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Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today’s diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

• Directory Structure
• How to cite this study
• Datasets
• Reproducing results
• License
• Contact

review-technology-dictates-algorithms-master├───1. figures├───2. multi_panel├───3. notebooks├───4. raw_data├───5. scripts├───6. summary_data

1. In the "figures" directory, you will find all figures used in our study.
2. In the "multi_panel" directory, you will find all figures used in our study.
3. In the "notebooks" directory, you will find all python scripts used to produce Figures 2, 3, 4, and supplementary figures.
4. In the "raw_data" directory, you will find the raw data used for generating the figures and running the python scripts in "notebooks" directory.
5. In the "scripts" directory, you will find R codes used for the statistical analyses.
6. In the "summary_data" directory, you will find csv files for the collected data about all studied read alignment tools from 1988 until 2021.

We used 10 WGS datasets with the following accession numbers: ERR009309, ERR013127, ERR013138, ERR045708, ERR050158, ERR162843, ERR181410, ERR183377, SRR061640, SRR360549

1. Install Jupyter Notebook

pip3 install jupyter

2. Install some dependencies

pip3 install wheelpip3 install pandaspip3 install seabornpip3 install ipysankeywidgetpip3 install floweaver

3. Run Jupyter Notebook and you will have a new tab in your web browser

jupyter notebook

4. Navigate to review-technology-dictates-algorithms-master/notebooks in your Jupyter Notebook session and make sure you have a trusted session (by clicking on "Not trusted" on the right top corner of the session page) so that you can save the figures into your machine.
5. Run the python code used to generate any of the subject figures by opening the code in the Notebook session and run the code using: "Cell --> Run All"

If you use our study in your work, please cite:

Mohammed Alser, Jeremy Rotman, Kodi Taraszka, Huwenbo Shi, Pelin Icer Baykal, Harry Taegyun Yang, Victor Xue, Sergey Knyazev, Benjamin D. Singer, Brunilda Balliu, David Koslicki, Pavel Skums, Alex Zelikovsky, Can Alkan, Onur Mutlu, Serghei Mangul."Technology dictates algorithms: Recent developments in read alignment"arXiv preprintarXiv:2003.00110 (2020).link

Below is bibtex format for citation.

@article{alser2020technology,title={Technology dictates algorithms: Recent developments in read alignment},author={Alser, Mohammed and Rotman, Jeremy and Taraszka, Kodi and Shi, Huwenbo and Baykal, Pelin Icer and Yang, Harry Taegyun and Xue, Victor and Knyazev, Sergey and Singer, Benjamin D and Balliu, Brunilda and others},journal={arXiv preprint arXiv:2003.00110},year={2020}}

This repository is under MIT license. For more information, please read ourLICENSE file.

Please do not hesitate to contact us (alserm@ethz.ch,mangul@usc.edu) if you have any comments, suggestions, or clarification requests regarding the study or if you would like to contribute to this resource.If you encounter bugs or have further questions or requests, you can raise an issue at theissue page.