5

Entrez requires the unique identifiers(UID) for fetching related info. The id you are using in the query is RID. I guess that why you are getting <ERROR>UID=1440: cannot get document summary</ERROR>. List of example uids using the term "ncbi+blast": https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=books&term=ncbi+blast Summary ...


4

These are NCBI taxonomy ID's rather than anything specific to UniProt. One way to parse them would be using BioPython via Bio.Entrez, for example: from Bio import Entrez def get_taxon_name_from_NCBI_id(id_): Entrez.email = 'name@provider.com' handle = Entrez.efetch(db='taxonomy', id= id_, retmode='xml') records = Entrez.read(handle)[0] ...


4

Recently I've developed a python lib for converting InChI to InChIKey: https://github.com/liwt31/chembl_ikey. Maybe it will help.


4

Further inspecting the source code of the downloaded page, I found the following: <li id="nav-sequences" class="module-load transcript" wname="sequences" href="/rest/widget/transcript/K06C4.12/sequences"> <span class="ui-icon ui-icon-close module-close" wname="sequences" </span> Sequences </li> This suggested me ...


4

Lambert et al. (2018, Cell. 172(4):650-665) provide a manually curated list of human TFs at this website.


3

The page you link to is a GenBank Flat file, the default format used by GenBank. So, to get that format, which includes the join lines, use: ./efetch -db nuccore -id CP003820.1 -format gb The join() lines give the different ways in which a sequence can be built from this gene. For example, one mRNA sequence produced by the locus you mention (CP003820) is: ...


3

There is not going to be a public database with this data. Apart from anything else, generating data for early human development is difficult and ethically tricky. Also, most people who care about development would probably think that a whole body average would be meaningless. The best you could do would be to select tissues you were interested in from ...


3

As arupgsh says, you need to use esearch to get a list of unique identifiers before using efetch to retrieve info about each result. I think the easiest way to do this is to use Entrez Direct, which allows you to simply pipe esearch output to efetch: esearch -db books -query NBK1440 | efetch -format docsum or esearch -db books -query NBK1440 | esummary


2

I'm not aware of a functionality in the KEGG service that would allow you to do that directly. I believe your solution (using e.g. ChEBI) is the correct one. You may have other useful service such as unichem, chemspider (you would need a login though for that one) could help as well. Disclaimer: I'm the bioservices main author


2

CRT (CRISPR Recognition Tool) http://www.room220.com/crt/ Crisprs Finder Online Tool https://crispr.i2bc.paris-saclay.fr/Server/ Piler-CR http://www.drive5.com/pilercr/ (Omic Tools List) https://omictools.com/crispr-detection-category


2

It seems like there are some files related to the databases at the ftp site: http://ftp.mcs.anl.gov/pub/WIT2/. The related files were last updated on 2002. I found it via searching at google: site:*.mcs.anl.gov/ WIT. Which reports also a page about the database. There seems to be a project to develop WIT3, because there is a mailing list for developers, ...


2

Here are some command lines I used for that purpose in bash. Simply prepare a text file containing each accession number (SRR/ERR) you want and create a for loop. Here I used prozilla to speed up downloads but you may use wget either. for index in $(cat list_of_accessions) ; do if [ ${#index} -eq 9 ]; then proz -k=6 --no-curses ftp.sra.ebi.ac.uk/...


2

One such database/tool is “[PharmacoGx][1]: an R package for analysis of large pharmacogenomic datasets.” Bioinformatics (Oxford, England). They brought together a large compendium of experimental data (cell lines and the like if I remember correctly from a talk), standardized these and made available within a special object class. I think there is genomic ...


2

data = pd.read_excel ("2019-02-27_161601 AtWAKL8 different version expressions.xls", sheet_name='Results').fillna(0) data.to_csv('df1' + '.csv', index=True) df1 = pd.read_csv ("df1.csv") This doesn't make much sense ... you read in Excel to a dataframe, you then read out to csv - the dataframe doesn't have an index, but you include an index and then read ...


1

Example in R. If you have a file of column names, names.txt, such as: mpg cyl disp and a data.frame like mtcars > mtcars mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 ...


1

There's a million different ways to do simple taks like this. Pick one and learn it:) Python is my go to as it can do pretty much everything you'd ever want. I saved your example as csv with no spaces called expression.txt. import pandas as pd df = pd.read_csv('expression.txt') df[['SM-5GZZ7','SM-5GIEN','SM-5EGGH']] SM-5GZZ7 SM-5GIEN SM-5EGGH 0 0 ...


1

They have an API you can interact with. If you need to get files for only a few different projects: Search for your accession ID in the browser (leading to https://www.ebi.ac.uk/ena/browser/view/PRJNA506829). Filter the show selected columns to only fastq_ftp, click download tsv to get the list of ftp links.


1

These data are in Experiment Hub in Bioconductor library(curatedTCGAData) brcaRNA <- curatedTCGAData("BRCA", "RNASeq2GeneNorm", dry.run = FALSE)


1

I personally find the GDC portal not very user-friendly. However, some of the data is available from other sources. For example, you can download TCGA-BRCA FPKMs and phenotypes from UCSC Xena here: https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Breast%20Cancer%20(BRCA) All the sample info is already combined and the labels are the classic TCGA ...


1

Blueprint does not have technical replicates, you will find that very few sequencing based experiments ever have technical replicates. As biological replicates: each individual is regarded as a replicate. This makes sense for the sort of experimental design which blueprint was designed to answer questions such as "Do individuals from group A have higher or ...


1

If you want to find version info you can either : Ask for another edirect tool version (it will be the same version for edirect) : efetch -version 6.80 Check the info contained in the edirect.pl script : grep -i 'version = ' edirect.pl $version = "6.80";


1

The issue here was I was using an older version of eDirect. There is no command to check version or updates. Your best bet is if you experience problems uninstall and reinstall.


1

That file is actually trivial to parse. You only care about lines that have N=$species, so you can simply do: sed -En 's/.* ([0-9]+): N=(.*)/\1\t\2/p' speclist That will return a tab separated list of taxID and species: $ sed -En 's/.* ([0-9]+): N=(.*)/\1\t\2/p' speclist | head 648330 Aedes albopictus densovirus (isolate Boublik/1994) 10804 Adeno-...


1

Not sure if directly possible in bioservices but one can do the following workaround using chemspider: import requests host = "http://www.chemspider.com" getstring = "/InChI.asmx/InChIToInChIKey?inchi=" inchi = 'InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,1-4,7-8H2,(H,9,10)/t5-/m0/s1' r = requests.get('{}{}{}'.format(host, getstring, inchi)) if r.ok: ...


1

Some information seems to be available on the module level utilising the API. Reaction R00352 points to the module M00173. While some information is in the Definition, it's not trivial to link it to the particular reaction. This linkage is however easier accessible via API:


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