PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets

@article{Deshpande2019PLITAA,
  title={PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets},
  author={Sumukh Deshpande and James Shuttleworth and Jianhua Yang and Sandy Taramonli and Matthew England},
  journal={Computers in biology and medicine},
  year={2019},
  volume={105},
  pages={
          169-181
        }
}
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22 Citations
Highly Influential Citations
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Background Citations
3
Methods Citations
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22 Citations

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Citation Type

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq
TLDR
This review discusses major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of nc RNAs using RNA-seq, to present a comprehensive bioinformatics toolbox for plant ncRNA researchers.
Databases and tools for long noncoding RNAs
Long Non-coding RNA for Plants Using Big Data Analytics—A Review
TLDR
The role of emergent systems and databases to store the data of lncRNAs of plants is presented and the importance of Big Data analytics in storage of data and Machine learning algorithms for implementation plays a major role.
Systematic and computational identification of Androctonus crassicauda long non-coding RNAs
TLDR
A stringent step-by-step filtering pipeline and machine learning-based tools were used to identify the specific Androctonus crassicauda lncRNAs and analyze the features of predicted scorpion lnc RNAs, uncovering that lower protein-coding potential, lower GC content, shorter transcript length, and less number of isoform per gene are outstanding features of A. crassic audiology transcripts.
PtLnc-BXE: Prediction of plant lncRNAs using a Bagging-XGBoost-ensemble method with multiple features
TLDR
A plant lncRNA prediction approach PtLnc-BXE is presented, which combines multiple sequence features in two steps to develop an ensemble mode and outperformed other state-of-the-art plant lNCRNA prediction methods, achieving higher AUC on the benchmark datasets.
Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants
TLDR
The biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs in plants are described and tools for analysis and prediction of plant nc RNAs are summarized, as well as databases.
Common Features in lncRNA Annotation and Classification: A Survey
TLDR
It is found that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences, and the distinction of lnc RNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.
The computational approaches of lncRNA identification based on coding potential: Status quo and challenges
ItLnc-BXE: A Bagging-XGBoost-Ensemble Method With Comprehensive Sequence Features for Identification of Plant lncRNAs
TLDR
The results show that ItLnc-BXE outperforms other state-of-the-art plant lncRNA identification methods, achieving better and robust performance, and the results indicate that dicots-based and monocot-based models can be used to accurately identify lncRNAs in lower plant species, such as mosses and algae.
Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction.
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