Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast

Statistics requantitates the central dogma

(2015) J. J. Li et al.   SCIENCE

Dynamic profiling of the protein life cycle in response to pathogens

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Poly(A)-tail profiling reveals an embryonic switch in translational control

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Translation inhibitors cause abnormalities in ribosome profiling experiments

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System wide analyses have underestimated protein abundances and the importance of transcription in mammals

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Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast

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Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data

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Critical assessment of proteome-wide label-free absolute abundance estimation strategies

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Causes and Effects of N-Terminal Codon Bias in Bacterial Genes

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Quantitative Analysis of Fission Yeast Transcriptomes and Proteomes in Proliferating and Quiescent Cells

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PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life

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Genome-wide ribosome profiling reveals complex translational regulation in response to oxidative stress

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Deep and Highly Sensitive Proteome Coverage by LC-MS/MS Without Prefractionation

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System-wide Perturbation Analysis with Nearly Complete Coverage of the Yeast Proteome by Single-shot Ultra HPLC Runs on a Bench Top Orbitrap

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A dynamic model of proteome changes reveals new roles for transcript alteration in yeast

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Global quantification of mammalian gene expression control

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Saccharomyces Genome Database: the genomics resource of budding yeast

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Transcriptional regulation is only half the story

(2010) Joshua B Plotkin   Molecular Systems Biology

Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line

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Tackling the widespread and critical impact of batch effects in high-throughput data

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There is a steady-state transcriptome in exponentially growing yeast cells

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A Comprehensive, Quantitative, and Genome-Wide Model of Translation

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A Universal Trend of Reduced mRNA Stability near the Translation-Initiation Site in Prokaryotes and Eukaryotes

(2010) Wanjun Gu et al.   PLoS Computational Biology

A weakly informative default prior distribution for logistic and other regression models

(2009) Andrew Gelman et al.   Annals of Applied Statistics

Full Dynamic Range Proteome Analysis of S. cerevisiae by Targeted Proteomics

(2009) Paola Picotti et al.   CELL

Correlation of mRNA and protein in complex biological samples

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Bayesian Correction for Attenuation of Correlation in Multi-Trial Spike Count Data

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Quantification of the yeast transcriptome by single-molecule sequencing

(2009) Doron Lipson et al.   NATURE BIOTECHNOLOGY

Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing

(2009) M. Yassour et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Coding-Sequence Determinants of Gene Expression in Escherichia coli

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Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling

(2009) N. T. Ingolia et al.   SCIENCE

Absolute quantification of the budding yeast transcriptome by means of competitive PCR between genomic and complementary DNAs

(2008) Fumihito Miura et al.   BMC GENOMICS

Integrative Analyses of Posttranscriptional Regulation in the Yeast Saccharomyces cerevisiae Using Transcriptomic and Proteomic Data

(2008) Gang Wu et al.   CURRENT MICROBIOLOGY

RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

(2008) J. C. Marioni et al.   GENOME RESEARCH

Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast

(2008) Lyris M. F. de Godoy et al.   NATURE

Single-RNA counting reveals alternative modes of gene expression in yeast

(2008) Daniel Zenklusen et al.   NATURE STRUCTURAL & MOLECULAR BIOLOGY

The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

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A disattenuated correlation estimate when variables are measured with error: Illustration estimating cross-platform correlations

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