Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants

Negri, Tatianne da Costa; Alves, Wonder Alexandre Luz; Bugatti, Pedro Henrique; Saito, Priscila T. M.; Domingues, Douglas Silva; Paschoal, Alexandre Rossi

doi:10.1093/bib/bby034

Cited by 56 publications

(41 citation statements)

References 32 publications

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“…A detailed list of plant lncRNA databases is in Table 3. Several important tools, such as CPPred [158], REPTree [159], Pfamscan [160], COME [161], PLIT [156], and CPC2 [162], are available to distinguish lncRNAs from mRNAs. Advances in bioinformatics tools and new algorithms could further boost our efforts in discovering novel lncRNAs and their accurate functional annotations.…”

Section: Database and Web-based Resources Of Lncrnasmentioning

confidence: 99%

Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation

et al. 2020

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Background The immobile nature of plants means that they can be frequently confronted by various biotic and abiotic stresses during their lifecycle. Among the various abiotic stresses, water stress, temperature extremities, salinity, and heavy metal toxicity are the major abiotic stresses challenging overall plant growth. Plants have evolved complex molecular mechanisms to adapt under the given abiotic stresses. Long non-coding RNAs (lncRNAs)—a diverse class of RNAs that contain > 200 nucleotides(nt)—play an essential role in plant adaptation to various abiotic stresses. Results LncRNAs play a significant role as ‘biological regulators’ for various developmental processes and biotic and abiotic stress responses in animals and plants at the transcription, post-transcription, and epigenetic level, targeting various stress-responsive mRNAs, regulatory gene(s) encoding transcription factors, and numerous microRNAs (miRNAs) that regulate the expression of different genes. However, the mechanistic role of lncRNAs at the molecular level, and possible target gene(s) contributing to plant abiotic stress response and adaptation, remain largely unknown. Here, we review various types of lncRNAs found in different plant species, with a focus on understanding the complex molecular mechanisms that contribute to abiotic stress tolerance in plants. We start by discussing the biogenesis, type and function, phylogenetic relationships, and sequence conservation of lncRNAs. Next, we review the role of lncRNAs controlling various abiotic stresses, including drought, heat, cold, heavy metal toxicity, and nutrient deficiency, with relevant examples from various plant species. Lastly, we briefly discuss the various lncRNA databases and the role of bioinformatics for predicting the structural and functional annotation of novel lncRNAs. Conclusions Understanding the intricate molecular mechanisms of stress-responsive lncRNAs is in its infancy. The availability of a comprehensive atlas of lncRNAs across whole genomes in crop plants, coupled with a comprehensive understanding of the complex molecular mechanisms that regulate various abiotic stress responses, will enable us to use lncRNAs as potential biomarkers for tailoring abiotic stress-tolerant plants in the future.

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Section: Database and Web-based Resources Of Lncrnasmentioning

confidence: 99%

Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation

et al. 2020

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“…Various tools are available to evaluate the coding potential of transcripts and distinguish noncoding RNAs from protein-coding ones using machine learning approaches. These methods have used a variety of models, such as support vector machine (Liu et al, 2006;Kong et al, 2007;Sun et al, 2013aSun et al, , 2013bLi et al, 2014;Kang et al, 2017;Vieira et al, 2017), random forest (Hu et al, 2017;Singh et al, 2017;Wucher et al, 2017), logistic regression (Wang et al, 2013), and REPTree (Negri et al, 2018). The features used by these machine learning approaches are usually extracted directly from nucleotide sequences.…”

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confidence: 99%

Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

Lin

et al. 2019

Plant Physiol.

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Long non-coding RNAs (lncRNAs) are defined as non-protein-coding transcripts that are at least 200 nucleotides long. They are known to play pivotal roles in regulating gene expression, especially during stress responses in plants. We used a large collection of in-house transcriptome data from various soybean (Glycine max and Glycine soja) tissues treated under different conditions to perform a comprehensive identification of soybean lncRNAs. We also retrieved publicly available soybean transcriptome data that were of sufficient quality and sequencing depth to enrich our analysis. In total, RNA-sequencing data of 332 samples were used for this analysis. An integrated reference-based, de novo transcript assembly was developed that identified ;69,000 lncRNA gene loci. We showed that lncRNAs are distinct from both protein-coding transcripts and genomic background noise in terms of length, number of exons, transposable element composition, and sequence conservation level across legume species. The tissue-specific and time-specific transcriptional responses of the lncRNA genes under some stress conditions may suggest their biological relevance. The transcription start sites of lncRNA gene loci tend to be close to their nearest proteincoding genes, and they may be transcriptionally related to the protein-coding genes, particularly for antisense and intronic lncRNAs. A previously unreported subset of small peptide-coding transcripts was identified from these lncRNA loci via tandem mass spectrometry, which paved the way for investigating their functional roles. Our results also highlight the present inadequacy of the bioinformatic definition of lncRNA, which excludes those lncRNA gene loci with small open reading frames from being regarded as protein-coding.

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“…The Random Forest (RF) classifier [33] was used to build the gene prediction model, obtaining better performance when compared to other state-of-the-art predictors. The RF method was chosen based on our previous studies [14] and it was used in similar cases with good performance [34] [35]. Four models having 100, 200, 500 and 700 decision trees with 5-fold cross-validation with 5 repetitions on the performance evaluation training set were built (Figure 4).…”

Section: Random Forest Parametrizationmentioning

confidence: 99%

geneRFinder: gene finding in distinct metagenomic data complexities

Silva

Padovani

Góes

et al. 2020

Preprint

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MotivationMicrobes perform a fundamental economic, social and environmental role in our society. Metagenomics makes it possible to investigate microbes in their natural environments (the complex communities) and their interactions. The way they act is usually estimated by looking at the functions they play in those environments and their responsibility is measured by their genes. The advances of next-generation sequencing technology have facilitated metagenomics research however it also create a heavy computational burden. Large and complex biological datasets are available as never before. There are many gene predictors available which can aid gene annotation process though they lack of handling appropriately metagenomic data complexities. There is no standard metagenomic benchmark data for gene prediction. Thus, gene predictors may inflate their results by obfuscating low false discovery rates.ResultsWe introduce geneRFinder, a ML-based gene predictor able to outperform state-of-the-art gene prediction tools across this benchmark by using only one pre-trained Random Forest model. Average prediction rates of geneRFinder differed in percentage terms by 54% and 64%, respectively, against Prodigal and FragGeneScan while handling high complexity metagenomes. The specificity rate of geneRFinder had the largest distance against FragGeneScan, 79 percentage points, and 66 more than Prodigal. According to McNemar’s test, all percentual differences between predictors performances are statistically significant for all datasets with a 99% confidence interval.ConclusionsWe provide geneRFinder, a approach for gene prediction in distinct metagenomic complexities, available at github.com/railorena/geneRFinder, and also we provide a novel, comprehensive benchmark data for gene prediction — which is based on The Critical Assessment of Metagenome Interpretation (CAMI) challenge, and contains labeled data from gene regions – avaliable at sourceforge.net/p/generfinder-benchmark.

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Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants

Cited by 56 publications

References 32 publications

Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation

Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation

Analysis of Soybean Long Non-Coding RNAs Reveals a Subset of Small Peptide-Coding Transcripts

geneRFinder: gene finding in distinct metagenomic data complexities

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