derfinder

Last updated on Tue, May 9, 2023
#derfinder Bioconductor
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The goal of my Ph.D. with Jeff Leek and Andrew Jaffe at JHBSPH was to develop statistical methods and software that enable researchers to differentiate the sources of variation observed in RNA-seq while minimizing the dependance on known annotation. This allows researchers to correct for technological variation and study the biological variation driving their phenotype of interest. This work lead to the development of the derfinder and regionReport Bioconductor packages. We then applied these methods to further our understanding of neuropsychiatric disorders using the Lieber Institute for Brain Development human brains collection.

derfinder bulk
Leonardo Collado-Torres
Leonardo Collado-Torres
Investigator @ LIBD, Assistant Professor, Department of Biostatistics @ JHBSPH

#rstats @Bioconductor/🧠 genomics @LieberInstitute/@lcgunam @jhubiostat @jtleek @andrewejaffe alumni/@LIBDrstats @CDSBMexico co-founder

Publications

Incomplete annotation has a disproportionate impact on our understanding of Mendelian and complex neurogenetic disorders
Growing evidence suggests that human gene annotation remains incomplete; however, it is unclear how this affects different tissues and …
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Incomplete annotation has a disproportionate impact on our understanding of Mendelian and complex neurogenetic disorders
Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis
Genome-wide association studies have identified 108 schizophrenia risk loci, but biological mechanisms for individual loci are largely …
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Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis
Flexible expressed region analysis for RNA-seq with derfinder
derfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis. The package is available from Bioconductor.
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Flexible expressed region analysis for RNA-seq with derfinder
Rail-RNA: Scalable analysis of RNA-seq splicing and coverage
We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners.
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Rail-RNA: Scalable analysis of RNA-seq splicing and coverage
regionReport: Interactive reports for region-level and feature-level genomic analyses
regionReport is an R package for generating detailed interactive reports from region-level genomic analyses as well as feature-level …
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regionReport: Interactive reports for region-level and feature-level genomic analyses
Annotation-agnostic RNA-seq differential expression analysis software
Poster presented at ASHG2015 and IDIES2015
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Annotation-agnostic RNA-seq differential expression analysis software
Developmental regulation of human cortex transcription and its clinical relevance at single base resolution
Transcriptome analysis of human brain provides fundamental insight into development and disease, but it largely relies on existing …
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Developmental regulation of human cortex transcription and its clinical relevance at single base resolution
Measurement, Summary, and Methodological Variation in RNA-sequencing in Statistical Analysis of Next Generation Sequencing Data
There has been a major shift from microarrays to RNA-sequencing (RNA-seq) for measuring gene expression as the price per measurement …
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Measurement, Summary, and Methodological Variation in RNA-sequencing in Statistical Analysis of Next Generation Sequencing Data
Fast Annotation-Agnostic Differential Expression Analysis
Poster presented at ENAR2014
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Fast Annotation-Agnostic Differential Expression Analysis
Fast Annotation-Agnostic Differential Expression Analysis
Poster presented at Genomics and Bioinformatics Symposium 2013, Delta Omega Poster Competition
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Fast Annotation-Agnostic Differential Expression Analysis
Differential expression RNA-seq analysis with a large data set from brain samples
Poster presented at JHU Biostatistics Department Retreat
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Differential expression RNA-seq analysis with a large data set from brain samples