Address of the bookmark: https://github.com/combogenomics/medusa

What is de novo genome assembly?
The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either “single ended” as described above or “paired end.”

Why genome assembly?
In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.

Raw NGS data
Reads can be saved as a Fasta file as text or in a FastQ file with their attributes. FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.

In a nutshell the protocol:
Get the sequence file(s) read from the sequencing machine (s).
Look at the readings - have an idea of what you have and what the standard is like.
If required, raw data cleanup/quality trimming.
Choose an adequate parameter set for assembly.
Assemble the data into scaffolds/contigs.
Examine the assembly performance and determine the efficiency of the assembly.

Read Quality Control:
Check the qualiy with fastQC.
Script
https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda

Quality trimming/cleanup of read files.
This function trims adapters, barcodes and other contaminants from the reads.
Script
https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command

Genome Assembly:
The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina

A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own.
Some of the assemblers available include:
Velvet
SOAP-denovo
MIRA
ALLPATHS

Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now. Few stuff you can note about the contigs you just created: They're the draft Contigs. Any mis-assemblies can occur.

Mis-assembly checking and assembly metric tools:
QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast
Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve
InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/
inGAP is also useful for finding structural variants between genomes from read mappings.

Genome finishing tools:
Semi-automated gap fillers:
Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/

IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page

Genome visualisers and editors:
Artemis - http://www.sanger.ac.uk/resources/software/artemis/
IGV - http://www.broadinstitute.org/igv/

Automated and semi automated annotation tools:
Prokka - https://github.com/tseemann/prokka
RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer
JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/

Frequent command use for the analysis are at:

https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly
https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools

Effective July 10, 2023, NCBI’s Assembly and Genome record pages now redirect to new NCBI Datasets pages. As previously announced, these updates are part of our ongoing effort to modernize and improve your user experience. NCBI Datasets is a new resource that makes it easier to find and download genome data.   

The following pages have been updated:

• The NCBI Assembly record pages now redirect to the new NCBI Datasets Genome record pages that describe assembled genomes and provide links to related NCBI tools such as Genome Data Viewer and BLAST.  
• The NCBI Genome record pages now redirect to the NCBI Datasets Taxonomy record pages that provide a taxonomy-focused portal to genes, genomes, and additional NCBI resources.   

During this transition, you will have the option to return to the legacy Genome and Assembly record pages. We will remove the legacy pages in early 2024.  

What is Genome Assembly?

Genome assembly involves piecing together short DNA sequence reads generated by sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) into longer, contiguous sequences called contigs. This can be performed as:

• De Novo Assembly: Without a reference genome.
• Reference-Guided Assembly: Using a reference genome to guide the assembly process.

Step 1: Preparing Your Data

Before starting the assembly, ensure that your raw sequencing data is high quality.

1. Input Data

   • Short Reads: Illumina sequencing generates short, accurate reads ideal for scaffolding.
   • Long Reads: PacBio and Nanopore sequencing provide long reads for resolving repetitive regions.

2. Quality Control (QC)
   Use tools like FastQC or MultiQC to assess the quality of your reads:

   fastqc reads.fastq multiqc .

   Look for issues like low-quality bases, adapter contamination, or overrepresented sequences.

3. Read Trimming and Filtering
   Trim low-quality bases and adapters using Trimmomatic or Cutadapt:

   trimmomatic PE reads_R1.fastq reads_R2.fastq trimmed_R1.fastq trimmed_R2.fastq \ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36

Step 2: Choosing an Assembly Strategy

Select an assembly strategy based on your data type:

• Short-Read Assemblers:

  • SPAdes: Popular for microbial genomes.
  • Velvet: Fast for smaller genomes.

• Long-Read Assemblers:

  • Canu: Ideal for long-read datasets.
  • Flye: Versatile for small and large genomes.

• Hybrid Assemblers:

  • MaSuRCA: Combines short and long reads.
  • Unicycler: Optimized for bacterial genomes.

Step 3: Running the Assembly

3.1. SPAdes (Short-Read Assembly)

SPAdes is an excellent choice for small genomes, such as bacteria.

The output includes assembled contigs (contigs.fasta) and scaffolds (scaffolds.fasta).

3.2. Canu (Long-Read Assembly)

Canu is designed for high-error long reads from PacBio or Nanopore.

The output will be in canu_output/genome.contigs.fasta.

3.3. Hybrid Assembly with Unicycler

Unicycler combines short and long reads for improved assemblies.

Step 4: Assessing Assembly Quality

After assembly, evaluate its quality using the following tools:

1. QUAST
   QUAST generates assembly statistics, such as N50, genome size, and GC content:

   quast contigs.fasta -o quast_output

2. BUSCO
   BUSCO checks genome completeness by identifying conserved genes:

   busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome

3. Assembly Graph Visualization
   Visualize assembly graphs with Bandage:

   Bandage load assembly_graph.gfa

Step 5: Post-Assembly Steps

1. Polishing
   Improve assembly accuracy using tools like Pilon (for short reads) or Racon (for long reads).

   racon long_reads.fasta mapped_reads.sam contigs.fasta > polished_contigs.fasta

2. Scaffolding
   Link contigs into scaffolds using tools like SSPACE or Opera-LG if required.

3. Annotation
   Annotate the assembled genome using Prokka for prokaryotes or Maker for eukaryotes.

   prokka --outdir annotation_output --prefix genome contigs.fasta

Step 6: Sharing and Archiving

1. Submit to Public Repositories
   Share your assembly in databases like NCBI GenBank, ENA, or DDBJ.

2. Metadata Preparation
   Include detailed metadata for your submission, such as organism name, sequencing platform, and coverage.

Best Practices

• Always perform quality checks at each stage to ensure data integrity.
• Use multiple tools to cross-validate results when working with complex genomes.
• Document parameters and software versions for reproducibility.

Conclusion

Genome assembly is a powerful process that transforms raw sequencing data into a coherent representation of an organism’s genome. By following this step-by-step guide, you can successfully assemble genomes and uncover valuable biological insights. Whether you’re assembling a microbial genome or tackling the complexities of a eukaryotic genome, these tools and strategies will set you on the path to success.

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. 

Address of the bookmark: https://sourceforge.net/projects/metassembler/?source=directory

Address of the bookmark: https://github.com/CMU-SAFARI/AirLift

• Homology-based misassembly correction
• Homology-based assembly scaffolding and patching
• Scaffold merging

Address of the bookmark: https://github.com/malonge/RagTag

Classificier of reads from metagenomic sequencing experiments.

•  Kawulok, J., Deorowicz, S., CoMeta: Classification of Metagenomes Using k-mers, PLOS ONE, 2015; 10(4):1–23,

CoMSA

Compressor of multiple sequence alignments of proteins.

•  Deorowicz, S., Walczyszyn, J., Debudaj-Grabysz, A., CoMSA: compression of protein multiple sequence alignment files, Bioinformatics, 2019; 35(2):22–234,

DSRC

Compressor of sequencing reads.

•  Roguski, L., Deorowicz, S., DSRC 2: Industry-oriented compression of FASTQ files, Bioinformatics, 2014; 30(15):2213–2215,
•  Deorowicz, S., Grabowski, Sz., Compression of DNA sequences in FASTQ format, Bioinformatics, 2011; 27(6):860–862,

FAMSA

Multiple sequence alignment designed for huge families of proteins (even containing hundreds of thousands of sequences).

•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., FAMSA: Fast and accurate multiple sequence alignment of huge protein families, Scientific Reports, 2016; 6(33964):

FaStore

Compressor of FASTQ files.

•  Roguski, L., Ochoa, I., Hernaez, M., Deorowicz, S., FaStore - a space-saving solution for raw sequencing data, Bioinformatics, 2018; 34(16):2748–2756,

FQSqueezer

Experimental high-end compressor of FASTQ files.

•  Deorowicz, S., FQSqueezer: k-mer-based compression of sequencing data, Scientific Reports, 2020; 10(578):

GDC

Compressor of collections of genome sequences.

•  Deorowicz, S., Danek, A., Niemiec, M., GDC 2: Compression of large collections of genomes, Scientific Reports, 2015; 5(11565):1–12,
•  Deorowicz, S., Grabowski, Sz., Robust relative compression of genomes with random access, Bioinformatics, 2011; 27(21):2979–2986,

GTC

Genotype databases compressor with support for fast queries.

•  Danek, A., Deorowicz, S., GTC: how to maintain huge genotype collections in a compressed form, Bioinformatics, 2018; 34(11):1834–1840,

GTShark

Genotypes compressor.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, Bioinformatics, 2019; 35(22):4791–4793,

KMC

Memory frugal k-mer counter.

•  Kokot, M., Długosz, M., Deorowicz, S., KMC 3: counting and manipulating k -mer statistics, Bioinformatics, 2017; 33(17):2759–2761,
•  Deorowicz, S., Kokot, M., Grabowski, Sz., Debudaj-Grabysz, A., KMC 2: Fast and resource-frugal k-mer counting, Bioinformatics, 2015; 31(10):1569–1576,
•  Deorowicz, S., Debudaj-Grabysz, A., Grabowski, Sz., Disk-based k-mer counting on a PC, BMC Bioinformatics, 2013; 14():Article no. 160,

Kmer-db

Tool for estimation of evolutionary distances in a collection of genomes.

•  Deorowicz, S., Gudys, A., Dlugosz, M., Kokot, M., Danek, A., Kmer-db: instant evolutionary distance estimation, Bioinformatics, 2019; 35(1):133–136,

MuGI

Index allowing queries for a collection of multiple genome sequences.

•  Danek, A., Deorowicz, S., Grabowski, Sz., Indexes of Large Genome Collections on a PC, PLOS ONE, 2014; 9(10):e109384,

ORCOM

Experimental compressor of sequencing reads.

•  Grabowski, Sz., Deorowicz, S., Roguski, L., Disk-based compression of data from genome sequencing, Bioinformatics, 2014; 31(9):1389–1395,

PgSA

Index allowing queries for a collection of sequencing reads.

•  Kowalski, T., Grabowski, Sz., Deorowicz, S., Indexing arbitrary-length k-mers in sequencing reads, PLOS ONE, 2015; 10(7):1–16,

QuickProbs

Multiple sequence alignment designed especially for GPU.

•  Gudys, A., Deorowicz, S., QuickProbs 2: towards rapid construction of high-quality alignments of large protein families, Scientific Reports, 2017; 7(41553):
•  Gudys, A., Deorowicz, S., QuickProbs – A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors, PLOS ONE, 2014; 9(2):e88901,

RECKONER

Read error corrector.

•  Maciej Długosz, M., Deorowicz, S., RECKONER: read error corrector based on KMC, Bioinformatics, 2017; 33(7):1086–1089,

TGC

Compressor of collections of genomes given in Variant Call Format (VCF) files.

•  Deorowicz, S., Danek, A., Grabowski, Sz., Genome compression: a novel approach for large collections, Bioinformatics, 2013; 29(20):2572–2578,

VCFShark

Compressor of VCF files.

•  Deorowicz, S., Danek, A., GTShark: Genotype compression in large projects, biorxiv.org, 2020; ():

Whisper

Experimental mapper of whole genome sequencing data.

•  Deorowicz, S., Gudys, A., Whisper 2: indel-sensitive short read mapping, bioRxiv.org, 2019; :
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Whisper: read sorting allows robust robust mapping of DNA sequencing data, Bioinformatics, 2019; 35(12):2043–2050,
•  Deorowicz, S., Debudaj-Grabysz, A., Gudys, A., Grabowski, Sz., Robust mapping of whole genome sequencing data, Poster at The Biology of Genomes Conference, 2017;

ALF
A Simulation Framework for Genome Evolution
http://www.cbrg.ethz.ch/alf

Bayesian Serial SimCoal
Bayesian Serial SimCoal, (BayeSSC) is a modification of SIMCOAL 1.0, a program written by Laurent Excoffier, John Novembre, and Stefan Schneider.
http://www.stanford.edu/group/hadlylab/ssc/index.html

BEERS
BEERS was designed to benchmark RNA-Seq alignment algorithms and also algorithms that aim to reconstruct different isoforms and alternate splicing from RNA-Seq data
http://cbil.upenn.edu/beers/

BOTTLENECK
Bottleneck is a program for detecting recent effective population size reductions from allele data frequencies
http://www.ensam.inra.fr/urlb/bottleneck/bottleneck.html

BottleSim
BottleSim is a computer simulation program for simulating the process of population bottlenecks
http://chkuo.name/software/bottlesim.html

CASS
Protein Sequence Simulation
http://www.wyomingbioinformatics.org/liberlesgroup/cass/

CDPOP
CDPOP is a landscape genetics tool for simulating the emergence of spatial genetic structure in populations resulting from specified landscape processes governing organism movement behavior.
http://cel.dbs.umt.edu/cdpop

CoalFace
CoalFace is a simulation of the coalescent process with the visual display of gene genealogies.
http://web.up.ac.za/default.asp?ipkcategoryid=3283

CoaSim
CoaSim is a tool for simulating the coalescent process with recombination and geneconversion under various demographic models.
http://users-birc.au.dk/mailund/coasim/index.html

cosi
The cosi package is written in C and is available as a tar file.
http://www.broadinstitute.org/~sfs/cosi/

CS-PSeq-Gen
A program to simulate the evolution of protein sequences under the constraints of the information of a particular reconstructed phylogeny
http://bioserv.rpbs.univ-paris-diderot.fr/software/cs-pseq-gen.html

DAWG
An application designed to simulate the evolution of recombinant DNA sequences in continuous time
http://scit.us/projects/dawg

Easypop
EASYPOP is an individual based model intended to simulate datasets under a very broad range of conditions
http://www.unil.ch/dee/page36926_fr.html

EggLib
EggLib is a C++/Python library and program package for evolutionary genetics and genomics.
http://egglib.sourceforge.net/

EvolSimulator
A simulation test bed for hypotheses of genome evolution
http://acb.qfab.org/acb/evolsim/

EvolveAGene
A realistic coding sequence simulation program that separates mutation from selection and allows the user to set selection conditions
http://bellinghamresearchinstitute.com/software/index.html

fastsimcoal
A continuous-¬‐time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios
http://cmpg.unibe.ch/software/fastsimcoal/

FastSLINK
Simulation of Marker and Phenotype Data in Pedigrees
http://watson.hgen.pitt.edu/

FFPopSim
C++/Python library for population genetics.
http://webdav.tuebingen.mpg.de/ffpopsim/

FLUX SIMULATOR
The Flux Simulator aims at providing a deterministic in silico reproduction of the experimental pipelines for RNA-Seq, employing a minimal set of parameters.
http://flux.sammeth.net/simulator.html

ForSim
ForSim: A Forward Evolutionary Computer Simulation
http://www.anthro.psu.edu/weiss_lab/research.shtml

ForwSim
The program given below is based on the algorithm described in Padhukasahasram et al. 2008 to simulate genetic drift in a standard Wright-Fisher process.
http://badri-populationgeneticsimulators.blogspot.com/

FPG
Forward Population Genetic simulation
http://genfaculty.rutgers.edu/hey/software#fpg

FREGENE
FREGENE is a C++ program that simulates sequence-like data over large genomic regions in large diploid populations.
http://www.ebi.ac.uk/projects/bargen/download/fregen/documentation_html.html

GAMETES
Genetic Architecture Model Emulator for Testing and Evaluating Software: Simulates complex SNP models with pure, strict epistatic interactions with n-loci.
http://sourceforge.net/projects/gametes/?source=navbar

GASP
Genometric Analysis Simulation Program. A software tool for testing and investigating methods in statistical genetics by generating samples of family data based on user specified models.
http://research.nhgri.nih.gov/gasp/

GemSIM
Next generation sequencing read simulator
http://sourceforge.net/projects/gemsim/

GeneArtisan
Simulation of Markers in Case-Control Study Designs
http://www.rannala.org/?page_id=241

GENOME
A rapid coalescent-based whole genome simulator
http://www.sph.umich.edu/csg/liang/genome/

GenomePop2
GenomePop2 is a specialization of the program GenomePop just to manage SNPs under more flexible and useful settings. If you need models with more than 2 alleles please use the GenomePop program version.
http://webs.uvigo.es/acraaj/genomepop2.htm

GenomeSimla
GenomeSIMLA is currently under development- however, we have a beta release that we are asking to be tested
http://chgr.mc.vanderbilt.edu/genomesimla/

GENS2
Simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions.
https://sourceforge.net/projects/gensim/

GWAsimulator
A rapid whole genome simulation program
http://biostat.mc.vanderbilt.edu/wiki/main/gwasimulator

HAP-SAMPLE
An association simulator for candidate regions or genome scans
http://www.hapsample.org/

HAPGEN
A simulator for the simulation of case control datasets at SNP markers
https://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html

HapSim
A simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients
http://cran.r-project.org/web/packages/hapsim/index.html

HAPSIMU
A program that simulates heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model
http://l.web.umkc.edu/liujian/

IBDsim
IBDSim is a computer package for the simulation of genotypic data under general isolation by distance models.
http://raphael.leblois.free.fr/

indel-Seq-Gen
A biological sequence simulation program that simulates highly divergent DNA sequences and protein superfamilies
http://bioinfolab.unl.edu/~cstrope/isg/

Indelible
A powerful and flexible simulator of biological evolution
http://abacus.gene.ucl.ac.uk/software/indelible/

invertFREGENE
InvertFREGENE is a forward-in-time simulator of inversions in population genetic data
http://www.ebi.ac.uk/projects/bargen/

kernalPop
A spatially explicit population genetic simulation engine
http://cran.r-project.org/src/contrib/archive/kernelpop/

MaCS
Markovian Coalescent Simulator
http://www-hsc.usc.edu/~garykche/

Mason
A package for the simulation of nucleotide data.
http://www.seqan.de/projects/mason/

mbs
modifying Hudson's ms software to generate samples of DNA sequences with a biallelic site under selection
http://www.sendou.soken.ac.jp/esb/innan/innanlab/software.html

Mendel's Accountant
Mendel's Accountant (MENDEL) is an advanced numerical simulation program for modeling genetic change over time and was developed collaboratively by Sanford, Baumgardner, Brewer, Gibson and ReMine
http://mendelsaccount.sourceforge.net/

MetaSim
A tool to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets
http://ab.inf.uni-tuebingen.de/software/metasim/

mlcoalsim
Multilocus Coalescent Simulations
http://code.google.com/p/mlcoalsim-v1/

ms
The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or statistical tests, and generally to aid in the interpretation of polymorphism data sets.
http://home.uchicago.edu/~rhudson1/source/mksamples.html

msHOT
The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or statistical tests, and generally to aid in the interpretation of polymorphism data sets.
http://home.uchicago.edu/~rhudson1/

msms
A coalescent Simlation tool with selection.
http://www.mabs.at/ewing/msms/index.shtml

MySSP
A program for the simulation of DNA sequence evolution across a phylogenetic tree
http://www.rosenberglab.net/software.php

Nemo
A forward-time, individual-based, genetically explicit, and stochastic simulation program designed to study the evolution of genetic markers, life history traits, and phenotypic traits in a flexible (meta-)population framework.
http://nemo2.sourceforge.net/

NetRecodon
Coalescent simulation of coding DNA sequences with recombination (inter and intracodon), migration and demography
http://code.google.com/p/netrecodon/

PEDAGOG
Software for simulating eco-evolutionary population dynamics
https://bcrc.bio.umass.edu/pedigreesoftware/node/5

phenosim
A tool to add phenotypes to simulated genotypes
http://evoplant.uni-hohenheim.de/doku.php?id=software:software

PhyloSim
An R package for the Monte Carlo simulation of sequence evolution
http://bit.ly/rlsim-git

pIRS
Profile-based Illumina pair-end reads simulator
https://code.google.com/p/pirs/

ProteinEvolver
Simulation of protein evolution along phylogenies under structure-based substitution models
http://code.google.com/p/proteinevolver/

QMSim
QTL and Marker Simulator
http://www.aps.uoguelph.ca/~msargol/qmsim/

quantiNEMO
An individual-based program for the analysis of quantitative traits with explicit genetic architecture potentially under selection in a structured population
http://www2.unil.ch/popgen/softwares/quantinemo/

RECOAL
Simulates new haplotype data from a reference population of haplotypes.
ftp://popgen.usc.edu/

Recodon
Coalescent simulation of coding DNA sequences with recombination, migration and demography
http://code.google.com/p/recodon/

rlsim
A package for simulating RNA-seq library preparation with parameter estimation
http://bit.ly/rlsim-git

Rmetasim
Rmetasim is a front-end for the metasim engine that is implemented as a package that runs in the statistical computing environment R
http://linum.cofc.edu/software.html#metasim

RNA Seq Simulator
RSS takes SAM alignment files from RNA-Seq data and simulates over dispersed, multiple replica, differential, non-stranded RNA-Seq datasets.
http://useq.sourceforge.net/cmdlnmenus.html#rnaseqsimulator

Rose
Random model of sequence evolution
http://bibiserv.techfak.uni-bielefeld.de/rose/

SelSim
SelSim is a program for Monte Carlo simulation of DNA polymorphism data for a recom- bining region within which a single bi-allelic site has experienced natural selection
http://www.well.ox.ac.uk/~spencer/selsim/

Seq-Gen
An application for the Monte Carlo simulation of molecular sequence evolution along phylogenetic trees.
http://tree.bio.ed.ac.uk/software/seqgen/

SEQPower
Statistical power analysis for sequence-based association studies
http://bioinformatics.org/spower/

SeqSIMLA
SeqSIMLA can simulate sequence data with user-specified disease and quantitative trait models. Family or unrelated case-control data can be simulated.
http://seqsimla.sourceforge.net/

Serial NetEvolve
A flexible utility for generating serially-sampled sequences along a tree or recombinant network
http://biorg.cis.fiu.edu/sne/

SFS_CODE
SFS_CODE can perform forward population genetic simulations under a general Wright-Fisher model with arbitrary migration, demographic, selective, and mutational effects.
http://sfscode.sourceforge.net/sfs_code/index/index.html

SIBSIM
Quantitative phenotype simulation in extended pedigrees
http://sourceforge.net/projects/sibsim/

SIMCOAL2
A coalescent program for the simulation of complex recombination patterns over large genomic regions under various demographic models
http://cmpg.unibe.ch/software/simcoal2/

SimCopy
An R package simulating the evolution of copy number profiles along a tree.
http://bit.ly/simcopy

SIMLA
SIMLA is a SIMuLAtion program that generates data sets of families for use in Linkage and Association studies.
http://www.chg.duke.edu/research/simla.html

SimPed
A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures
http://www.hgsc.bcm.tmc.edu/content/simped

Simprot
A program to simulate protein evolution by substitution, insertion and deletion
http://www.uhnresearch.ca/labs/tillier/software.htm#3

SimRare
Rare variant simulation and analysis tool
http://code.google.com/p/simrare/

simuGWAS
A forward-time simulator that simulates realistic samples for genome-wide association studies.
http://simupop.sourceforge.net/cookbook/simucomplexdisease

simuPOP
simuPOP is a general-purpose individual-based forward-time population genetics simulation environment.
http://simupop.sourceforge.net/

SISSI
A software tool to generate data of related sequences along a given phylogeny, taking into account user defined system of neighbourhoods and instantaneous rate matrices.
http://www.cibiv.at/software/sissi/

SNPsim
Coalescent simulation of hotspot recombination
http://code.google.com/p/phylosoftware/

SPIP
SPIP simulates the transmission of genes from parents to offspring in a population having demographic structure defined by the user
http://swfsc.noaa.gov/textblock.aspx?division=fed&id=3434

Splatche
Spatial and Temporal Coalescences in Heterogeneous Environment
http://www.splatche.com/

srv
Simulator of Rare Varaints (srv) is a simulator for the simulation of the introduction and evolution of (rare) genetic variants.
http://simupop.sourceforge.net/cookbook/simurarevariants

SUP
SLINK/FastSLINK utility program
http://mlemire.freeshell.org/software.html

TreesimJ
A flexible, forward-time population genetic simulator
http://code.google.com/p/treesimj/

Vortex
VORTEX is an individual-based simulation model for population viability analysis (PVA).
http://www.vortex9.org/vortex.html

References:

Image www.evolution-of-life.com

www.cancer.gov