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Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data [article]

Chen Jia
2019 bioRxiv   pre-print
statistically characterized in terms of a zero-inflated negative binomial (ZINB) model.  ...  Single-cell RNA sequencing data have complex features such as dropout events, over-dispersion, and high-magnitude outliers, resulting in complicated probability distributions of mRNA abundances that are  ...  Zhang, Min Chen, and Cong Zhang at the University of Texas at Dallas, Yuxuan Liu at the University of Texas Southwestern Medical Center, Bochao Liu at Rutgers  ... 
doi:10.1101/827840 fatcat:evs6tx2fcrhyhobau34favszpm

Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data [article]

Chen Jia
2019 arXiv   pre-print
statistically characterized in terms of a zero-inflated negative binomial (ZINB) model.  ...  Single-cell RNA sequencing data have complex features such as dropout events, over-dispersion, and high-magnitude outliers, resulting in complicated probability distributions of mRNA abundances that are  ...  Zhang, Min Chen, and Cong Zhang at the University of Texas at Dallas, Yuxuan Liu at the University of Texas Southwestern Medical Center, Bochao Liu at Rutgers  ... 
arXiv:1911.00356v1 fatcat:ajo44vi5izgjtoy5ebysrwswvm

A mechanistic model for the negative binomial distribution of single-cell mRNA counts [article]

Lisa Amrhein, Kumar Harsha, Christiane Fuchs
2019 bioRxiv   pre-print
Several tools analyze the outcome of single-cell RNA-seq experiments, and they often assume a probability distribution for the observed sequencing counts.  ...  Our results indicate that the negative binomial distribution arises as steady-state distribution from a mechanistic model that produces mRNA molecules in bursts.  ...  Acknowledgments Our research was supported by the German Research Foundation within the SFB 1243, Subproject A17, by the German Federal Ministry of Education and Research under grant number 01DH17024,  ... 
doi:10.1101/657619 fatcat:2ukdcjrccrgutjtaowpx52wnju

RNA Sequencing and Analysis

Kimberly R. Kukurba, Stephen B. Montgomery
2015 Cold Spring Harbor Protocols  
K.R.K. is supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship from the U.S. Department of Defense, and S.B.M. is funded by the Edward Mallinckrodt, Jr. Foundation.  ...  Mauro Pala, for their valuable comments.  ...  binomial distribution DESeq Exon-based approach using the negative binomial model DEGSeq Isoform-based approach using the Poisson model EdgeR Count-based approach using empirical Bayes method based  ... 
doi:10.1101/pdb.top084970 pmid:25870306 pmcid:PMC4863231 fatcat:iqwyhy37jrh5bmrfw4wjukraja

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing

Tarmo Äijö, Christian L Müller, Richard Bonneau, Alfonso Valencia
2017 Bioinformatics  
Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data  ...  The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial and non-parametric  ...  This work was supported by Simons Foundation. Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/btx549 pmid:28968799 pmcid:PMC5860357 fatcat:petx735ga5c4xhu5esvpslhila

Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing [article]

Tarmo Aijo, Christian Lorenz Mueller, Richard Bonneau
2016 bioRxiv   pre-print
Statistical analysis and the validity of conclusions drawn from (time series) 16S rRNA and other metagenomic sequencing data is hampered by the presence of significant amount of noise and missing data  ...  The proposed Temporal Gaussian Process Model for Compositional Data Analysis (TGP-CODA) shows superior modeling performance compared to commonly used Dirichlet-multinomial, multinomial, and non-parametric  ...  Acknowledgements We acknowledge the computational resources provided by the computing group of Simons Center for Data Analysis.  ... 
doi:10.1101/076836 fatcat:lorq7uvzufegjihedgm5uplu7u

Single-cell RNA sequencing technologies and bioinformatics pipelines

Byungjin Hwang, Ji Hyun Lee, Duhee Bang
2018 Experimental and Molecular Medicine  
Single-cell RNA sequencing (scRNA-seq), for example, can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track the trajectories of distinct cell lineages in  ...  In this review, we will focus on technical challenges in single-cell isolation and library preparation and on computational analysis pipelines available for analyzing scRNA-seq data.  ...  To account for various sources of noise in single-cell data, however, a better fit can be obtained by using a Negative Binomial model (variance = mean + over-dispersion×mean 2 ; for most genes, overdispersion  ... 
doi:10.1038/s12276-018-0071-8 pmid:30089861 pmcid:PMC6082860 fatcat:5hbi2tvtcrh4ble5ibj6eudjem

Chromatin accessibility dynamics of myogenesis at single cell resolution [article]

Hannah Pliner, Jonathan Packer, Jose McFaline-Figueroa, Darren Cusanovich, Riza Daza, Sanjay Srivatsan, Xiaojie Qiu, Dana Jackson, Anna Minkina, Andrew Adey, Frank Steemers, Jay Shendure (+1 others)
2017 bioRxiv   pre-print
We introduce Cicero, a statistical method that connects regulatory elements to target genes using single cell chromatin accessibility data.  ...  The methodological framework described here constitutes a powerful new approach for elucidating the architecture, grammar and mechanisms of cis-regulation on a genome-wide basis.  ...  Acknowledgements We gratefully acknowledge Stephen Tapscott, William Noble, and Daniela Witten as well as members of the Shendure and Trapnell labs for their advice.  ... 
doi:10.1101/155473 fatcat:2ocky2kwerda7kn4khxkbbbsqi

Transcriptional Programming of Normal and Inflamed Human Epidermis at Single-Cell Resolution

Jeffrey B. Cheng, Andrew J. Sedgewick, Alex I. Finnegan, Paymann Harirchian, Jerry Lee, Sunjong Kwon, Marlys S. Fassett, Justin Golovato, Matthew Gray, Ruby Ghadially, Wilson Liao, Bethany E. Perez White (+14 others)
2018 Cell Reports  
Here we report single-cell RNA sequencing profiles of 92,889 human epidermal cells from 9 normal and 3 inflamed skin samples.  ...  We also identify molecular fingerprints of inflammatory skin states, including S100 activation in the interfollicular epidermis of normal scalp, enrichment of a CD1C+CD301A+ myeloid dendritic cell population  ...  This work was supported in part by funds from NIH grant R01CA163336 and the Grainger Engineering Breakthroughs DECLARATION OF INTERESTS The authors declare no competing interests.  ... 
doi:10.1016/j.celrep.2018.09.006 pmid:30355494 pmcid:PMC6367716 fatcat:jqq5elx2afczhlmpyu3ci54eda

In vivo negative regulation of SARS-CoV-2 receptor, ACE2, by interferons and its genetic control

M. Azim Ansari, Emanuele Marchi, Narayan Ramamurthy, Dominik Aschenbrenner, Sophie Morgan, Carl-Philipp Hackstein, Shang-Kuan Lin, Rory Bowden, Eshita Sharma, Vincent Pedergnana, Suresh Venkateswaran, Subra Kugathasan (+15 others)
2021 Wellcome Open Research  
Negative correlation was also found in the gastrointestinal tract where inflammation driven IFN-stimulated genes were negatively correlated with ACE2 expression and in lung tissue from a murine model of  ...  Angiotensin I converting enzyme 2 (ACE2) is a receptor for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and differences in its expression may affect susceptibility to infection.  ...  Acknowledgements The authors would like to thank Gilead Sciences for the provision of samples and data from the BOSON clinical study for use in these analyses.  ... 
doi:10.12688/wellcomeopenres.16559.1 fatcat:746ykc5zmvg7vpz6hktk4lfy2q

Improved Ribosome-Footprint and mRNA Measurements Provide Insights into Dynamics and Regulation of Yeast Translation

David E. Weinberg, Premal Shah, Stephen W. Eichhorn, Jeffrey A. Hussmann, Joshua B. Plotkin, David P. Bartel
2016 Cell Reports  
of Ribo-Zero-treated RNA compared to those measured by RNA-seq of total unselected RNA.  ...  on mapped RNA-seq reads from only the 30 ends of genes (Fig- and RNA-seq data.  ... 
doi:10.1016/j.celrep.2016.01.043 pmid:26876183 pmcid:PMC4767672 fatcat:ohs2tg37arc3va6hfmqbknjbjm

Improved ribosome-footprint and mRNA measurements provide insights into dynamics and regulation of yeast translation [article]

David E Weinberg, Premal Shah, Stephen W Eichhorn, Jeffrey A Hussmann, Joshua B Plotkin, David P Bartel
2015 biorxiv/medrxiv   pre-print
with a simple model that considered mRNA abundance, upstream open reading frames, cap-proximal structure and nucleotide composition, and lengths of the coding and 5′- untranslated regions.  ...  Collectively, our results reveal key features of translational control in yeast and provide a framework for executing and interpreting ribosome- profiling studies.  ...  of Ribo-Zero-treated RNA compared to those measured by RNA-seq of total unselected RNA.  ... 
doi:10.1101/021501 fatcat:ogb63bvmrjcvdir73qsbanvqhy

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Chong Peng, Yan Lin, Hao Luo, Feng Gao
2017 Frontiers in Microbiology  
The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico.  ...  Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes.  ...  Feng-Biao Guo's laboratory for providing the standalone version of Geptop.  ... 
doi:10.3389/fmicb.2017.02331 pmid:29230204 pmcid:PMC5711816 fatcat:flf2b6ljnbcmbayijjemtfa56q

A quantitative and predictive model for RNA binding by human Pumilio proteins [article]

Inga Jarmoskaite, Sarah K. Denny, Pavanapuresan P. Vaidyanathan, Winston R. Becker, Johan O.L. Andreasson, Curtis J. Layton, Kalli Kappel, Varun Shivashankar, Raashi Sreenivasan, Rhiju Das, William J. Greenleaf, Daniel Herschlag
2018 bioRxiv   pre-print
We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2  ...  High-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs).  ...  We thank Namita Bisaria, Greg Hogan, Julia Salzman, Erik Van Nostrand and members of the Herschlag lab for helpful discussions and comments on the manuscript.  ... 
doi:10.1101/403006 fatcat:6ccykl2j45ab3dte2kwz3t4qfa

Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
2019 Information Fusion  
No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease.  ...  The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view.  ...  single-cell DNA methylation and single-cell RNA-seq data.  ... 
doi:10.1016/j.inffus.2018.09.012 pmid:30467459 pmcid:PMC6242341 fatcat:mjhnzxxv4fbrlgufb7vkg3pz5u
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