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Category Archives: next generation sequencing
From cats to rats: two studies on domestication and tameness
Anyone who has ever read Charles Darwin is acutely aware of his fascination with domestication – particularly how he fancied fancy pigeons. Darwin drew on his domestication obsession while writing his book, The Variation of Animals and Plants under Domestication, … Continue reading →
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Posted in adaptation, association genetics, domestication, genomics, methods, next generation sequencing, phylogenetics, quantitative genetics
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Tagged cats, domestication, eQTL, QTL, rats, tameness
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The forest resounding at rare intervals with the note of … reproductive isolation
Hybrid zones are often used as a window with which to gaze upon the evolutionary process (Barton and Hewitt 1989). With the advent of genomic tools, it is possible to detect the genomic signatures and the architecture underlying reproductive isolation. In … Continue reading →
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WTF (What's The Function?)
Jay Shendure’s editorial, “Life after genetics”, points out that we, as geneticists, should shift our focus from variant-finding (e.g., GWAS) to understanding the functional implications of disease-associated variants: “We are in a period of rich discovery in human genetics and genomics. The … Continue reading →
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Posted in genomics, medicine, mutation, next generation sequencing
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Tagged gene function, genome-editing, GWAS, perspectives
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How many markers does it take to make a dataset "genomic"?
A new paper in Ecology Letters by Matthew Fitzpatrick and Stephen Keller proposes to use some a class of statistical methods developed for understanding the distribution of species in different environments to understand the distribution of genetic variants in different … Continue reading →
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Spontaneous mutations—friend or foe?
The following is a cross-posting from the Stanford CEHG Blog by Ryo (Ryosuke) Kit, a graduate student in Hunter Fraser’s lab at Stanford University. Evolution has conflicting opinions about spontaneous mutations. Spontaneous mutations produce the genetic variation that drives evolution … Continue reading →
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Posted in mutation, next generation sequencing, population genetics
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Tagged yeast
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Scanning the genome for local adaptation
One of the most obvious and important applications of evolutionary genetics is in figuring out whether natural biological communities are going to be able to adapt to global climate change. The projected rate of climate change over then next century … Continue reading →
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Posted in adaptation, association genetics, genomics, next generation sequencing
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Tagged Medicago truncatula, mixed linear model, TASSEL
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2014 NGS Field Guide: Resistance is Futile (mostly, at least for a while)
This year, to introduce the 2014 update to his Next Generation Sequencing Field Guide—perennially our most-accessed community resource—Travis Glenn has a bit more to say than just what goes in the tables. So here it is as a guest post! … Continue reading →
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No reference genome? No problem! Demographic inference from genomic data in nonmodel insect populations
This guest article by Martin Sikora is cross-posted from the Computational, Evolutionary and Human Genomics blog at Stanford University. Reconstructing the demographic history of species and populations is one of the major goals of evolutionary genetics. Inferring the timing and … Continue reading →
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Want to share your code?
In this line of work, we have all encountered tasks that are tedious, time consuming, and repetitive. (Or if not, maybe give it a bit more time.) When confronted with these situations, people tend to fall into one of two … Continue reading →
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Phylogeny-aware comparisons of microbial communities – EdgePCA and Squash Clustering
I’m jumping on the bandwagon with a blog post about this new PLoS ONE paper (taking the lead from the man in charge in my lab) because the algorithms are just so exciting: Matsen FA IV, Evans SN. (2013) Edge Principal … Continue reading →
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Posted in bioinformatics, genomics, next generation sequencing, software
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Tagged 454, edgePCA, Illumina, microbial community, pplacer, squash clustering, UniFrac, UPGMA
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