# Tag Info

6

Do you mean the NCBI taxonomy IDs? If so, I've personally always preferred the way UniProt displays NCBI taxonomy, and it's easier to download IDs in mass. For example, if you find E.coli's entry: https://www.uniprot.org/taxonomy/562 You can then click on "all lower taxonomy nodes", which will show all E.coli strains. If you click 'Preview first 10'...

5

At Ensembl, we categorise synonyms as anything that a gene might also be known as. This includes older names for them, since those names will be in the literature, including where a gene has been split in two.

4

This has been answered before. See: https://stackoverflow.com/a/55402322/6262370 In short, you need to either use rettype='gbwithparts' or rettype='gb', style='withparts' to download the entire genbank flat file.

3

You'll want something like: esearch -db sra -query SRX1596422 | efetch -format runinfo This will produce a CSV output to the screen with columns containing the meta information available in SRA: Run,ReleaseDate,LoadDate,spots,bases,spots_with_mates,avgLength,size_MB,AssemblyName,download_path,Experiment,LibraryName,LibraryStrategy,LibrarySelection,...

2

Answer from @devon-ryan, converted from comment: Both, that's why NAT5 is no longer a human gene symbol. NAT5 does not equal NAA20, it's an out-dated name for it. Names change over time as people realize that there are more genes for something than originally thought.

2

I've been corresponding with NLM about this issue and I finally took the time to try out their suggestion (which personally I found hard to see between the lines and which is not a discrete solution, but rather a very time-consuming manual process containing false positives because they say that to get a formal and comprehensive response to my query: ...

2

In order to download the FASTA sequence of transcript variants using Entrez eutilities, you will have to use Entrez.elink to get a list of transcript accessions for the gene of interest and then use Entrez.efetch to fetch the sequence(s). Just to give you an idea, you can use Entrez Direct for this as follows: elink -db gene -id 682 -target nuccore -name ...

2

Hugo names don't really have an associated assembly. They are just the official name of the gene in human. You will find them in all assemblies (except if they hadn't been annotated/identified in one of the older ones). Entrez entries are also not tied to a specific genome assembly. They usually have mappings to multiple ones. For example, Hugo gene name ...

1

1

It's not a curl problem. (The 22 error is just passing along the server-side HTTP 400 error, which is because it dumped an error message into a URL.) I think terdon is right and there's something wrong with your edirect installation. You have esearch and efetch on your PATH but apparently not xtract and/or xtract.Darwin. More detail: Look toward the end ...

1

Did you try to fetch from the database nucleotide instead of gene ? # load modules from Bio import SeqIO from Bio import Entrez # Lookup ID search = Entrez.esearch(db='nucleotide', term='Tobacco mosaic virus[Orgn] AND replicase') read = Entrez.read(search) idlist = read["IdList"] # Fetch sequence search = Entrez.efetch(db='nucleotide', id=idlist[...

1

The following bash script downloads the summaries into an XML file. #!/usr/bin/env bash # This requires the ncbi tool gene2xml # sudo apt install ncbi-tools-bin DIR="download_cache" mkdir -p "$DIR" URL="ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/ASN_BINARY/Mammalia/Mus_musculus.ags.gz" wget -S -O - "$URL" | gzip -d | ...

1

You could wrap the call too entrez_summary in a loop. For example to cut the data into chunks of five genes: gene.vector # this would be the genes you are interested in gene.chunks <- ceiling(length(gene.vector) / 5) # initializing an empty list summary.list <- vector(mode = "list", length = gene.chunks) for(i in gene.chunks){ # select the ...

1

You should check out NCBI Datasets for this sort of thing. Since this is bacteria, you won't be able to use the web interface but there's an API and a command-line tool that you can use. For example, you can use the datasets command line tool to download the E. coli RefSeq genome assemblies as follows: ## see how many genomes are in scope $datasets summary ... 1 For this particular query, try host=Bemisia tabaci B[WORD]. However, you may also be interested in related queries like host=Bemisia tabaci B biotype[WORD]. But, I am not sure a general solution exists, as I do not believe all qualifiers are indexed. Also, you can see how your query is interpreted on the right side under "Search details". For ... 1 I don't program in R, but replace the last line of you code with the following and I suspect you will find it works, for(i in seq(1,10000,1)){ recs <- entrez_summary(db="pubmed", web_history=pubmed_search$web_history, retmax=100, retstart=i) cat(seq_start+99, "sequences downloaded\r") } Rationale I would guess seq is similar to the linspace function in ...

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