Tuning the engine

Dassi, Erik; Quattrone, Alessandro

doi:10.4161/rna.22035

Cited by 10 publications

(4 citation statements)

References 52 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 1 - 3 In consequence to this interest, huge amounts of data have been and are being collected every day primarily thanks to the advent of the new high-throughput techniques such as polysomal profiling, 3 , 4 ribosome footprinting, 5 and protein-RNA interaction mapping methods such as RIP, 6 CLIP and its several variants. 7 - 9 This massive quantity of information cannot be exploited at its best, due to the fact that most of it lies isolated and scattered throughout several databases, 10 or in published papers reporting single PTR interactions. The lack of a comparative platform hampers the possibility to obtain a comprehensive view of the multiple factors binding to UTRs, which could both enlighten about the biological meaning of their combination and allow us to trace regulatory networks.…”

Section: Introductionmentioning

confidence: 99%

Aura 2

Dassi

Leo

et al. 2014

Translation

Self Cite

View full text Add to dashboard Cite

Post-transcriptional regulation (PTR) of gene expression is now recognized as a major determinant of cell phenotypes. The recent availability of methods to map protein-RNA interactions in entire transcriptomes such as RIP, CLIP and their variants, together with global polysomal and ribosome profiling techniques, are driving the exponential accumulation of vast amounts of data on mRNA contacts in cells, and of corresponding predictions of PTR events. However, this exceptional quantity of information cannot be exploited at its best to reconstruct potential PTR networks, as it still lies scattered throughout several databases and in isolated reports of single interactions. To address this issue, we developed the second and vastly enhanced version of the Atlas of UTR Regulatory Activity (AURA 2), a meta-database centered on mapping interaction of trans-factors with human and mouse UTRs. AURA 2 includes experimentally demonstrated binding sites for RBPs, ncRNAs, thousands of cis-elements, variations, RNA epigenetics data and more. Its user-friendly interface offers various data-mining features including co-regulation search, network generation and regulatory enrichment testing. Gene expression profiles for many tissues and cell lines can be also combined with these analyses to display only the interactions possible in the system under study. AURA 2 aims at becoming a valuable toolbox for PTR studies and at tracing the road for how PTR network-building tools should be designed. AURA 2 is available at http://aura.science.unitn.it.

show abstract

Section: Introductionmentioning

confidence: 99%

Aura 2

Dassi

Leo

et al. 2014

Translation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Motifs are usually defined as short nucleotide sequence patterns of length k ( k -mers) and represented with matrices containing the probabilities to find nucleotides in specific positions (position weighted matrices PWMs). In the past decade, the advancement of high-throughput technologies contributed to the generation of a large amount of genomic data [ 4 ], promoting development of computational methods to detect regulatory elements such as transcription and splicing factor binding sites [ 5 ]. One fundamental requirement of methods for large-scale analysis is that relevant features (e.g., recognition motifs) are identified with good accuracy and in reasonable time [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences

et al. 2014

View full text Add to dashboard Cite

BackgroundThe large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites.ResultsHere, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature.ConclusionsSeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-925) contains supplementary material, which is available to authorized users.

show abstract

“…Although the essential role of the 3′‐UTR in the expression of FAF1/2c was described in this study, the underlying molecular mechanism of how the T‐DNA insertion in the 3′‐UTR leads to the accumulation of the FAF1/2c mRNA transcript awaits further study. On the one hand, this may be related to the possibility that the 3′‐UTR is involved in the post‐transcriptional regulation of genes, affecting mRNA stability or translation efficiency (Dassi and Quattrone, 2012 ). The CREs of the 3′‐UTR have been shown to promote mRNA degradation under specific conditions (Simone and Keene, 2013 ; Tavares et al ., 2000 ; Vlasova and Bohjanen, 2008 ).…”

Section: Discussionmentioning

confidence: 99%

EARLY FLOWERING is a dominant gain‐of‐function allele of FANTASTIC FOUR 1/2c that promotes early flowering in tomato

Zhang,

Ai,

et al. 2023

Plant Biotechnology Journal

View full text Add to dashboard Cite

SummaryFlowering time, an important factor in plant adaptability and genetic improvement, is regulated by various genes in tomato (Solanum lycopersicum). In this study, we characterized a tomato mutant, EARLY FLOWERING (EF), that developed flowers much earlier than its parental control. EF is a dominant gain‐of‐function allele with a T‐DNA inserted 139 bp downstream of the stop codon of FANTASTIC FOUR 1/2c (FAF1/2c). The transcript of SlFAF1/2c was at elevated levels in the EF mutant. Overexpressing SlFAF1/2c in tomato plants phenocopied the early flowering trait of the EF mutant. Knocking out SlFAF1/2c in the EF mutant reverted the early flowering phenotype of the mutant to the normal flowering time of the wild‐type tomato plants. SlFAF1/2c promoted the floral transition by shortening the vegetative phase rather than by reducing the number of leaves produced before the emergence of the first inflorescence. The COP9 signalosome subunit 5B (CSN5B) was shown to interact with FAF1/2c, and knocking out CSN5B led to an early flowering phenotype in tomato. Interestingly, FAF1/2c was found to reduce the accumulation of the CSN5B protein by reducing its protein stability. These findings imply that FAF1/2c regulates flowering time in tomato by reducing the accumulation and stability of CSN5B, which influences the expression of SINGLE FLOWER TRUSS (SFT), JOINTLESS (J) and UNIFLORA (UF). Thus, a new allele of SlFAF1/2c was discovered and found to regulate flowering time in tomato.

show abstract

Tuning the engine

Cited by 10 publications

References 52 publications

Aura 2

Aura 2

SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences

EARLY FLOWERING is a dominant gain‐of‐function allele of FANTASTIC FOUR 1/2c that promotes early flowering in tomato

Contact Info

Product

Resources

About