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Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.

Spies D, Ciaudo C - Comput Struct Biotechnol J (2015)

Bottom Line: New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks.This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments.Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

View Article: PubMed Central - PubMed

Affiliation: Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland ; Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

ABSTRACT
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. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

No MeSH data available.


RNA-seq analysis workflow.
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f0005: RNA-seq analysis workflow.

Mentions: Time course experiments follow the same workflow as static RNA-seq experiments, starting with preprocessing and normalization of the data, followed by differential gene expression (DEG) and downstream analysis by clustering and network construction (Fig. 1).


Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis.

Spies D, Ciaudo C - Comput Struct Biotechnol J (2015)

RNA-seq analysis workflow.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4564389&req=5

f0005: RNA-seq analysis workflow.
Mentions: Time course experiments follow the same workflow as static RNA-seq experiments, starting with preprocessing and normalization of the data, followed by differential gene expression (DEG) and downstream analysis by clustering and network construction (Fig. 1).

Bottom Line: New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks.This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments.Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

View Article: PubMed Central - PubMed

Affiliation: Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland ; Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

ABSTRACT
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. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.

No MeSH data available.