TAILIEUCHUNG - Gene-level differential analysis at transcript-level resolution

Compared to RNA-sequencing transcript differential analysis, gene-level differential expression analysis is more robust and experimentally actionable. However, the use of gene counts for statistical analysis can mask transcript-level dynamics. We demonstrate that ‘analysis first, aggregation second,’ where the p values derived from transcript analysis are aggregated to obtain gene-level results, increase sensitivity and accuracy. | Yi et al. Genome Biology 2018 19 53 https s13059-018-1419-z METHOD Open Access Gene-level differential analysis at transcript-level resolution Lynn Yi1 2 Harold Pimentel3 Nicolas L. Bray4 and Lior Pachter2 5 Abstract Compared to RNA-sequencing transcript differential analysis gene-level differential expression analysis is more robust and experimentally actionable. However the use of gene counts for statistical analysis can mask transcript-level dynamics. We demonstrate that analysis first aggregation second where the p values derived from transcript analysis are aggregated to obtain gene-level results increase sensitivity and accuracy. The method we propose can also be applied to transcript compatibility counts obtained from pseudoalignment of reads which circumvents the need for quantification and is fast accurate and model-free. The method generalizes to various levels of biology and we showcase an application to gene ontologies. Keywords RNA-sequencing Differential expression Meta-analysis P value aggregation Lancaster method Fisher s method Šidák correction RNA-seq quantification RNA-seq alignment Pseudoalignment Transcript compatibility counts Gene ontology Background A remedy to this problem is to estimate gene abun- Direct analysis of RNA abundance by sequencing comple- dances . in transcript-per-million units by summing mentary DNAs cDNAs using RNA-sequencing RNA-seq transcript abundances 7 but regularization methods for offers the possibility of analyzing expression at the resolution variance estimation of gene counts 8 cannot be directly of individual transcripts 1 . Nevertheless RNA-seq con- applied to abundances. For this reason recent workflows tinues to be mostly studied at the gene level partly because for gene-level differential analysis rely on converting gene such analyses appear to be more robust 2 and also because abundance estimates to gene counts 2 9 . Such methods gene-level discoveries are more experimentally actionable .

• Sinh học
• Genome Biology
• RNA sequencing
• Differential expression
• Meta analysis
• P value aggregation
• Lancaster method
• Fisher’s method
• RNA seq quantification
• Pan genome
• Accessory genome
• Whole genome sequencing
• Next generation sequencing
• Genome wide association studies
• Synthetic biology
• Synthetic genomics
• Genome engineering
• Microbial evolution
• Laboratory evolution
• Summary of Doctoral Thesis in Biology
• Doctoral Thesis in Biology
• Analysis of mitochondrial genome
• Structure of mitochondrial genome
• Genetic characteristics of pig breeds
• Mango genome
• Whole genome duplication
• Notably cashew
• Family Anacardiaceae
• Genome resequencing clarifies
• Genome reconstruction
• Genome sequences
• Genome analysis
• Large scale genomic data sets
• Ancient DNA
• Mapping precision
• False positive rate
• Core genome
• Presence absence variation
• Genome assembly
• Population specific variation
• Base composition
• Genome divergence
• UV damage repair
• Plant domestication provides
• Genome structure
• Medfly genome
• Tephritid genomics
• Insect orthology
• Gene family evolution
• Chromosomal synteny
• Insect invasiveness
• Insect adaptation
• Genome surgery
• Genome editing
• Computational biology
• Distinguishing alleles
• High Yield cell and molecular biology
• Cell and molecular biology
• Chromosome replication
• Meiosis and genetic recombination
• The human nuclear genome
• The human mitochondrial genome
• Luận án Tiến sĩ
• Summary of Biology doctoral thesis
• Biology doctoral thesis
• Mitochondrial genome
• Northern provinces of Vietnam
• Functional redundancy
• Eukaryotic genome
• Escape selective constraints
• Genetic map
• K mer
• Genotypeby sequencing
• Chromosome scale genome assembly
• Ashkenazi human
• Human reference genome
• Single reference genome
• Population specific reference genomes
• Genome quality
• Genome contamination
• Metagenome assembled genomes
• Quantifies genome chimerism
• Genome graph
• Bacterial adaptability
• Human genome
• Transcriptional repression
• Genome higherorder organization
• Transcriptional activity
• Bacteriophage resistance
• CRISPR Cas9 genome editing tools
• Biological processes
• Genome sequence
• Automated circularization
• Genome assemblies
• Plasmodium falciparum
• Therapeutic genome
• Precisely modifying
• Infectious diseases
• 3D genome structure
• Higher order genome organization
• Population based modeling
• Homologous pairing
• Drosophila melanogaster
• Genome duplication
• Interspecific hybridization
• Genome evolution
• Regulatory elements
• Non coding regions
• Genome wide quantification
• Endogenous functionality
• Predictive modeling
• Synthetic organisms
• Multiplex genome engineering
• Hitchhiking mutations