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 ...
I suggest you take a look at rna-pdb-tools we do way more than you need! :-) The tools can get you a sequence, secondary structure and much more using various algorithms, and all is well documented http://rna-pdb-tools.readthedocs.io/en/latest/
To get sequence http://rna-pdb-tools.readthedocs.io/en/latest/main.html#get-sequence
$ rna_pdb_tools.py --...
To achieve (at least some of) your goals, I would recommend the Variant Effect Predictor (VEP). It is a flexible tool that provides several types of annotations on an input .vcf file. I agree that ExAC is the de facto gold standard catalog for human genetic variation in coding regions. To see the frequency distribution of variants by global subpopulation ...
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 ...
I don't know if there's a database that does exactly what you want, but there are some places that might help you figure this out, especially if you already have a list of CNAs/genes/regions in mind.
ICGC is the first that comes to mind, as it has samples from many types of cancer and copy number data for most of them. Depending on what you want though, you ...
Conversion using R:
mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
genes <- getBM(
Where ensembl.genes is a vector of Ensembl gene IDs.
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 ...
I don't know whether there is an API, but ENCODE's website does provide an interactive data matrix where you can filter data based on assay and sample type, place data sets in a "shopping cart", and then proceed to "checkout" to download the files of interest.
There is now a post on Biostars:
We are starting to define the release process for the Human Cell
The first couple of HCA releases will be primarily single-cell RNA-Seq
and we are interested to know what you would expect our releases to
Please fill out our survey if you would like to help us define our
This can be done by using the "Search details" as a search term in Entrez.esearch:
OPJ = os.path.join
base_dir = os.getcwd()
from Bio import Entrez
Entrez.email = "my_email_here"
query = "ANOS1[gene name] AND refseq[filter]"
handle = Entrez.esearch(term=query, db="nuccore", retmax=400)
ids = Entrez.read(handle)["IdList"]
with open(OPJ(base_dir, "...
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 greatest protein coding variant catalogue is definitely ExAC (>65k individuals). They also published a blogpost where they describe how to reproduce figures in the paper (it is a good start how to get familiar with the dataset).
For the whole-genome variants I would look at the data created by 1000 genomes project (the latest release has more than 3k ...
By reading this thread on seqanswers and by comparing the data to TCGA, I figured out
raw_read_count is the read count which you use as input for e.g. DESeq2. It has been estimated using RSEM
normalized_read_count is equivalent to the scaled_estimate from TCGA. This is the estimated fraction of transcripts made up by a given gene, as estimated by RSEM. ...
You can just add /?format=json to any page to get the JSON output.
ENCODE REST API documentation: https://www.encodeproject.org/help/rest-api/
Example scripts: https://github.com/ENCODE-DCC/submission_sample_scripts
The PDB file format is a fixed-column file format designed in 1970s for storing structural models of macromolecules. The format has been around for long time, has many uses, and although it has official spec the files in circulation may not strictly conform to it. It always has a list of atoms with coordinates
(the first two lines are added to ...
Your best bet is to use programs that provide you an complete annotation of variants present in your VCF. Two examples are snpEff and Annovar. These programs work on known variants deem different sources and provide you with information on each item in your file, that you can filter after to try to understand the effects of each variant.
I have used STRING pretty heavily, and have compared it to various other databases of protein interactions and signaling pathways. I do feel like it has a lot of quality interaction annotations, but you have to sift through a lot of noise to get to them. The simplest method I have found for doing this is to look at the individual scores for each ...
This depends on what you are trying to do and whether you value specificity over sensitivity. We can't tell you since it is entirely dependent on the biological question you want to answer.
However, I would recommend two things:
Don't use STRING. The creators of STRING made the choice to value sensitivity over all else, so they include any interaction they ...
In the simplest case, if you just want to stop after the first record was printed, you can just add exit (I also corrected the syntax errors you had and added use strict and use warnings; I suggest you get into the habit of using those two, they save you from a lot of grief in the long run):
Reposting my answer from the related ticket in the Biology section:
I think there is an issue with the terminology. The "primary" accession number, is the first accession number in cases where an entry has more than one accession number, as described in http://www.uniprot.org/help/accession_numbers:
Entries can have more than one accession number. This ...
For rare diseases, you can use Orphanet. You can download an xml file with prevalence from orphadata.
There is an OWL version of Orphanet called ORDO, you can browse it on the EBI Ontology Lookup Service. See for example Arrhythmogenic right ventricular cardiomyopathy and point prevalence relationships in the lower right box. There are a variety of ...
Further inspecting the source code of the downloaded page, I found the following:
<span class="ui-icon ui-icon-close module-close" wname="sequences"
This suggested me ...
For the correspondence between PDB and Uniprot entries you can use SIFTS -- a semi-automated mapping between PDB and UniProt maintained by PDBe.
The pipeline that creates the mappings uses BLAST and a few other criteria to decide which UniProt entry should be assigned to each PDB entry. The SIFTS website has all the data in CSV files.
It sounds like your easiest option is to just install blast on your local machine and set up the databases there. It really will only take a few minutes to blast 6,000 sentences against a yeast genome on a modern laptop. Any solution interfacing with web results or parsing the output will be painfully convoluted. There are executables for blast that will ...
The GOLD database can be used to retrieve a list of species names according to their Gram staining: go to the "Advanced search" page and select the either "gram-" or "gram+" under "Organism Fields" -> "Gram Stain". Then on the results page click on the number under "Organisms". On the results page you can select to have 1000 results per page, meaning that ...