Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families

Kalvari, Ioanna; Argasinska, Joanna; Quinones-Olvera, Natalia; Nawrocki, Eric P.; Rivas, Elena; Eddy, Sean R.; Bateman, Alex; Petrov, Anton I.

doi:10.1093/nar/gkx1038

Cited by 872 publications

(729 citation statements)

References 27 publications

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“…To identify small RNAs in these three pests, cleaned reads were annotated by Blastn against the nr database (https://www.ncbi.nlm.nih.gov), Rfam (Kalvari et al ., ) and RepBase (Jurka et al ., ), and then two algorithms were used to identify the miRNAs. One algorithm involved homology‐searching against arthropod miRNAs in the miRbase (Kozomara and Griffiths‐Jones, ) using a cut‐off of 0‐2 nt mismatches or deletions.…”

Section: Methodsmentioning

confidence: 99%

Transgenic microRNA‐14 rice shows high resistance to rice stem borer

Xiao

Sun

et al. 2018

Plant Biotechnology Journal

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Rice stem borer (RSB, Chilo suppressalis) is an insect pest that causes huge economic losses every year. Control efforts rely heavily on chemical insecticides, which leads to serious problems such as insecticide resistance, environment pollution, and food safety issues. Therefore, developing alternative pest control methods is an important task. Here, we identified an insect-specific microRNA, miR-14, in RSB, which was predicted to target Spook (Spo) and Ecdysone receptor (EcR) in the ecdysone signalling network. In-vitro dual luciferase assays using HEK293T cells confirmed the interactions of Csu-miR-14 with CsSpo and with CsEcR. Csu-miR-14 exhibited high levels of expression at the end of each larval instar stage, and its expression was negatively correlated with the expression of its two target genes. Overexpression of Csu-miR-14 at the third day of the fifth instar stage led to high mortality and developmental defects in RSB individuals. We produced 35 rice transformants to express miR-14 and found that three lines had a single copy with highly abundant miR-14 mature transcripts. Feeding bioassays using both T and T generations of transgenic miR-14 rice indicated that at least one line (C#24) showed high resistance to RSB. These results indicated that the approach of miRNAs as targets has potential for improving pest control methods. Moreover, using insect-specific miRNAs rather than protein-encoding genes for pest control may prove benign to non-insect species, and thus is worthy of further exploration.

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Section: Methodsmentioning

confidence: 99%

Transgenic microRNA‐14 rice shows high resistance to rice stem borer

Xiao

Sun

et al. 2018

Plant Biotechnology Journal

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“…Often, noncoding RNA databases are also frequently used in finding noncoding RNA. Some such databases are RNAdb (Pang et al ., ), NONCODE (Zhao et al ., ), Rfam (Kalvari et al ., ), miRBase (Kozomara and Griffiths‐Jones, ) and snoRNABase (Xie et al ., ). Prediction of protein‐coding genes The identification of protein‐coding genes is the most important part of structural annotation. There are three approaches to predict protein‐coding genes from the genome: (1) identifying homologues of known protein‐coding genes through sequence similarity; (2) de novo predicting the protein‐coding genes with software developed via machine learning of protein‐coding gene structures; and (3) determining the exonic regions by direct transcriptome sequencing [eg RNA sequencing (RNA‐seq) or expressed sequence tags (ESTs)] and aligning to the assembled scaffolds.…”

Section: Insect Genome Assembly and Annotationmentioning

confidence: 99%

Insect genomes: progress and challenges

Zhao

et al. 2019

Insect Molecular Biology

138

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In the wake of constant improvements in sequencing technologies, numerous insect genomes have been sequenced. Currently, 1219 insect genome‐sequencing projects have been registered with the National Center for Biotechnology Information, including 401 that have genome assemblies and 155 with an official gene set of annotated protein‐coding genes. Comparative genomics analysis showed that the expansion or contraction of gene families was associated with well‐studied physiological traits such as immune system, metabolic detoxification, parasitism and polyphagy in insects. Here, we summarize the progress of insect genome sequencing, with an emphasis on how this impacts research on pest control. We begin with a brief introduction to the basic concepts of genome assembly, annotation and metrics for evaluating the quality of draft assemblies. We then provide an overview of genome information for numerous insect species, highlighting examples from prominent model organisms, agricultural pests and disease vectors. We also introduce the major insect genome databases. The increasing availability of insect genomic resources is beneficial for developing alternative pest control methods. However, many opportunities remain for developing data‐mining tools that make maximal use of the available insect genome resources. Although rapid progress has been achieved, many challenges remain in the field of insect genomics.

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“…Starting with release 13.0, Rfam provides ncRNA annotations of a non-redundant set of complete genomes (Kalvari et al, 2017). The text search enables viewing RNA families found in a certain species or taxonomic group, comparing RNAs found in different genomes, and viewing annotations of individual ncRNA sequences found in a genome.…”

Section: Introductionmentioning

confidence: 99%

Non‐Coding RNA Analysis Using the Rfam Database

Kalvari

Nawrocki

Argasinska

et al. 2018

CP in Bioinformatics

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352

234

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Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site. The data produced by Rfam have a broad application, from genome annotation to providing training sets for algorithm development. This article gives an overview of how to search and navigate the Rfam Web site, and how to annotate sequences with RNA families. The Rfam database is freely available at http://rfam.org. © 2018 by John Wiley & Sons, Inc.

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Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families

Cited by 872 publications

References 27 publications

Transgenic microRNA‐14 rice shows high resistance to rice stem borer

Transgenic microRNA‐14 rice shows high resistance to rice stem borer

Insect genomes: progress and challenges

Non‐Coding RNA Analysis Using the Rfam Database

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