Genetic analysis: genes, genomes, and networks in eukaryotes
Gespeichert in:
| Vorheriger Titel: | Advanced genetic analysis |
|---|---|
| 1. Verfasser: | |
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2014
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVI, 552 S. zahlr. Ill., graph. Darst. |
| ISBN: | 9780199651818 9780199681266 |
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| 245 | 1 | 0 | |a Genetic analysis |b genes, genomes, and networks in eukaryotes |c Philip Meneely |
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| 650 | 4 | |a Eukaryotic Cells |x physiology | |
| 650 | 4 | |a Eukaryotic cells |x Research | |
| 650 | 4 | |a Gene Expression Regulation | |
| 650 | 4 | |a Genetic Techniques | |
| 650 | 4 | |a Genetics |x Research |x Methodology | |
| 650 | 4 | |a Genomics | |
| 650 | 4 | |a Models, Genetic | |
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Datensatz im Suchindex
| _version_ | 1804152326232997889 |
|---|---|
| adam_text | Genetic Analys
Genes, Genomes, and Networks in Eukaryotes
Philip Meneely
Haverford College
Second Edition
OXFORD
UNIVERSITY PRESS
BRIEF CONTENTS
Detailed contents vi
Preface xviii
Acknowledgments xxiii
Figure acknowledgments xxiv
Unit I Genes and genomes 2
1 The logic of genetic analysis 5
2 Model organisms and their genomes 44
3 Genomes, chromosomes, and epigenetics 89
Unit II Genes and mutations 142
i
4 Identifying and classifying mutants 145
5 Connecting a phenotype to a DNA sequence 216
6 Finding mutant phenotypes for cloned genes 262
7 Genome-wide mutant screens 283
Unit III Genes and populations 314
8 Genome-wide associations 317
9 Genetic analysis of complex traits 339
10 Genetic analysis using natural variation
Unit IV Genes and pathways 388
11 Using one gene to identify functionally
12 Epistasis and genetic pathways 445
13 Pathways, networks, and systems 476
14 Genes, systems, and phenotypes 518
related genes 391
Glossary 539
Index 547
DETAILED CONTENTS
Preface xviii
Acknowledgments xxiii
Figure acknowledgments xxiv
Unit I Genes and genomes
1 The logic of genetic analysis 5
Topic Summary 5
Introduction 6
1 1 The logic of genetic analysis: a historical overview 8
1 2 Genes are the units of inheritance 8
1 3 Genes are found on chromosomes 10
1 4 One gene-one protein 12
Alternative splicing results in one gene-many proteins 13
Some genes have RNA rather than a protein as their functional
product 15
1 5 Genes consist of DNA 17
Text Box 1 1 The analysis of gene expression: reporter genes 19
New features in the structure and organization of genomes are
emerging routinely 25
Text Box 1 2 The analysis of gene expression: microarrays
and genome-wide transcriptional analysis 29
Gene families are extremely common in the genome 34
Family relationships are detected from aligning the
nucleotide or amino acid sequences 37
Genomes of widely divergent species share many of the same genes 40
The functions of some genes cannot be inferred from their sequence 40
1 6 Summary: genetic analysis 41
Chapter Capsule: Genetic analysis 43
Further reading 43
2 Model organisms and their genomes 44
Topic Summary 44
Introduction 45
2 1 Model organisms: an overview 46
Model organisms and human biology 47
2 2 The awesome power of yeast genetics 50
Yeast is often grown as a haploid 53
Transformation in yeast involves naturally occurring plasmids 54
Protein trafficking is one example of a fundamental process in
eukaryotic cells studied by genetic analysis in yeast 57
• •
Detailed contents VII
2 3 Caenorhabditis elegans: amenable to genetic analysis 59
C elegans has two sexes, but no females 59
Nematodes have precisely defined cell lineages 61
Transformation of C elegans is done by microinjection 63
Mutations and the cell lineage patterns are used together for
genetic analysis in C elegans 64
These mutations identified two different cellular pathways
involved in human cancers 66
2 4 Drosophila melanogaster: If you have to ask 66
D melanogaster has some peculiar features 68
Transformation of Drosophila uses a transposable element 69
Sex determination in Drosophila involves a pathway of alternative
splicing 70
Both universal and taxon-specific properties are revealed by sex
determination in Drosophila 73
2 5 Arabidopsis thaliana: a weed with a purpose 73
The Arabidopsis life cycle is shorter than many flowering plants 75
( Transformation of Arabidopsis is done using Agrobacterium
tumefaciens 76
The brassinosteroid hormone biosynthetic and signaling pathways
have been delineated by genetic analysis in Arabidopsis 78
Mutations demonstrated the presence of brassinosteroid
pathways in Arabidopsis 78
2 6 Mus musculus: mighty mouse 79
2 7 Five model organisms: a summary 81
2 8 Other useful model organisms 82
Are more model organisms needed? 82
Genetic analysis in non-model organisms 84
Chapter Capsule: Model organisms 84
Case Study 2 1 Regeneration in planaria 85
Further reading 88
3 Genomes, chromosomes, and epigenetics 89
Topic Summary 89
Introduction 90
3 1 Genomes, chromosomes, and epigenetics: an overview 90
3 2 An overview of eukaryotic gene expression 94
Transcription initiates at specialized sites 95
Initiation of transcription is regulated by promoters and
enhancers 97
Transcription pauses during elongation 98
The primary transcript is modified and spliced 98
Regulation of gene expression and protein activity also occurs
post-transcriptionally 100
Regulation by microRNAs is now observed for many genes in
multicellular organisms 100
Long non-coding RNAs are also widespread 102
Not all annotated sequence elements are transcribed 103
Gene regulation in a nutshell 103
3 3 Genome-wide methods to analyze sequence elements 104
Microarrays record the expression profile of many genes
simultaneously 104
Microarrays can provide answers for other questions about gene
structure and expression 106
Massively parallel or next-generation sequencing is much faster
and less expensive than previous sequencing methods 107
Text Box 3 1 Massively parallel or next-generation sequencing 107
Large-scale sequencing is an alternative to microarrays 113
None of the sequencing methods is designed to look at protein
expression 113
3 4 Gene structure and gene expression: some examples 114
3 5 Chromatin structure 116
Histones and the nucleosome core particle 117
Histone modifications and a histone code 118
Histone modifications are targets for other chromatin-associated
proteins 119
DNA cytosine methylation is a regulatory mechanism in
many species 120
3 6 Methods to annotate chromatin 121
DNase hypersensitive sites were among the first indications that
chromatin structure affected gene expression and function 121
Chromatin immunoprecipitation (ChIP) is a protein-based
approach to survey genome-wide binding sites 121
Bisulfite sequencing is used to identify DNA methylation sites
genome-wide 122
Formaldehyde-assisted isolation of regulatory elements (FAIRE)
detects regions with few nucleosomes 123
3 7 Chromatin structure and functions: some examples 124
Transcribed genes and replication origins have characteristic but
different chromatin signatures 124
Regulatory regions are identified by transcription-factor binding 126
HOT regions are an unexpected and unexplained feature of
metazoan genomes that were found during the ENCODE projects 128
Heterochromatin re-examined 129
3 8 Limitations of the ENCODE-related projects 131
Chapter Capsule: Genomes, chromosomes, and epigenetics 132
Case Study 3 1 Imprinting 133
Further reading 141
Unit II Genes and mutations
4 Identifying and classifying mutants
Topic Summary
Introduction
I •
Detailed contents IX
4 1 Finding mutations: an overview 146
Genetic analysis can begin with a mutant or with a gene 147
4 2 Producing mutations 149
Chemical mutagens modify or replace nucleotide bases 149
Radiation induces chromosome breaks and structural
rearrangements 151
Insertional mutagens are among the most useful laboratory
mutagens 152
Transposable elements are effective mutagens for inducing
single gene mutations 153
A summary of mutagenic effects 155
4 3 Finding mutations 155
Genetic tricks are simply well-established principles of genetics 157
Selections can be used to reduce the number of individuals that
have to be examined 158
Selection can be done using linked lethal mutations 159
Lethal mutations can still be maintained in culture 159
TextBox4 1 Conditional mutations and the time of gene activity 161
Balanced heterozygotes 164
I Mutations can affect more than one phenotype, or may not
produce a mutant phenotype 166
Text Box 4 2 Balancer chromosomes and genetic screens 167
4 4 Mapping genes 173
A mutant can be mapped using recombination 173
Text Box 4 3 Mapping genes in humans 174
A mutation can be mapped using deletions and duplications 178
4 5 Complementation tests to assign mutants to genes 181
Complementation tests define a gene but complementation
tests are not perfect 182
Complementation tests can be used to screen for new mutant
alleles of a gene 182
The total number of genes that could be found by a mutagenesis
procedure can be estimated by statistical methods 183
Text Box 4 4 Estimating the number of genes 183
Gene names are usually but not always based on mutant phenotypes 185
4 6 Classifying mutants 187
Recessive mutations generally have a loss of or a reduction in the
normal function of the gene 188
Text Box 4 5 Dominance and the evolution of gene expression 188
Null and hypomorphic alleles of a gene can be placed in an
allelic series 191
Conditional mutations are often hypomorphs 192
Dominant mutants generally arise from over-producing a normal
function 192
Mutations producing an unexpected function can also
be dominant 194
Haplo-insufficient mutants are dominant and define
dose-dependent genes 197
Summarizing the effects of mutations with an analogy 199
X Detailed contents
4 7 Summary: a mutant provides a crucial starting part for genetic
analysis of a biological process 199
Chapter Capsule: Classifying mutants 200
Case Study 4 1 Find a mutant: segmentation in Drosophila embryos 201
Further reading 215
5 Connecting a phenotype to a DNA sequence 216
Topic Summary 216
Introduction 217
5 1 Cloning genes: an overview 217
5 2 Identifying candidate genes: map position 220
Locating a gene with respect to cloned markers and genes 220
Cloning the region between two molecular markers 222
5 3 Identifying candidate genes: expression pattern 223
Genes can be cloned based on their expression patterns 223
RNA-based expression cloning 223
An intermediate method: homology-based cloning 225
Text Box 5 1 Cloning genes based on homology 225
i
5 4 Evaluating the candidate genes: complementation and mutations 226
Complementation testing or transformation rescue can be used
in model organisms to identify the best candidate gene 226
Mutations, or DNA sequence variations, are the most widely used
property to confirm the relationship between the cloned gene
and the mutant phenotype 229
Tiling arrays allow direct comparisons between wild-type and
mutant organisms 229
5 5 Direct searches for causative mutations: exome sequencing 231
Exome sequencing 231
Miller syndrome was among the first rare genetic diseases to be
analyzed by exome sequencing 233
Refinements to the procedure used for Miller syndrome have
made exome sequencing even more powerful 237
Relatively few individuals are needed to identify the gene,
even if no other information is available 238
Diagnosis can be done more accurately 238
Different disorders can be caused by the same gene 238
Similar disorders can arise from distinct genes 239
Exome sequencing is also used for somatic mutations 240
5 6 Summary: connecting a phenotype to a DNA sequence 241
Chapter Capsule: Cloning a gene 242
Case Study 5 1 Positional cloning of the cystic fibrosis gene in humans 243
Case Study 5 2 Cloning the patched gene from Drosophila 252
References and further reading 261
6 Finding mutant phenotypes for cloned genes 262
Topic Summary 262
Introduction 263
Detailed contents XI
6 1 An overview of reverse genetics: targeted gene disruptions and
targeted gene replacements 263
Reverse genetics addresses different questions than forward genetics 264
In reverse genetics in yeast and mice, a cloned gene is inserted at a
specific site in the genome 265
6 2 Targeted gene insertions in yeast 267
6 3 Targeted gene knockouts in the mouse 268
Embryonic stem cells can give rise to mouse embryos 269
ES cells can be targeted for gene insertion using both positive and
negative selections 271
The ability to target gene knockouts has changed mouse genetics 273
Genes can also be targeted by Cre-lox or other recombination systems 274
Tissue-specific mutations can be made by regulating Cre expression 275
Knock-in mutations replace the coding region with an altered function 276
Knockouts and knock-ins have proved to be versatile tools in
mouse genetics 276
I hear you knocking 277
,64 Summary: reverse genetics allows specific types of mutations
to be made and analyzed 278
Chapter Capsule: Reverse genetics 279
Case Study 6 1 patched knockout mutations in mice 280
Further reading 282
7 Genome-wide mutant screens 283
Topic Summary 283
Introduction 284
7 1 Genome-wide mutant screens: an overview 285
Genome-wide screens identify new genes affecting well-described
biological processes 287
7 2 Identifying the genes to be mutated 288
7 3 Disrupting and perturbing genes 289
Genome-wide mutant screens in yeast have been performed by
targeted gene disruption 290
Molecular bar codes allow identification of individual genes from
among a pool 291
Text Box 7 1 Molecular bar codes 292
Gene disruptions are targeted by recombination 293
7 4 RNAi and large-scale mutant analysis 294
The antecedents of RNAi lie in other experiments 294
Text Box 7 2 Antisense experiments using morpholinos 297
Text Box 7 3 MicroRNAs: a brief history 299
The method to introduce dsRNA depends on the cells and
organisms 300
The mechanism of RNAi relies on normal cellular functions 301
RNAi has some known limitations 305
The problem of cross-reactivity 305
7 5 Screening for mutant phenotypes 306
xii Detailed contents
7 6 Confirming the effects 308
7 7 Lessons from genome-wide screens 309
7 8 Summary: genome-wide screens attempt to identify
phenotypes for every gene in the genome 311
Chapter Capsule: Genome-wide screens 311
Further reading 312
Unit III Genes and populations
8 Genome-wide associations 317
Topic Summary 317
Introduction 318
8 1 Genome-wide associations: an overview 318
8 2 Variation in the human genome 319
Some locations in the human genome are observed to vary in
predictable frequencies 319
Different types of polymorphisms are found in the genome 321
t83 The strategy of GWAS 323
Polymorphisms have been compiled and assayed throughout the
human genome 323
Text Box 8 1 Detecting a SNP using microarrays 325
The genome structure reflects human history 326
Haplotypes and LD are conceptually similar to balancer
chromosomes on a population scale 329
Selection can have a larger effect than genetic drift on local
population structures 331
8 4 The process of GWAS 333
Microarrays can be used to assess common polymorphisms
throughout the genome 333
The Common Disease Common Variant hypothesis is used to find
disease-gene associations 335
8 5 Summary: genome-wide associations 336
Chapter Capsule: Genome-wide associations 338
Further reading 338
9 Genetic analysis of complex traits 339
Topic Summary 339
Introduction 340
9 1 Genetics of complex traits 340
Complex traits do not follow simple inheritance patterns 340
Quantitative complex traits have both genetic and environmental
variance » 341
Twin studies have been used in humans to estimate genetic and
environmental variance 344
Text Box 9 1 Heritability 346
Detailed contents | xiii
9 2 Genome-wide association studies 348
Crohn s disease is an example of a complex trait 350
GWAS have been used for hundreds of complex traits 351
GWAS are now a common and powerful tool in human
genetics 353
9 3 Limitations and the interpretation of GWAS 353
Population stratification 353
Gene deserts 355
Gene deserts may not be functional deserts 356
The missing heritability 356
9 4 Summary: genomes and disease gene identification 359
Chapter Capsule: Complex traits 360
References and further reading 360
10 Genetic analysis using natural variation 362
Topic Summary 362
Introduction 363
• 10 1 Genetic analysis using natural variation: an overview 364
10 2 Evolutionary forces and natural variation 365
Mutation is the original source of all natural genetic variation 365
Migration and genetic drift are related to variation in population
structure 366
Selection has profound effects on shaping natural variation 366
Selection can be detected from genome sequencing 370
Adaptation is genetic variation seen in local populations 371
10 3 Flowering time adaptations in Arabidopsis 372
The photoperiod depends on the transcription of the FT gene 372
Vernalization requires inhibition of repressors of the flowering
response 374
Arabidopsis can be used to study changes in flowering times in
other plants 376
10 4 Coat color adaptations in deer mice 376
Coat color differences show a selective advantage 377
Distinct coloration patterns correlated with specific changes
at the Agouti locus 378
10 5 EDAR and the evolution of hair texture in humans 381
EDAR has been under strong positive selection in some human
populations 381
EDAR V370A also affects hair morphology in transgenic mice 383
The adaptive advantage from EDAR V370A may arise from
sweat glands 384
10 6 Summary: genetic analysis using natural variation 385
Chapter Capsule: Natural variation 385
Further reading 386
Unit IV Genes and pathways
11 Using one gene to identify functionally related genes 391
Topic Summary 391
Introduction 392
11 1 Using one gene to find more genes involved in the same
biological process: an overview 392
11 2 Using a mutant phenotype as a tool to find related genes 393
Suppressors and enhancers modify the phenotype of another mutation 393
Suppressor and enhancer gene nomenclature can be extremely
confusing 395
11 3 Suppressor mutations: more similar to wild-type 396
Suppressor mutations can be either intragenic or extragenic 397
Text Box 11 1 General strategy for mapping a suppressor 398
Extragenic suppressors fall into three main functional classes 399
Interactional suppressors are specific to both the gene and the allele 401
Informational suppressors affect the molecular lesion in the
specific allele but not the function of the gene 403
t Gene-specific suppressors affect many different alleles of a gene,
but no or only a few other genes 405
High-copy suppression involves the use of a wild-type cloned gene 407
Text Box 11 2 Suppression of dominant mutations in Arabidopsis 408
11 4 Synthetic enhancers: modifying mutations that make a mutant
phenotype more severe 410
Synthetic enhancers are mutations that exacerbate the effect
of the original mutation 410
Synthetic enhancement can involve duplicate or paralogous genes 411
Synthetic enhancement and bypass suppression can be
indications of the same effect 412
Synthetic enhancement can involve parallel biological pathways 412
Non-allelic non-complementation is a type of synthetic enhancement
that occurs when both mutations are heterozygous 415
Text Box 11 3 Non-allelic non-complementation 416
11 5 Summary: finding related genes using mutant phenotypes 418
11 6 Using a cloned gene to find interacting genes 418
Yeast two-hybrid assays use a genetic approach to discover
protein-protein interactions 419
A Y2H assay was used to examine brassinosteroid signaling in
Arabidopsis 421
Co-immunoprecipitation is a physiological standard for
protein-protein interactions 422
The power of protein interactions: an example from plant pathology 423
11 7 Summary: finding more genes 425
Chapter Capsule: Finding related genes 427
Case Study 11 1 Genetic analysis of spindle morphogenesis in
budding yeast 429
Further reading 444
Detailed contents XV
12 Epistasis and genetic pathways 445
Topic Summary 445
Introduction 446
12 1 Epistasis and genetic pathways: an overview 446
The logic of epistasis requires close attention 448
12 2 Combining mutants and molecular expression assays 448
A ptc mutation changes the expression pattern of other proteins
expressed in the wing 449
12 3 Epistasis and genetic pathways 451
General amino acid control in budding yeast is regulated by
two types of genes 452
Sex determination in C elegans involves both positive and
negative genetic interactions 455
Negative pathways involve mutations with opposite phenotypes 456
Positive pathways involve mutations with similar phenotypes 459
Using subtle differences in the mutant phenotype 460
Illustrating positive pathways using non-biological examples 461
Returning to somatic sex determination 461
Using a dominant allele to confirm the results 461
The pathway inferred from epistasis informs further experiments
to understand sex determination 462
Epistatic analysis has known limitations 463
12 4 A branched pathway involving dauer larva formation in C elegans 464
Suppression analysis was used to find additional genes affecting
dauer formation 464
Interactions among the dauer mutations are not always simple
to interpret 466
Synthetic enhancers support the presence of two pathways 466
The two pathways share some common steps 467
12 5 The pathways unveiled 468
Chapter Capsule: Epistasis and genetic pathways 470
Case Study 12 1 Epistasis and the patched pathway 471
Further reading 475
13 Pathways, networks, and systems 476
Topic Summary 476
Introduction 477
13 1 Pathways, networks, and systems: an overview 478
Some properties in a network emerge from the interactions
of its components 479
13 2 Properties of networks: background and definitions 481
The network can be used to infer the functions of its components 481
Paths, degree distributions, and hubs describe biological networks 483
13 3 The interactions between transcription factors and
DNA sequences 485
The interactions between transcription factors and their
binding sites are often found by ChIP experiments 486
xvi Detailed contents
Genome-wide ChIP assays so far have given the most insights into
transcriptional regulation 486
A yeast one-hybrid assay is a gene-centered approach to identify
the transcription factors that bind to specific regulatory regions 488
Different types of transcriptional regulation are seen among the
networks 489
13 4 microRNA and mRNA interactions 493
Transcriptional regulation of microRNA might reflect timing
of events 493
13 5 The interactions between proteins 496
Interactomes reveal possible functions for unknown genes 496
The protein interactome is also characterized by hubs 497
Types of hubs can be distinguished by the times of their interactions 499
13 6 Gene regulation networks 500
The secretory pathway shows many of the basic principles of
synthetic interactions 501
A nearly complete and unbiased set of gene interactions reveals
the genetic landscape of the yeast cell 502
13 7 Summary: pathways, networks, and systems 509
Chapter Capsule: Pathways, networks, and systems 510
Case Study 13 1 A systems analysis ofTGF-p signaling in C elegans 511
References and further reading 517
14 Genes, systems, and phenotypes 518
Topic Summary 518
Introduction 519
14 1 Genes, systems, and phenotypes: an overview 519
14 2 Interpreting mutant phenotypes 521
Mutant phenotypes tend to affect particular developmental
stages and tissues 522
Terminal phenotypes might not indicate the time or location
of gene activity 523
Most genes have pleiotropic effects 524
The mutant phenotype may not reflect when and where the
gene is expressed 524
14 3 Types of mutation 526
Dominant mutations have not been widely studied on a
genome-wide scale 526
14 4 Gene expression levels and biological noise 528
How much change in gene expression is biologically significant? 529
The level of biological noise varies among genes affecting different
processes 529
Biological noise has several different origins 531
The phenotypic consequences of biological noise 532
Detailed contents I XVII
14 5 Genetic regulatory networks, robustness, and risk factors:
a speculation 536
Genetic interactions could affect our risk for many genetic diseases 536
Chapter Capsule: Genes, systems, and phenotypes 537
Further reading 538
Glossary
Index
|
| any_adam_object | 1 |
| author | Meneely, Philip |
| author_facet | Meneely, Philip |
| author_role | aut |
| author_sort | Meneely, Philip |
| author_variant | p m pm |
| building | Verbundindex |
| bvnumber | BV041947102 |
| callnumber-first | Q - Science |
| callnumber-label | QH440 |
| callnumber-raw | QH440 |
| callnumber-search | QH440 |
| callnumber-sort | QH 3440 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WC 4460 WG 1000 WG 4150 |
| classification_tum | BIO 180f |
| ctrlnum | (OCoLC)879593279 (DE-599)BVBBV041947102 |
| dewey-full | 576.5072 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 576 - Genetics and evolution |
| dewey-raw | 576.5072 |
| dewey-search | 576.5072 |
| dewey-sort | 3576.5072 |
| dewey-tens | 570 - Biology |
| discipline | Biologie |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV041947102 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T01:08:56Z |
| institution | BVB |
| isbn | 9780199651818 9780199681266 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027390151 |
| oclc_num | 879593279 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM DE-188 DE-19 DE-BY-UBM DE-11 DE-29T DE-355 DE-BY-UBR |
| owner_facet | DE-M49 DE-BY-TUM DE-188 DE-19 DE-BY-UBM DE-11 DE-29T DE-355 DE-BY-UBR |
| physical | XXVI, 552 S. zahlr. Ill., graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| spelling | Meneely, Philip Verfasser aut Genetic analysis genes, genomes, and networks in eukaryotes Philip Meneely 2. ed. Oxford [u.a.] Oxford Univ. Press 2014 XXVI, 552 S. zahlr. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Eukaryotic Cells physiology Eukaryotic cells Research Gene Expression Regulation Genetic Techniques Genetics Research Methodology Genomics Models, Genetic Eukaryoten (DE-588)4070991-7 gnd rswk-swf Genanalyse (DE-588)4200230-8 gnd rswk-swf Eukaryoten (DE-588)4070991-7 s Genanalyse (DE-588)4200230-8 s DE-604 Früher u.d.T. Advanced genetic analysis (DE-604)BV035404207 HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027390151&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Meneely, Philip Genetic analysis genes, genomes, and networks in eukaryotes Eukaryotic Cells physiology Eukaryotic cells Research Gene Expression Regulation Genetic Techniques Genetics Research Methodology Genomics Models, Genetic Eukaryoten (DE-588)4070991-7 gnd Genanalyse (DE-588)4200230-8 gnd |
| subject_GND | (DE-588)4070991-7 (DE-588)4200230-8 |
| title | Genetic analysis genes, genomes, and networks in eukaryotes |
| title_auth | Genetic analysis genes, genomes, and networks in eukaryotes |
| title_exact_search | Genetic analysis genes, genomes, and networks in eukaryotes |
| title_full | Genetic analysis genes, genomes, and networks in eukaryotes Philip Meneely |
| title_fullStr | Genetic analysis genes, genomes, and networks in eukaryotes Philip Meneely |
| title_full_unstemmed | Genetic analysis genes, genomes, and networks in eukaryotes Philip Meneely |
| title_old | Advanced genetic analysis |
| title_short | Genetic analysis |
| title_sort | genetic analysis genes genomes and networks in eukaryotes |
| title_sub | genes, genomes, and networks in eukaryotes |
| topic | Eukaryotic Cells physiology Eukaryotic cells Research Gene Expression Regulation Genetic Techniques Genetics Research Methodology Genomics Models, Genetic Eukaryoten (DE-588)4070991-7 gnd Genanalyse (DE-588)4200230-8 gnd |
| topic_facet | Eukaryotic Cells physiology Eukaryotic cells Research Gene Expression Regulation Genetic Techniques Genetics Research Methodology Genomics Models, Genetic Eukaryoten Genanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027390151&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT meneelyphilip geneticanalysisgenesgenomesandnetworksineukaryotes |