CLEAR: coverage-based limiting-cell experiment analysis for RNA-seq

[1]  Jian Huang,et al.  Optimal Gene Filtering for Single-Cell data (OGFSC) - a gene filtering algorithm for single-cell RNA-seq data , 2018, Bioinform..

[2]  Ahmed Mahfouz,et al.  Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells , 2018, Nature Biotechnology.

[3]  Kevin R. Moon,et al.  Recovering Gene Interactions from Single-Cell Data Using Data Diffusion , 2018, Cell.

[4]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[5]  Kiyoko Kato,et al.  Ectopic neurogenesis induced by prenatal antiepileptic drug exposure augments seizure susceptibility in adult mice , 2018, Proceedings of the National Academy of Sciences.

[6]  Q. Morris,et al.  QAPA: a new method for the systematic analysis of alternative polyadenylation from RNA-seq data , 2018, Genome Biology.

[7]  I. Nikaido,et al.  Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs , 2018, Nature Communications.

[8]  N. Weston,et al.  The Potential of Stem Cells in Treatment of Traumatic Brain Injury , 2018, Current Neurology and Neuroscience Reports.

[9]  S. Linnarsson,et al.  Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing , 2018, Nature Neuroscience.

[10]  Mauro J. Muraro,et al.  A Single-Cell RNA Sequencing Study Reveals Cellular and Molecular Dynamics of the Hippocampal Neurogenic Niche. , 2017, Cell reports.

[11]  Zheng Guo,et al.  Robust transcriptional signatures for low-input RNA samples based on relative expression orderings , 2017, BMC Genomics.

[12]  S. Teichmann,et al.  A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications , 2017, Genome Medicine.

[13]  Juehua Yu,et al.  Single-cell transcriptomics reveals gene signatures and alterations associated with aging in distinct neural stem/progenitor cell subpopulations , 2017, Protein & Cell.

[14]  B. Barres,et al.  Reactive Astrocytes: Production, Function, and Therapeutic Potential. , 2017, Immunity.

[15]  Sandrine Dudoit,et al.  Normalizing single-cell RNA sequencing data: challenges and opportunities , 2017, Nature Methods.

[16]  S. Picelli Single-cell RNA-sequencing: The future of genome biology is now , 2017, RNA biology.

[17]  Kevin R. Moon,et al.  MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data , 2017, bioRxiv.

[18]  H. Ueda,et al.  Erratum to: Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity , 2017, Genome Biology.

[19]  A. Brunet,et al.  Single-Cell Transcriptomic Analysis Defines Heterogeneity and Transcriptional Dynamics in the Adult Neural Stem Cell Lineage. , 2017, Cell reports.

[20]  Aaron T. L. Lun,et al.  Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R , 2017, Bioinform..

[21]  Joshua W. K. Ho,et al.  CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data , 2016, Genome Biology.

[22]  David A. Knowles,et al.  Batch effects and the effective design of single-cell gene expression studies , 2016, Scientific Reports.

[23]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[24]  Nir Yosef,et al.  FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data , 2016, BMC Bioinformatics.

[25]  Aleksandra A. Kolodziejczyk,et al.  Classification of low quality cells from single-cell RNA-seq data , 2016, Genome Biology.

[26]  Stinus Lindgreen,et al.  AdapterRemoval v2: rapid adapter trimming, identification, and read merging , 2016, BMC Research Notes.

[27]  I. Hellmann,et al.  Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.

[28]  Fabian J. Theis,et al.  destiny: diffusion maps for large-scale single-cell data in R , 2015, Bioinform..

[29]  D. Poeppel,et al.  Cortical Tracking of Hierarchical Linguistic Structures in Connected Speech , 2015, Nature Neuroscience.

[30]  P. Linsley,et al.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.

[31]  Wen J. Li,et al.  Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation , 2015, Nucleic Acids Res..

[32]  A. Oudenaarden,et al.  Design and Analysis of Single-Cell Sequencing Experiments , 2015, Cell.

[33]  Hui Wang,et al.  SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis , 2015, PLoS Comput. Biol..

[34]  Aleksandra A. Kolodziejczyk,et al.  Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.

[35]  David W. Nauen,et al.  Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis. , 2015, Cell stem cell.

[36]  Jianwei Jiao,et al.  Molecular Biomarkers for Embryonic and Adult Neural Stem Cell and Neurogenesis , 2015, BioMed research international.

[37]  Xuegong Zhang,et al.  mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data , 2015, Nature Communications.

[38]  Catalina A. Vallejos,et al.  BASiCS: Bayesian Analysis of Single-Cell Sequencing Data , 2015, PLoS Comput. Biol..

[39]  Aleksandra A. Kolodziejczyk,et al.  The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.

[40]  N. Jafari,et al.  Evaluation of commercially available RNA amplification kits for RNA sequencing using very low input amounts of total RNA. , 2015, Journal of biomolecular techniques : JBT.

[41]  Steven L Salzberg,et al.  HISAT: a fast spliced aligner with low memory requirements , 2015, Nature Methods.

[42]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[43]  Aaron M. Streets,et al.  Microfluidic single-cell whole-transcriptome sequencing , 2014, Proceedings of the National Academy of Sciences.

[44]  Cole Trapnell,et al.  Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions , 2014, Nature Biotechnology.

[45]  B. Williams,et al.  From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing , 2014, Genome research.

[46]  Steven R. Head,et al.  Technical Variations in Low-Input RNA-seq Methodologies , 2014, Scientific Reports.

[47]  Pearlly Yan,et al.  Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline , 2014, Cancer informatics.

[48]  Åsa K. Björklund,et al.  Full-length RNA-seq from single cells using Smart-seq2 , 2014, Nature Protocols.

[49]  Gioele La Manno,et al.  Quantitative single-cell RNA-seq with unique molecular identifiers , 2013, Nature Methods.

[50]  N. Neff,et al.  Quantitative assessment of single-cell RNA-sequencing methods , 2013, Nature Methods.

[51]  Aleksandra A. Kolodziejczyk,et al.  Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.

[52]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[53]  R. Elkon,et al.  Alternative cleavage and polyadenylation: extent, regulation and function , 2013, Nature Reviews Genetics.

[54]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[55]  Aviv Regev,et al.  Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples , 2013, Nature Methods.

[56]  H. Ueda,et al.  Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity , 2013, Genome Biology.

[57]  W. Shi,et al.  The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote , 2013, Nucleic acids research.

[58]  Bronwen L. Aken,et al.  GENCODE: The reference human genome annotation for The ENCODE Project , 2012, Genome research.

[59]  David Haussler,et al.  The UCSC genome browser and associated tools , 2012, Briefings Bioinform..

[60]  Wei Li,et al.  RSeQC: quality control of RNA-seq experiments , 2012, Bioinform..

[61]  R. Sandberg,et al.  Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.

[62]  A. Sivachenko,et al.  RNA-SeQC: RNA-seq metrics for quality control and process optimization , 2012, Bioinform..

[63]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[64]  F. Gage,et al.  New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory? , 2010, Nature Reviews Neuroscience.

[65]  Shimyn Slomovic,et al.  Addition of poly(A) and poly(A)-rich tails during RNA degradation in the cytoplasm of human cells , 2010, Proceedings of the National Academy of Sciences.

[66]  Catalin C. Barbacioru,et al.  mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.

[67]  A. Shetty,et al.  Hippocampal neurogenesis and neural stem cells in temporal lobe epilepsy , 2009, Epilepsy & Behavior.

[68]  J. Harrow,et al.  GENCODE: producing a reference annotation for ENCODE , 2006, Genome Biology.

[69]  D. Steindler,et al.  Neural stem and progenitor cells in nestin‐GFP transgenic mice , 2004, The Journal of comparative neurology.

[70]  J. Warner,et al.  The economics of ribosome biosynthesis in yeast. , 1999, Trends in biochemical sciences.

[71]  Richard M Myers,et al.  Transposase mediated construction of RNA-seq libraries. , 2012, Genome research.

[72]  Aaron R. Quinlan,et al.  Bioinformatics Applications Note Genome Analysis Bedtools: a Flexible Suite of Utilities for Comparing Genomic Features , 2022 .

[73]  A. Tartakoff,et al.  Supplementary Issue: Network and Pathway Analysis of Cancer Susceptibility (b) , 2022 .