TY - CHAP
T1 - Workflow development for the functional characterization of ncRNAs
AU - Wolfien, Markus
AU - Brauer, David Leon
AU - Bagnacani, Andrea
AU - Wolkenhauer, Olaf
N1 - Publisher Copyright:
© Springer Science+Business Media, LLC, part of Springer Nature 2019.
PY - 2019
Y1 - 2019
N2 - During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.
AB - During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.
KW - Co-expression analysis
KW - Data analysis
KW - Experimental RNA discovery
KW - Machine learning
KW - Network analysis
KW - Next Generation Sequencing
KW - Transcript identification
KW - Workflow
KW - ncRNA
UR - https://www.scopus.com/pages/publications/85059906574
U2 - 10.1007/978-1-4939-8982-9_5
DO - 10.1007/978-1-4939-8982-9_5
M3 - Chapter
C2 - 30635892
AN - SCOPUS:85059906574
T3 - Methods in Molecular Biology
SP - 111
EP - 132
BT - Methods in Molecular Biology
PB - Humana Press Inc.
ER -