Single-cell genome sequencing: current state of the science

By Charles Gawad, W. Koh, Stephen R. Quake. 2016

1,233 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Casey S. Greene
2. G. Way
3. Aviv Regev
4. Jinbo Xu
5. Nir Yosef
6. Noah Fierer
7. Allon Wagner

In Molecular Biology

1. Jinbo Xu
2. Curtis Huttenhower
3. Nicola Segata
4. Nir Yosef
5. Jian Peng
6. Edoardo Pasolli
7. Casey S. Greene
8. Adrian Tett

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Single-cell methods are generating excitement as biologists recognize the vast heterogeneity within unicellular species and between cells of the same tissue type in the same organism [211].”
• Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “This Here we review key questions, progress, and open challenges in the development of computational methods in single-cell functional genomics, focusing primarily on scRNA-seq (we do not discuss single-cell genome analysis, as it was recently reviewed elsewhere 37 ).”
• Major New Microbial Groups Expand Diversity and Alter our Understanding of the Tree of Life. (2018, neutral): “The method has been especially useful for study of eukaryotic cells (Gawad et al., 2016).”
• Embracing the unknown: disentangling the complexities of the soil microbiome (2017, methodology): “These data are increasingly derived from cultivation-independent approaches, including single-cell genomics 100 and the assembly of individual genomes from metagenomes 20,101 .”
• Methods and applications for single-cell and spatial multi-omics (2023, background): “For a view on how challenges associated with each modality specifically could be tackled, we refer to previous reviews on single-cell and spatial mono-omic profiling 11,86,164–168 .”
• Topology Prediction of α-Helical Transmembrane Proteins (2016, background): “This has allowed for fields such as single-cell sequencing to emerge [3], producing even more data.”
• Microbial strain-level population structure and genetic diversity from metagenomes (2017, positive): “2016) or single-cell (Gawad et al. 2016) sequencing makes StrainPhlAn profiling of large metagenomes collections a key tool for the understanding of the ecology of the human gut and other microbial communities.”
• Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks (2018, background): “Single-cell RNA sequencing (scRNA-seq) (Gawad et al., 2016), which profiles transcriptomes of individual cells rather than bulk samples, has been a key tool in dissecting the intercellular variation in a wide range of domains, including cancer biology (Wang et al.”
• Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2017, background): “Improvements in biochemistry 11, 12 and microfluidics 13, 14 continue to increase the number of cells and transcripts profiled per experiment.”