Best3 in semester1-2020

Best 3

  1. Simone Zaccaria and Benjamin J Raphael. Characterizing allele-and haplotype-specific copy numbers in single cells with chisel. Nature biotechnology, pages 1–8, 2020. URL: https://www.nature.com/articles/s41587-020-0661-6.

  2. Volker Bergen, Marius Lange, Stefan Peidli, F Alexander Wolf, and Fabian J Theis. Generalizing rna velocity to transient cell states through dynamical modeling. Nature biotechnology, pages 1–7, 2020. URL: https://www.nature.com/articles/s41587-020-0591-3.

  3. Deepmind. ‘alphafold: a solution to a 50-year-old grand challenge in biology. 2020. URL: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology.

Rongting

1. CHISEL: It is really the relevant one to what I’m doing now. The integration of BAF and RDR of scDNA-seq data in chisel is a step froward in calling CNV more accurtely. Though CaSpER also uses BAF and RDR information of scRNA-seq at the same time, the phasing strategy in chisel can be more helpful for getting more information.

2. Methods Review article in single cell data: The review outline the major challenges in analyzing single-cell cancer genomics data and survey the current computational tools available to tackle these. They further outline unsolved problems that may be considered as major opportunities for future methods development to help interpret the vast quantities of data being generated.

3. AlphaFold2: As the title of the share says, “game has changed in the field of solving protein structures!” It can be helpful in many research works. However, “This isn’t the end of something,” “It’s the beginning of many new things.”

  1. Simone Zaccaria and Benjamin J Raphael. Characterizing allele-and haplotype-specific copy numbers in single cells with chisel. Nature Biotechnology, pages 1–8, 2020. URL: https://www.nature.com/articles/s41587-020-0661-6.

  2. Allen W Zhang and Kieran R Campbell. Computational modelling in single-cell cancer genomics: methods and future directions. arXiv preprint arXiv:2005.01549, 2020. URL: https://arxiv.org/abs/2005.01549.

  3. Robert F Service. ‘the game has changed.’ai triumphs at protein folding. 2020. URL: https://www.sciencemag.org/news/2020/11/game-has-changed-ai-triumphs-solving-protein-structures.

Xianjie

1. Proteins prediction-Deep learning Cool stuff

2. scVelo Life process is assumed to be dynamically regulated. ScVelo uses kinetics to build the dynamical model trying to characterize and predict the change of gene expression level. I learned kinetics from the course of physical chemistry and it’s pleasantly surprised to find that how knowledge from textbook could apply to the real-world problem and benefit to the community.

  1. Benefit from the CHISEL model, especially the EM part.

  1. Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, and others. Improved protein structure prediction using potentials from deep learning. Nature, 577(7792):706–710, 2020. URL: https://www.nature.com/articles/s41586-019-1923-7?fbclid=IwAR37LQHolvzYLj9Dj5wGbaH48oKcKFEX4jaGFwl1oxspEvxtlC6uyDgrCKg.

  2. Volker Bergen, Marius Lange, Stefan Peidli, F Alexander Wolf, and Fabian J Theis. Generalizing rna velocity to transient cell states through dynamical modeling. Nature biotechnology, pages 1–7, 2020. URL: https://www.nature.com/articles/s41587-020-0591-3.

  3. Simone Zaccaria and Benjamin J Raphael. Characterizing allele-and haplotype-specific copy numbers in single cells with chisel. Nature biotechnology, pages 1–8, 2020. URL: https://www.nature.com/articles/s41587-020-0661-6.

Yuanhua

  1. AlphaFold2: it addressed a long-history problem on protein structure prediction with near perfect performance, and opens many exciting opportunities for functional and interaction analysis in biology. It is also a very encouraging example that data driven (often black-box) methods have great potentials on revealing complex patterns in science.

  2. CHISEL: the multi-step phasing of the gremline SNPs effectively increases the power of using the allelic information to identify copy number variations. The modelling of the two-module data further motivates the integration pivot of multiple mutations and multiple omics data for cancer mutations.

  3. scVelo: it offers an interesting extension of RNA velocity and nice implementation. The inferred splicing kinetic parameters through the dynamic model also inspires further investigation on its regulatory patterns.

  1. Deepmind. ‘alphafold: a solution to a 50-year-old grand challenge in biology. 2020. URL: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology.

  2. Simone Zaccaria and Benjamin J Raphael. Characterizing allele-and haplotype-specific copy numbers in single cells with chisel. Nature biotechnology, pages 1–8, 2020. URL: https://www.nature.com/articles/s41587-020-0661-6.

  3. Volker Bergen, Marius Lange, Stefan Peidli, F Alexander Wolf, and Fabian J Theis. Generalizing rna velocity to transient cell states through dynamical modeling. Nature biotechnology, pages 1–7, 2020. URL: https://www.nature.com/articles/s41587-020-0591-3.

Julia

  1. It provided insight on factors affecting tumor development and progression, which was very useful to me to reference to when I was writing my literature review.

  2. Also used as a reference when i was writing my literature review. cause it provided insight on different computational analysis method used for analysing cell heterogeneity.

  3. Even though it is not really relevant to what I’m doing for my fyp, but it is probably one of the article that i understand the most regarding the methodology.

  1. Anna S Nam, Ronan Chaligne, and Dan A Landau. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nature Reviews Genetics, pages 1–16, 2020. URL: https://www.nature.com/articles/s41576-020-0265-5.

  2. Gabriela S Kinker, Alissa C Greenwald, Rotem Tal, Zhanna Orlova, Michael S Cuoco, James M McFarland, Allison Warren, Christopher Rodman, Jennifer A Roth, Samantha A Bender, and others. Pan-cancer single cell rna-seq uncovers recurring programs of cellular heterogeneity. Nature Genetics, pages 807552, 2019. URL: https://www.biorxiv.org/content/10.1101/807552v1.abstract.

  3. Tatiana A. Gurbich and Valery Vladimirovich Ilinsky. Classifycnv: a tool for clinical annotation of copy-number variants. Scientific Reports, 10(1):20375, Nov 2020. URL: https://doi.org/10.1038/s41598-020-76425-3, doi:10.1038/s41598-020-76425-3.

Jerry

1. Network properties derived from deep sequencing of human B-cell receptor repertoires delineate B-cell populations I like it because this paper provided a new angle – Network Analysis to look into single cell BCR repertoire, which can clearly visualize the condition of diseased and healthy subjects. This paper also explains quite a bit on the mechanism of BCR differentiation and mutation.

2. A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis This paper described the B cell clonal expansion of multiple sclerosis and proved the effectiveness of B cell depletion therapy. This paper inspired me to compare the B cell expansion between diseased patient and healthy subject.

3. Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs I like it because this paper applied transcriptomic analysis into many parts of our body, which can reveal a clear pathway of immune cell differentiation. This paper also provide marker genes of many cell types, which are useful for future analysis

  1. Rachael JM Bashford-Rogers, Anne L Palser, Brian J Huntly, Richard Rance, George S Vassiliou, George A Follows, and Paul Kellam. Network properties derived from deep sequencing of human b-cell receptor repertoires delineate b-cell populations. Genome research, 23(11):1874–1884, 2013. URL: https://genome.cshlp.org/content/23/11/1874.short.

  2. Akshaya Ramesh, Ryan D Schubert, Ariele L Greenfield, Ravi Dandekar, Rita Loudermilk, Joseph J Sabatino, Matthew T Koelzer, Edwina B Tran, Kanishka Koshal, Kicheol Kim, and others. A pathogenic and clonally expanded b cell transcriptome in active multiple sclerosis. Proceedings of the National Academy of Sciences, 117(37):22932–22943, 2020. URL: https://www.pnas.org/content/117/37/22932.short.

  3. Shuai He, Linhe Wang, Yang Liu, Yiqi Li, Haitian Chen, Jinghong Xu, Wan Peng, Guowang Lin, Panpan Wei, Bo Li, and others. Single-cell transcriptome profiling an adult human cell atlas of 15 major organs. bioRxiv, 2020. URL: https://www.biorxiv.org/content/10.1101/2020.03.18.996975v1.abstract.

Weizhong

1. Momentum Contrast for Unsupervised Visual Representation Learning Reason : this work improve the contrastive learning by a simple queuing technique, intuitively and at the same time effectively.

2. Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign Reason : This paper focuses on an important question : how to map cell clusters across different condition. The creativity is mainly that we only build one probability density function, making the changes of coefficient explainable.

3. Characterising the loss-of-function impact of 5’ untranslated region variants in 15,708 individuals Reason ： Of great biological significance, this paper covers a large scale population genetics analysis on 5‘ UTR sequences, showing the strength of purification selection on the sequence motif.

  1. Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9729–9738. 2020. URL: https://openaccess.thecvf.com/content_CVPR_2020/html/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html.

  2. Sisi Chen, Paul Rivaud, Jong H Park, Tiffany Tsou, Emeric Charles, John R Haliburton, Flavia Pichiorri, and Matt Thomson. Dissecting heterogeneous cell populations across drug and disease conditions with popalign. Proceedings of the National Academy of Sciences, 117(46):28784–28794, 2020. URL: https://www.pnas.org/content/117/46/28784.short.

  3. Nicola Whiffin, Konrad J Karczewski, Xiaolei Zhang, Sonia Chothani, Miriam J Smith, D Gareth Evans, Angharad M Roberts, Nicholas M Quaife, Sebastian Schafer, Owen Rackham, and others. Characterising the loss-of-function impact of 5’untranslated region variants in 15,708 individuals. Nature communications, 11(1):1–12, 2020. URL: https://www.nature.com/articles/s41467-019-10717-9.

Qiaochen

1. Strategies for Pre-training Graph Neural Networks Strategies for pretraining graph neural networks are summarized and validated, pretrained models can also be downloaded. Like Bert-triggered pretrained language models in the NLP filed, this work has the potential to benefit many downstream tasks.

2. scVelo: Generalizing RNA velocity to transient cell states through dynamical modeling An interesting extension of steady-state estimation of RNA velocity to maximum likelihood estimation.

3. Rethinking the Value of Labels for Improving Class-Imbalanced Learning: A lite-weighted yet effective strategy to tackle class-imbalance problems in classification.

  1. Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265, 2019. URL: https://openreview.net/forum?id=HJlWWJSFDH.

  2. Volker Bergen, Marius Lange, Stefan Peidli, F Alexander Wolf, and Fabian J Theis. Generalizing rna velocity to transient cell states through dynamical modeling. Nature biotechnology, pages 1–7, 2020. URL: https://www.nature.com/articles/s41587-020-0591-3.

  3. Yuzhe Yang and Zhi Xu. Rethinking the value of labels for improving class-imbalanced learning. Advances in Neural Information Processing Systems, 2020. URL: https://papers.nips.cc/paper/2020/file/e025b6279c1b88d3ec0eca6fcb6e6280-Paper.pdf.

Yoyo

1.Jointly defining cell types from multiple single-cell datasets using LIGER The tool may be useful for analyzing scRNA datasets, although I haven’t used it

2.Identification of a novel cancer stem cell subpopulation that promotes progression of human fatal renal cell carcinoma by single-cell rna-seq analysis. This paper involves a couple of fancy tools I’m interested in, e.g. RNA velocity, cell-cell communication analysis

3.Reprogramming roadmap reveals route to human induced trophoblast stem cells The scRNA-seq data is relevant to my research, and I am exploring this

  1. Jialin Liu, Chao Gao, Joshua Sodicoff, Velina Kozareva, Evan Z Macosko, and Joshua D Welch. Jointly defining cell types from multiple single-cell datasets using liger. Nature Protocols, 15(11):3632–3662, 2020. URL: https://www.nature.com/articles/s41596-020-0391-8.

  2. Xiu-wu Pan, Hao Zhang, Jia-xin Chen Da Xu, Wen-jin Chen, Si-shun Gan, Fa-jun Qu, Chuan-min Chu, Jian-wei Cao, Ying-hui Fan, Xu Song, and others. Identification of a novel cancer stem cell subpopulation that promotes progression of human fatal renal cell carcinoma by single-cell rna-seq analysis. International journal of biological sciences, 16(16):3149, 2020. URL: https://www.ijbs.com/v16p3149.htm.

  3. Xiaodong Liu, John F Ouyang, Fernando J Rossello, Jia Ping Tan, Kathryn C Davidson, Daniela S Valdes, Jan Schröder, Yu BY Sun, Joseph Chen, Anja S Knaupp, and others. Reprogramming roadmap reveals route to human induced trophoblast stem cells. Nature, 586(7827):101–107, 2020. URL: https://www.nature.com/articles/s41586-020-2734-6.