By Erik Dassi (eds.)
ISBN-10: 1493930664
ISBN-13: 9781493930661
ISBN-10: 1493930672
ISBN-13: 9781493930678
This quantity goals to supply the latest advances in concepts for learning gene expression legislation on the post-transcriptional point. Post-Transcriptional Gene rules, moment Edition is equipped in six sections describing bioinformatics techniques for learning post-transcriptional law, a number of expression profiling techniques, protein-RNA interplay and non-coding RNA research suggestions, tools for profiling RNA ameliorations, and different innovations comparable to substitute translation initiation or polyadenylation websites decision. Written for the Methods in Molecular Biology sequence, chapters contain introductions to their respective themes, lists of the required fabrics and reagents, step by step, without difficulty reproducible laboratory protocols, and tips about troubleshooting and keeping off identified pitfalls.
Authoritative and useful, Post-Transcriptional Gene rules, moment variation is a flexible source to researchers learning post-transcriptional rules, introducing the newest strategies and supplying a entire advisor to their implementation.
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Extra resources for Post-Transcriptional Gene Regulation
Sample text
From the list the set of targets for this RBP can be implicitly inferred, along with processes and pathways in which these are Bioinformatics Resources for PTR 25 involved. We can thus obtain an overall view of the potential role that this RBP has in the studied system. While this information is extremely useful, we still lack, at this point, a definition of how this RBP chooses the regions it targets on the mRNAs. To address this issue, the next step, performed by means of the RBPmotif [69] tool, exploits the set of bound sequences (and a corresponding set of unbound ones, also derived from the PAR-CLIP assay) to learn the binding preference of the RBP and derive both a sequence logo and a structural preference indicator.
Long RNA weighted fragmentation (for transcripts > 10,000 nt) [14]: The use of RNA fragments is introduced to identify RNA regions involved in protein binding. The RNALfold algorithm from Vienna package is employed to select RNA fragments in the range between 100 and 200 nt with predicted stable secondary structure. 3 catRAPID Strength We previously observed that the strength correlates with chemical affinities [14], which suggests that the interaction propensity can be used to estimate the strength of association [16].
0: a database of selenoprotein genes, proteins and SECIS elements. Nucleic Acids Res 36:D332–D338 Campillos M, Cases I, Hentze MW, Sanchez M (2010) SIREs: searching for iron-responsive elements. Nucleic Acids Res 38:W360–W367 Jacobs GH, Chen A, Stevens SG, Stockwell PA, Black MA, Tate WP, Brown CM (2009) Transterm: a database to aid the analysis of regulatory sequences in mRNAs. Nucleic Acids Res 37:D72–D76 Agostini F, Zanzoni A, Klus P, Marchese D, Cirillo D, Tartaglia GG (2013) catRAPID omics: a web server for large-scale prediction of protein-RNA interactions.
Post-Transcriptional Gene Regulation by Erik Dassi (eds.)
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