lh3/samtools-...
For metadata, I would use a SQL schema something like the following:
CREATE TABLE Project (
ac TEXT, -- project/Study accession
PRIMARY KEY (ac)
CREATE TABLE Sample ( -- biological sample/biopsy
PRIMARY KEY (ac)
CREATE TABLE AnalysisSample (
prj_ac TEXT, -- project acccession (Project.ac)
symbol TEXT, -- a short name ...
I agree that there is no ideal data model that is going to be stable for very long in a quick-moving field like genome informatics. Perhaps a schema-less (NoSQL or some other document-based system, such as MongoDB) database approach would work better? This gives you ultimate flexibility to attach whatever information is relevant to database entries you're ...
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 ...
This can be done quite easily using Ensebl's BioMart.
Choose the Ensembl Regulation database:
Select the "Human Regulatory Features" dataset:
That's basically it right there, just click on "Results":
Export to file and click "Go":
This will download a file called mart_export.txt which looks like this (I chose TSV for tab separated values):
$ head ...
Ensembl contains this information: When you go to the “phenotype” menu item of a given gene, you will see a list of variants (potentially after clicking on “ALL associated variants”) with their associated genomic position.
You can subtract the transcript’s start position from that position to find out the residue (of the translated sequence, and be careful ...
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 ...