Translational Biomedical Informatics: A Precision Medicine Perspective

331 Pages·2016·6.277 MB·English

by  Bairong Shen, Haixu Tang, Xiaoqian Jiang (eds.)

Advances in Experimental Medicine and Biology 939
Bairong Shen
Haixu Tang
Xiaoqian Jiang Editors
Translational
Biomedical
Informatics
A Precision Medicine Perspective
Advances in Experimental Medicine and Biology
Volume 939
Editorial Board
IRUN R. COHEN, The Weizmann Institute of Science, Rehovot, Israel
N.S. ABEL LAJTHA, Kline Institute for Psychiatric Research, Orangeburg,
NY, USA
JOHN D. LAMBRIS, University of Pennsylvania, Philadelphia, PA, USA
RODOLFO PAOLETTI, University of Milan, Milan, Italy
More information about this series at http://www.springer.com/series/5584
Bairong Shen (cid:129) Haixu Tang (cid:129) Xiaoqian Jiang
Editors
Translational Biomedical
Informatics
A Precision Medicine Perspective
Editors
BairongShen HaixuTang
CenterforSystemsBiology SchoolofInformaticsandComputing
SoochowUniversity IndianaUniversity
Jiangsu,China Bloomington,IA,USA
XiaoqianJiang
DepartmentofBiomedicalInformatics
UniversityofCaliforniaSanDiego
LaJolla,CA,USA
ISSN0065-2598 ISSN2214-8019 (electronic)
AdvancesinExperimentalMedicineandBiology
ISBN978-981-10-1502-1 ISBN978-981-10-1503-8 (eBook)
DOI10.1007/978-981-10-1503-8
LibraryofCongressControlNumber:2016957788
©SpringerScience+BusinessMediaSingapore2016
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Contents
1 NGSforSequenceVariants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
ShaoleiTeng
2 RNABioinformaticsforPrecisionMedicine. . . . . . . . . . . . . . . . . . 21
JiajiaChenandBairongShen
3 ExploringHumanDiseasesandBiologicalMechanisms
byProteinStructurePredictionandModeling. . . . . . . . . . . . . . . . 39
JuexinWang,JosephLuttrellIV,NingZhang,SaadKhan,
NianQingShi,MichaelX.Wang,Jing-QiongKang,
ZhengWang,andDongXu
4 ComputationalMethodsinMassSpectrometry-
BasedProteomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
SujunLiandHaixuTang
5 InformaticsforMetabolomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
KanthidaKusonmano,WanwipaVongsangnak,
andPramoteChumnanpuen
6 MetagenomicsandSingle-CellOmicsDataAnalysisforHuman
MicrobiomeResearch. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
MaozhenHan,PengshuoYang,HaoZhou,HongjunLi,
andKangNing
7 TextMiningforPrecisionMedicine:BringingStructuretoEHRs
andBiomedicalLiteraturetoUnderstandGenesandHealth. . . . . . . 139
MichaelSimmons,AyushSinghal,andZhiyongLu
8 MedicalImagingInformatics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
WilliamHsu,SuzieEl-Saden,andRickyK.Taira
9 LIMSandClinicalDataManagement. . . . . . . . . . . . . . . . . . . . . . 225
YalanChen,YuxinLin,XuyeYuan,andBairongShen
v
vi Contents
10 BiobanksandTheirClinicalApplicationandInformatics
Challenges. . . . .. . . . . . . . .. . . . . . . . . .. . . . . . . . .. . . . . . . . . .. 241
LanYang,YalanChen,ChunjiangYu,andBairongShen
11 XML,Ontologies,andTheirClinicalApplications. . . . . . . . . . . . . 259
ChunjiangYuandBairongShen
12 BayesianComputationMethodsforInferringRegulatory
NetworkModelsUsingBiomedicalData. . . . . . . . . . . . . . . . . . . . . 289
TianhaiTian
13 Network-BasedBiomedicalDataAnalysis. . .. . . .. . . .. . . .. . . .. 309
YuxinLin,XuyeYuan,andBairongShen
Chapter 1
NGS for Sequence Variants
ShaoleiTeng
Abstract Recent technological advances in next-generation sequencing (NGS)
provideunprecedentedpowertosequencepersonalgenomes,characterizegenomic
landscapes, and detect a large number of sequence variants. The discovery of
disease-causing variants in patients’ genomes has dramatically changed our per-
spective on precision medicine. This chapter provides an overview of sequence
variant detection and analysis in NGS study. We outline the general methods for
identifyingdifferenttypesofsequencevariantsfromNGSdata.Wesummarizethe
common approachesfor analyzingand visualizingcasualvariants associated with
complexdiseasesonprecisionmedicineinformatics.
Keywords Sequence variants (cid:129) Next-generation sequencing (cid:129) Sequence
alignment (cid:129) Variant calling (cid:129) Association testing (cid:129) Visualization (cid:129) Precision
medicineinformatics
1.1 Introduction
Overthelastdecade,next-generationsequencing(NGS)hasdramaticallychanged
the precision medicine field by characterizing patients’ genomic landscapes and
identifying the casual variants associated with human diseases. The Sanger-based
sequencing [48] (“first-generation sequencing”) was used to sequence the first
human reference genome for the Human Genome Project [3], which took
13yearstofinish thedraftgenome at atotal costof$3 billion. NGS technologies
makethesequencingatremarkablepriceandunprecedentedspeedbycarryingout
hundredsofmillionsofsequencingreactionsatonce[52,57].Withtherevolution-
arytechnology,wecansequencethousandsofgenomesinjust1month,addressthe
biological questions at a large scale, identify the genetic risk factors for human
diseases,andprovideamoreprecisewaytohealthcare[24].Inparticular,NGScan
beusedtodetectalargenumberofsequencevariantsinthepatients’genomesand
identifythecasualvariantsassociatedwithhumandiseases,whichhasdramatically
S.Teng(*)
DepartmentofBiology,HowardUniversity,Washington,DC20059,USA
e-mail:[email protected]
©SpringerScience+BusinessMediaSingapore2016 1
B.Shenetal.(eds.),TranslationalBiomedicalInformatics,Advancesin
ExperimentalMedicineandBiology939,DOI10.1007/978-981-10-1503-8_1
2 S.Teng
changed our perspective on genetic variants, human diseases, and precision
medicine.
Discoveryofcasual sequence variants associatedwith certain traits ordiseases
hasbecomeafundamentalaimofgeneticsandbiomedicalresearch.Thesequence
variantscanbeclassifiedtosinglenucleotidevariants(SNVs),smallinsertionsand
deletions(INDELs),andlargestructuralvariants(SVs)basedontheirsequencesin
length. SNVs, the mostcommon type of sequence variants, are single DNA base-
pairdifferencesinindividuals.INDELsaredefinedassmallDNApolymorphisms
includingbothinsertionsanddeletionsrangingfrom1to50bpinlength.SVsare
largegenomicalterations(>50bp)includingunbalancedvariants(deletions,inser-
tions,orduplications)andbalancedchanges(translocationsandinversions).Copy
numbervariants(CNVs),alargecategoryofunbalancedSVs,areDNAalterations
thatresultintheabnormalnumberofcopiesofparticularDNAsegments.Somatic
mutationsaretumor-specificvariantsincancer–normalsamplepairs.Thedifferent
types of sequence variants play important roles in the development of human
complex diseases. For example, the SNVs associated with major depression were
found in the genes encoding serotonin transporter, serotonin receptor, catechol-o-
methyltransferase, tryptophan hydroxylase, and tyrosine hydroxylase [29]. These
sequence variants can influence the neurotransmitter functions in multiple ways
including changing gene expression level, altering substrate binding affinity, or
affectingtransportkinetics[19].Abalancedt(1;11)(q42.1;q14.3)translocationin
disrupted in schizophrenia 1 (DISC1) gene was discovered in a large Scottish
family highly burdened for severe mental illnesses, and the family members with
the translocation showed a reduced P300 event-related potential associated with
schizophrenia[9].Identifyingthecasualvariantsandtheirclinicaleffectsprovides
important insight to understand the roles of sequence variants in the causation of
humandiseases.
Discovery of disease-causing variants from a large number of sequence poly-
morphisms detected from NGS data is a major challenge in precision medicine.
Bioinformaticsandstatisticalmethodshavebeendevelopedfordetectingsequence
variantsandidentifyingdisease-relatedcasualvariants.Theschematicdiagramof
NGS variant analysison precision medicine informatics is shown inFig.1.1. The
DNAsamplesareextractedfrompatients(ornormalindividuals)andsequencedon
NGS platforms. The billions of short sequence reads are produced by the
sequencers, and sequence information is stored in FASTQ format files. From
here, NGS variant analysis falls into two major frameworks. The first framework
is the variant detection. The high-quality sequence reads passed quality control
(QC)filtersarealignedtoareferencegenome,andthesequencealignmentdatais
depositedinSAM/BAMformatfiles.Severalvariantdetectiontoolsareusedtocall
small variants including SNVs and INDELs. The somatic mutation callers are
applied to tumor–normal patient samples. Multiple SV callers are developed to
detectlargestructuralvariants.Thevariantscalledfromthesetoolscanbestoredin
Variant Call Format (VCF) files or BED format files. The next framework is the
variant analysis. The annotation tools are used to predict the functional effects of
coding and regulatory variants.Theassociation analysis can identifythe common
1 NGSforVariants 3
Fig.1.1 AflowchartofNGSvariantanalysisinprecisionmedicine
and rare variants associated with certain diseases or traits. Visualization tools are
used to view the small and large candidate sequence variants. By combining
numerous analyzing tools, the causal variants can be identified and connected
with clinical information for precision medicine research. On the one hand,
disease-related causal variants provide the genetic biomarkers for diagnostics of
complex diseases. On the other hand, the candidate variants offer the targets for
developing more precise treatments and drugs for patients. In the following sec-
tions, we will review the bioinformatics approaches and provide a guide for
detectingandanalyzingthesequencevariantsfromNGSdata.
1.2 Variant Detection
Variantdetectionconsistsofqualitycontrol(QC),sequencealignment,andvariant
calling. The raw data contains a large number of short reads generated by NGS
sequencers. Preprocessing and post-processing QC are carried out to remove the