Process Design for Natural Scientists: An Agile Model-Driven Approach

263 Pages·2014·20.525 MB·English

by  Anna-Lena Lamprecht, Tiziana Margaria (eds.)

Anna-Lena Lamprecht
Tiziana Margaria (Eds.)
Communications in Computer and Information Science 500
Process Design
for Natural Scientists
An Agile Model-Driven Approach
123
Communications
in Computer and Information Science 500
EditorialBoard
SimoneDinizJunqueiraBarbosa
PontificalCatholicUniversityofRiodeJaneiro(PUC-Rio),
RiodeJaneiro,Brazil
PhoebeChen
LaTrobeUniversity,Melbourne,Australia
AlfredoCuzzocrea
ICAR-CNRandUniversityofCalabria,Cosenza,Italy
XiaoyongDu
RenminUniversityofChina,Beijing,China
JoaquimFilipe
PolytechnicInstituteofSetúbal,Setúbal,Portugal
OrhunKara
TÜBI˙TAKBI˙LGEMandMiddleEastTechnicalUniversity,Ankara,Turkey
IgorKotenko
St.PetersburgInstituteforInformaticsandAutomationoftheRussian
AcademyofSciences,St.Petersburg,Russia
KrishnaM.Sivalingam
IndianInstituteofTechnologyMadras,Chennai,India
DominikS´le˛zak
UniversityofWarsawandInfobright,Warsaw,Poland
TakashiWashio
OsakaUniversity,Osaka,Japan
XiaokangYang
ShanghaiJiaoTongUniversity,Shangai,China
Moreinformationaboutthisseriesathttp://www.springer.com/series/7899
·
Anna-Lena Lamprecht Tiziana Margaria (Eds.)
Process Design
for Natural Scientists
An Agile Model-Driven Approach
ABC
Editors
Anna-LenaLamprecht TizianaMargaria
ChairServiceandSoftwareEngineering ChairSoftwareEngineering
InstituteofComputerScience ComputerScienceandInformationSystems
UniversityofPotsdam Department
Potsdam UniversityofLimerick
Germany and
Lero,TheIrishSoftwareResearchCenter
Limerick
Ireland
ISSN1865-0929 ISSN1865-0937 (electronic)
ISBN978-3-662-45005-5 ISBN978-3-662-45006-2 (eBook)
DOI978-3-662-45006-2
LibraryofCongressControlNumber: 2014950464
SpringerHeidelbergNewYorkDordrechtLondon
(cid:2)c Springer-VerlagBerlinHeidelberg2014
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Preface I
In contrast with our seemingly endless ability to generate more biological data,
faster,andatlowercost,thereareincreasinglyworrisomeobservationsofhuman
limitationswithrespecttomanagingandmanipulatingthesemassiveandhighly
complex datasets. With data at this scale, mistakes are easily made, and as
Baggerly noted “the most common errors are simple... the most simple errors
are common” when it comes to biological data management. Many, possibly
most,biologicalresearcherslacktheskillstoprogrammaticallymanipulatelarge
datasets, and therefore continue to use inappropriate tools to manage the “big
data” that even a modestly resourcedlaboratory can now create. Serious errors
introducedduring data managementandmanipulationare difficult to detect by
the researcher and, because they go unrecorded, are nearly impossible to trace
during peer-review.
Beyond data manipulation errors, the statistical expertise required to cor-
rectly analyze high-throughput data is rare, and biological researchers—even
those who are extremely competent in rigorously executing the data-generating
“omics” experiments—are seldom adequately trained in appropriate statistical
analysis of the output. As such, inappropriate approaches, including trial-and-
error,maybe applieduntil a “sensible”answeris found. Finally, becausemanu-
ally driven analyses of high-throughput data can be extremely time-consuming
and monotonous, researchers will sometimes inappropriately use a hypothesis-
guided approach—examining only possibilities that they already believe are
likely based on their interpretation of prior biological knowledge, or personal
biastowardswheretheybelievetheanswershouldbe.Thus,thescientificlitera-
ture becomes contaminatedwith errorsresulting from“fishing for significance,”
from research bias, and even from outright errors.
Theseproblemsarebecomingpervasiveinomics-scalescience.Theaffordabil-
ityandaccessibilityofhigh-throughputtechnologiesissuchthatnowevensmall
groups and individual laboratories can generate datasets that far exceed their
capacity, computationally and statistically, to adequately manage and correctly
analyze.Theendresultisaglutofnon-reproduciblesciencemakingitswayinto
the primary literature and databases. Recent “forensic audits” of the scientific
literature, such as those executed by Begley and Elles, have shown that a large
proportion of bioinformatics research cannot be replicated independently, with
numbers ranging from a low of 25% up to a staggering 89% of non-replicable
researchin a recent study of oncology publications from 2001 to 2011.
While an independent study by Baggerly into the accuracy and quality of
published high-throughput analyses triggered retractions (and even a scientific
misconduct investigation!) a study by Ioannidis revealed that, even in the pres-
tigious Nature Genetics, more than half of the peer-reviewed, high-throughput
studies cannot be replicated. Yet, ostensibly, these studies have all passed
VI Preface I
“rigorous peer review.” The failure of peer-review to detect non-reproducible
research is, at least in part, because the analytical methodology is not ade-
quatelydescribed,butperhapsequallybecausethereviewersfindtheseanalyses
as challenging as the original investigator did; even if the original data were
made available to them, together with a clear and comprehensive description of
the analytical methodology (as we would hope are present in the Materials and
Methods section of a manuscript), it still might not be reasonable to expect a
reviewer to rigorously evaluate the study because the analytical infrastructure
is difficult to envision or replicate from a textual description alone.
In recognition of these limitations, the Institute of Medicine in 2012 pub-
lished several recommendations relating to proper conduct of high-throughput
analyses.Theseinclude:rigorouslydescribed,annotated,andfolloweddataman-
agement procedures; “locking down” the computational analysis pipeline once
it has been selected; and publishing the workflowof this analytical pipeline in a
formalmanner,togetherwiththefullstartingandresultdatasets.Theserecom-
mendations help ensure that (a) errorsare not introducedthroughmanual data
manipulation, (b) there can be no human intervention in the data as it passes
through the analytical process, and (c) that third parties can properly evaluate
the data, the analytical methodology, and the result at the time of peer-review,
even to the point of re-running the analysis for validation purposes.
Formalworkflowtechnologieshaveprovedeffectiveatresolvingmanyofthese
issues,andinfact, beyondjusttransparencyandreproducibility,they evenpro-
vide opportunities for time-saving andconvenience. For example, the use of for-
mal workflows provides an excellent opportunity to automate the collection of
provenanceinformation(purpose,source,dataused,date,algorithms,interfaces,
versions,nameofdatapublisher,etc.),farbeyondwhatiscommonlycapturedby
eventhe mostattentive biologicalresearcher.Itis perhaps surprising,therefore,
that integration of formal workflows into the scientific “culture” is lagging, and
ad hoc. Nevertheless, as regulatory and funding agencies become increasingly
concerned and dubious about the quality of the research they are supporting,
the pressure to improve the way we “do science” grows.It is clear that the only
path forwardinvolves mechanization of much of the scientific analysis process.
It is for all the reasons above that I am so pleased to see this book on sci-
entific workflows, directly aimed at the bench researcher as its target audience.
The broad scope of research questions covered in these chapters will speak to
the full breadth of researchers who, until now, have been reluctant to consider
adoptingthesepowerfulnewapproachestodatamanagementandanalysis.Hav-
ing a few key exemplars such as these—formal workflows solving real scientific
problems—willhopefully “blazethe trail”forotherresearchersto follow.Atthe
sametime, readingthese use caseswill no doubtspur thoseofus who study sci-
entific workflow technologies to produce even better tools that make life easier
for the biologists whose data we care so much about.
May 2014 Mark D. Wilkinson
Isaac Peral Distinguished Researcher
Center for Plant Biotechnology and Genomics, UPM, Madrid, Spain
Preface II
At present, 95% of all digital data are estimated to have a geospatial reference
(Hamilton,inPerkins,2010).Geospatialreferenceisthedefinitionoftheabsolute
or relative position of objects or geographical phenomena. This is determined,
in the first case, by coordinates, and, in the latter case, by spatial relations
or neighborhood of geospatial objects. For centuries, spatial data have been
processedtogeneratemapsormap-likecartographicrepresentations.Indoingso,
cartographersmadeuseofthefactthatmapsaretheonlyvisualrepresentations
of geographic space simultaneously showing both the absolute and the relative
positions of spatial objects. In addition, they have served as an effective data
storage well beyond the advent of ICT in cartography in the late 1960s.
Unlike remote sensingimagery,mapsare notimagesbutgraphicalmodels of
geospatial reality. Map models are generated in a systematic process by trans-
forming non-graphic spatial data of the real worldinto spatially related graphic
symbols. Selection and processing of the geospatial source data are subject to
the specific thematic and/or geographical requirements of a given application.
The data-to-symbol transformation is controlled by a sequence of processes im-
plementing asetofcartographicrepresentationmethods andothercartographic
regulations. This regulatory framework determines the thematic as well as the
scale-relatedmodeling andsymbolizationofthe preprocessedgeospatialdata.It
is, in fact, this processed-based, application-orientedtransformation of graphic-
free geodata into graphic map data that accounts for the professional carto-
graphic quality of the maps produced.
To generate quality cartographic representations or, in more modern terms,
geovisualizations, expertise in both methods and techniques is required on how
to processnon-graphicgeospatialdata into propergraphicmaps. This expertise
has, for centuries, been passed on personally from master to apprentice. Hence,
different “schools” of geodata processing into maps can easily be distinguished.
Around the turn of the nineteenth to twentieth centuries the process of map-
making has been codified and formalized in early textbooks on cartography. It
is obvious that the written description of the transformation process of geospa-
tial data into maps required strict formalization beforehand of the processing
steps involved for interpersonal application. Only this will put a trained person
into a position to produce quality maps by applying the mapping processesto a
geospatialdataset.Depending onthedata,mappurpose,application,andscale,
a finite number of processes is required to generate the respective map types.
To make sure the application of the relevant processes to similar datasets will
yield identical map representations, the range of individual processes has been
formalizedin abouta dozenso-calledcartographicrepresentationmethods. The
representation methods describe the processes involved in transforming specific
geodata and their features into precisely matching map models or map types.
VIII Preface II
Hence,cartographicrepresentationmethodscanbeconsideredasetofformalized
processing rules sets applied to visualize geodata expressively and effectively.
Today, almost without exception, geodata are handled and processed with
ICT systems more or less dedicated to this purpose. Thus, the methodical need
for the formalization of geovisual processes is complemented by the technical
requirements of formalization when it comes to implementing geovisualization
processes in a software environment. Software engineering, in particular, has a
distinctive history and substantial record of modeling processes. Accordingly, it
deals with developing as well as implementing software solutions, which can be
dedicated or generic, proprietary, or open-source. Formalization requirements
both from a methodical and a technical perspective can thus be considered a
common ground of geoinformation processing and software engineering. It goes
withoutsayingthatgeoinformationprocessingwillbenefitfromsoftwaresystems
facilitating the formalization and implementation of data-to-map transforma-
tions in an intuitive, easy-to-comprehend way. One such software environment
is the open-source jABC framework used in the range of applications collected
in this volume. Systems like jABC help to code a set of processing rules into
a formalized technical framework that can be implemented in ICT systems for
geoinformation processing.
The use cases presented demonstrate both the potential of jABC as well as
the benefits the implementation of this framework holds for the applications in
question. From a geoinformation perspective it furthermore shows the added
value a fruitful collaboration of the two disciplines will hold for geoinformation
applications as well as for geoinformation scientists.
June 2014 Hartmut Asche
Professor of Geoinformation Science
Department of Geography, University of Potsdam, Germany
Contents
Framework
Scientific Workflows and XMDD ................................... 1
Anna-Lena Lamprecht and Tiziana Margaria
Modeling and Execution of Scientific Workflows with the jABC
Framework...................................................... 14
Anna-Lena Lamprecht, Tiziana Margaria, and Bernhard Steffen
The Course’s SIB Libraries........................................ 30
Anna-Lena Lamprecht and Alexander Wickert
Lessons Learned ................................................. 45
Anna-Lena Lamprecht, Alexander Wickert, and Tiziana Margaria
Bioinformatics Applications
Protein Classification Workflow.................................... 65
Judith Reso
Data Mining for Unidentified Protein Sequences ..................... 73
Leif Blaese
Workflow for Rapid Metagenome Analysis........................... 88
Gunnar Schulze
Constructing a Phylogenetic Tree .................................. 101
Monika Lis
Exploratory Data Analysis ........................................ 110
Janine Vierheller
Identification of Differentially Expressed Genes ...................... 127
Christine Schu¨tt
Geovisualization Applications
Visualization of Data Transfer Paths ............................... 140
Christian Kuntzsch
Spotlocator – Guess Where the Photo Was Taken! ................... 149
Marcel Hibbe