Bioinformatics Database Systems

290 Pages·2016·6.416 MB·English

by  Kevin Byron, Katherine G. Herbert, Jason T. L. Wang

Bioinformatics
Database Systems

Bioinformatics
Database Systems
Kevin Byron

New Jersey Institute of Technology, Newark, USA
Katherine G. Herbert

Montclair State University, New Jersey, USA

Jason T. L. Wang

New Jersey Institute of Technology, Newark, USA
CRC Press
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Library of Congress Cataloging-in-Publication Data
Names: Byron, Kevin, 1954- , author. | Herbert, Katherine G., author. | Wang,
Jason T. L., author.
Title: Bioinformatics database systems / Kevin Byron, Katherine G. Herbert,
Jason T. L. Wang.
Description: Boca Raton : Taylor & Francis, 2017. | Includes bibliographical
references and index.
Identifiers: LCCN 2016027400 | ISBN 9781439812471 (hardback : alk. paper)
Subjects: LCSH: Bioinformatics. | Biology--Databases.
Classification: LCC QH324.2 .B97 2017 | DDC 570.285--dc23
LC record available at https://lccn.loc.gov/2016027400
Visit the Taylor & Francis Web site at
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Contents
LIST OF FIGURES vii
LIST OF TABLES xi
PREFACE xv
ACKNOWLEDGMENTS xix
Chapter 1 (cid:4) Overview of Bioinformatics Databases 1
1.1 INTRODUCTION 1
1.2 SEQUENCE DATABASES 2
1.3 PHYLOGENETIC DATABASES 7
1.4 STRUCTURE AND PATHWAY DATABASES 12
1.5 MICROARRAY AND BOUTIQUE DATABASES 14
Chapter 2 (cid:4) Biological Data Cleaning 17
2.1 INTRODUCTION 17
2.2 GENERAL DATA CLEANING 24
2.3 CASE STUDY IN BIOLOGICAL DATA CLEANING 34
Chapter 3 (cid:4) Biological Data Integration 47
3.1 INTRODUCTION 47
3.2 GENERAL DATA INTEGRATION 48
3.3 TOPICS IN BIOLOGICAL DATA INTEGRATION 50
Chapter 4 (cid:4) Biological Data Searching 57
4.1 INTRODUCTION 57
4.2 BIOLOGICAL DATA SEARCHING USING BLAST 58
4.3 BIOLOGICALDATASEARCHINGUSINGTHEUCSC
GENOME BROWSER 58
v
vi (cid:4) Contents
4.4 CASESTUDYINPHYLOGENETICTREEDATABASE
SEARCH 59
4.5 CASE STUDY IN RNA PSEUDOKNOT DATABASE
SEARCH 73
Chapter 5 (cid:4) Biological Data Mining 91
5.1 INTRODUCTION 91
5.2 GENERAL DATA MINING 93
5.3 BIOLOGICAL DATA MINING 94
5.4 CASE STUDY IN BIOLOGICAL MOTIF DISCOVERY 101
5.5 CASE STUDY IN BIOLOGICAL DATA MINING 111
Chapter 6 (cid:4) Biological Network Inference 133
6.1 INTRODUCTION 133
6.2 GENE REGULATORY NETWORK INFERENCE 135
Chapter 7 (cid:4) Cloud-Based Biological Data Processing 193
7.1 INTRODUCTION 193
7.2 DATA PROCESSING IN THE CLOUD 198
Bibliography 217
Index 261
List of Figures
2.1 A framework for biological data cleaning. 23
3.1 Scientific data integration. 52
3.2 Data integration in a single repository. 54
3.3 Data integration in multiple repositories. 55
4.1 UpDistance. 61
4.2 Up operations and down operations. 62
4.3 Arbitrary five-node tree and up and down matrices. 63
4.4 Arbitrary tree with eight nodes. 65
4.5 Arbitrary tree with seven nodes. 66
4.6 Example of a query tree P and a data tree D. 67
4.7 Further examples of a query tree and a data tree. 68
4.8 PDB crystal structures 1E8O and 2B57. 77
4.9 Histograms for the base mismatch ratios of the align-
ments produced by RKalign, CARNA, RNA Sampler,
DAFS, R3D Align and RASS. 80
4.10 Boxplotsforthebase mismatchratiosofthealignments
produced by RKalign, CARNA, RNA Sampler, DAFS,
R3D Align and RASS. 81
4.11 Exampleshowingbasemismatchesinanalignmentpro-
duced by DAFS, R3D Align, and RKalign. 82
4.12 Histograms for the base mismatch ratios of the align-
ments produced by RKalign, CARNA, RNA Sampler
and DAFS. 83
4.13 Boxplotsforthebase mismatchratiosofthealignments
producedbyRKalign,CARNA,RNASamplerandDAFS. 84
4.14 Comparison of the stem mismatch ratios yielded by
RKalign and CARNA. 85
vii
viii (cid:4) LIST OF FIGURES
4.15 Multiple alignment: PDB crystal structures 1E8O,
1XP7, 2F4X, 2OOM, 2D19 and 1BAU. 87
5.1 Three RNA topological families, A, B, and C, of three-
way RNA junctions containing coaxial helical stacking. 113
5.2 Secondary structure plot produced by VARNA of nu-
cleotides 5 through 49 of chain A from PDB molecule
3E5C. 116
5.3 Three-dimensional plot produced by Jmol of nucleotides
5 through 49 of chain A from PDB molecule 3E5C. 117
5.4 Hypothetical three-way RNA junction to illustrate fea-
tures used by random forests classifier. 118
5.5 Stockholm format multiple sequence alignment of
ncRNAmoleculesfromsixsamplesrecordedinthe PDB
with identifiers 2GDI, 2CKY, 2AVY, 1S72, 2AW4 and
2J01. 122
5.6 CSminer’s prediction result on the genome of D. radio-
durans. 123
5.7 Junction Explorer Screenshot 1: home page. 127
5.8 Junction Explorer Screenshot 2: sample input in dot-
bracket format. 128
5.9 JunctionExplorerScreenshot3:details ofjunction type,
junction location,junctionloopregionsandcoaxialheli-
cal stacking prediction. 129
5.10 Junction Explorer Screenshot 4: visualizations of pre-
dicted topology (family). 130
6.1 Hypothetical five-gene network. 137
6.2 Another hypothetical five-gene network. 138
6.3 Goldstandard-directedgraphforDREAM410-genenet-
work 1. 154
6.4 Top 10 GRN edges as predicted by ARACNE algorithm. 158
6.5 Top 10 GRN edges as predicted by CLR algorithm. 160
6.6 Top 10 GRN edges as predicted by GENIE3 algorithm. 161
6.7 Top 10 GRN edges as predicted by MRNET algorithm. 163
6.8 GRN edges as predicted by Inferelator algorithm. 166
6.9 GRN edges as predicted by Jump3 algorithm. 168
6.10 GRN edges as predicted by NetworkBMA algorithm. 170
LIST OF FIGURES (cid:4) ix
6.11 GRN edges as predicted by TimeDelay-ARACNE algo-
rithm using DREAM4 10-gene dataset 1. 171
6.12 Diagramshowingtrue regulatoryrelationshipsbetween
E. coli transcriptionfactor FNR and severaltargetgenes.175
6.13 Algorithm of semi-supervised link prediction methods. 178
6.14 Performancecomparisonofthetransductiveandinduc-
tive learning approaches based on the E. coli transcrip-
tion factorARCA anddatasetGSE21869with the SVM
algorithm and the RF algorithm. 182
6.15 Performancecomparisonofthetransductiveandinduc-
tive learningapproachesbasedonthe S. cerevisiae tran-
scription factor REB1 and dataset GSE12222 with the
SVM algorithm and the RF algorithm. 183
6.16 PerformancecomparisonoftheSVMandRFalgorithms
with the transductive learning approach on five gene
expression datasets GSE10158, GSE12411, GSE33147,
GSE21869and GSE17505,andtwo transcriptionfactors
of E. coli. 184
6.17 PerformancecomparisonoftheSVMandRFalgorithms
with the transductivelearning approachonfive gene ex-
pression datasets and two other transcription factors of
E. coli. 185
6.18 PerformancecomparisonoftheSVMandRFalgorithms
with the transductive learning approach on five gene
expression datasets GSE30052, GSE12221, GSE12222,
GSE40817 and GSE8799, and two transcription factors
of S. cerevisiae. 187
6.19 PerformancecomparisonoftheSVMandRFalgorithms
with the transductivelearning approachonfive gene ex-
pression datasets and two other transcription factors of
S. cerevisiae. 188
7.1 MapReduce example 1. 206
7.2 MapReduce example 2. 214