Methods papers for MD997

I am now running a new module for masters students, MD997. The aim is to introduce the class to a range of advanced research methods and to get them to think about how to formulate their own research question(s).

The module is built around a paper which is allocated in the first session. I had to come up with a list of methods-type papers, which I am posting below. There are 16 students and I picked 23 papers. I aimed to cover their interests, which are biological but with some chemistry, physics and programming thrown in. The papers are a bit imaging-biased but I tried to get some ‘omics and neuro in there. There were some preprints on the list to make sure I covered the latest stuff.

The students picked their top 3 papers and we managed to assign them without too much trouble. Every paper got at least one vote. Some papers were in high demand. Fitzpatrick et al. on cryoEM of Alzheimer’s samples and the organoid paper from Lancaster et al. had by far the most votes.

The students present this paper to the class and also use it to formulate their own research proposal. Which one would you pick?

  1. Booth, D.G. et al. (2016) 3D-CLEM Reveals that a Major Portion of Mitotic Chromosomes Is Not Chromatin Mol Cell 64, 790-802.
  2. Chai, H. et al. (2017) Neural Circuit-Specialized Astrocytes: Transcriptomic, Proteomic, Morphological, and Functional Evidence Neuron 95, 531-549 e9.
  3. Chang, J.B. et al. (2017) Iterative expansion microscopy Nat Methods 14, 593-599.
  4. Chen, B.C. et al. (2014) Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution Science 346, 1257998.
  5. Chung, K. & Deisseroth, K. (2013) CLARITY for mapping the nervous system Nat Methods 10, 508-13.
  6. Eichler, K. et al. (2017) The Complete Connectome Of A Learning And Memory Center In An Insect Brain bioRxiv.
  7. Fitzpatrick, A.W.P. et al. (2017) Cryo-EM structures of tau filaments from Alzheimer’s disease Nature 547, 185-190.
  8. Habib, N. et al. (2017) Massively parallel single-nucleus RNA-seq with DroNc-seq Nat Methods 14, 955-958.
  9. Hardman, G. et al. (2017) Extensive non-canonical phosphorylation in human cells revealed using strong-anion exchange-mediated phosphoproteomics bioRxiv.
  10. Herzik, M.A., Jr. et al. (2017) Achieving better-than-3-A resolution by single-particle cryo-EM at 200 keV Nat Methods.
  11. Jacquemet, G. et al. (2017) FiloQuant reveals increased filopodia density during breast cancer progression J Cell Biol 216, 3387-3403.
  12. Jungmann, R. et al. (2014) Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT Nat Methods 11, 313-8.
  13. Kim, D.I. et al. (2016) An improved smaller biotin ligase for BioID proximity labeling Mol Biol Cell 27, 1188-96.
  14. Lancaster, M.A. et al. (2013) Cerebral organoids model human brain development and microcephaly Nature 501, 373-9.
  15. Madisen, L. et al. (2012) A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing Nat Neurosci 15, 793-802.
  16. Penn, A.C. et al. (2017) Hippocampal LTP and contextual learning require surface diffusion of AMPA receptors Nature 549, 384-388.
  17. Qin, P. et al. (2017) Live cell imaging of low- and non-repetitive chromosome loci using CRISPR-Cas9 Nat Commun 8, 14725.
  18. Quick, J. et al. (2016) Real-time, portable genome sequencing for Ebola surveillance Nature 530, 228-232.
  19. Ries, J. et al. (2012) A simple, versatile method for GFP-based super-resolution microscopy via nanobodies Nat Methods 9, 582-4.
  20. Rogerson, D.T. et al. (2015) Efficient genetic encoding of phosphoserine and its nonhydrolyzable analog Nat Chem Biol 11, 496-503.
  21. Russell, M.R. et al. (2017) 3D correlative light and electron microscopy of cultured cells using serial blockface scanning electron microscopy J Cell Sci 130, 278-291.
  22. Strickland, D. et al. (2012) TULIPs: tunable, light-controlled interacting protein tags for cell biology Nat Methods 9, 379-84.
  23. Yang, J. et al. (2015) The I-TASSER Suite: protein structure and function prediction Nat Methods 12, 7-8.

If you are going to do a similar exercise, Twitter is invaluable for suggestions for papers. None of the students complained that they couldn’t find three papers which matched their interests. I set up a slide carousel in Powerpoint with the front page of each paper together with some key words to tell the class quickly what the paper was about. I gave them some discussion time and then collated their choices on the board. Assigning the papers was quite straightforward, trying to honour the first choices as far as possible. Having an excess of papers prevented too much horse trading for the papers that multiple people had picked.

Hopefully you find this list useful. I was inspired by Raphaël posting his own list here.

The Sound of Clouds: wordcloud of tweets using R

Another post using R and looking at Twitter data.

As I was typing out a tweet, I had the feeling that my vocabulary is a bit limited. Papers I tweet about are either “great”, “awesome” or “interesting”. I wondered what my most frequently tweeted words are.

Like the last post you can (probably) do what I’ll describe online somewhere, but why would you want to do that when you can DIY in R?

First, I requested my tweets from Twitter. I wasn’t sure of the limits of rtweet for retrieving tweets and the request only takes a few minutes. This gives you a download of everything including a csv of all your tweets. The text of those tweets is in a column called ‘text’.


## for text mining
## for building a corpus
## for making wordclouds
## read in your tweets
tweets <- read.csv('tweets.csv', stringsAsFactors = FALSE)
## make a corpus of the text of the tweets
tCorpus <- Corpus(VectorSource(tweets$text))
## remove all the punctation from tweets
tCorpus <- tm_map(tCorpus, removePunctuation)
## good idea to remove stopwords: high frequency words such as I, me and so on
tCorpus <- tm_map(tCorpus, removeWords, stopwords('english'))
## next step is to stem the words. Means that talking and talked become talk
tCorpus <- tm_map(tCorpus, stemDocument)
## now display your wordcloud
wordcloud(tCorpus, max.words = 100, random.order = FALSE)

For my @clathrin account this gave:


So my most tweeted word is paper, followed by cell and lab. I’m quite happy about that. I noticed that great is also high frequency, which I had a feeling would also be the case. It looks like @christlet, @davidsbristol, @jwoodgett and @cshperspectives are among my frequent twitterings, this is probably a function of the length of time we’ve been using twitter. The cloud was generated using 10.9K tweets over seven years, it might be interesting to look at any changes over this time…

The cloud is a bit rough and ready. Further filtering would be a good idea, but this quick exercise just took a few minutes.

The post title comes from “The Sound of Clouds” by The Posies from their Solid States LP.