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Nathan Skene edited this page May 1, 2024 · 4 revisions

Videos giving an overview of the field

Possibly the quickest survey of the field available currently is in a series of videos made by Manolis Kellis. I suggest the videos on Single cell genomics, Regulatory genomics, GWAS, 3D genomes,LDSC, fine mapping & QTLs

Learning computational biology

See these lectures prepared by Prof Jia Liu at Harvard for a good overview of computational biology. They cover everything from next gen sequencing through to single cell RNA-seq.

Short intro to complex trait genetics (via twitter)

These two twitter threads are great summaries about what is known: https://twitter.com/SashaGusevPosts/status/1647421238799736833

And what is not known: https://twitter.com/SashaGusevPosts/status/1649511866744336384

The lab library

I've bought copies of the following books so anyone in the lab can borrow them. They are all easy reading and intended to give you a general background in current understanding of molecular biology, evolution, and human genetics that forms a good background for the work the lab is doing.

  • "Arrival of the Fittest: Solving Evolution's Greatest Puzzle" by Andreas Wagner
    • This book details practical issues with how metabolic pathways could have evolved, i.e. how many possible pathways could result in production of a single metabolite? This is useful to understand to grasp how complex traits work.
  • "The Beak Of The Finch: Story of Evolution in Our Time" by Jonathan Weiner
    • Explains some of the most important practical studies on evolution, involving carefully monitoring of finch colonies on islands in the Galapagos. Helps understand how evolution actually works day to day, year by year.
  • "Who We Are and How We Got Here: Ancient DNA and the new science of the human past" by David Reich
    • Explains the history of our species from a genetic perspective
  • "A Life Decoded: My Genome: My Life" by Craig Venter and "Avoid Boring People" by James Watson
    • Sequencing of the human genome was one of the greatest scientific achievements of man. These two books explain very different perspectives on how it was done: Venter tried to do it using private funding, Watson fought to make academia rise to the challenge.
  • “At the water’s edge” by Carl Zimmmer
    • One of the most remarkable transitions in biology is the evolution of whales from land-born mammmals. This book explains what we know about how this happened.
  • "Born Together-Reared Apart" by Nancy Segal
    • This book explains the history of one of the most important twin studies. Gets a bit dry as the book goes on but the early parts of the book give a valuable introduction.

Learning biology / genomics

When I moved into biology, I read Genomes by T.A. Brown. This was some years ago and the field has moved rapidly, so I cannot speak to the current book, but four new versions have been released since then which is reassuring. The 2nd edition is available online but it's probably worth getting the latest one from the university library as it's not a slow moving field.

This article gives an computer scientist perspective on how to go about learning biology, and references many books which may be useful in doing so: https://jsomers.net/i-should-have-loved-biology/

Epigenetics

We spend a lot of time in the lab chatting about various histone marks, so you'll want to get an familiar with the different effects they have on genome regulation. These papers will give you a good overview:

Regulation of nucleosome dynamics by histone modifications | Nature Structural & Molecular Biology

Functions of DNA methylation: islands, start sites, gene bodies and beyond | Nature Reviews Genetics

Transcriptional enhancers: from properties to genome-wide predictions | Nature Reviews Genetics

The relationship between genome structure and function | Nature Reviews Genetics

Chromatin accessibility profiling methods | Nature Reviews Methods Primers

You'll want to know how single cell methods are being used within neuroscience, and this is well reviewed here:

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes - ScienceDirect

Single cell genomics

The field of single cell has exploded rapidly. There are some core techniques that are useful to understand. This video by Manolis Kellis gives a great introduction to the field, how the techniques have developed and the problems that are encountered within it. This paper explains how methodological advances lead to the exponential growth in the methods over recent years.

Unique Molecular Identifiers are used to label each molecular of mRNA prior to amplification to avoid bias resulting from exponential PCR. They were initially shown to improve the accuracy of mRNA-seq just before the single cell era. Shortly after it was shown that they can be used to generate accurate expression profiling from single cell data. This was done using the Fluidigm C1 system, which was then used for the [first major single cell analysis of brain cell types(https://science.sciencemag.org/content/347/6226/1138.full).

Until recently, all UMI based approaches were unable to provide whole-transcript coverage, which drove a broad split of protocols into two camps. The recent development of Smart-Seq3 changed this by enabling UMI's to be used along with whole-transcript coverage which enabled higher recovery rates from scRNA-seq.

Many grand challenges remain associated with single cell analysis, so there is much work left to be done!

These papers are worth reading for an understanding of the state of genetics today:

  • Visscher, Peter M., and Michael E. Goddard. "From RA Fisher’s 1918 Paper to GWAS a Century Later." Genetics 211.4 (2019): 1125-1130.

  • Ashbrook, David G., et al. "The expanded BXD family of mice: A cohort for experimental systems genetics and precision medicine." bioRxiv (2019): 672097.

    • A good understanding of complex trait genetics in mice is important for really understanding the field of genetics
    • Worth watching this video to get an overview of the complex mouse genetics field and it's importance first

  • Boyle, Evan A., Yang I. Li, and Jonathan K. Pritchard. "An expanded view of complex traits: from polygenic to omnigenic."

  • "Common disease is more complex than implied by the core gene omnigenic model." Wray, Naomi R., et al.

  • van Rheenen, Wouter, et al. "Genetic correlations of polygenic disease traits: from theory to practise"

  • Watanabe, Kyoko, et al. "A global overview of pleiotropy and genetic architecture in complex traits."

  • Sullivan, Patrick F., and Daniel H. Geschwind. "Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders."

  • Reshef, Y. A. et al. Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nat. Genet. 50, 1483–1493 (2018).

  • Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228 (2015).

  • Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. (2019).

  • Soskic, B. et al. Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases. bioRxiv 566810 (2019). doi:10.1101/566810

  • Colantuoni, C. et al. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2011).

Also important to remember the importance of rare variants: not all traits are complex

  • Vissers, Lisenka ELM, Christian Gilissen, and Joris A. Veltman. "Genetic studies in intellectual disability and related disorders." Nature Reviews Genetics 17.1 (2016): 9-18.

  • Study, Deciphering Developmental Disorders, et al. "Prevalence and architecture of de novo mutations in developmental disorders." Nature 542.7642 (2017): 433-438.

These papers are worth reading to understand single cell methods in neuroscience:

  • Zeisel, Amit, et al. "Molecular architecture of the mouse nervous system."

  • Harris, Kenneth D., et al. "Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics." PLoS biology 16.6 (2018): e2006387.

  • Hodge, Rebecca D., et al. "Conserved cell types with divergent features in human versus mouse cortex." Nature 573.7772 (2019): 61-68.

  • Codeluppi, Simone, et al. "Spatial organization of the somatosensory cortex revealed by osmFISH." Nature methods 15.11 (2018): 932.

  • Goldmann, Tobias, et al. "Origin, fate and dynamics of macrophages at central nervous system interfaces." Nature immunology 17.7 (2016): 797.

Papers worth reading about broader neuroscience:

  • Komiyama, Noboru H., et al. "Synaptic combinatorial molecular mechanisms generate repertoires of innate and learned behavior." BioRxiv (2018): 500389.

  • Kopanitsa, Maksym V., et al. "A combinatorial postsynaptic molecular mechanism converts patterns of nerve impulses into the behavioral repertoire." BioRxiv (2018): 500447.

  • Grant, Seth GN. "Synapse diversity and synaptome architecture in human genetic disorders." Human molecular genetics (2019).

  • Luo, Liqun, Edward M. Callaway, and Karel Svoboda. "Genetic dissection of neural circuits: a decade of progress." Neuron 98.2 (2018): 256-281.

Papers worth reading about broader neurogenomics:

  • Raj, Bushra, and Benjamin J. Blencowe. "Alternative splicing in the mammalian nervous system: recent insights into mechanisms and functional roles." Neuron 87.1 (2015): 14-27.

  • Vuong, Celine K., Douglas L. Black, and Sika Zheng. "The neurogenetics of alternative splicing." Nature Reviews Neuroscience 17.5 (2016): 265.

  • Kosik, Kenneth S. "Life at low copy number: how dendrites manage with so few mRNAs." Neuron 92.6 (2016): 1168-1180.

  • Holt, Christine E., and Erin M. Schuman. "The central dogma decentralized: new perspectives on RNA function and local translation in neurons." Neuron 80.3 (2013): 648-657.

Worth reading about the genetics of neurodegenetative disease:

  • Singleton, Andrew, and John Hardy. "The evolution of genetics: Alzheimer’s and Parkinson’s diseases." Neuron 90.6 (2016): 1154-1163.

Worth reading about statistics

You should be familiar with the papers refered to in the section on reproducible bioinformatics. It may be worth you watching this video on 'the dance of the p-values' to build your intuitive sense around statistics.

The best textbook I've encountered on statistics is Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. I highly recommend getting a copy and working through the examples in R. It's about Bayesian statistics, but by going through it you'll probably get a better understanding of frequentist stats that you would studying them directly.

If you will be working on the computational side of the lab, then I strongly recommend working through a copy of Pattern Recognition and Machine Learning by Chris Bishop.

Empirical Bayes methods are commonly used in genomics.

This Rmarkdown file has the key maths for major genetics concepts such as SNP heritability, GREML and LDSC. It is associated with this paper.

Interesting project going on elsewhere in neuroscience:

  • Abbott, Larry F., et al. "An international laboratory for systems and computational neuroscience." Neuron 96.6 (2017): 1213-1218.

You might find these lectures notes useful:

Documentaries

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