This document discusses computational methods and challenges for genome assembly using next-generation sequencing data. It describes the four main stages of genome assembly as preprocessing filtering, graph construction, graph simplification, and postprocessing filtering. Each stage processes the data from the previous stage to build the assembly graph and reduce complexity, though some assemblers delay filtering steps.

1. Methods

2. Old-school

3. “Molecular” Ecology
• Allozymes
• PCR
• RFLP
• AFLP
• qPCR
• qRT-PCR
• Microsatellites
• Sanger sequencing
• PCR with degenerate primers

4. RFLP : Restriction Fragment Length Polymorphism

5. AFLP:Amplified fragment length
polymorphism
• restriction enzymes to digest genomic DNA,
• followed by ligation of adaptors to ends of restriction
fragments.
• PCR amplification using primers complementary to the
adaptor sequence, the restriction site sequence (and a few
nucleotides inside the restriction site fragments)

7. Genomics approaches/challenges
for ecology & evolution

8. Computer simulations: tools for
population and evolutionary genetics
Sean Hoban1,2
, Giorgio Bertorelle2
and Oscar E. Gaggiotti1
Abstract | Computer simulations are excellent tools for understanding the evolutionary
and genetic consequences of complex processes whose interactions cannot be
analytically predicted. Simulations have traditionally been used in population genetics
by a fairly small community with programming expertise, but the recent availability of
dozens of sophisticated, customizable software packages for simulation now makes
simulation an accessible option for researchers in many fields. The in silico genetic data
produced by simulations, along with greater availability of population-genomics data,
are transforming genetic epidemiology, anthropology, evolutionary and population
genetics and conservation. In this Review of the state-of-the-art of simulation software,
STUDY DESIGNS
EWS

9. Genome-wide genetic marker
discovery and genotyping using
next-generation sequencing
John W. Davey*, Paul A. Hohenlohe‡
, Paul D. Etter§
, Jason Q. Boone||
,
Julian M. Catchen‡
and Mark L. Blaxter*¶
Abstract | The advent of next-generation sequencing (NGS) has revolutionized genomic
and transcriptomic approaches to biology. These new sequencing tools are also valuable
for the discovery, validation and assessment of genetic markers in populations. Here we
review and discuss best practices for several NGS methods for genome-wide genetic
marker development and genotyping that use restriction enzyme digestion of target
genomes to reduce the complexity of the target. These new methods — which include
reduced-representation sequencing using reduced-representation libraries (RRLs) or
complexity reduction of polymorphic sequences (CRoPS), restriction-site-associated DNA
STUDY DESIGNS

10. Sequencing costs have fallen so dramatically that a sin- with some basic UNIX skills, ‘do-it-yourself’ genomenotation
A beginner’s guide to eukaryotic
genome annotation
Mark Yandell and Daniel Ence
Abstract | The falling cost of genome sequencing is having a marked impact on the
research community with respect to which genomes are sequenced and how and where
they are annotated. Genome annotation projects have generally become small-scale
affairs that are often carried out by an individual laboratory. Although annotating
a eukaryotic genome assembly is now within the reach of non-experts, it remains a
challenging task. Here we provide an overview of the genome annotation process and
the available tools and describe some best-practice approaches.
STUDY DESIGNS
REVIEWS

11. 2014, pages 1–6
BIOINFORMATICS EDITORIAL doi:10.1093/bioinformatics/btu492
Genome analysis Advance Access publication July 26, 2014
Big data and other challenges in the quest for orthologs
Erik L.L. Sonnhammer1,2,3,
*, Toni Gabaldon4,5,6
, Alan W. Sousa da Silva7
, Maria Martin7
,
Marc Robinson-Rechavi8,9
, Brigitte Boeckmann10
, Paul D. Thomas11
,
Christophe Dessimoz9,12,
* and the Quest for Orthologs consortiumy
1
Stockholm Bioinformatics Center, Science for Life Laboratory, Box 1031, SE-17121 Solna, Sweden, 2
Swedish
eScience Research Center, Stockholm, 3
Department of Biochemistry and Biophysics, Stockholm University, SE-106 91
Stockholm, Sweden, 4
Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), 08003
Barcelona, Spain, 5
Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain, 6
Institucio Catalana de Recerca i Estudis
Avanc¸ats (ICREA), 08010 Barcelona, Spain, 7
EMBL-European Bioinformatics Institute, Hinxton CB10 1SD, UK,
8
Department of Ecology and Evolution, University of Lausanne, 9
Swiss Institute of Bioinformatics, 1015 Lausanne,
Switzerland, 10
SwissProt, Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland, 11
Division of Bioinformatics,
Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089, USA and 12
Department of
Genetics, Evolution and Environment, and Department of Computer Science, University College London, Gower St,
London WC1E 6BT, UK
Associate Editor: John Hancock
Bioinformatics Advance Access published August 25, 2014
http://bioinformDownloadedfrom

12. PRIMER
Principles of transcriptome analysis and gene expression
quantification: an RNA-seq tutorial
JOCHEN B. W. WOLF*†
*Department of Evolutionary Biology, Uppsala University, Uppsala, Sweden, †Science of Life Laboratory, Uppsala, Sweden
Abstract
Genome-wide analyses and high-throughput screening was long reserved for biomedical applications and genetic
model organisms. With the rapid development of massively parallel sequencing nanotechnology (or next-generation
sequencing) and simultaneous maturation of bioinformatic tools, this situation has dramatically changed. Genome-
wide thinking is forging its way into disciplines like evolutionary biology or molecular ecology that were histori-
cally confined to small-scale genetic approaches. Accessibility to genome-scale information is transforming these
fields, as it allows us to answer long-standing questions like the genetic basis of local adaptation and speciation or
the evolution of gene expression profiles that until recently were out of reach. Many in the eco-evolutionary sciences
Molecular Ecology Resources (2013) 13, 559–572 doi: 10.1111/1755-0998.12109

13. Review
Next-Generation Sequence Assembly: Four Stages of
Data Processing and Computational Challenges
Sara El-Metwally1
, Taher Hamza1
, Magdi Zakaria1
, Mohamed Helmy2,3
*¤
1 Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt, 2 Botany Department, Faculty of Agriculture, Al-Azhar
University, Cairo, Egypt, 3 Biotechnology Department, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
Abstract: Decoding DNA symbols using next-generation
sequencers was a major breakthrough in genomic
research. Despite the many advantages of next-genera-
tion sequencers, e.g., the high-throughput sequencing
rate and relatively low cost of sequencing, the assembly of
the reads produced by these sequencers still remains a
major challenge. In this review, we address the basic
framework of next-generation genome sequence assem-
blers, which comprises four basic stages: preprocessing
filtering, a graph construction process, a graph simplifi-
cation process, and postprocessing filtering. Here we
discuss them as a framework of four stages for data
construction process, a graph simplification process, and post-
processing filtering [7–35]. A series of communication messages
are transferred between these stages and each stage works on its
respective inputs to produce the outputs that reflect its function.
These stages are found in most working assemblers (see below) in
the next-generation environment but some assemblers delay
preprocessing filtering until the later stages. In this review, we
discuss the complete framework and address the most basic
challenges in each stage. Furthermore, we survey a wide range of
software tools, which represent all of the different stages in the
assembly process while also representing most of the paradigms
available during each stage. Most of the tools reviewed are freely

14. Choose a method
•What it is for?
•Why its necessary and/or interesting for EEG? (i.e.
how does it facilitate EEG work? what kind of work?)
•How do you do it? How does it work?
•What are the shortcomings/difficulties/challenges?
•In your groups:
•Single A4 page summary (on shared document for all)
•8 minute presentation.