Matt Shirley ([email protected])
25 April 2013
sudo pip install Biopython. If pip is not installed, try easy_install.To test whether you successfully installed Biopython, run python -c 'import Bio'. If you don't see an error message, you're done.
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
dna = Seq('ATGGGGTTCGATCGTCCTTTCCCAGCTCTGACTAG', generic_dna)
print str(dna)
rna = dna.transcribe()
print str(rna)
prot = dna.translate()
print str(prot)## ATGGGGTTCGATCGTCCTTTCCCAGCTCTGACTAG
## AUGGGGUUCGAUCGUCCUUUCCCAGCUCUGACUAG
## MGFDRPFPALTfrom Bio.Seq import Seq
from Bio.Alphabet import generic_dna
dna = Seq('ATGGGGTTCGATCGTCCTTTCCCAGCTCTGACTAG', generic_dna)
print str(dna)
print str(dna.complement())
print str(dna.reverse_complement())
prot = dna.translate()
print str(prot)
try:
prot.complement()
except ValueError, e:
print e## ATGGGGTTCGATCGTCCTTTCCCAGCTCTGACTAG
## TACCCCAAGCTAGCAGGAAAGGGTCGAGACTGATC
## CTAGTCAGAGCTGGGAAAGGACGATCGAACCCCAT
## MGFDRPFPALT
## Proteins do not have complements!Why can we not complement a Protein? Simple: Biopython Seq objects have an "alphabet" associated with them that prevents you from doing such silly things.
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna
dna = Seq('ATGGGGTTCGATCGTCCTTTCCCAGCTCTGACTAG', generic_dna)
comp = dna.complement()
print str(dna)
print [(base, dna.count(base)) for base in ['A','C','T','G']]
print [(base, comp.count(base)) for base in ['A','C','T','G']]## ATGGGGTTCGATCGTCCTTTCCCAGCTCTGACTAG
## [('A', 5), ('C', 10), ('T', 11), ('G', 9)]
## [('A', 11), ('C', 9), ('T', 5), ('G', 10)]Note that you don't really need Biopython for this, except for the complement function.
from textwrap import fill
from Bio import Entrez
Entrez.email = '[email protected]'
query = Entrez.einfo()
result = Entrez.read(query)
keys = result.keys()
print keys
results = ' '.join(result['DbList'])
print fill(results, 60)## [u'DbList']
## pubmed protein nuccore nucleotide nucgss nucest structure
## genome assembly gcassembly genomeprj bioproject biosample
## biosystems blastdbinfo books cdd clinvar clone gap gapplus
## dbvar epigenomics gene gds geoprofiles homologene journals
## medgen mesh ncbisearch nlmcatalog omia omim pmc popset probe
## proteinclusters pcassay pccompound pcsubstance pubmedhealth
## seqannot snp sra taxonomy toolkit toolkitall toolkitbook
## unigene unists gencollUse einfo() to list the properties of each database. In this example, we return a result listing all of the available databases to query.
from textwrap import fill
from Bio import Entrez
Entrez.email = '[email protected]'
query = Entrez.einfo(db='pubmed')
result = Entrez.read(query)
keys = result.keys()
print keys
results = ' '.join(result['DbInfo'])
print fill(results, 60)
print result['DbInfo']['Count']## [u'DbInfo']
## Count LastUpdate MenuName Description LinkList FieldList
## DbName
## 22690440from Bio import Entrez
Entrez.email = '[email protected]'
query = Entrez.einfo(db='pubmed')
result = Entrez.read(query)
fieldlist = result['DbInfo']['FieldList']
for field in fieldlist:
print '%(Name)s, %(FullName)s, %(Description)s' % field## ALL, All Fields, All terms from all searchable fields
## UID, UID, Unique number assigned to publication
## FILT, Filter, Limits the records
## TITL, Title, Words in title of publication
## WORD, Text Word, Free text associated with publication
## MESH, MeSH Terms, Medical Subject Headings assigned to publication
## MAJR, MeSH Major Topic, MeSH terms of major importance to publication
## AUTH, Author, Author(s) of publication
## JOUR, Journal, Journal abbreviation of publication
## AFFL, Affiliation, Author's institutional affiliation and address
## ECNO, EC/RN Number, EC number for enzyme or CAS registry number
## SUBS, Supplementary Concept, CAS chemical name or MEDLINE Substance Name
## PDAT, Date - Publication, Date of publication
## EDAT, Date - Entrez, Date publication first accessible through Entrez
## VOL, Volume, Volume number of publication
## PAGE, Pagination, Page number(s) of publication
## PTYP, Publication Type, Type of publication (e.g., review)
## LANG, Language, Language of publication
## ISS, Issue, Issue number of publication
## SUBH, MeSH Subheading, Additional specificity for MeSH term
## SI, Secondary Source ID, Cross-reference from publication to other databases
## MHDA, Date - MeSH, Date publication was indexed with MeSH terms
## TIAB, Title/Abstract, Free text associated with Abstract/Title
## OTRM, Other Term, Other terms associated with publication
## INVR, Investigator, Investigator
## COLN, Author - Corporate, Corporate Author of publication
## CNTY, Place of Publication, Country of publication
## PAPX, Pharmacological Action, MeSH pharmacological action pre-explosions
## GRNT, Grant Number, NIH Grant Numbers
## MDAT, Date - Modification, Date of last modification
## CDAT, Date - Completion, Date of completion
## PID, Publisher ID, Publisher ID
## FAUT, Author - First, First Author of publication
## FULL, Author - Full, Full Author Name(s) of publication
## FINV, Investigator - Full, Full name of investigator
## TT, Transliterated Title, Words in transliterated title of publication
## LAUT, Author - Last, Last Author of publication
## PPDT, Print Publication Date, Date of print publication
## EPDT, Electronic Publication Date, Date of Electronic publication
## LID, Location ID, ELocation ID
## CRDT, Date - Create, Date publication first accessible through Entrez
## BOOK, Book, ID of the book that contains the document
## ED, Editor, Section's Editor
## ISBN, ISBN, ISBN
## PUBN, Publisher, Publisher's name
## AUCL, Author Cluster ID, Author Cluster ID
## EID, Extended PMID, Extended PMIDUse Entrez.esearch to actually search one of the databases, providing a search query term using the fields we identified using Entrez.einfo.
from Bio import Entrez
Entrez.email = '[email protected]'
query = Entrez.esearch(db='pubmed', \
term='Shirley M[AUTH] AND Johns Hopkins School of Medicine[AFFL]')
result = Entrez.read(query)
print result['Count']
print result['IdList']## 3
## ['23549483', '23185369', '22374857']Dang. Only three publications as of:
## 2013-04-24Use Entrez.efetch to return a result from an accession number. Here we're grabbing the FASTA format sequence from a GenBank accession.
from Bio import Entrez
Entrez.email = '[email protected]'
query = Entrez.efetch(db='nucleotide', \
id='312176363', \
rettype='gb', \
retmode='text')
sequence = query.read()
print sequence
with open('example.gb', 'w') as out:
out.write(sequence)## LOCUS NM_002072 2215 bp mRNA linear PRI 17-APR-2013
## DEFINITION Homo sapiens guanine nucleotide binding protein (G protein), q
## polypeptide (GNAQ), mRNA.
## ACCESSION NM_002072
## VERSION NM_002072.3 GI:312176363
## KEYWORDS .
## SOURCE Homo sapiens (human)
## ORGANISM Homo sapiens
## Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi;
## Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini;
## Catarrhini; Hominidae; Homo.
## REFERENCE 1 (bases 1 to 2215)
## AUTHORS Xiang,B., Zhang,G., Stefanini,L., Bergmeier,W., Gartner,T.K.,
## Whiteheart,S.W. and Li,Z.
## TITLE The Src family kinases and protein kinase C synergize to mediate
## Gq-dependent platelet activation
## JOURNAL J. Biol. Chem. 287 (49), 41277-41287 (2012)
## PUBMED 23066026
## REMARK GeneRIF: Src family kinases, PI3 kinase and protein kinase C
## synergize to mediate Gq-dependent platelet activation
## REFERENCE 2 (bases 1 to 2215)
## AUTHORS Daniels,A.B., Lee,J.E., MacConaill,L.E., Palescandolo,E., Van
## Hummelen,P., Adams,S.M., DeAngelis,M.M., Hahn,W.C., Gragoudas,E.S.,
## Harbour,J.W., Garraway,L.A. and Kim,I.K.
## TITLE High throughput mass spectrometry-based mutation profiling of
## primary uveal melanoma
## JOURNAL Invest. Ophthalmol. Vis. Sci. 53 (11), 6991-6996 (2012)
## PUBMED 22977135
## REMARK GeneRIF: The vast majority of primary large uveal melanomas harbor
## mutually-exclusive mutations in GNAQ or GNA11, but very rarely have
## the oncogenic mutations that are reported commonly in other
## cancers.
## Publication Status: Online-Only
## REFERENCE 3 (bases 1 to 2215)
## AUTHORS Drastichova,Z. and Novotny,J.
## TITLE Identification and subcellular localization of molecular complexes
## of Gq/11alpha protein in HEK293 cells
## JOURNAL Acta Biochim. Biophys. Sin. (Shanghai) 44 (8), 641-649 (2012)
## PUBMED 22710260
## REMARK GeneRIF: analysis of molecular complexes of Gq/11alpha protein in
## HEK293 cells
## REFERENCE 4 (bases 1 to 2215)
## AUTHORS Golebiewska,U., Guo,Y., Khalikaprasad,N., Zurawsky,C.,
## Yerramilli,V.S. and Scarlata,S.
## TITLE gamma-Synuclein interacts with phospholipase Cbeta2 to modulate G
## protein activation
## JOURNAL PLoS ONE 7 (8), E41067 (2012)
## PUBMED 22905097
## REMARK GeneRIF: gamma-synuclein has a role in promoting a more robust G
## protein Galphaq activation of PLCbeta2
## REFERENCE 5 (bases 1 to 2215)
## AUTHORS Svensson,L., Stanley,P., Willenbrock,F. and Hogg,N.
## TITLE The Galphaq/11 proteins contribute to T lymphocyte migration by
## promoting turnover of integrin LFA-1 through recycling
## JOURNAL PLoS ONE 7 (6), E38517 (2012)
## PUBMED 22701657
## REMARK GeneRIF: Vesicle-associated Galphaq/11 is required for the turnover
## of LFA-1 adhesion that is necessary for immune cell migration.
## REFERENCE 6 (bases 1 to 2215)
## AUTHORS Dong,Q., Shenker,A., Way,J., Haddad,B.R., Lin,K., Hughes,M.R.,
## McBride,O.W., Spiegel,A.M. and Battey,J.
## TITLE Molecular cloning of human G alpha q cDNA and chromosomal
## localization of the G alpha q gene (GNAQ) and a processed
## pseudogene
## JOURNAL Genomics 30 (3), 470-475 (1995)
## PUBMED 8825633
## REFERENCE 7 (bases 1 to 2215)
## AUTHORS Lesch,K.P. and Manji,H.K.
## TITLE Signal-transducing G proteins and antidepressant drugs: evidence
## for modulation of alpha subunit gene expression in rat brain
## JOURNAL Biol. Psychiatry 32 (7), 549-579 (1992)
## PUBMED 1333286
## REFERENCE 8 (bases 1 to 2215)
## AUTHORS Berstein,G., Blank,J.L., Jhon,D.Y., Exton,J.H., Rhee,S.G. and
## Ross,E.M.
## TITLE Phospholipase C-beta 1 is a GTPase-activating protein for Gq/11,
## its physiologic regulator
## JOURNAL Cell 70 (3), 411-418 (1992)
## PUBMED 1322796
## REFERENCE 9 (bases 1 to 2215)
## AUTHORS Shenker,A., Goldsmith,P., Unson,C.G. and Spiegel,A.M.
## TITLE The G protein coupled to the thromboxane A2 receptor in human
## platelets is a member of the novel Gq family
## JOURNAL J. Biol. Chem. 266 (14), 9309-9313 (1991)
## PUBMED 1851174
## REFERENCE 10 (bases 1 to 2215)
## AUTHORS Banno,Y., Yada,Y. and Nozawa,Y.
## TITLE Purification and characterization of membrane-bound phospholipase C
## specific for phosphoinositides from human platelets
## JOURNAL J. Biol. Chem. 263 (23), 11459-11465 (1988)
## PUBMED 2841328
## COMMENT REVIEWED REFSEQ: This record has been curated by NCBI staff. The
## reference sequence was derived from DB474490.1, BC069520.1,
## BC057777.1 and CV570171.1.
## This sequence is a reference standard in the RefSeqGene project.
## On Nov 17, 2010 this sequence version replaced gi:40254461.
##
## Summary: This locus encodes a guanine nucleotide-binding protein.
## The encoded protein, an alpha subunit in the Gq class, couples a
## seven-transmembrane domain receptor to activation of phospolipase
## C-beta. Mutations at this locus have been associated with problems
## in platelet activation and aggregation. A related pseudogene exists
## on chromosome 2.[provided by RefSeq, Nov 2010].
##
## Publication Note: This RefSeq record includes a subset of the
## publications that are available for this gene. Please see the Gene
## record to access additional publications.
##
## ##Evidence-Data-START##
## Transcript exon combination :: BC057777.1, U43083.1 [ECO:0000332]
## RNAseq introns :: single sample supports all introns
## ERS025084 [ECO:0000348]
## ##Evidence-Data-END##
## PRIMARY REFSEQ_SPAN PRIMARY_IDENTIFIER PRIMARY_SPAN COMP
## 1-1 DB474490.1 1-1
## 2-1361 BC069520.1 1-1360
## 1362-2098 BC057777.1 1335-2071
## 2099-2099 CV570171.1 220-220
## 2100-2215 BC057777.1 2073-2188
## FEATURES Location/Qualifiers
## source 1..2215
## /organism="Homo sapiens"
## /mol_type="mRNA"
## /db_xref="taxon:9606"
## /chromosome="9"
## /map="9q21"
## gene 1..2215
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /note="guanine nucleotide binding protein (G protein), q
## polypeptide"
## /db_xref="GeneID:2776"
## /db_xref="HGNC:4390"
## /db_xref="MIM:600998"
## exon 1..204
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=1
## STS 51..1167
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /db_xref="UniSTS:482300"
## CDS 69..1148
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /note="guanine nucleotide-binding protein alpha-q"
## /codon_start=1
## /product="guanine nucleotide-binding protein G(q) subunit
## alpha"
## /protein_id="NP_002063.2"
## /db_xref="GI:40254462"
## /db_xref="CCDS:CCDS6658.1"
## /db_xref="GeneID:2776"
## /db_xref="HGNC:4390"
## /db_xref="MIM:600998"
## /translation="MTLESIMACCLSEEAKEARRINDEIERQLRRDKRDARRELKLLL
## LGTGESGKSTFIKQMRIIHGSGYSDEDKRGFTKLVYQNIFTAMQAMIRAMDTLKIPYK
## YEHNKAHAQLVREVDVEKVSAFENPYVDAIKSLWNDPGIQECYDRRREYQLSDSTKYY
## LNDLDRVADPAYLPTQQDVLRVRVPTTGIIEYPFDLQSVIFRMVDVGGQRSERRKWIH
## CFENVTSIMFLVALSEYDQVLVESDNENRMEESKALFRTIITYPWFQNSSVILFLNKK
## DLLEEKIMYSHLVDYFPEYDGPQRDAQAAREFILKMFVDLNPDSDKIIYSHFTCATDT
## ENIRFVFAAVKDTILQLNLKEYNLV"
## exon 205..389
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=2
## exon 390..544
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=3
## exon 545..673
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=4
## exon 674..803
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=5
## exon 804..957
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=6
## exon 958..2198
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /inference="alignment:Splign:1.39.8"
## /number=7
## STS 1596..1815
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /standard_name="A002Y36"
## /db_xref="UniSTS:59945"
## STS 1977..2180
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## /standard_name="SHGC-53345"
## /db_xref="UniSTS:63543"
## polyA_signal 2168..2173
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## polyA_site 2198
## /gene="GNAQ"
## /gene_synonym="G-ALPHA-q; GAQ"
## ORIGIN
## 1 gggagggtgt gtgtgcgcgc tgtgagcagg gggtgccggc ggggctgcag cggaggcact
## 61 ttggaagaat gactctggag tccatcatgg cgtgctgcct gagcgaggag gccaaggaag
## 121 cccggcggat caacgacgag atcgagcggc agctccgcag ggacaagcgg gacgcccgcc
## 181 gggagctcaa gctgctgctg ctcgggacag gagagagtgg caagagtacg tttatcaagc
## 241 agatgagaat catccatggg tcaggatact ctgatgaaga taaaaggggc ttcaccaagc
## 301 tggtgtatca gaacatcttc acggccatgc aggccatgat cagagccatg gacacactca
## 361 agatcccata caagtatgag cacaataagg ctcatgcaca attagttcga gaagttgatg
## 421 tggagaaggt gtctgctttt gagaatccat atgtagatgc aataaagagt ttatggaatg
## 481 atcctggaat ccaggaatgc tatgatagac gacgagaata tcaattatct gactctacca
## 541 aatactatct taatgacttg gaccgcgtag ctgaccctgc ctacctgcct acgcaacaag
## 601 atgtgcttag agttcgagtc cccaccacag ggatcatcga ataccccttt gacttacaaa
## 661 gtgtcatttt cagaatggtc gatgtagggg gccaaaggtc agagagaaga aaatggatac
## 721 actgctttga aaatgtcacc tctatcatgt ttctagtagc gcttagtgaa tatgatcaag
## 781 ttctcgtgga gtcagacaat gagaaccgaa tggaggaaag caaggctctc tttagaacaa
## 841 ttatcacata cccctggttc cagaactcct cggttattct gttcttaaac aagaaagatc
## 901 ttctagagga gaaaatcatg tattcccatc tagtcgacta cttcccagaa tatgatggac
## 961 cccagagaga tgcccaggca gcccgagaat tcattctgaa gatgttcgtg gacctgaacc
## 1021 cagacagtga caaaattatc tactcccact tcacgtgcgc cacagacacc gagaatatcc
## 1081 gctttgtctt tgctgccgtc aaggacacca tcctccagtt gaacctgaag gagtacaatc
## 1141 tggtctaatt gtgcctccta gacacccgcc ctgcccttcc ctggtgggct attgaagata
## 1201 cacaagaggg actgtatttc tgtggaaaac aatttgcata atactaattt attgccgtcc
## 1261 tggactctgt gtgagcgtgt ccacagagtt tgtagtaaat attatgattt tatttaaact
## 1321 attcagagga aaaacagagg atgctgaagt acagtcccag cacatttcct ctctatcttt
## 1381 tttttaggca aaaccttgtg actcagtgta ttttaaattc tcagtcatgc actcacaaag
## 1441 ataagacttg tttctttctg tctctctctc tttttctttt ctatggagca aaacaaagct
## 1501 gatttccctt ttttcttccc ccgctaattc atacctccct cctgatgttt ttcccaggtt
## 1561 acaatggcct ttatcctagt tccattcttg gtcaagtttt tctctcaaat gatacagtca
## 1621 ggacacatcg ttcgatttaa gccatcatca gcttaattta agtttgtagt ttttgctgaa
## 1681 ggattatatg tattaatact tacggtttta aatgtgttgc tttggataca cacatagttt
## 1741 cttttttaat agaatatact gtcttgtctc actttggact gggacagtgg atgcccatct
## 1801 aaaagttaag tgtcatttct tttagatgtt taccttcagc catagcttga ttgctcagag
## 1861 aaatatgcag aaggcaggat caaagacaca caggagtcct ttcttttgaa atgccacgtg
## 1921 ccattgtctt tcctcccttc tttgcttctt tttcttaccc tctctttcaa ttgcagatgc
## 1981 caaaaaagat gccaacagac actacattac cctaatggct gctacccaga acctttttat
## 2041 aggttgttct taattttttt gttgttgttg ttcaagcttt tcctttcttt tttttcttgg
## 2101 tgtttgggcc acgattttaa aatgactttt attatgggta tgtgttgcca aagctggctt
## 2161 tttgtcaaat aaaatgaata cgaacttaaa aaataaaaaa aaaaaaaaaa aaaaa
## //from Bio import SeqIO
count = SeqIO.convert('example.gb', 'genbank', 'example.fasta', 'fasta')
print 'converted {0} records'.format(count)## converted 1 recordsfrom textwrap import fill
from Bio import SeqIO
with open('example.fasta', 'rU') as infile:
sequence = SeqIO.read(infile, 'fasta')
print sequence.id
print fill(str(sequence.seq))## NM_002072.3
## GGGAGGGTGTGTGTGCGCGCTGTGAGCAGGGGGTGCCGGCGGGGCTGCAGCGGAGGCACTTTGGAAGAAT
## GACTCTGGAGTCCATCATGGCGTGCTGCCTGAGCGAGGAGGCCAAGGAAGCCCGGCGGATCAACGACGAG
## ATCGAGCGGCAGCTCCGCAGGGACAAGCGGGACGCCCGCCGGGAGCTCAAGCTGCTGCTGCTCGGGACAG
## GAGAGAGTGGCAAGAGTACGTTTATCAAGCAGATGAGAATCATCCATGGGTCAGGATACTCTGATGAAGA
## TAAAAGGGGCTTCACCAAGCTGGTGTATCAGAACATCTTCACGGCCATGCAGGCCATGATCAGAGCCATG
## GACACACTCAAGATCCCATACAAGTATGAGCACAATAAGGCTCATGCACAATTAGTTCGAGAAGTTGATG
## TGGAGAAGGTGTCTGCTTTTGAGAATCCATATGTAGATGCAATAAAGAGTTTATGGAATGATCCTGGAAT
## CCAGGAATGCTATGATAGACGACGAGAATATCAATTATCTGACTCTACCAAATACTATCTTAATGACTTG
## GACCGCGTAGCTGACCCTGCCTACCTGCCTACGCAACAAGATGTGCTTAGAGTTCGAGTCCCCACCACAG
## GGATCATCGAATACCCCTTTGACTTACAAAGTGTCATTTTCAGAATGGTCGATGTAGGGGGCCAAAGGTC
## AGAGAGAAGAAAATGGATACACTGCTTTGAAAATGTCACCTCTATCATGTTTCTAGTAGCGCTTAGTGAA
## TATGATCAAGTTCTCGTGGAGTCAGACAATGAGAACCGAATGGAGGAAAGCAAGGCTCTCTTTAGAACAA
## TTATCACATACCCCTGGTTCCAGAACTCCTCGGTTATTCTGTTCTTAAACAAGAAAGATCTTCTAGAGGA
## GAAAATCATGTATTCCCATCTAGTCGACTACTTCCCAGAATATGATGGACCCCAGAGAGATGCCCAGGCA
## GCCCGAGAATTCATTCTGAAGATGTTCGTGGACCTGAACCCAGACAGTGACAAAATTATCTACTCCCACT
## TCACGTGCGCCACAGACACCGAGAATATCCGCTTTGTCTTTGCTGCCGTCAAGGACACCATCCTCCAGTT
## GAACCTGAAGGAGTACAATCTGGTCTAATTGTGCCTCCTAGACACCCGCCCTGCCCTTCCCTGGTGGGCT
## ATTGAAGATACACAAGAGGGACTGTATTTCTGTGGAAAACAATTTGCATAATACTAATTTATTGCCGTCC
## TGGACTCTGTGTGAGCGTGTCCACAGAGTTTGTAGTAAATATTATGATTTTATTTAAACTATTCAGAGGA
## AAAACAGAGGATGCTGAAGTACAGTCCCAGCACATTTCCTCTCTATCTTTTTTTTAGGCAAAACCTTGTG
## ACTCAGTGTATTTTAAATTCTCAGTCATGCACTCACAAAGATAAGACTTGTTTCTTTCTGTCTCTCTCTC
## TTTTTCTTTTCTATGGAGCAAAACAAAGCTGATTTCCCTTTTTTCTTCCCCCGCTAATTCATACCTCCCT
## CCTGATGTTTTTCCCAGGTTACAATGGCCTTTATCCTAGTTCCATTCTTGGTCAAGTTTTTCTCTCAAAT
## GATACAGTCAGGACACATCGTTCGATTTAAGCCATCATCAGCTTAATTTAAGTTTGTAGTTTTTGCTGAA
## GGATTATATGTATTAATACTTACGGTTTTAAATGTGTTGCTTTGGATACACACATAGTTTCTTTTTTAAT
## AGAATATACTGTCTTGTCTCACTTTGGACTGGGACAGTGGATGCCCATCTAAAAGTTAAGTGTCATTTCT
## TTTAGATGTTTACCTTCAGCCATAGCTTGATTGCTCAGAGAAATATGCAGAAGGCAGGATCAAAGACACA
## CAGGAGTCCTTTCTTTTGAAATGCCACGTGCCATTGTCTTTCCTCCCTTCTTTGCTTCTTTTTCTTACCC
## TCTCTTTCAATTGCAGATGCCAAAAAAGATGCCAACAGACACTACATTACCCTAATGGCTGCTACCCAGA
## ACCTTTTTATAGGTTGTTCTTAATTTTTTTGTTGTTGTTGTTCAAGCTTTTCCTTTCTTTTTTTTCTTGG
## TGTTTGGGCCACGATTTTAAAATGACTTTTATTATGGGTATGTGTTGCCAAAGCTGGCTTTTTGTCAAAT
## AAAATGAATACGAACTTAAAAAATAAAAAAAAAAAAAAAAAAAAAfrom Bio import SeqIO
with open('example.fasta', 'rU') as infile:
sequence = SeqIO.read(infile, 'fasta')
sequence.seq = sequence.seq.translate()
print sequence.seq[:36]
with open('example_prot.fasta', 'w') as out:
SeqIO.write(sequence, out, 'fasta')## GRVCVRAVSRGCRRGCSGGTLEE*LWSPSWRAA*ARReading our example FASTA file, translating it to protein, then writing a new FASTA file.
There's no homework this week. Let's use this extra time to discuss your final project proposals and go through any issues with last week's homework.