Quick start

First, import the relevant package:

from prunito import uniprot as up

A simple search for all reviewed UniProtKB entries with tax in their names.

result = up.search_reviewed('name:tax')

Check how many hits this search retrieved. As results are sequence-like, they support __len__.

result.size()
# or
#len(result)

What would happen if a given query had many hits? By default, the maximum number of hits retrieved is 2000. This can be changed using the parameter limit.

Let’s re-run the previous search but this time not just for reviewed entries but all of UniProtKB:

huge = up.search('name:tax', limit=1000)

Partial dataset retrieved. Size: 1890. Retrieved: 1000.
Consider increasing the limit and/or using offset.

If the set limit is lower than the actual number of search hits, the above hint is printed. One could then set limit=2000.

Back to the initial result. What does size mean? UniProtKB entries. And these entries we would like to parse. As prunito provides functionality for both searching and parsing UniProt, one can directly iterate over the entries in a search result for convenience:

entries = list(result)
# or
# for entry in result: ...

Iterate over the entries, printing out primary accessions and recommended full names. Both fields are provided for convenience.

for entry in entries:
    print(entry.primary_accession, entry.recommended_full_name)

Q06507 Cyclic AMP-dependent transcription factor ATF-4
P18848 Cyclic AMP-dependent transcription factor ATF-4
Q9Y6D9 Mitotic spindle assembly checkpoint protein MAD1
P47911 60S ribosomal protein L6
P10070 Zinc finger protein GLI2
Q0VGT2 Zinc finger protein GLI2
...

Which methods and fields are available on a Record object? Basically, all the ones Biopython’s REcord objects provide plus as few more for convenience. See prunito.uniprot.parsers.parser_knowledgebase_txt.Record.

Get isoforms for those entries that have them. We use the presence of a keyword, Alternative splicing, as a filter here.

for e in entries:
    if 'Alternative splicing' in e.keywords:
        for i in e.isoforms():
            print(i)

>sp|Q9Y6D9-2|MD1L1_HUMAN Isoform 2 of Mitotic spindle assembly checkpoint protein MAD1 OS=Homo sapiens (Human). OX=['9606']
MLPARGCVRKRTVWPRLARVLIVTLLTLELSYAPLPCQLSGVPYNTGDPVGRWARPCIWP
CPWHTTINALKGRISELQWSVMDQEMRVKRLESEKQELQEQLDLQHKKCQEANQKIQELQ
...
>sp|P10070-1|GLI2_HUMAN Isoform 1 of Zinc finger protein GLI2 OS=Homo sapiens (Human). OX=['9606']
MALTSINATPTQLSSSSNCLSDTNQNKQSSESAVSSTVNPVAIHKRSKVKTEPEGLRPAS
PLALTQGQVSGHGSCGCALPLSQEQLADLKEDLDRDDCKQEAEVVIYETNCHWEDCTKEY
...
>sp|P10070-2|GLI2_HUMAN Isoform 2 of Zinc finger protein GLI2 OS=Homo sapiens (Human). OX=['9606']
MALTSINATPTQLSSSSNCLSDTNQNKQSSESAVSSTVNPVAIHKRSKVKTEPEGLRPAS
PLALTQEQLADLKEDLDRDDCKQEAEVVIYETNCHWEDCTKEYDTQEQLVHHINNEHIHG
...

We would like to run a FASTA similarity search against Swiss-Prot for one of the sequences. Let’s take the canonical sequence of the first entry in entries.

Here we use the ebiwebservices module from prunito. The EBI web services require an email address to be set.

from prunito import ebiwebservices as ews

ews.set_email('some@gmx.de')

first_entry = entries[0]
similar = ews.fasta_search(first_entry.as_fasta())

print(similar.text[:600])

# /nfs/public/release/wp-jdispatcher/latest/appbin/linux-x86_64/fasta-36.3.7b/fasta36 -l /nfs/public/ro/es/data/idata/latest/fastacfg/fasta3db -L -T 8 -p -m "F9 fasta-R20180501-155642-0060-16766253-p1m.m9" @:1- +uniprotkb_swissprot+
FASTA searches a protein or DNA sequence data bank
 version 36.3.7b Jun, 2015(preload9)
Please cite:
 W.R. Pearson & D.J. Lipman PNAS (1988) 85:2444-2448

Query: @
  1>>>sp|Q06507|ATF4_MOUSE Cyclic AMP-dependent transcription factor ATF-4 OS=Mus musculus (Mouse). OX=['10090'] - 349 aa
Library: UniProtKB/Swiss-Prot
  199856860 residues in 557275 sequences

Statistic...

How about using InterPro’s HMMER search instead of FASTA?

from prunito import interpro as ip

ip_similar = ip.search_phmmer(first_entry.as_fasta())
print(ip_similar.summary())

acc2        acc     desc    species kg      evalue
Q06507      ATF4_MOUSE      Cyclic AMP-dependent transcription factor ATF-4 Mus musculus    Eukaryota       1.0e-232
Q9ES19      ATF4_RAT        Cyclic AMP-dependent transcription factor ATF-4 Rattus norvegicus       Eukaryota       2.6e-216
P18848      ATF4_HUMAN      Cyclic AMP-dependent transcription factor ATF-4 Homo sapiens    Eukaryota       2.4e-195
Q3ZCH6      ATF4_BOVIN      Cyclic AMP-dependent transcription factor ATF-4 Bos taurus      Eukaryota       1.9e-169
Q6NW59      ATF4_DANRE      Cyclic AMP-dependent transcription factor ATF-4 Danio rerio     Eukaryota       5.0e-34
Q9Y2D1      ATF5_HUMAN      Cyclic AMP-dependent transcription factor ATF-5 Homo sapiens    Eukaryota       6.3e-20
Q6P788      ATF5_RAT        Cyclic AMP-dependent transcription factor ATF-5 Rattus norvegicus       Eukaryota       5.8e-18
Q9GPH3      ATFC_BOMMO      Activating transcription factor of chaperone    Bombyx mori     Eukaryota       2.4e-16
O70191      ATF5_MOUSE      Cyclic AMP-dependent transcription factor ATF-5 Mus musculus    Eukaryota       3.5e-13
Q8TFF3      HAC1_HYPJE      Transcriptional activator hac1  Hypocrea jecorina (strain QM6a) Eukaryota       5.4e-05

The result summary is also available as a dataframe if pandas is.

df_hmmer = ip_similar.as_dataframe()

Do some of the entries contain the same PubMed IDs? Let’s find the 5 most common ones.

from collections import Counter

c = Counter()
for e in entries:
    c.update(e.all_pubmed_ids)
print(c.most_common(5))

[('15489334', 24), ('20068231', 9), ('14702039', 8), ('23186163', 8), ('21269460', 7)]

Which are the accession numbers and species of those 24 entries containing the most common one (15489334)?

for e in entries:
    if '15489334' in e.all_pubmed_ids:
        print(e.primary_accession, e.organism)

Q06507 Mus musculus (Mouse).
P18848 Homo sapiens (Human).
Q9Y6D9 Homo sapiens (Human).
P47911 Mus musculus (Mouse).
Q0VGT2 Mus musculus (Mouse).
...

So, which paper is hiding behind this PMID 15489334? Here we use another module for accessing EuropePMC <https://europepmc.org> from prunito. EuropePMC returns data for example in JSON format. We can iterate over the results.

from prunito import europepmc as epmc

paper = epmc.get_pmid_metadata('15489334')
for p in paper:
    print(p['title'])
    print(p['abstractText'])

The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC).

"The National Institutes of Health's Mammalian Gene Collection (MGC) project was designed to generate and
sequence a publicly accessible cDNA resource containing a complete open reading frame (ORF) for every human
and mouse gene. The project initially used a random strategy to select clones from a large number of cDNA
libraries from diverse tissues. Candidate clones were chosen based on 5'-EST sequences, and then fully sequenced
to high accuracy and analyzed by algorithms developed for this project. Currently, more than 11,000 human and
10,000 mouse genes are represented in MGC by at least one clone with a full ORF. The random selection approach
is now reaching a saturation point, and a transition to protocols targeted at the missing transcripts is now
required to complete the mouse and human collections. Comparison of the sequence of the MGC clones to reference
genome sequences reveals that most cDNA clones are of very high sequence quality, although it is likely that some
cDNAs may carry missense variants as a consequence of experimental artifact, such as PCR, cloning, or reverse
transcriptase errors. Recently, a rat cDNA component was added to the project, and ongoing frog (Xenopus) and
zebrafish (Danio) cDNA projects were expanded to take advantage of the high-throughput MGC pipeline."

The paper mentions the Mammalian Gene Collection. Why not search EuropePMC for articles mentioning the collection in their abstracts?

mgc_papers = epmc.search('abstract:"Mammalian Gene Collection"')
mgc_papers.size()
#
# len(mgc_papers)
for idx, hit in enumerate(mgc_papers):
    print(idx, hit['title'])

0 Identification of candidate transcription factor binding sites in the cattle genome.
1 Selenoproteins in bladder cancer.
2 NSrp70 is a novel nuclear speckle-related protein that modulates alternative pre-mRNA splicing in vivo.
3 Generation of a genome scale lentiviral vector library for EF1α promoter-driven expression of human ORFs ...
4 The completion of the Mammalian Gene Collection (MGC).
5 A high-throughput platform for lentiviral overexpression screening of the human ORFeome.
6 PRFdb: a database of computationally predicted eukaryotic programmed -1 ribosomal frameshift signals.
7 Transcriptome analysis of a cDNA library from adult human epididymis.
...

Each hit/paper has many extra data fields including DOI, PubMed ID etc. If the abstract is needed, resulttype='core' has to be specified as a search parameter.

for k, v in list(mgc_papers)[3].items():
    print(k + ':\t' + str(v))

id: 23251614
source:     MED
pmid:       23251614
pmcid:      PMC3520899
doi:        10.1371/journal.pone.0051733
title:      Generation of a genome scale lentiviral vector library for EF1α promoter-driven expression of human ORFs and identification of human genes affecting viral titer.
authorString:       Å kalamera D, Dahmer M, Purdon AS, Wilson BM, Ranall MV, Blumenthal A, Gabrielli B, Gonda TJ.
journalTitle:       PLoS One
issue:      12
journalVolume:      7
pubYear:    2012
journalIssn:        1932-6203
pageInfo:   e51733
pubType:    research support, non-u.s. gov't; research-article; journal article;
isOpenAccess:       Y
inEPMC:     Y
inPMC:      Y
hasPDF:     Y
hasBook:    N
hasSuppl:   Y
citedByCount:       8
hasReferences:      Y
hasTextMinedTerms:  Y
hasDbCrossReferences:       Y
dbCrossReferenceList:       {'dbName': ['EMBL']}
hasLabsLinks:       Y
hasTMAccessionNumbers:      Y
tmAccessionTypeList:        {'accessionType': ['gen']}
firstPublicationDate:       2012-12-12