A <i>k</i>-mer scheme to predict piRNAs and characterize locust piRNAs

Zhang, Yi; Wang, Xianhui; Kang, Le

doi:10.1093/bioinformatics/btr016

Cited by 121 publications

(173 citation statements)

References 30 publications

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“…Non-piRNA sequences (negative samples) were obtained from Zhang et al (2011). Because there are far less nonpiRNA small ncRNAs in one species than piRNAs, only 34,675 real non-piRNA sequences from 861 organisms are contained in NONCODE (Bu et al, 2011), which was collected by Zhang et al (2011). The remaining 158,646 sequences are well-designed and were generated by random processes according to real data, which was developed by Zhang et al (2011).…”

Section: Training Dataset and Length Filtermentioning

confidence: 99%

“…Because there are far less nonpiRNA small ncRNAs in one species than piRNAs, only 34,675 real non-piRNA sequences from 861 organisms are contained in NONCODE (Bu et al, 2011), which was collected by Zhang et al (2011). The remaining 158,646 sequences are well-designed and were generated by random processes according to real data, which was developed by Zhang et al (2011). Furthermore, most non-piRNA sequences are considerably longer than piRNA sequences; therefore, if a sequence is too long, it is clearly not a piRNA and should be removed.…”

Section: Training Dataset and Length Filtermentioning

confidence: 99%

“…TFs can be used to identify certain regions of biomolecules such as DNA or proteins (Zhang et al, 2011). Furthermore, it has been determined that TFs are species-or taxon-specific Karlin and Mrázek, 1997;Madera et al, 2010).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Only 17-20% of mammalian piRNA repeat sequences correspond to transposons and retrotransposons (Houwing et al, 2007). Limited computational studies have addressed piRNAs detection because of the lack of efficient secondary structure motifs and sequence homology in different species (Zhang et al, 2011). Furthermore, given the large size of ncRNAs, it is challenging to effectively and efficiently identify piRNAs (Reuter et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al (2011) developed a piRNA predictor (piRNA predictor) based on the k-mer scheme and Fisher discriminant analysis. With a precision of over 90%, this approach is clearly superior to that proposed by Betel et al However, the sensitivity of this method is not satisfactory (60-70%; except for fruit flies).…”

Section: Introductionmentioning

confidence: 99%

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Detection of Piwi-interacting RNAs based on sequence features

Liu

Zhang

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs. Distinguishing piRNAs from other non-coding RNAs is important because of their important role in the physiological regulation of spermatogenesis, genome protection from transposons, and regulation of mRNAs and long non-coding RNAs. Few computational studies have addressed piRNAs detection, and both effectiveness and efficiency of piRNA detection tools require improvement. In this study, a piRNA detection method based on sequence features and a support vector machine was developed. Four types of features are proposed: weighted k-mer, weighted k-mer with wildcards, position-specific base, and piRNA length. The piRNA sequences from human, mouse, rat, and drosophila were respectively used in this experiment. Compared to existing algorithms, the proposed method provides a better balance between precision and sensitivity (both are approximately 90%), and although these values were slightly slower than those obtained using the piRNA annotation approach, the proposed method was four-fold faster than piRPred and 229-fold faster than piRNA predictor.

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Section: Training Dataset and Length Filtermentioning

confidence: 99%

Section: Training Dataset and Length Filtermentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Piwi-interacting RNAs based on sequence features

Liu

Zhang

et al. 2016

Genet. Mol. Res.

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Roles of Noncoding RNAs in Islet Biology

Guay

Jacovetti

Bayazit

et al. 2020

Comprehensive Physiology

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The discovery that most mammalian genome sequences are transcribed to RNA has revolutionized our understanding of the mechanisms governing key cellular processes and of the causes of human diseases, including diabetes mellitus. Pancreatic islet cells were found to contain thousands of noncoding RNAs, including microRNAs, Piwi-associated RNAs, small nucleolar RNAs, tRNAderived fragments, long non-coding RNAs and circular RNAs. While the involvement of microRNAs in islet function and in the etiology of diabetes is now well documented, there is emerging evidence indicating that other classes of non-coding RNAs are also participating in different aspects of islet physiology. The aim of this review will be to provide a comprehensive and updated view of the studies carried out in human samples and rodent models over the last 15 years on the role of non-coding RNAs in the control of α-and β-cell development and function and to highlight the recent discoveries in the field. We will not only describe the role of non-coding RNAs in the control of insulin and glucagon secretion but will also address the contribution of these regulatory molecules in the proliferation and survival of islet cells under physiological and pathological conditions. It is now well established that most cells release part of their non-coding RNAs inside small extracellular vesicles allowing the delivery of genetic material to neighboring or distantly located target cells. The role of these secreted RNAs in cell-to-cell communication between β-cells and other metabolic tissues as well as their potential use as diabetes biomarkers will be discussed.-Circulating non-coding RNAs represent promising biomarkers.

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Recent advances of PIWI‐interacting RNA in cardiovascular diseases

Li,

Wang,

Cheng

et al. 2024

Clinical & Translational Med

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BackgroundThe relationship between noncoding RNAs (ncRNAs) and human diseases has been a hot topic of research, but the study of ncRNAs in cardiovascular diseases (CVDs) is still in its infancy. PIWI‐interacting RNA (piRNA), a small ncRNA that binds to the PIWI protein to maintain genome stability by silencing transposons, was widely studied in germ lines and stem cells. In recent years, piRNA has been shown to be involved in key events of multiple CVDs through various epigenetic modifications, revealing the potential value of piRNA as a new biomarker or therapeutic target.ConclusionThis review explores origin, degradation, function, mechanism and important role of piRNA in CVDs, and the promising therapeutic targets of piRNA were summarized. This review provide a new strategy for the treatment of CVDs and lay a theoretical foundation for future research.Key points piRNA can be used as a potential therapeutic target and biomaker in CVDs. piRNA influences apoptosis, inflammation and angiogenesis by regulating epigenetic modificaions. Critical knowledge gaps remain in the unifying piRNA nomenclature and PIWI‐independent function.

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A k-mer scheme to predict piRNAs and characterize locust piRNAs

Cited by 121 publications

References 30 publications

Detection of Piwi-interacting RNAs based on sequence features

Detection of Piwi-interacting RNAs based on sequence features

Roles of Noncoding RNAs in Islet Biology

Recent advances of PIWI‐interacting RNA in cardiovascular diseases

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