Developmental regulation of human cortex transcription and its clinical relevance at single base resolution

Description

These tracks show results from differential expression analysis of the developing frontal cortex at single base resolution [1]. We performed RNA sequencing (RNA-seq) in 36 dorsolateral prefrontal cortex (DLPFC) samples across 6 important life stages — fetal (2nd trimester), infant, child, teen, adult and elderly. We implemented an annotation-agnostic differential expression analysis to leverage the power of RNA-seq without the difficulties of transcript assembly called derfinder [2]. These tracks display RNA-seq coverage across age, and evidence for differential expression.

The ageBrainDERs track shows significant differentially expression regions (DERs) across genome across the 6 age groups (at FWER < 5%), and further replicated in an independent set of 36 DLPFC samples.

The sixGroup_F track displays the evidence for differential expression across 6 age groups across the genome via F-statistics. Contiguous bases above a particular F-statistic cutoff (horizontal line on the track) were grouped into differentially expressed regions (DERs).

The sixGroupMulti tracks show base-level coverage from the RNA-seq alignments. Each sub-track represents the average adjusted coverage (normalized to an 80M read library size, RP80M) averaged across the 6 samples in each age group, and smoothed using running means with a 5bp window. Briefly, polyA+ RNA-seq was performed using 100bp paired end reads, and aligned to the genome and transcriptome using TopHat [2].

Nuclear/Cytosolic tracks show coverage data from independent samples where RNA was extracted from cytosolic and nuclear fractions from 3 fetal and 3 adult samples. As above, adjusted coverage (RP80M) was averaged by fraction and age group.

Caveats

• Coverage tracks do not incorporate biological variability. Visually inferring regions of differential expression from these tracks alone is not recommended. Instead, information across all 36 samples should be used to determine differential expression.
• Unlike previous "bump hunting" approaches [4], differential expression statistics were not smoothed to retain spatial resolution at exon boundaries, which can result in fragmented DERs. However, the average coverage within each age group, and/or cellular fraction was subjected to a running mean smoother with a 5bp window.

Methods

RNA-seq reads were aligned to the genome and transcriptome using TopHat2. Coverage (the number of reads at each base) was extracted from the bam files, averaged across age group, and smoothed with a 5bp running mean window. derfinder was performed across the 36 samples fitting a model that incorporated age stage and library size, and compared to a model with just library size to generate F-statistics for differential expression by age.

Credits

Questions should be directed to Andrew Jaffe.

References

1 Jaffe AE, Shin J, Collado-Torres L, Leek JT, Tao R, Li C, Gao Y, Jia Y, Maher BJ, Hyde TM, Kleinman JE, Weinberger DR. Developmental regulation of human cortex transcription and its clinical relevance at base resolution. Nature Neuroscience. 2014 Dec 15. PMID: 25501035

2 Collado-Torres L, Frazee AC, MI Love, Irizarry RA, Jaffe AE Leek JT. derfinder: Software for annotation-agnostic RNA-seq differential expression analysis. Available on bioRxiv

3 Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013 Apr 25;14(4):R36. PMID: 23618408

4 Jaffe AE, Murakami P, Lee H, Fallin MD, Leek JT, Feinberg AP, Irizarry RA. Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int. J. Epidemiol. (2012) 41 (1):200-209. PMID: 22422453