BOL: Related items

TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads

Surabhi Chaudhary — Tue, 15 May 2018 02:53:11 -0500

TAndem REpeat ANalyzer -TAREAN – is a computational pipeline for unsupervised identification of satellite repeats from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.

How to use TAREAN:

Install a local instance of the pipeline using its source code available from bitbucket repository.
Use public Galaxy-based server at https://repeatexplorer-elixir.cerit-sc.cz/. The server is provided in frame of the Elixir CZ project and is maintained by CESNET and CERIT-SC. Simple registration is required to use this service.

Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

References

Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.

Shruti Paniwala — Sat, 08 Aug 2020 08:25:21 -0500

How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

Metabuli 분리 improves metagenomic read classification

Abhi — Sat, 03 Jun 2023 20:15:04 -0500

Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

ChromEvol

Jit — Sun, 25 Jan 2015 00:33:11 -0600

Chromosome number is a remarkably dynamic feature of eukaryotic evolution. Chromosome numbers can change by a duplication of the whole genome (a process termed polyploidy), or by single chromosome changes (ascending dysploidy via, e.g., chromosome fission or descending dysploidy via, e.g., chromosome fusion).
Of the various mechanisms of chromosome number change, polyploidy has received significant attention because of the impact such an event may have on the organism.
ChromEvol implements a series of likelihood models for the evolution of chromosome numbers. By comparing the fit of the different models to biological data, it may be possible to gain insight regarding the pathways by which the evolution of chromosome number proceeds. For each model, the program estimates the rates for the possible transitions assumed by the model, infers the set of ancestral chromosome numbers, and estimates the location along the tree for which polyploidy events (and other chromosome number changes) occurred. For further methodological details, see the publications and manual on the Downloads page.

http://www.tau.ac.il/~itaymay/cp/chromEvol/about.html

Address of the bookmark: http://www.tau.ac.il/~itaymay/cp/chromEvol/downloads.html

Computer simulation of genetic mechanism !!

Jit — Sun, 13 Mar 2016 09:29:56 -0500

Computer simulation is the discipline of designing a model of an actual or theoretical physical/biological system, executing the model on a digital computer, and analyzing the execution output. Simulation embodies the principle of ``learning by doing'' --- to learn about the system we must first build a model of some sort and then operate the model. The use of simulation is an activity that is as natural as a child who role plays. Children understand the world around them by simulating (with toys and figurines) most of their interactions with other people, animals and objects. As adults, we lose some of this childlike behavior but recapture it later on through computer simulation. To understand reality and all of its complexity, we must build artificial objects and dynamically act out roles with them. Computer simulation is the electronic equivalent of this type of role playing and it serves to drive synthetic environments and virtual worlds. Within the overall task of simulation, there are three primary sub-fields: model design, model execution and model analysis

Simulation models have become important tools in Bioinformatics studies. There are many reasons for this, but we emphasize three of the more important:

(1) they enable exploration of hypotheses, and as such, have become invaluable means to guide research;

(2) they are unique approaches to integrate (in the literal term of the word) biological knowledge, in the form of experimental results; and

(3) they enable connecting biology with other fields of study ranging from physiology to genomics;

This blog, and this software list, is intended to guide the potential user of simulation models.
It is not, in any way, meant to be comprehensive on the very diverse simulation tools that already exist, but focuses on mechanistic, dynamic models. Similarly, it is not meant to provide any coverage of the breadth of applications; however, for interested readers, we provide references to use as a possible starting point.

Simulation models are meant to answer questions which scientists have in a dynamic, quantitative, and often, a pictorial way. Much of the bioinformatics research and its applications, in particular, involve a large number of components, actors, and factors. Assembling these in a coherent framework may seem a daunting task, especially for beginners, and can lead to confusion, even for experienced scientists, especially if the objectives of such an exercise are not well defined. Followings are the list of tools bioinformatician may use to analyze and provide answers to complex biological mechanisms and related problems.

Software Resource	Brief Description and Homepage
Aladyn	Tools to investigate how demographic parameters, populations genetics and abiotic conditions affect the rate of adaptation http://www.katja-schiffers.eu/research.html
ALF	A Simulation Framework for Genome Evolution http://www.cbrg.ethz.ch/alf
ART	ART is a set of simulation tools to generate synthetic next-generation sequencing data by mimicking real sequencing process with empirical error models or quality profiles. http://www.niehs.nih.gov/research/resources/software/biostatistics/art/
BAMSurgeon	Methods for realistic simulation of mutations in real data. https://github.com/adamewing/bamsurgeon
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
BaySICS	An integral platform with a graphical interface for statistical inference based on approximate Bayesian computation. https://sites.google.com/site/baysicsabc/
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 https://liberles.cst.temple.edu/software/cass/index.html
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
Classical Genetics Simulator	Web-based simulation software http://www.cgslab.com/
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/
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/en/home/menuinst/softwares--dataset/softwares/easypop.html
EggLib	EggLib is a C++/Python library and program package for evolutionary genetics and genomics. http://egglib.sourceforge.net/
EpiSIM	EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis https://sourceforge.net/projects/episimsimulator/files/
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 https://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://sammeth.net/confluence/display/sim/home
forqs	Forward-in-time simulation of Recombination, Quantitative Traits, and Selection https://bitbucket.org/dkessner/forqs
ForSim	ForSim: A Forward Evolutionary Computer Simulation http://anth.la.psu.edu/research/weiss-lab/research/research
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 https://bio.cst.temple.edu/~hey/software/software.htm#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
FIGG	FIGG is a genome simulation tool that uses known or theorized variation frequency, per a given fragment size and grouped by GC content across a genome to model new genomes in FASTA format while tracking applied mutations for use in analysis http://insilicogenome.sourceforge.net/
fwdpp	A C++ template library for implementing efficient forward simulations. http://molpopgen.github.io/fwdpp/
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/
GCTA	Genome-wide Complex Trait Analysis http://www.complextraitgenomics.com/software/gcta/download.html
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. https://ritchielab.psu.edu/research/research-areas/statistical-genetics-and-gen-epi/methods/genomesimla
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/
Marlin	Marlin provides a user-friendly interface for performing forward-in-time population genetic simulations. http://www.patrickmeirmans.com/software/marlin.html
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/
MetaPopGen	Simulates genetics in large size metapopulations https://sites.google.com/site/marcoandrello/metapopgen
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.html
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/
OncoSimulR	BioConductor package for Forward Genetic Simulation of Cancer Progresion with Epistasis https://github.com/rdiaz02/oncosimul
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://www.ebi.ac.uk/goldman-srv/phylosim/
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://cran.r-project.org/web/packages/rmetasim/index.html
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/
scrm	A coalescent simulator optimized for long sequences and large samples. https://scrm.github.io/
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/
SimAdapt	A spatially explicit, individual-based, forward-time, landscape-genetic simulation model combined with a landscape cellular automaton. https://www.openabm.org/model/3137
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://dmpi.duke.edu/simla-simulation-software-version-32
SimPed	A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures http://bioinformatics.org/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/simugwas
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/
SMARTPOP	Simulating Mating Alliance as a Reproductive Tactic for Populations http://smartpop.sourceforge.net/
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/
Variant Simulation Tools	A simulation tool for post-GWAS genetic epidemiological studies using whole-genome or whole-exome next-gen sequencing data, with an emphasis on user-friendliness and reproducibility. http://varianttools.sourceforge.net/simulation/homepage
Vortex	VORTEX is an individual-based simulation model for population viability analysis (PVA). http://www.vortex9.org/vortex.html
Wessim	Whole Exome Sequencing SIMulator http://sak042.github.io/wessim/

Online resources on must-read papers in evolutionary biology

BioStar — Fri, 26 Jul 2024 01:39:14 -0500

Online resources on must-read papers in evolutionary biology, for a literature club.

Below is a summary of all answers that we received.

All the best,

Jana and Xiaoyan

1.       *Nick Barton:*

- The textbook "Evolution" by Nick Barton, with resources for
  exploring the literature: Barton, N. H., Briggs, D. E. G., Eisen, J.
  A., Goldstein, D. B., & Patel, N. H. (2007). Evolution. Cold Spring
  Harbor Laboratory Press.

- Papers from a course named "Classics in Evolutionary Biology":

Evolutionary Synthesis
1. Haldane, J. B. S. 1932. The causes of evolution. Longmans. New York.
   (esp. Ch. IV).
2. Fisher, R. A. 1930. The genetical theory of natural selection. Oxford
   University Press, Oxford. Selected Sections - Fundamental Theorem.

Genetic Variation
1a. Lewontin, R. C., and J. L. Hubby. 1966. A molecular approach to
the study of genic heterozygosity in natural populations. II. Amount
of variation and degree of heterozygosity in natural populations of
Drosophila pseudoobscura. Genetics. 54:595-609.

1b. Sachidandam et al. 2001. A map of human genome sequence variation
containing 1.42 million single nucleotide polymorphisms. 409: 928-33.

2. Wright S., Dobzhansky T., Hovanitz W. 1942 Genetics of natural
populations VII The allelism of lethals in the third chromosome of
Drosophila pseudoobscura. Genetics 27: 363-394.

Recombination and evolution
1. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits
to artificial selection. Genet. Res. 8:269-294.

2. Maynard Smith and Haigh. 1974. The hitch-hiking effect of a favourable
gene. Genet. Res. 23: 23-35.

Understanding sequence variation
1. Begun D. J., Aquadro C. F., 1992 Levels of naturally occurring DNA
polymorphism correlate with recombination rate in Drosophila melanogaster.
Nature 356: 519-520.

2. Green R. E., Reich D., Pääbo S., 2010 A draft sequence of the
Neandertal genome. Science 328: 710-722.

Quantitative Genetics:  variation in complex traits
1. Galton F., 1877 Typical laws of heredity. Nature 15: 492-495-
512-514- 532-533.

2. Turelli M., 1984 Heritable genetic variation via
mutation-selection balance: Lerch's Zeta meets the abdominal
bristle. Theor. Popul. Biol. 25: 138-193.

Quantitative Genetics:  finding the genes
1. Shrimpton A. E., Robertson A., 1988 The Isolation of polygenic factors
controlling bristle score in Drosophila melanogaster II Distribution of
third chromosome bristle effects within chromosome sections. Genetics
118: 445-459.

2. Boyle E. A., Li Y. I., Pritchard J. K., 2017 An expanded view of
complex traits: from polygenic to omnigenic. Cell 169: 1177-1186.

Neutral Evolution
1. Kimura, M. 1968. Evolutionary rate at the molecular level. Science.
217:624-626.

2a. Kern A. D., Hahn M. W., 2018 The Neutral Theory in Light of Natural
Selection. Molecular Biology and Evolution 110: 21077-6.

2b. Jensen J. D., Payseur B. A., Stephan W., Aquadro C. F., Lynch M.,
Charlesworth D., Charlesworth B., 2018 The importance of the Neutral Theory
in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 112:
2109-4.

2c. Ellegren & Galtier. 2016. Determinants of genetic diversity. Nature
Reviews Genetics.

Mutation and Genetic Variability
1. Luria, S. E., and M. Delbrück. 1943. Mutations of Bacteria from Virus
Sensitivity to Virus Resistance. Genetics. 28(6):491-511.

2. Hill, W G. 1982. "Rates of Change in Quantitative Traits From Fixation
of New Mutations." Proceedings of the National Academy of Sciences (U.S.A.)
79: 142-45.

Testing for selection
1. McDonald & Kreitman. 1991. Adaptive protein evolution at the Adh locus
in Drosophila. Nature.

2. Begun, et al. Mol. Biol. Evol. 16, 1816-1819 (1999).

3. Siddiq et al. 2016. Experimental test and refutation of a classic case
of molecular adaptation in Drosophila melanogaster.  Nature Ecology &
Evolution.

The shifting balance
1. Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and
selection in evolution. Proceedings of the VI International Congress of
Genetics: 1. pp 356-366.

2. Coyne, J.A., N.H. Barton, and M. Turelli. 1997. A critique of Wright's
shifting balance theory of evolution.  Evolution 51: 643-671.

3. Barton. 2016. Sewall Wright on Evolution in Mendelian Populations and
the "Shifting Balance". Genetics.

Evolution of Sex
1.  Muller, H.J. 1964. The relation of recombination to mutational advance.
Mutation Res. 1(1):2-9

2. McDonald et al. 2016. Sex speeds adaptation by altering the dynamics of
molecular evolution. Nature.

Kin Selection, Cooperation, and Conflict
1. Hamilton, W. D. 1964. The genetical evolution of social behaviour I.
Journal of Theoretical Biology. 7:1-52.

2. Trivers, R. L. 1974 Parent-offspring conflict. American Zoologist.
14(1):249-264.

Sexual Selection
1. Zahavi, A. 1975. Mate selection - a selection of a handicap. J. Theor.
Biol. 53:205-214.

2. Kirkpatrick, M., and Ryan, M.J. 1991. The evolution of mating
preferences and the paradox of the lek. Nature. 350:33-38.

Fitness Landscapes
1. Dean, A. 1995. A Molecular Investigation of Genotype by Environment
Interactions. Genetics. 139:19-33.

2. Costanzo et al. 2010. The Genetic Landscape of a Cell. Science.

Speciation
1. Coyne, J. A., and H. A. Orr. 1989. Patterns of speciation in Drosophila.
Evolution. 43:362-381.

2. Corbett-Detig et al. 2013. Genetic incompatibilities are widespread
within species. Nature.

2.       *Marcos Antezana:*

Valen, L. v. 1975. Energy and Evolution. University of Chicago, Department
of Biology.

3.       *Remco Folkertsma:*

1. The work by Hopi Hoekstra on local adaptation and oldfield mice

2. Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Müller,
I., ... & Wolf, J. B. (2014). The genomic landscape underlying phenotypic
integrity in the face of gene flow in crows. Science, 344(6190), 1410-1414.

4.       *Joshka Kaufmann and Leslie Turner*

They offer us a link to 'papers every evolutionary biologist should read',
the papers are collected by Leslie Turner.
https://static1.squarespace.com/static/53e8cb7ce4b02c4bc3aeeee4/t/5ab8fcb670a6ad55c67fcdf4/1522072758665/EvoBioClassicsRefList.pdf

5.       *Sarah Stockwell*

Matt Ridley collected classic papers in evolutionary biology and printed
part of these papers in his book Evolution (see Matt Ridley. Evolution
(Univ. of Oxford Press, 2nd edition, 2004))

Genomic Basis of Evolutionary Innovations (GEvol)

BioStar — Sat, 06 Dec 2025 06:11:00 -0600

The Priority Programme (SPP 2349) funded by German Science Foundation (DFG) started 2022: „Genomic Basis of Evolutionary Innovations (GEvol)“

GEvol is unique as it will use, for the first time, a large taxonomic group to focus on one goal: to characterise the dynamics and mechanisms of genomic innovations underlying novel traits using comparative evolutionary genomics (and related data).
Thus, projects participating in GEvol we will ask fundamental evolutionary questions such as:
1. At what level is evolution repeatable?
2. How does genomic plasticity interfere with phenotypic plasticity during evolution?
3. How do inter- and intra-specific interactions influence genomic architectures?
4. How predictable is phenotypic variation given some knowledge about the dynamics and mechanisms of underlying genome evolution?

Address of the bookmark: https://g-evol.uni-muenster.de/open-positions/

The Gene Detectives: How a New Computational Tool Is Helping Us Read the History Written in Our Genes

BioStar — Thu, 10 Sep 2026 21:23:42 -0500

Imagine opening an ancient family album with no names, dates, or captions. Some photographs are missing, others have been copied, and a few show people who look almost identical. Your challenge is to discover who is related to whom and reconstruct the family story. In evolutionary biology, scientists face a similar mystery when they study genes. Every genome contains clues about its past, but genes can be duplicated, lost, and changed over millions of years, making their history difficult to trace.

A recent study published in PLOS Computational Biology introduces REvolutionH-tl 2.0 (https://pypi.org/project/revolutionhtl/), a computational tool designed to help solve this mystery. The software analyzes protein sequences and identifies evolutionary relationships between genes. It can help researchers detect important events such as gene duplication, gene loss, and speciation, allowing them to reconstruct how genes evolved across different organisms.

What makes the tool especially interesting is its focus on both speed and interpretation. The researchers compared REvolutionH-tl 2.0 with several existing computational tools and reported that it achieved competitive accuracy while requiring less computational time in their tests. The tool also includes visualization features, helping researchers turn complicated genetic data into evolutionary stories that are easier to explore.

Genes are more than simple sequences of DNA; they are records of life’s long history. Each surviving gene carries traces of the changes that shaped it. By bringing together computational analysis and visualization, REvolutionH-tl 2.0 acts like a detective, helping scientists uncover these hidden stories. Ultimately, tools like this can improve our understanding of how genomes evolved and how genes acquired the roles they perform today.

More at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013017

SOWDHAMINI Lab

Sun, 15 Sep 2013 09:19:12 -0500

Genome sequencing projects have enormous potential for benefiting human endeavors. However, just as acquiring a language's vocabulary does not enable one to speak it, databases that list the amino acid composition of proteins do not directly tell us much about these proteins' higher-level structure and function. The most productive way to indirectly exploit these databases has been to start with the small number of proteins that are fully-characterised and to assume that other "similar" proteins will have a related structure and function. Proteins with very similar amino acid sequence are "no-brainers", but the real test, which our group largely focuses on, is to detect the "essential" similarity in proteins whose non-critical sections have experienced random rearrangements during evolution. In such cases functionally similar proteins may have less than 25% sequence overlap.

More @ http://www.ncbs.res.in/sowdhamini/groups_sowdhamini.htm