In o me Técnico / Technical Repo
Re . #:
P oS-TR-XXXX
Ti le:
So wa e o he Gene ic Analysis Domain
Au ho (s):
Osca Pas o , F ancisco Val e de, and Ma ia Jose Villanue a
Co esponding
au ho (s):
opas o @dsic.up .es
al [email p o ec ed].es
m illanue [email p o ec ed].es
Documen e sion numbe :
Final e sion:
Pages:
Release da e:
Key wo ds:
So wa e o he Gene ic Analysis Domain
Osca Pas o , F ancisco Val e de, and Ma ia Jose Villanue a
1
Con en
So wa e ools ............................................................................................................................... 3
Gene ic Analysis ........................................................................................................................ 3
In oduc ion .......................................................................................................................... 3
Sequenche ............................................................................................................................ 4
SeqScape ............................................................................................................................... 6
Codon Code Aligne ............................................................................................................... 7
Mu a ion Su eyo ................................................................................................................ 8
Polyph ed ............................................................................................................................ 10
InSnp .................................................................................................................................... 12
Compa ison ......................................................................................................................... 13
Conclusion ........................................................................................................................... 15
Re e ences ........................................................................................................................... 15
Genomic Analysis .................................................................................................................... 16
In oduc ion ........................................................................................................................ 16
Re ie ing and anno a ing da a manually ........................................................................... 16
Bioma ................................................................................................................................ 17
VCFTools .............................................................................................................................. 18
Anno a ............................................................................................................................... 19
VEP ...................................................................................................................................... 20
SamTools ............................................................................................................................. 22
SNPE .................................................................................................................................. 22
GATK .................................................................................................................................... 24
Pipeline De elopmen En i onmen s.......................................................................................... 24
Biopy hon, BioPe l, Bioja a, Bio* ............................................................................................ 24
Ta e na .................................................................................................................................... 24
Repo .................................................................................................................................. 26
Wo k low 1: Diagen (Disease diagnosis BREAST Cance om a ia ion de ec ion) ........... 27
Galaxy ...................................................................................................................................... 32
Repo .................................................................................................................................. 33
Wo k low 1: Diagen (Disease diagnosis BREAST Cance om a ia ion de ec ion) ........... 33
EBioFlow .................................................................................................................................. 35
Repo .................................................................................................................................. 35
BSIS .......................................................................................................................................... 36
Repo .................................................................................................................................. 36
2
1 In oduc ion
The main pu pose ha p omo ed he esea ch o he exis en comme cial alignmen ools is o
lea n he die en unc ionali y ha his kind o ools a e oe ing. In o de o accomplish his
a ge will be necessa y o ob ain he die ences be ween hem and o nd he s onges poin s
and deciencies o each one; bu mos ly which unc ionali y may be missing in all o hem.
Ha ing a be e comp ehension abou wha has been al eady de eloped, wha is ac ually
being used and wha a e he needs o he use s o ha ools, could cla i y i he objec i es o
he p esen p ojec o he Genoma g oup could be a eal con ibu ion o he eld.
The s a poin o his p ojec had as a main objec i e o c ea e a so wa e ha mee s
he equi emen s o he biologis s when pe o ming a DNA analysis om a pa ien sample
sea ching o mu a ions ha may cause some disease. As a consequence he so wa e we in end
o de elop will y o co e all he ac i i ies in ol ed on he p ocess in o de o p o ide a
comple e unc ionali y ha his expe s claim all he comme cial ools lack.
Fi s o all i is essen ial o es ablish and delimi all he ac i i ies ha he men ioned p ocess
comp ises. Howe e , inside his collec ion o ac i i ies we will nd ha some o hem canno be
con olled by ou so wa e o canno be au oma ed by any so wa e; ac i i ies like he sequencing
he DNA sample and decision-making he co ec ness o he basecalling espec i ely.
The  s s ep while analyzing a sample o a pa ien is o ex ac a e y small agmen
and pe o m he sequencing. This ac i i y is done by he sequence machine and i is no an
ac i i y ha he so wa e will ake in o accoun . Howe e , he ou pu o his ac i i y, les in
.ABI (Applied Biosys ems Inc.) o ma , will be he inpu o he so wa e. The sequences, he
samples and he e e ence can be exp essed in his o ma bu also in he o ma s .SEQ, .GB
(GeneBank) o FASTA. Be o e ge ing deepe in de ails abou he analysis decomposi ion, i
is impo an o emphasize ha no mally he analysis is es ic ed in only one gene a a ime.
Mo eo e , due biological dicul ies on he sequencing p ocess he DNA sample is sequenced in
pieces abou 800 bp (called con igs). Fo his eason, some imes i is sequenced only he egion
o in e es o he analysis, bu a leas i is sequenced wice (one e e se and o he o wa d
di ec ion) in o de o ensu e he accu acy o he esul . F om bo h sequences i is ob ained a
consensus sequence.
DNA sequence analysis ha he new so wa e will ha e o manage consis s on se e al phases:
1. Assembly each con ig in o i s co ec posi ion: Independen ly o he numbe o con igs
sequenced each one has o be loca ed p ope ly.
2. Clean he esul s om he sequence machine: Using he ool and i s biological knowledge
biologis s pe o m manually a cleaning on he basecalling o he con igs. Expe s ha e
o decide, aided wi h he e e ence sequence, i he basecalling o each sample and he
consensus sequence eec s he e aci y o he o iginal sample, co ec ing hus all he
e o s ha he sequence machine has in oduced. The ool will ha e o pe o m as well a
d opping o he beginnings and ends o sequenced con igs ha no mally a e no use ul o
analysis because o he quali y o he signal. This las cleaning is e e ed as  imming.
3. Compa e consensus wi h he e e ence sequence sea ching o a ia ions: Each die ence
in he consensus sequence espec he e e ence sequence will be conside ed as a a ia ion.
The ool will ha e o sea ch o inse ions, dele ions and indels. He e ozygosis ( wo die -
en signals codi ying wo bases in he same posi ion) is also impo an o he de ec ion o
a ia ions.
4. Sea ch wha does mean each a ia ion: One a ia ion may be p o oking some pheno ype
depending on he base changes and he posi ion o hem. Fo each possible a ia ion-
pheno ype pai i would be c ucial o know he  s publica ion ha suppo s he nding.
3
In his e iew he e has been analyzed he ollowing ools: Sequenche , SeqScape, Mu a ion
Su eyo , CodonCodeAligne , Polyph ed and InSNP. The ollowing 6 sec ions will analyze he
essen ials o each ool conce ning he ins alla ion and use o he ool, he a ia ions de ec ed
in compa ison wi h he concep ual model, he possible connec ion wi h bibliog aphy and some
o he in e es ing ea u es. The las wo sec ions will con ain he compa ison be ween ools and
he conclusion and ecommenda ions o he au ho o his e iew. In he anex i is explained a
 s con ac abou he beha iou o all ools unde he same condi ions.
2 Sequenche
Sequenche is a p op ie a y So wa e o Gene Codes Co po a ion [3] ha oe s he possibili y o
in oduce sequences, pe o m assemblies, he explo a ion and edi ion o sequences in connec ion
wi h i s e e ence and nally he de ec ion o he a ia ions ha die om his e e ence.
Gene Codes Co po a ion oe s a Demo e sion wi h es ic ed unc ionali y and he possi-
bili y o pu chase all he so wa e wi h a license ha once ob ained i ne e expi es.
I is a ailable o MAC and Windows and i is possible o ins all i on a single compu e o
ins all he ne wo k-enabled e sion, wi h a se e and i s clien s.
In o de o help he use s o amilia ize wi h he ool he e is a collec ion o easy and comple e
u o ials on i s webpage (
h p://www.genecodes.com
).
Figu e 1: Die en windows on Sequenche
Sequenche p o ides die en iews o he da a (See Figu e 1): les assembly (le and up
window), p o iding he lis o les con aining he sequences assembled in he same con ig; dis-
ibu ion o con igs a ound he gene ( igh and up window), displaying he e e ence sequence
4
(blue line) and all he con igs si ua ed in i s posi ion (g een lines o o wa d and ed lines o
e e se); disco e ed a ia ions epo ( igh and down window), desc ibing he ound changes
in se e al elds inside a able; bases o each sequence (middle window), si ua ing each sequence
sequen ially and he ch oma og ams o he samples (le and down window). When a a i-
ion is double clicked he bases in ol ed a e highligh ed in he bases ep esen a ion and in he
ch oma og ams.
2.1 Types o de ec ed mu a ions
Inse ions
,
dele ions
and
indels
in homozygosis a e de ec ed and all o hem a e exp essed base
by base, as a ia ions o leng h 1. I , o example, he e is an inse ion o 3 nucleo ides, he
a ia ion is exp essed as 3 inse ions o 1 nucleo ide.
Howe e ,
inse ions
and
dele ions
in he e ozygosis canno be de ec ed. When hey occu ,
he ch oma og ams o he con igs seem a mix o wo signals ha should be almos iden ical bu
now appea shi ed se e al posi ions. This so wa e de ec s
indels
in he e ozygosis because his
shi does no happen.
In o de o iden i y a he e ozygosis base ( wo die en alues in he same posi ion) and die -
en ia e i om a homozygosis one wi h noise, a sensibili y alue can be xed. This sensibili y is
exp essed in pe cen age and when he p esence o wo signican o e lapping uo escence peaks
occu s in a conc e e posi ion, i ep esen s he ela ion be ween he signal o lowe in ensi y
espec he o he .
2.2 Re u n o ma s
All he sequences included he consensus sequence (once all con igs a e assembled and cleaned)
can be impo ed and expo ed in se e al o ma s: in plain ex as ASCII plain o ma o un o -
ma ed (bases only); in specialis and legacy o ma s like AFDIL, Genen ech, IG and S ide ;
in da abases o ma s as GenBank, NBRF and EMBL; in commonly used o ma s like FASTA
(no mal and conca ena ed) and GCG; in phylogenic p og am o ma s like NEXUS/PAUP (in-
e lie ed and sequen ial), Phylip (no mal, 3 and 4) and e en in s anda d ch oma og am o ma s
like SCF (2.0 and 3.0). Fo some o hose o ma s can be indica ed as well he ollowing op ions:
expo wi h uppe /lowe case, selec he o ien a ion o he sequence and lea e o emo e he
gaps.
When he e a e a ia ions in he sample hey a e epo ed in a able. The epo ga he s he
da a abou posi ion and alue o he base on he e e ence, alue o he base on he sample and
numbe o die en samples ha ha e his a ia ion. This a ia ion able can only be expo ed
in TXT and PDF.
2.3 Connec ion wi h bibliog aphy
I is no possible o make any connec ion wi h da a abou a ia ions nei he in he e e ence
sequence no he a ia ions able.
2.4 O he in e es ing ea u es
Du ing he impo and assembly o con igs Sequenche oe s he op ion o classi y he samples
au oma ically only using he name o he le. The samples can be di ided in o wa d and e e se
and a he same ime in die en specimen. I also allows he simul aneous analysis o se e al
pa ien s and all he die ences among hem would be eec ed in he epo s.
5

Figu e 2: SeqScape window
3 SeqScape
SeqScape is a p op ie a y So wa e o Applied Biosys ems [1] wi hou demo a ailable. I s unc-
ionali y goes om impo ing, assembling, edi ing and analyzing samples sea ching o a ia ions
un il compa ison o he segmen s sea ching o pa e ns.
I is a ailable only o Windows and can be congu ed a manage access secu i y con ol wi h
he use o logins and passwo ds allowing h ee die en pe mission p oles. In addi ion o he
secu i y, i can p epa e documen a ion o u u e audi ion, ha is, i is possible o p og am
some e en s o be eco ded.
SeqScape changes he me hodology o ope a ion om ac ions-objec i e, whe e o each ob-
jec i e ha he use wan o achie e has o pe o m se e al conc e e ac ions, o congu a ion-
objec i es, whe e i is necessa y o congu e all he needed op ions be o e pe o ming any ac ion
and once all congu e all o he objec i es a e execu ed oge he . Then i is necessa y o lea n
how o congu e he p ojec s. I has se e al impo an congu a ion phases ha nally will
lead o one bu on ha execu es all he unc ionali y pe o med in one s ep. Then he use will
sea ch o he da a ha wan s o know in each momen (See Figu e 2).
3.1 Types o de ec ed mu a ions
Inse ions
,
dele ions
and
indels
in homozygosis a e de ec ed and SeqScape claims ha i also
de ec s
inse ions
and
dele ions
in he e ozygosis since he e sion 2.5. Howe e he e sion
p o ide by IMEGEN is he 2.0 and his kind o mu a ions can no be loca ed. In e sion 2.0
when hey occu hese con igs a e no included in he assembled ones. S ill
indels
in he e ozygosis
a e de ec ed and i is possible o x a sensibili y alue like was possible in Sequenche .
6
3.2 Re u n o ma s
The consensus sequence can be expo ed as FASTA, SEQ and QUAL o ma . I has he op ion
o eplacing unknown bases wi h a desi ed symbol, in case o gaps o bad quali y bases.
The o ma s o expo ing he epo s abou he a ia ions a e TXT, HTML, PDF o XML.
The expo ed les will con ain some analysis a iables o each specimen (success o analysis,
specimen sco e, mu a ions ound) and in o ma ion abou a ia ions (sample, posi ion, size).
3.3 Connec ion wi h bibliog aphy
I is possible o add known a ia ions o he e e ence sequence in o de o he SeqScape o be
able o iden i y hem as known o unknown. I is also possible o au oma e his in oduc ion o
da a by c ea ing an XLS le ha will con ain all he elds needed o desc ibe each a ia ion.
The a ia ions in oduced can be classi y as inse ions, dele ions, o basechange, indica ing he
ROI, he posi ion, he e e ence base(s), he a ian base(s) and i s desc ip ion.
3.4 In e es ing ea u es
In addi ion o all he secu i y con ol a ound he access i is as well possible o expo da a
signed elec onically.
When he e e ence sequence has been impo ed om GeneBank, SeqScape c ea es egions
o in e es and laye s ha sepa a es he die en exons au oma ically.
In addi ion i exis s he possibili y o c ea e lib a ies sea ching o pa e ns. A lib a y is a
collec ion o se e al segmen s (alleles, geno ypes and haplo ypes) wi h a xed leng h o a egion
o in e es (ROI). The consensus sequence is compa ed wi h he lib a y sea ching o ma ches
on any segmen .
4 CodonCodeAligne
4.1 Types o de ec ed a ia ions
CodonCodeAligne is p opie a y so wa e om CodonCode Co po a ion [2] used o sequence
assembly, con ig edi ing, and mu a ion de ec ion.
CodonCode Co po a ionI makes a ailable a 30 day-demo ha can be yed unde Windows
and Mac.
I s in e ace oe s a isualiza ion (See Figu e 3) o he assembled con igs and he e e -
ence sequence simul aneously wi h he codi ying egions o he gene o (depending o he use
p e e ences) he isualiza ion o he die ences be ween bases.
4.2 Re u n o ma s
CodonCodeAligne can expo he samples wi h he o ma s FASTA and SCF bu he consensus
sequence only in FASTA (No mal bases o haplo ypes). Fo bo h o hem exis s he op ions o
including gaps, append he commen s, eplace p oblem cha ac e s in names and w i e FASTA
quali y les. I he a ge sequences a e he disposi ion inside he whole assembly, i is possible
o sa e i in o an ACE p ojec , a NEXUS/PAUD (in e lea ed and sequen ial) o a Phylip
(in e lea ed and sequen ial) o ma .
The a ia ions ound a e ga he ed in a epo ha can be expo ed in TXT and PDF. This
epo will con ain he ea u e, he sou ce, he ype o sou ce whe e he mu a ion has been
ounded, he pa en con ig, he s a , he end, and he con en .
7
Figu e 3: CodonCode Aligne
4.3 Connec ion wi h bibliog aphy
The e is no op ion o add any in o ma ion abou a ia ions, bibliog aphy o pheno ypes.
4.4 In e es ing Fea u es
G aphical in e ace achie es he comple e na iga ion along sequences, edi ion o sequences al-
lowing all ypes o ope a ions and isualiza ion o all equi ed da a simul aneously.
Fu he mo e he e e ence sequence can be downloaded au oma ically om GeneBank by
only indica ing he accession numbe .
5 Mu a ion Su eyo
Mu a ion Su eyo is a p op ie a y So wa e o So Gene ics [8] ha compa es se e al samples
wi h a e e ence sequence sea ching o a ia ions.
So Gene ics oe s a Demo e sion, he possibili y o ying a ully unc ionali y 30 days ail
and a ee aining o gene ic expe s ha will be he use s o he ool.
The so wa e is a ailable o Windows (NT, 2000, XP, Vis a and 7) and MAC (al hough i
is necessa y a le con e e o PC les o hose les ha will be inpu o he so wa e) and i is
possible o ins all i on a single compu e o ins all he ne wo k-enabled e sion (wi h a se e
and i s clien s).
The use -in e ace has been designed ollowing Mic oso pla o m design guides o be a use -
iendly so wa e (See Figu e 4). Die en iews compose he s uc u e o he in e ace: he ex
iew ( igh ) and he g aphical iew (le ), na iga ing easily along hem.
The e is an addi ional so wa e called Mu a ion Su eyo Au o un ha ha pe mi s he
una ended analysis o mul iple p ojec s and a Log File Edi o o congu e he pa ame e s o
he hole una ended p ocess.
8
Figu e 4: Mu a ion Su eyo iews
5.1 Types o de ec ed mu a ions
Inse ions
,
dele ions
and
indels
homozygosis a e de ec ed and all o hem a e exp essed wi h
i s co ec leng h. A e also de ec ed
indels
in he e ozygosis and a sensibili y alue, now called
d opping ac o , can be xed o nd he he e ozygosis bases.
Rega ding
inse ions
and
dele ions
in he e ozygosis, i has he abili y o iden i y he e ozy-
gous indels down o 5% o he p ima y peak. The sample is decomposed in o wo die en
samples ep esen ing bo h s ands (one om he a he , one o he mo he ). Then one o hose
is shi ed and displayed below acco ding he mu a ion de ec ed in o de o ma ch he e e ence
sequence. Some imes due he na u e o his kind o a ia ions is dicul o he so wa e o
iden i y au oma ically he s a poin . Fo his eason, i allows he expe o e i y i he
ob ained posi ion is he co ec one.
5.2 Re u n o ma s
I is no possible o expo he consensus sequence because his ool does no c ea e any due
he ac i sea ches o a ia ions di ec ly in each o wa d and e e se sample sepa a ely.
Pe con a, he e a e a lo o possibili ies o expo and congu e epo s abou he a ia ions
ound. An s anda d epo could con ain he ex a da a: numbe , sample and e e ence le
names, di ec ion o he GeneBank e e ence sequence, eading ame, s a and end o he
sample, quali y, a mu a ion code o each one ound and se e al mo e in o ma ion. All his
in o ma ion can be expo ed in TXT, XLS, HTML o XML.
The able wi h he mu a ions appea ed on each sample can be expo ed only in a TXT le.
9
Genomic Analysis
In oduc ion
The genomic analysis add esses h ee s eps: 1) i s akes as a base a comple e VCF (con aining
one indi idual o iple s); 2) hen adds all ele an in o ma ion o he VCF ile (anno a ion
p ocess) and inally 3) depending on he analysis he VCF anno a ed is il e ed acco ding some
c i e ia:
The ele an in o ma ion hey wan o anno a e is: 1) S uc u al Ids (no mally om ENSEMBL)
(and hg s no a ion); 2) Snp ids ( s om dbSNP); 3) Popula ion Allele equency ( om 1000G da a
o Exome Sequencing P ojec ); 4) Co e age; 5) Pai E ec p edic ion-T ansc ip (Algo i hm: SIFT,
POLYPHEN o combined, and ansc ip s: all ansc ip s ha a e a ec ed, ansc ip o he issue
whe e i exp esses he mos damaging consequence, mos impo an ansc ip o disease); and
6) combined analysis wi h iple s (Phasing and Combined he e ocigosis o se e al a ia ions
ha a ec a gene le el).
And he di e en il e ing c i e ia a e: 1) S uc u al: Ch , Gene, Type o a ia ion (ins, del, indel,
MNPs, SNPs and CNVs) ype o polymo phism (homozygosis, he e ozygosis, combined
he e ozygosis, de-no o); 2) Posi ion ange; 3) Allele equency; 4) Co e age; 5) T ansc ip ; 6)
Loss o unc ion.
Anno a ions can be classi ied in h ee ca ego ies: 1) s uc u al in o ma ion o he a ia ion, such
as gene, exon, ansc ip s; 2) da abase in o ma ion, such as he s om dbSNP; and 3) e ec s in
di e en ansc ip s, including sco es o SIFT and POLYPHEN and he hg s no a ion.
In o de o e ie e hese da a and anno a e he a ia ion ile, se e al op ions a e a ailable:
• Download da abase ile manually –in GVF, GFF, BED o ma s- o using bioma (in TXT
ile) and anno a e he ile wi h his da a a e wa ds.
• Run he sui able commands o he sui es Anno a , SnpE and/o VEP.
Re ie ing and anno a ing da a manually
A1. The GFF Fo ma
The GFF o ma (Gene ic Fea u e Fo ma Ve sion) speci ies genomic/gene ic ea u es (genes,
exons, CDS, e c.) and hei p ope ies (name, sequence, dbx e , e c.) using some p ede ined
ields and ules and also on ology e ms. I is widely used by he communi y o anno a e
a ia ions wi h s uc u al da a.
Example (cu en e sion 3):
0 ##g - e sion 3
1 ##sequence- egion c g123 1 1497228
2 c g123 . gene 1000 9000 . + . ID=gene00001;Name=EDEN
3 c g123 . TF_binding_si e 1000 1012 . + . ID= bs00001;Pa en =gene00001
4 c g123 . mRNA 1050 9000 . + . ID=mRNA00001;Pa en =gene00001;
5 c g123 . mRNA 1050 9000 . + . ID=mRNA00002;Pa en =gene00001;
6 c g123 . mRNA 1300 9000 . + . ID=mRNA00003;Pa en =gene00001;
7 c g123 . exon 1300 1500 . + . ID=exon00001;Pa en =mRNA00003
8 c g123 . exon 1050 1500 . + . ID=exon00002;Pa en =mRNA00001,mRNA00002
9 c g123 . exon 3000 3902 . + . ID=exon00003;Pa en =mRNA00001,mRNA00003
10 c g123 . exon 5000 5500 . + . ID=exon00004;Pa en =mRNA00001,mRNA00002,mRNA00003
11 c g123 . exon 7000 9000 . + . ID=exon00005;Pa en =mRNA00001,mRNA00002,mRNA00003
16

I can be downloaded om he NCBI p: e _GRCh37.p13_ op_le el.g 3.gz (No e: I choose his
ile obse ing he name and assuming ha i is he one ha con ains all he in o ma ion)
A2. The BED o ma
The BED o ma is de ined by he USCS also o desc ibe anno a ions.
b owse posi ion ch 7:127471196-127495720
b owse hide all
ack name="I emRGBDemo" desc ip ion="I em RGB demons a ion" isibili y=2
i emRgb="On"
ch 7 127471196 127472363 Pos1 0 + 127471196 127472363 255,0,0
ch 7 127472363 127473530 Pos2 0 + 127472363 127473530 255,0,0
ch 7 127473530 127474697 Pos3 0 + 127473530 127474697 255,0,0
ch 7 127474697 127475864 Pos4 0 + 127474697 127475864 255,0,0
ch 7 127475864 127477031 Neg1 0 - 127475864 127477031 0,0,255
ch 7 127477031 127478198 Neg2 0 - 127477031 127478198 0,0,255
ch 7 127478198 127479365 Neg3 0 - 127478198 127479365 0,0,255
ch 7 127479365 127480532 Pos5 0 + 127479365 127480532 255,0,0
ch 7 127480532 127481699 Neg4 0 - 127480532 127481699 0,0,255
A3. The GVF Fo ma
The o ma GVF (Genome Va ia ion Fo ma ) is a specializa ion o he GFF o ma o desc ibe
a ia ions ela i e o a e e ence genome. I is widely used by he communi y o anno a e
a ia ions wi h da abase da a.
GVFLinkGCF
##g - e sion 1.06
##genome-build NCBI B36.3
##sequence- egion ch 16 1 88827254
ch 16 sam ools SNV 49291141 49291141 . + . ID=ID_1;Va ian _seq=A,G;Re e ence_seq=G;
ch 16 sam ools SNV 49291360 49291360 . + . ID=ID_2;Va ian _seq=G;Re e ence_seq=C;
ch 16 sam ools SNV 49302125 49302125 . + . ID=ID_3;Va ian _seq=T,C;Re e ence_seq=C;
ch 16 sam ools SNV 49302365 49302365 . + . ID=ID_4;Va ian _seq=G,C;Re e ence_seq=C;
ch 16 sam ools SNV 49302700 49302700 . + . ID=ID_5;Va ian _seq=T;Re e ence_seq=C;
ch 16 sam ools SNV 49303084 49303084 . + . ID=ID_6;Va ian _seq=G,T;Re e ence_seq=T;
ch 16 sam ools SNV 49303156 49303156 . + . ID=ID_7;Va ian _seq=T,C;Re e ence_seq=C;
ch 16 sam ools SNV 49303427 49303427 . + . ID=ID_8;Va ian _seq=T,C;Re e ence_seq=C;
ch 16 sam ools SNV 49303596 49303596 . + . ID=ID_9;Va ian _seq=T,C;Re e ence_seq=C;
Di e en da ase s con aining his da a can be downloaded om ENSEMBL( p) and NCBI( p).
Acco ding o he README in he ENSEMBL p, we should choose he ile
“homo_sapiens_incl_consequences.g .gz”.
Bioma
Bioma p o ides a use in e ace o que y and e ie e abula iles wi h he da a o hei
da abases.
In o de o e ie e he equi ed da a we can:
1) Choose he da abase o in e es (FROM),
2) Res ic ou que y choosing among he di e en il e ing c i e ia p o ided ( egions, gene
on ology e ms, e c…) (WHERE)
3) Res ic he ields o he esul choosing among he di e en ields (ENSEMBL Ids, HGCN Ids,
e c….) (SELECT)
Gene S a (bp) Gene End (bp) Ensembl Gene ID
17
16573334 16678949 ENSG00000037637
78028101 78149104 ENSG00000036549
29814705 29823405 ENSG00000225011
30117392 30117525 ENSG00000221126
30181698 30182394 ENSG00000228176
VCFTools
Desc ip ion
Pe l sc ip s ha pe o m ope a ion o e c iles
T oubleshoo ing
Requi es ins alling o he linux packages ( ambix, gbzip) and pe l
modules (Tes ::Mos ), and se ing se e al en i onmen a iables
(PATH and PERL5LIB).
Requi es p ep ocessing o c iles: comp essing and index c ea ion.
Few documen a ion abou in e nal de ails o commands (Ex, c -
anno a e has a –d op ion no documen ed
, bu equi ed when
anno a ing a c ile)
Ve sion ins alled
c _ ools_0.1.11
Commands e iewed
c - o- ab, c -que y, c -anno a e
Gene al opinion om
SwEnginee ing
pe spec i e
Ve y in e es ing commands o e c iles, howe e , hose ope a ions
(me g
e, il e ing, in e sec ion, anno a ion), should be pe o med
o e a ia ions ins ead o i s co esponding ex ep esen a ion.
c -que y: Con e s VCF iles in o o ma de ined by he use .
Usage: c -que y [OPTIONS] ile. c .gz
Op ions:
-c, --columns < ile|lis > Lis o comma-sepa a ed column names o
one column name pe line in a ile.
- , -- o ma <s ing> The de aul is '%CHROM:%POS %REF[ %SAMPLE=%GT] n'
-l, --lis -columns Lis columns.
- , -- egion ch : om- o Re ie e he egion. (Runs abix.)
--use-old-me hod Use old e sion o API, slowe bu mo e obus .
Exp essions:
%CHROM The CHROM column (simila ly also o he columns)
%GT T ansla ed geno ype (e.g. C/A)
%GTR Raw geno ype (e.g. 0/1)
%INFO/TAG Any ag in he INFO column
%LINE P in s he whole line
%SAMPLE Sample name
[] The b acke s loop o e all samples
%*<A><B> All o ma ields p in ed as KEY<A>VALUE<B>
Examples:
c -que y ile. c .gz 1:1000-2000 -c NA001,NA002,NA003
c -que y ile. c .gz - 1:1000-2000 -
'%CHROM:%POS %REF %ALT[ %SAMPLE:%*=,] n'
c -que y ile. c .gz - '[%GT ]%LINE n'
c -que y ile. c .gz - '[%GT ]%LINE n'
c -que y ile. c .gz - '%CHROM _%POS %INFO/DP %FILTER n'
18
c - o- ab Con e s he VCF ile in o a ab-delimi ed ex ile lis ing he ac ual a ian s ins ead
o ALT indexes
Usage: c - o- ab [OPTIONS] < in. c > ou . ab
Op ions: -i, --iupac Use one-le e IUPAC codes
c -anno a ion Adds cus om anno a ions o VCF iles.
Usage: c -anno a e [OPTIONS] > ou . c
Op ions:
-a, anno a ion.gz
-d key=INFO,ID=ANN,Numbe =1,Type=In ege ,Desc ip ion='MyAnno a ion'
-c CHROM,FROM,TO,INFO/ANN > ou . c
Vc - il e : Add il e ing c i e ia o il e VCF Files: C omosome, gene, sId (one o a ile), Allele
equency
Usage: c ools – c ile. c –-bed BED ilewi hVa ia ions>
c . il e ed
Anno a
Desc ip ion
Pe l sc ip s ha anno a e c iles wi h s uc u al da a and da abase
in o ma ion.
T oubleshoo ing
Requi es a lo o space in disk. 80M package, 12M Re Gene and 8,5G
dbsnp.
Ve sion ins alled
Dowloaded la es e sion in Oc obe 2013
Commands e iewed
anno a e_ a ia ion, con e 2anno a
Gene al opinion om
SwEnginee ing
pe spec i e
Commands only wo k wi h hei cus om o ma , so use s should
manage hemsel es he ans o ma ion o hei iles o his o ma
(e en o he s anda d o ma c ). I equi es downloading all
da abases locally, which o some use s could be p oblema ic. S ill,
e y in e es ing unc ionali y abou anno a ions, s uc u al da a and
da abase da a and i s sepa a ion in di e en iles. Howe e , I ind
ex ual anno a ions in gene al p oblema ic because ex ual da a is
edious o ead, misses he well-
o med s uc u ed ules o con ol
e o s, and leads o edundancy-which could a ec o access la ency
and space.
Con e 2anno a : Con e s VCF iles in o o ma used by anno a (.a inpu )
Command: con e 2anno a .pl - o ma c 4 ile. c > ile.a inpu
Anno a e_ a ia ion:
• To download a da abase: Conc e ely e Seq, dbsnp and 1000G p ojec (allele equency
da a)
19
Command: anno a e_ a ia ion.pl -downdb -build e hg19 -web om anno a e Gene
humandb
Command:anno a e_ a ia ion.pl -downdb -build e hg19 -web om anno a dbsnp135
humandb
Command:anno a e_ a ia ion.pl -downdb 1000g2012ap humandb –build e hg19
• To sea ch a ia ion p ope ies: Gene, egion.
Command: anno a e_ a ia ion.pl -build e hg19 –geneanno ile.a inpu humandb/
This command c ea es he ile *. a ia ion_ unc ion, which ells whe he he a ian hi a
s uc u al egion and he name o he gene (o neighbou ing genes).
• To sea ch a ia ion in da abases: Conc e ely in dbsnp e ie ing he sId
Command: anno a e_ a ia ion.pl -build e hg19 – il e –db ype dbsnp135
ile.a inpu humandb/
This command c ea es wo iles: *. il e ed ha con ains he SNPs no in dbSNP and
*.d opped ha con ains a ian s ha a e anno a ed in dbSNP oge he wi h hei s iden i ie s
• To sea ch a ia ion p ope ies: S uc u al e ec and hg s no a ion.
Command: anno a e_ a ia ion.pl -build e hg19 -hg s ile.a inpu humandb/
This command c ea es he ile *.exonic_ a ian _ unc ion ha con ains he amino acid
changes as a esul o he exonic a ian (se e al hg sNo a ion wi h i s e SeqIden i ie s)
VEP
Desc ip ion
Pe l Sc ip s ha anno a e a ia ion iles (VCF, TXT and BED) wi h he
e ec s o a a ia ion.
T oubleshoo ing
Requi es o download 5,5 G. Re seq didn’ download co ec ly and I
had o sol e he p oblem manually wi h linux commands (download
and ex ac in he sui able di ec o y: home/. ep/human).
Ve sion ins alled
Dowloaded la es e sion in Oc obe 2013
Commands e iewed
Va ian _e ec _p edic o , il e ep
Gene al opinion om
SwEnginee ing
pe spec i e
Ve y in e es ing unc ionali y abou e ec s. Howe e , he ex ual
o ma has he same p oblem men ioned in anno a and Snpe , he
di e ence is ha VEP exp esses in he
ield “ex a” a se o
p ope ies exp essed using a pai “Key= alue” and sepa a ed wi h
“;”. S ill, o imp o e isualiza ion he analysis c ea es a epo
con aining s a is ics a isual diag ams.
20
Va ian _e ec _p edic o : Anno a es a a ia ion ile (VCF, Pileup, HGVSIden i ie s) wi h s uc u al
da a: Gene (HGNC iden i ie , symbol), CDS posi ions, In on/Exon numbe , and da abase da a: sId
om dbSNP.
Command: pe l a ian _e ec _p edic o –-cache –i ile. c –-symbol –o
ile.o. c
Va ian _e ec _p edic o : Anno a es a a ia ion ile (VCF, Pileup, HGVSIden i ie s) wi h aminoacid
and codon change and allele equency.
Command: pe l a ian _e ec _p edic o –-cache –i ile. c –o ile.o. c
I can also anno a e a ia ions wi h he e ec o SIFT and POLYPHEN, which analyse i he a ian
changes he p o ein unc ion; i calcula es he HGVS no a ion (genomic, coding and some imes
p o ein).
Command: pe l a ian _e ec _p edic o –-cache –i ile. c –-hg s –- e seq –-
si b –-polyphen b –o ile.o. c
Fil e _ ep: Fil e s he ou pu ile o VEP using a ex ual exp ession
Command: pe l il e _ ep –-cache –i ile. c –-hg s –-si b –-polyphen b –o
ile.o. c
21

SamTools
SNPE
Desc ip ion
Ja a p og am ha anno a e a ia ion iles (VCF, TXT and BED) wi h
he e ec s o a a ia ion.
T oubleshoo ing
Requi es o download 1,7G. And ha e a disk Space o 10G.
Ve sion ins alled
Dowloaded la es e sion in Oc obe 2013 ( 3.3)
Commands e iewed
SnpSi , snpE
Gene al opinion om
SwEnginee ing
pe spec i e
I equi es downloading all da abases locally, which o some use s
could be p oblema ic. Ve y in e es ing unc ionali y abou
anno a ions o e ec s and da abase da a. Howe e , he ex ual
o ma has he same p oblem men ioned in anno a . S i
ll, o
imp o e isualiza ion he analysis c ea es a epo con aining
s a is ics a isual diag ams. SnpE p o ides a e y powe ul op ion
o il e a ia ions, as i suppo exp essions in ol ing a big se o
ope a ions and any ield o a VCF ile. Howe e , his ope a ion should
no be pe o med a he ex ual le el.
SnpSi : Anno a es he ID ield o a a ia ion ile (VCF, TXT o BED). This unc ionali y can be used
o anno a e he s o dbSNP. The dbsnp. c can be downloaded om ncbi.
Command: ja a –ja SnpSi .ja anno a e – dbSnp. c ile. c > ile.dbSnp. c
SnpE :
• To download he da abase: F om he e e ence Genome.
Command: ja a –ja SnpE .ja download – GRCh37.69
Command: ja a –ja SnpE .ja download – hg19
• To calcula e s uc u al da a: Gene, e e ence ids. #TODO es his command
Command: ja a –Xmx4g –ja SnpE .ja – GRCh37.69 ile.dbSnp. c > ile.s . c
• To calcula e a ia ion e ec s: Lis o e ec s, hei impac , codon/aminoacid changes,
gene, e e ence ids and he loss o unc ion. Also he hg s no a ion.
Command: ja a –Xmx4g –ja SnpE .ja e –hg s – GRCh37.69 ile.dbSnp. c >
ile.e . c
*Mo e op ions can be used in he analysis: Apply il e s, choose egions o applica ion, p o ide
a lis o ansc ip s, use cus om anno a ions, e c.
22
• To download da abases wi h a ia ion e ec s: The da abase dbNSFP con ains
in o ma ion abou SIFT and POLPHEN as well as MAFs om 1000G among o he s.
Command:wge h p://dbns p.hous onbioin o ma ics.o g/dbNSFPzip/dbNSFP 2.3.zip
Command: unzip dbNSFP2.3.zip
And anno a e a ia ions wi h da a om his da abase
Command: ja a -ja SnpSi .ja dbns p - dbNSFP2.3. x – SIFT_sco e,
Polyphen2_HVAR_p ed ile.anno a ed. c > ile.anno a ed2. c
23
GATK
Pipeline De elopmen En i onmen s
Biopy hon, BioPe l, Bioja a, Bio*
Bio* is a amily o lib a ies o he de elopmen o pe sonalized gene ic analysis ools on op o
well-es ablished p og amming languages. BioPe l (STAJICH2002) in Pe l, BioPy hon (COCK2009)
in Py hon, o BioJa a (HOLLAND2008) in Ja a.
Thei common aim is o p o ide common unc ionali y ega ding DNA sequences manipula ion
and unc ionali y ega ding in eg a ion among so wa e componen s. These lib a ies p o ide a
se o modules, classes and me hods ha implemen algo i hms, da a s uc u es, ad anced
s ing manipula ion ope a ions and so on. Addi ionally, as he domain s ill lacks o a s anda d
nomencla u e o exp ess he ou pu esul s, hey p o ide se e al o ma con e sion ope a ions
o ans o m hese esul s among di e en ools.
Fo example, BioPe l p o ides suppo o : a) Indexa ion, ans o ma ion and anno a ion o
sequences; b) Sequence alignmen ; c) Sequence sea ch; d) Fo ma ans o ma ions; e) Pa e n
ma ching algo i hms o sequence analysis; ) W appe s o da abase e ie al o online se ices
execu ion; and g) 3D ep esen a ion o p o eins.
Figu e 1 BioPe l example
Figu e 6 shows an example w i en wi h BioPe l o access o he EMBL da abase in o de o
e ie e a sequence whose iden i ie is “U14680”. A e e ie al, his sequence is ans o med
o he Genebank o ma .
Ta e na
Ta e na p o ides an en i onmen o design, edi and execu e wo k lows using g aph
ep esen a ions: nodes ha ep esen asks, and a ows ha ep esen links o
communica ions among asks. Gene icis s can choose om a lis o componen s ( ha p o ide
gene ic unc ionali y), he asks hey wan o accomplish, d ag and d op hem on a g aphical
wo kshee and e en ually hey compose a wo k low ha execu es he desi ed gene ic analysis.
Ta e na in eg a es unc ionali y h ough myExpe imen (De ou e e al. 2008), a social ne wo k
o sha e scien i ic wo k lows, and he Bioca alogue (Bhaga e al.), a cu a ed ca alogue o web
se ices o he li e sciences.
Ta e na o e s a use in e ace (Figu e 2) o acili a e he c ea ion o wo k lows. The igh side,
o e s he use a se o abs o wo k wi h he en i onmen ; o example, a ab o disco e se ices
o o o e iew he de ails o a wo k low. The le side o he in e ace shows he g aphical
ep esen a ion o all he asks and da a low among asks ga he ed in he wo k low.
use Bio::DB::EMBL;
use Bio::SeqIO;
my $db= new Bio::DB::EMBL();
my $seq=$db->ge _Seq_by_acc("U14680);
my $seqou =new Bio::SeqIO(- o ma => "genbank");
i (de ined $seq){
$seqou ->w i e_seq($seq);
}
24
Figu e 2 Wo k low Example on Ta e na
Bene i s
A e a wo k low execu ion, i p o ides in e media y esul s om all se ices ha allow he
aceabili y o he esul s.
I p o ides a high abs ac ion o in eg a e command line ools, es se ices, bioma se ices,
and o he s. This abs ac ion makes easie he in eg a ion, bu echnological de ails a e s ill
equi ed.
Disad an ages:
The Ta e na in e ace has a edious wo k low ep esen a ion: Bo h simple and complex
examples con ain o e loading elemen s (a big amoun o se ices).
I is no possible o ep esen he objec s o a da a model.
I does no p o ide clea desc ip ion o unc ionali y and pa ame e s om he in eg a ed
se ices.
Gene al Issues:
- Se ice disco e y: Se ices no o de ed seman ically.
o Same amily se ices a e sca e ed: The a ailable axonomy shows he
echnology and he p o ide in he op. As a second le el some seman ics a e
used, howe e , hey con ain simila and o e lapping ca ego ies such as
{con e sion, con e ing} and {Alignmen , Bioin o ma ics}. In he hi d le el,
seman ics is los again, and se ices a e o de ed by package name.
o Fil e no enough help ul: Se ice sea ch is acili a ed by a key wo d il e .
Howe e , al hough se ice lis is educed disco e y is s ill di icul because o
he ca alog axonomy p e iously desc ibed.
25
4) Re ie e Lis SNPs o GeneBRCA1 om dbSNP, Re ie e Lis mu a ions o Gene om
HGMD, LOVD, BIC.
All his asks can be pe o med in Ta e na, bu in o ma ion is e ie ed om “ENSEMBL
VARIATION 67 (SANGER UK) “
P oblems de ec ed
• Non-consis en da a: Fields e ie ed om Bioma se ices ha e non-
es ic ed con en . Some ields a e no ul illed, o he s do no co espond wi h
he ield meaning and o he s a e exp essed in na u al language. In ou case, i
is no possible o e ie e he ype o a ia ion: inse ion, dele ion, indel.
• The equi ed en i ies o sa ing da a e ie ed a e no a ailable: Fields
e ie ed om any da a sou ce should be sa ed in one h ml/cs /xls ile o
linked o a po .
5) Compa e Lis di e ences s Lis SNPs (To be done)
Galaxy
Galaxy (Gia dine e al. 2005) is an open-sou ce web-based en i onmen o he execu ion o
biological se ices. I s main pu pose is o help gene icis s wi h hei da a in ensi e biological
esea ch h ough he de ini ion o web in e aces o biological da a e ie al and se ices
execu ion. Wi h his pu pose, i p o ides di e en in e aces ha access o some popula
gene ic da abases and oolki s.
Galaxy o e s a use in e ace (Figu e 3) o acili a e he c ea ion o wo k lows made o h ee
panels. The igh panel, shows a lis o ools ha can be execu ed and used o he wo k low.
The cen al panel shows he g aphical ep esen a ion o all he asks and da a low among asks
ga he ed in he wo k low. And inally, he le panel shows he de ails o he ool selec ed in he
cen al panel so ha i can be con igu ed by he use .
32

Figu e 3 Wo k low Example on Galaxy
Repo
Summa y: Galaxy is an en i onmen ha p o ides he use he possibili y o un di e en
biological se ices and c ea e wo k lows combining hose se ices. The en i onmen can be
execu ed locally, using a web in e ace o in he cloud. Galaxy allows he use s o e ie e
local and USCS da abase’ da a se s o be used in hei expe imen s. I allows o combine da a
om independen que ies, o pe o m calcula ions o e hese da a se s (such as il e ing a
da a se , combining se e al da a se s and ans o ming da a using a biological se ice) and,
inally, o isualize he esul s.
PROS: The mos common biological se ices used by gene icis s a e in eg a ed in Galaxy
(such as da abase e ie al, biological algo i hms and isual display uni s). The use s a e
p o ided wi h a use in e ace o each se ice, conc e ely a o m wi h all he pa ame e s o
be illed. Galaxy allows he use o eco d all he s eps o se ices ha a e execu ed, and
a e wa ds, hose selec ed by he use can be composed in a wo k low. All hese asks a e
a ailable o execu e as many imes as equi ed.
CONS: Galaxy ope a es using low le el da a desc ip ions: i s unc ionali y is based in he use
o aw da a se s sa ed in iles. All se ices ecei e da a om a ile and, as a esul , hey ob ain
ano he ile. Da a is o ganized in ows and columns whe e each ield implies a speci ic
meaning. As a consequence, use s con igu e he se ice’s pa ame e s aking in o accoun he
ows and he columns ins ead o he unde lying concep s.
Addi ionally, when composing a wo k low, he use has o wo y abou da a low. The
se ices’ in e aces a e con igu ed o accep iles exp essed in a speci ic o ma , so i can
di e wi hin di e en se ices. I he o ma is di e en a ans o ma ion is equi ed. Hence,
he use has o ind he way o p o ide inside he Galaxy en i onmen a mechanism ha
execu es his ans o ma ion. The au ho s claim ha new unc ionali y can be added, bu
deep knowledge o Galaxy and some p og amming skills a e equi ed.
Wo k low 1: Diagen (Disease diagnosis BREAST Cance om a ia ion de ec ion)
1) Desc ibe Gene, e ie e Pa ien .Sequence, e ie e Gene{BRCA1}.sequence om NCBI.
The h ee asks canno be pe o med in Ta e na.
33
P oblems de ec ed
o En i ies canno be desc ibed: Da a is managed using a “da ase en i y” wi h
some me ada a associa ed (such a name, o ma , e c). Gene and Pa ien a e
ep esen ed as a da ase ha con ains one o se e al sequences espec i ely.
o NCBI da a e ie al canno be pe o med: USCS Genome b owse and
ensemble da abase can be que ied. Addi ionally, gene ic da a can be easily
e ie ed using a Bioma se ice, which p o ides a use in e ace when he
use is guided o c ea e a que y agains a conc e e da a sou ce. This use
in e ace p o ides he a ailable ields o il e he da a sou ce and he en i ies
ha can be e ie ed and hei a ibu es. Using he bioma se ice, NCBI is
no a ailable, bu we used ano he da abase called VEGA (Sange UK), whe e
all he equi ed p ope ies o his wo k low we e a ailable.
o
Desc ip ion: P o eus is a p oblem sol ing en i onmen based on he G id echnology o
composing, compiling and unning bioin o ma ic applica ions. P o eus is based on he use o
on ologies o aid he use s o de ine hei applica ions. The bioin o ma ic on ology used
ep esen s he bioin o ma ics domain by means o he de ini ion o biological da a sou ces,
so wa e componen s and bioin o ma ic p ocesses o asks. The use designs hei
pe sonalized ool b owsing and que ying he on ology o ind o he componen s ha will
be used. Addi ionally, each componen is p o ided wi h me ada a o allow he use o
con igu e i acco ding hei equi emen s.
34
PROS: The on ology is sui able o ind so wa e componen s ha i biologis s’ equi emen s.
Addi ionally, he b owse is easy o use because he on ology is ca ego ized in di e en
axonomies and displayed using showing labeled ela ionships wi h o he i ems o he
on ology.
CONS: The en i onmen does no explain he in e ac ion be ween a domain on ology and
he bioin o ma ics on ology, ei he how he wo k low managemen sys em ans o ms he
g aphical design in o g id sc ip s. As a consequence, i is no clea how da a low among asks
is designed o execu ed.
This wo k was de eloped in 2004-2005, and he en i onmen has no been made a ailable.
Thei au ho s ocused hei e o s in applying hese ideas in mass-spec ome y p o eomics
and c ea ed a speci ic pla o m (MS-Analyze ) ha in eg a es algo i hms and ools o design
ools ega ding his domain.
EBioFlow
eBioFlow (WASSINK2010) is an open-sou ce wo k low managemen sys em o design and execu e
biological wo k lows de eloped in he academic en i onmen as a p oo o concep o a se ies
o Phd disse a ions. I s main pu pose is o imp o e o he wo k low de elopmen en i onmen s
by p o iding a be e usabili y o wo k low design (mul iple pe spec i es o model da a and
con ol low), a be e wo k low enac men wi h suppo o la e binding o se ices, and inally,
he suppo o da a p o enance o imp o ing wo k low sha ing and euse.
Figu e 14 shows he in e ace o eBio low. On he le panel, a se o abs a e a ailable o
na iga e be ween wo k lows, manage da a p o enance, sea ch o se ices, and see p e ious
wo k low uns. On he igh panel, se e al abs p o ide he di e en pe spec i es a ailable o
manage all he di e en conce ns while designing, execu ing and sha ing wo k lows.
Figu e 4 Wo k low Example on eBioFlow
Repo
Desc ip ion: BiosFlow is a wo k low pla o m ha allows he use o disco e web se ices
using on ology cons uc s. I de ines he en i y “node” as a basic uni wi h a se o ea u es
ha ep esen s a se ice. This pla o m p o ides a use en i onmen o disco e , compose
and execu e p ede ined se ice nodes in eg a ed in he pla o m.
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PROS: This wo k explains he need o simpli y he c ea ion o applica ions by biologis s. Wi h
his pu pose, hey add seman ics o wo k low composi ion, using on ologies, and de elop an
easy use in e ace, based on icons, o d ag and d op se ices and allow hei subsequen
execu ion.
CONS: The e is only a pape ha desc ibes his wo k (2009) and he pla o m is no a ailable.
They claim ha on ologies a e used o sea ch o web se ices, bu i does no explain how
se ices a e ela ed wi h on ologies o allow he se ice disco e y, nei he which on ologies
hey use, and nei he an example o use.
BSIS
Repo
Desc ip ion: BioSe ice In eg a ion Sys em (BSIS) is a amewo k o he de elopmen o
biological wo k lows. BSIS suppo s seman ic disco e y and composi ion o se ices because
p o ides a mechanism o anno a e webse ices wi h on ologies. Conc e ely he on ologies
used a e: 1) Se ice on ologies, which desc ibe p ocesses and ans o ma ions implemen ed
by bioin o ma ic se ices; and 2) Da a on ologies, which desc ibe biological da a.
BSIS p o ides a g aphical wo k low language ( o mally desc ibed) ha allows he use o
c ea e conc e e o abs ac wo k lows ha execu e web se ices. The conc e e wo k lows
a e speci ied by he use s while he abs ac ones a e ins an ia ed by he amewo k
a ending seman ic cons ains added by he use .
PROS: The amewo k p o ides a g aphical use in e ace easy o use o he use . Mo eo e ,
his p oposal akes in o accoun he use o seman ics o he de ini ion o bioin o ma ic
wo k lows. As a consequence, he use is p o ided wi h a high le el o abs ac ion o
implemen a ion de ails. Conc e ely, he use can b owse he biological on ology (se ice o
da a) and use i di ec ly in he wo k low design en i onmen by d ag and d op.
CONS: The phd was de eloped in 2007 and i s main publica ion was on 2010. The con ac
in o ma ion is no alid and he amewo k is no a ailable. As i is no a ailable and he
p ojec seems o be o e , ecen biological on ologies co e age canno be ensu ed. The
“se ice” basic uni is only applicable o web se ice.
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