This is an incomplete list of links to pages that I think are interesting, important, useful, helpful, or just plain cool.
The Castoe Lab at UT Arlington: Todd Castoe studies snake genomics. He leverages the diversity and extreme biology of squamates to investigate questions relating to adaptation, speciation, and vertebrate physiology development and evolution. http://www.snakegenomics.org
The Boissinot Lab at NYUAD: Stephane Boissinot studies evolutionary genomics, transposable element biology, and east African wildlife biogeography. http://boissinotlab.squarespace.com/
The Pollock Lab at University of Colorado School of Medicine: David Pollock studies evolutionary genomics, transposable element biology, protein sequence, structure, and functional evolution, adaptive coevolution and ancestral state reconstruction. http://www.evolutionarygenomics.com/Welcome2.html
Genome-Media.com: An online resource for helping you find interesting news in Genomics.
The Handbook of Biological Statistics: An online textbook by John H. McDonald, and a great resource for choosing the appropriate statistical test for an experiment, applying that test and interpreting the results. http://www.biostathandbook.com/index.html
Ten Simple Rules for a Computational Biologist’s Laboratory Notebook: A key reference for getting new researchers started on the right track, forming good habits documenting their progress. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004385
The best Computational Biologist’s resource I’ve found is Ming Tang’s GitHub page: I may copy the long and remarkably useful list of resources found here, including sites on computational biology, statistics, LINUX, R, visualization …
OneZoom tree of life explorer: An interactive map of the evolutionary relationships between 2,123,183 species of life on our planet. Maybe one of the most relaxing science websites. http://www.onezoom.org/
Cryptogenomicon: This is a web page from the Eddy/Rivas labs at Harvard that has useful updates about HMMER, and their work in general. I am very envious of this clever domain name because I agree with the perspective it suggests about the genome and code breaking, and because I am also a Neal Stephenson fan. https://cryptogenomicon.org/
The UCSC Genome Browser: https://genome.ucsc.edu/
RStudio Online Learning Resources: This is a great place to start if you want to develop your quantitative reasoning skills. It includes several links to great resources not just for using R but also for learning statistics and data analysis. https://www.rstudio.com/online-learning/#R If that's is too much at once, here's a simple but useful intro http://web.cs.ucla.edu/~gulzar/rstudio/basic-tutorial.html
Stack Overflow: Somebody knows why your code isn't working. https://stackoverflow.com/
RepBase: http://www.girinst.org/repbase/
RepeatMasker: http://repeatmasker.org/
Phylogenetic Comparative Methods by Luke J. Harmon: An exciting, open-source text that looks like it will both easy to read and informative. https://lukejharmon.github.io/pcm/
Methods in Population Genomics: A great collection of methods to infer population structure and explore population-scale datasets. http://methodspopgen.com/methods-to-infer-population-structure/
BIOCONDA: An incredible time saver https://bioconda.github.io/ often paired with this cheatsheet https://conda.io/docs/_downloads/conda-cheatsheet.pdf
Basic Statistics: A thoughtful and interesting blog that covers many common pitfalls and has a nice list of some essential readings https://garstats.wordpress.com/essential-readings/
NYU-NYUAD Center for Genomics and Systems Biology Bioinformatics Bootcamp: Notes for anyone who wants to master next-gen sequencing analysis. http://learn.gencore.bio.nyu.edu/
My essential toolbox for bioinformatics by Daniela C. Soto, a Fulbright Scholar in the Dennis Lab at U.C. Davis: A great, concise guide for setting up your bioinformatics battle station. I strongly endorse the Rubber Duck Debugging approach as described here. https://dcsoto.github.io/2018/07/06/bioinformatics-toolbox/
The pipefishguy R course by Adam Jones: The simplest, clearest introduction to R that I've come across (https://pipefishguysite.wordpress.com/r-course/ ). It's so succinct that I'm starting his textbook C++ for Biologists: Evolutionary Models.
Iowa State Bioinformatics Workbook: A cool new log of bioinformatics and a particularly good resource for beginners. It's from the Genome Informatics Facility at Iowa State University. https://isugenomics.github.io/bioinformatics-workbook/
Fundamentals of Data Visualization by Claus O. Wilke: A great guide to "making visualizations that accurately reflect the data, tell a story, and look professional... The entire book is written in R Markdown..." http://serialmentor.com/dataviz/
Algorithms for DNA Sequencing: Video lectures from Ben Langmead's Algorithms for DNA Sequencing Coursera class. https://www.youtube.com/playlist?list=PL2mpR0RYFQsBiCWVJSvVAO3OJ2t7DzoHA
A Bioinformatician's UNIX Toolbox: The time I could have saved if I had this open on my screen the first time I sat down and opened a terminal. http://lh3lh3.users.sourceforge.net/biounix.shtml