• Corpus ID: 16272065

Detection of construction biases in biological databases: the case of miRBase

@article{Saturnino2014DetectionOC,
  title={Detection of construction biases in biological databases: the case of miRBase},
  author={Guilherme Bicalho Saturnino and C P Godinho and Denise Fagundes-Lima and Alcides Castro e Silva and Gerald Weber},
  journal={arXiv: Molecular Networks},
  year={2014}
}
Biological databases can be analysed as a complex network which may reveal some its underlying biological mechanisms. Frequently, such databases are identified as scale-free networks or as hierarchical networks depending on connectivity distributions or clustering coefficients. Since these databases do grow over time, one would expect that their network topology may undergo some changes. Here, we analysed the historical versions of miRBase, a database of microRNAs where we performed an… 

Figures and Tables from this paper

Decreasing miRNA sequencing bias using a single adapter and circularization approach
TLDR
A new method for preparing miRNA sequencing libraries, RealSeq®-AC, that involves ligating the miRNAs with a single adapter and circularizing the ligation products, provides greatly reduced mi RNA sequencing bias and allows the identification of the largest variety of mi RNAs in biological samples.

References

SHOWING 1-10 OF 39 REFERENCES
Detecting Network Communities: An Application to Phylogenetic Analysis
TLDR
It is concluded that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis.
The powerful law of the power law and other myths in network biology.
TLDR
Network analysis provides a powerful frame for understanding the function and evolution of biological processes, provided it is brought to an appropriate level of description, by focussing on smaller functional modules and establishing the link between their topological properties and their dynamical behaviour.
Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies
TLDR
It is shown that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers in protein superfamilies, and sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.
Ultra-fast sequence clustering from similarity networks with SiLiX
TLDR
Comparing state-of-the-art software, SiLiX presents the best up-to-date capabilities to face the problem of clustering large collections of sequences.
High-quality sequence clustering guided by network topology and multiple alignment likelihood
TLDR
This paper proposes a strategy that aims at clustering sequences homologous over their entire length, and that takes into account the pattern of substitution specific to each gene family, and shows that HiFiX is the only method robust to both sequence divergence and domain rearrangements.
Identity Transposon Networks in D. melanogaster
TLDR
This work built a network based on a score which represents the transposon identity, calculated by comparing all currently known D. melanogastertransposons to each other using a Neddleman-Wunsch alignment algorithm, and shows that this score leads to a transition in the topology oftransposon networks from scale-free to almost fully-connected.
Computational identification of Drosophila microRNA genes
TLDR
A computational strategy succeeded in identifying bona fide miRNA genes and suggests that miRNAs constitute nearly 1% of predicted protein-coding genes in Drosophila, a percentage similar to the percentage of miRN as recently attributed to other metazoan genomes.
Reliable prediction of Drosha processing sites improves microRNA gene prediction
TLDR
It is suggested that expressed hairpins should not be annotated as miRNAs until they are verified to be Drosha and Dicer substrates, and another classifier that is trained on the output from the Microprocessor SVM outperforms existing methods for prediction of unconserved mi RNAs.
...
...