Create awesome lists with Manubot

A proof of concept for using Manubot to automate awesome lists. An awesome list is a themed list of resources in the README.md file in the master branch of a GitHub repository.

In this repository, README.md is created via continuous integration and should not be edited directly. Edit README-BASE.md to update this text. Update the reference lists in the content directory to add new sections or references. This is only a proof of concept that is not robust against errors in the scripts or merge conflicts.

The .travis.yml, deploy.sh, and environment.yml files were derived from https://github.com/manubot/rootstock (CC0 1.0 license).

This repository also contains a GitHub Actions workflow that uses Manubot to automatically extract reference information from the identifier in an issue title. The workflow only runs on issues with the label reference. See #7 for an example.

Manubot

1. Open collaborative writing with Manubot
   Daniel S. Himmelstein, Vincent Rubinetti, David R. Slochower, Dongbo Hu, Venkat S. Malladi, Casey S. Greene, Anthony Gitter
   (2019-07-09) https://greenelab.github.io/meta-review/

2. Opportunities and obstacles for deep learning in biology and medicine
   Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, … Casey S. Greene
   Journal of The Royal Society Interface (2018-04-04) https://doi.org/gddkhn
   DOI: 10.1098/rsif.2017.0387 · PMID: 29618526 · PMCID: PMC5938574

3. Python utilities for Manubot: Manuscripts, open and automated: manubot/manubot
   Manubot
   (2019-07-18) https://github.com/manubot/manubot

Protein-engineering

1. Machine-learning-guided directed evolution for protein engineering
   Kevin K. Yang, Zachary Wu, Frances H. Arnold
   Nature Methods (2019-07-15) https://doi.org/gf43h4
   DOI: 10.1038/s41592-019-0496-6 · PMID: 31308553

2. Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
   Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue
   arXiv (2019-04-17) https://arxiv.org/abs/1904.08102v1

3. Unified rational protein engineering with sequence-only deep representation learning
   Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, George M. Church
   Cold Spring Harbor Laboratory (2019-03-26) https://doi.org/gf48g2
   DOI: 10.1101/589333

4. Navigating the protein fitness landscape with Gaussian processes
   P. A. Romero, A. Krause, F. H. Arnold
   Proceedings of the National Academy of Sciences (2012-12-31) https://doi.org/f4k8bz
   DOI: 10.1073/pnas.1215251110 · PMID: 23277561 · PMCID: PMC3549130

Single-cell-pseudotime

1. A comparison of single-cell trajectory inference methods
   Wouter Saelens, Robrecht Cannoodt, Helena Todorov, Yvan Saeys
   Nature Biotechnology (2019-04-01) https://doi.org/gfxsgd
   DOI: 10.1038/s41587-019-0071-9 · PMID: 30936559

2. An overview of algorithms for estimating pseudotime in single-cell RNA-seq data: agitter/single-cell-pseudotime
   Anthony Gitter
   (2019-07-19) https://github.com/agitter/single-cell-pseudotime

3. Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data
   Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter
   Cold Spring Harbor Laboratory (2019-01-30) https://doi.org/gft4bb
   DOI: 10.1101/534834