RAiSD (Raised Accuracy in Sweep Detection) is a stand-alone software implementation of the \u03bc statistic for selective sweep detection. Unlike existing implementations, including our previously released tools (SweeD and OmegaPlus), RAiSD scans whole-genome SNP data based on a composite evaluation scheme that captures multiple sweep signatures at once.<\/p>\n
The main article describing RAiSD and the \u03bc statistic is published in Communications Biology:<\/p>\n
Other related publications:<\/p>\n
The source code of RAiSD is available on github: https:\/\/github.com\/alachins\/raisd<\/a><\/p>\n SweeD implements a composite likelihood ratio test to detect selective sweeps from whole genome data.<\/p>\n The main article describing SweeD is published in Molecular Biology and Evolution:<\/p>\n SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes<\/a> The github page of SweeD is here: OmegaPlus detects selective sweeps by employing the omega-statistic. The omega-statistic (first described by Kim and Nielsen, 2004) is a statistic that receives high values when there are two neighboring regions in an alignment, that each has high LD levels, but the LD level between them is low.<\/p>\n The main publication is here: https:\/\/academic.oup.com\/bioinformatics\/article\/28\/17\/2274\/245799<\/a><\/p>\n The github page of OmegaPlus is here: Demography inference is an interesting topic of population genetics because it allows us to understand the history of species. It’s also a vital part of selection inference. Because both demography and selection occur simultaneously on populations and often in similar time scales it’s difficult to disentangle one from the other. We implemented msABC to facilitate the inference of the demographic model.<\/p>\n Publication: https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/j.1755-0998.2010.02832.x<\/p>\n github page:<\/p>\n https:\/\/github.com\/idaios\/msABC<\/a><\/p>\n Evonet simulates the evolution of GRNs by means of genetic drift and selection. Based on the work of Dr. Andreas Wagner on boolean GRNs,\u00a0 Evonet expands those ideas by implementing two regulatory regions on each network gene. Evonet is written in C to ensure maximum compatibility and permit faster execution times. Github + Readme:\u00a0 https:\/\/github.com\/antokioukis\/evonet<\/a><\/p>\n During a retrovirus infection, a DNA copy of the viral RNA genome is permanently integrated into the nuclear DNA of the host cell as a provirus. Endogenous retroviruses (ERVs) have contributed to more than 8% of the human genome. In this project, we compare the binding sites of TFs from the ENCODE Consortium against random points and we perform hypothesis testing and statistical tests for the location of TFs and Histone Modifiers relative to the boundaries of LADs and TADs. Furthermore, we examine the distribution of Transcription Start Sites (TSS) from the GENCODE project within or outside TADs\u2019 and LADs\u2019 boundaries, and we measure the distance of the former from the latter, as well as from the center of each 3D DNA conformation.\u00a0 Simultaneously, we address the type of transcript that arises from each TSS, with our focus being placed on protein coding genes. In addition, we identify the recent evolutionary forces that shape the patterns of polymorphisms in relation to LAD and TAD boundaries within Human and Mice populations. Thus, we provide insights into whether recent, within populations evolution acts on the 3D structures of DNA within the nucleus. Finally, using Dn\/Ds measurements we examine whether older selective forces have been applied on genes located within or outside \u00a0TADs and LADs.<\/p>\n We have used the Protein Interaction Statistics (PrInS) algorithm to statistically describe interactions between amino acids using protein structures. PrInS produces a scoring matrix to describe the frequency of amino acid interactions in the protein structures. Then, this matrix was converted to several different types of matrices in order to be compared with the model substitution matrices, such BLOSUM62 and PAM120, in the prediction of protein evolution.<\/p>\n RAiSD: Selective Sweep Detection based on Multiple Signatures RAiSD (Raised Accuracy in Sweep Detection) is a stand-alone software implementation of the \u03bc statistic for selective sweep detection. Unlike existing implementations, including our previously released tools (SweeD and OmegaPlus), RAiSD scans whole-genome SNP data based on a composite evaluation scheme that captures multiple sweep signatures at … <\/p>\nSweeD: Selective Sweep Detection based on SFS<\/h2>\n
\nPavlos Pavlidis, Daniel \u017divkovi\u0107, Alexandros Stamatakis, Nikolaos Alachiotis
\nMolecular Biology and Evolution, Volume 30, Issue 9, September 2013, Pages 2224\u20132234, https:\/\/doi.org\/10.1093\/molbev\/mst112<\/p>\n
\nhttps:\/\/github.com\/alachins\/sweed<\/a><\/p>\nOmegaPlus: Selective Sweep Detection based on LD<\/h2>\n
\nBioinformatics.<\/a><\/span> 2012 Sep 1;28(17):2274-5. doi: 10.1093\/bioinformatics\/bts419. Epub 2012 Jul 3.<\/p>\n
\nhttps:\/\/github.com\/alachins\/omegaplus<\/a><\/p>\nDemography Inference (ABC)<\/h2>\n
\nMol Ecol Resour. 2010 Jul;10(4):723-7. doi: 10.1111\/j.1755-0998.2010.02832.x. Epub 2010 Feb 2.
\nmsABC: a modification of Hudson’s ms to facilitate multi-locus ABC analysis.
\nPavlidis P1, Laurent S, Stephan W.<\/p>\nSPS: Forward-Backward Spatial Simulator for Genetic Data<\/h2>\n
FEG: Forward Evolutionary Game Simulator<\/h2>\n
\n<\/span><\/div>\n<\/div>\nEVONET: Evolution of Gene Regulatory Networks<\/h2>\n
Endogenous Virus Evolution<\/a><\/h2>\n
\nThis project\u2019s goal is to locate endogenous retroviruses DNA copies into host\u2019s genome and their lineage and then to consider whether is a mutation in genes close to and what the consequences are.<\/p>\nMitochondrial-Nuclear genes Co-Evolution<\/h2>\n
HiC Data analysis — Evolution of DNA 3D Structure<\/a><\/h2>\n
Population Metagenomics<\/h2>\n
Evolutionary models of amino acids substitutions based on their neighborhood tertiary structure<\/a><\/h2>\n
Analysis of RNA-Seq and Chip-Seq Data<\/h2>\n","protected":false},"excerpt":{"rendered":"