This book details protocols and methods that are applicable to species where genomic resources are scarce. It addresses how to implement the best method to obtain the required data given constraints as well as the other genetic resources already available.

Population genomics is a recently emerged discipline, which aims at understanding how evolutionary processes influence genetic variation across genomes. Today, in the era of cheaper next-generation sequencing, it is no longer as daunting to obtain whole genome data for any species of interest and population genomics is now conceivable in a wide range of fields, from medicine and pharmacology to ecology and evolutionary biology. However, because of the lack of reference genome and of enough a priori data on the polymorphism, population genomics analyses of populations will still involve higher constraints for researchers working on non-model organisms, as regards the choice of the genotyping/sequencing technique or that of the analysis methods. Therefore, Data Production and Analysis in Population Genomics purposely puts emphasis on protocols and methods that are applicable to species where genomic resources are still scarce. It is divided into three convenient sections, each one tackling one of the main challenges facing scientists setting up a population genomics study. The first section helps devising a sampling and/or experimental design suitable to address the biological question of interest. The second section addresses how to implement the best genotyping or sequencing method to obtain the required data given the time and cost constraints as well as the other genetic resources already available, Finally, the last section is about making the most of the (generally huge) dataset produced by using appropriate analysis methods in order to reach a biologically relevant conclusion. Written in the successful Methods in Molecular Biology™ series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, advice on methodology and implementation, and notes on troubleshooting and avoiding known pitfalls.

Authoritative andeasily accessible, Data Production and Analysis in Population Genomics serves a wide readership by providing guidelines to help choose and implement the best experimental or analytical strategy for a given purpose.

Includes cutting-edge methods and protocols Provides step-by-step detail essential for reproducible results Contains key notes and implementation advice from the experts Includes supplementary material: sn.pub/extras

Inhalt

Sampling in Landscape Genomics.- oligoTag: A Program for Designing Sets of Tags for Next-Generation Sequencing of Multiplexed Samples.- SNP Discovery in Non-Model Organisms Using 454 Next Generation Sequencing.- ISIF (In Silico Fingerprinting): A User-Friendly in silico AFLP Program.- Diversity Arrays Technology (DArT) A Generic Genome Profiling Technology on Open Platforms.- Two Methods to Easily Obtain Nucleotide Sequences from AFLP Loci of Interest.- Roche Genome Sequencer FLX Based High-Throughput Sequencing of Ancient DNA.- Preparation of Normalized cDNA Libraries for 454 Titanium Transcriptome Sequencing.- RAD Paired-End Sequencing for Local de novo Assembly and SNP Discovery in Non-Model Organisms.- Automated Scoring of AFLPs Using RawGeno v 2.0, A Free R CRAN Library.- Haplotype Inference.- Allele Identification in Assembled Genomic Sequence Datasets.- Multiple-Testing in Large-Scale Genetic Studies.- Population Genomic Analysis of Model and Non-Model Species Using Sequenced RAD Tags.- Analysis and Management of Gene and Allelic Diversity in Subdivided Populations Using the Software Program METAPOP.- DETSEL: An R-Package to Detect Marker Loci Responding to Selection.- Use of Qualitative Environmental and Phenotypic Variables in the Context of Allele Distribution Models: Detecting Signatures of Selection in the Genome of Lake Victoria Cichilds.- Genomic Scan as a Tool for Assessing the Genetic Component of Phenotypic Variance in Wild Populations.