Unveiling Gene Expression with RNA-Seq Technology
Explore the power of RNA-Seq in quantifying gene expression levels and uncovering core scientific questions related to developmental stages, evolutionary relationships, tissue specificity, disease phenotypes, and individual variations. Discover how data science leverages the data exhaust for creative analysis while considering privacy implications.
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Presentation Transcript
Suggested slides for the Baylor talk Prashant Emani
Expression of genes is quantified by transcription: RNA-Seq measures mRNA transcript amounts Genes (DNA) Transcription Regulation RNA transcripts Non-coding regulatory RNAs Protein coding mRNA Gene Expression measured by RNA-seq Translation Proteins [ NATURE 459: 927; NAT. REV. GEN. 10: 57 ]
ATACAAGCAAGTATAAGTTCGTATGCCGTCTT GGAGGCTGGAGTTGGGGACGTATGCGGCATAG TACCGATCGAGTCGACTGTAAACGTAGGCATA ATTCTGACTGGTGTCATGCTGATGTACTTAAA Fastq sequence files ~5-10 GB Index-building + Alignment to reference genome BAM files ~1-2-fold reduction Successive steps of Data Reduction Conversion to signal track by overlapping reads BigWig files ~25-fold reduction Mapping to genes Gene/Transcript expression matrix ~20-fold reduction Reads => Signal Quantitative information from RNA-seq signal: average signals at exon level (RPKMs) [NAT. REV. 10: 57; PLOS CB 4:e1000158; PNAS 4:107: 5254 ]
Some Core Science Qs Addressed by RNA-seq Gene activity as a function of: Developmental stage: basic patterns and clusters of co-active genes across an organisms development Evolutionary relationships: behavior preserved across a wide range of organisms; patterns and clusters in model organisms in relation to those in humans Tissue- and cell-type: relationship of expression and specialized function Disease phenotypes: disruption of patterns in disease Individual variation: person-to-person discrepancies; personalized medicine
The Data Exhaust option 1 Key aspect of data science is creative analyses of the data Not necessarily core purpose of the data Mining the exhaust of the activity of many researchers generating large-scale data sets An aspect of exhaust is the inadvertent compromising of study participants privacy [PHOTO: RELAXNEWS; from http://www.lapresse.ca]
The Data Exhaust option 2 Metadata Front End Back end Data Exhaust Core scientific purposes Data collection and analysis Data on Collaboration, publication and Infrastructure Data Exhaust = Exploitable byproducts of big data collection and analysis [PHOTO: RELAXNEWS; from http://www.lapresse.ca]
