Advancements in RNA-Seq Time Course and Downstream Analysis

Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome.

RNA-seq allows the assessment of the whole transcriptome (known and novel transcripts), including: allele specific expression, gene fusions, non coding transcripts such as long non coding RNAs (lncRNA), enhancer RNAs (eRNA) and the possibility to detect alternatively spliced variants. Compared to microarrays approach, RNA-seq data is highly reproducible and allows the identification of alternative splice variants as well as novel transcripts. Expression or tiling microarrays and capture arrays are still used intensively in biology and medicine for specialized tasks and diagnosis due to the standardized protocols and gold standard bioinformatics analysis.


RNA-Seq analysis workflow

Several RNA-seq protocols for differential expression or detection of novel transcripts have been developed and can be classified into two main methods: enrichment of messenger RNA (mRNA) or depletion of ribosomal RNA (rRNA). For eukaryote genomes, the most common and so far standardized protocol is the selection of poly(A +) transcripts (mRNA) via oligo-dT beads enriching non rRNA fractions. The second category consists of the depletion of ribosomal RNA. Several of these protocols, have been compared and reviewed in regards to different applications.

When studying dynamic biological processes such as development or drug responses, datasets have to be captured continually in a Time Course (TC) experiment. Therefore, these data are sampled at several Time Points (TP) in order to recapitulate the whole regulatory network involved, identifying possible regulators and genes switches responsible e.g. for cyclic behavior or correct differentiation of cells…

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Spies D, Ciaudo C. (2015) Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis. Comput Struct Biotechnol J 13:469-77. [article]

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