sPARTA: a parallelized pipeline for integrated analysis of plant miRNA and cleaved mRNA data sets, including new miRNA target-identification software

Kakrana, Atul; Hammond, Reza; Patel, Parth; Nakano, Mayumi; Meyers, Blake C.

doi:10.1093/nar/gku693

Cited by 77 publications

(65 citation statements)

References 43 publications

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“…Using standard miRNA-target interaction criteria, prior reports failed to find targets of reproductive phasiRNAs, yet reported few details of these negative results (Song et al, 2012;Zhai et al, 2015b). We revisited this topic because new, more powerful, faster and flexible target prediction methods are available; prior work used a 'seed-based' sRNA-target interaction pipeline, which may not accurately capture plant target similarity (Kakrana et al, 2014). sPARTA (Kakrana et al, 2014) uses a 'seed-free' approach and allows flexibility in pairing parameters.…”

Section: Predicted Targets Of Reproductive Phasirnas As a Means To Inmentioning

confidence: 99%

Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

et al. 2018

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Little is known about the characteristics and function of reproductive phased, secondary, small interfering RNAs (phasiRNAs) in the Poaceae, despite the availability of significant genomic resources, experimental data, and a growing number of computational tools. We utilized machine-learning methods to identify sequence-based and positional features that distinguish phasiRNAs in rice and maize from other small RNAs (sRNAs). We developed Random Forest classifiers that can distinguish reproductive phasiRNAs from other sRNAs in complex sets of sequencing data, utilizing sequence-based (k-mers) and features describing position-specific sequence biases. The classification performance attained is > 80% in accuracy, sensitivity, specificity, and positive predicted value. Feature selection identified important features in both ends of phasiRNAs. We demonstrated that phasiRNAs have strand specificity and position-specific nucleotide biases potentially influencing AGO sorting; we also predicted targets to infer functions of phasiRNAs, and computationally assessed their sequence characteristics relative to other sRNAs. Our results demonstrate that machine-learning methods effectively identify phasiRNAs despite the lack of characteristic features typically present in precursor loci of other small RNAs, such as sequence conservation or structural motifs. The 5'-end features we identified provide insights into AGO-phasiRNA interactions. We describe a hypothetical model of competition for AGO loading between phasiRNAs of different nucleotide compositions.

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Section: Predicted Targets Of Reproductive Phasirnas As a Means To Inmentioning

confidence: 99%

Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

et al. 2018

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“…Degradome analysis usually defines a category of transcripts predicted to be endonucleolytically cleaved and then are cleavage positions compared to predicted miRNA binding sites (Li and Sunkar, 2013;Xing et al, 2014;Ding et al, 2016;Fan et al, 2016;Wang et al, 2016). Degradome analysis and target prediction has been integrated in to a web resource comPARE for plant miRNA target analysis (Kakrana et al, 2014). Degradome analysis can be also used in animals to identify rare miRNA targets suppressed by slicing .…”

Section: Degradome Analysismentioning

confidence: 99%

Literature review of baseline information to support the risk assessment of RNAi‐based GM plants

Pačes

Nič²,

Novotný³

et al. 2017

EFS3

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This report is the outcome of an EFSA procurement aiming at investigating and summarising the state of knowledge on (I) the mode-of-action of dsRNA and miRNA pathways, (II) the potential for nontarget gene regulation by dsRNA-derived siRNAs or miRNAs, (III) the determination of siRNA pools in plant tissues and the importance of individual siRNAs for silencing. The report is based on a comprehensive and systematic literature search, starting with the identification and retrieval of~190,000 publications related to the research area and further filtered down with keywords to produce focused collections used for subsequent screening of titles and abstracts. The report is comprised of an (I) Introduction to the field of small RNAs, (II) a Data and Methodologies section containing strategies used for literature search and study selection, and (III) the Results of the literature review organized according to the three main procurement tasks. The outcome of the first task reviews dsRNA and miRNA pathways in mammals (including humans), birds, fish, arthropods, annelids, molluscs, nematodes, and plants. Eight taxon-dedicated chapters are based on~1,400 cumulative references chosen from~10,000 inspected titles and abstracts. We review conserved and divergent aspects of small RNA pathways and dsRNA responses in animals and plants including structure and function of key proteins as well as four basic mechanisms: genome-encoded posttranscriptional regulations (miRNA), degradation of RNAs by short interfering RNA pools generated from long dsRNA (RNAi), transcriptional silencing, and sequence-independent responses to dsRNA. The outcome of the second task focuses on base pairing between small RNAs and their target RNAs and predictability of biological effects of small RNAs in animals and plants. The outcome of the last task reviews methodology, siRNA pools, and movement of small RNAs in plants. Potential transfer of small RNAs between species and circulating miRNAs in mammals is described in the final chapter.

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“…Validation was performed using the using the sPARTA package (Kakrana et al, 2015). Target prediction was performed using sPARTA's built-in target prediction module miRferno with standard scoring schema and score cutoff #7, followed by PARE-based validation of predicted targets.…”

Section: Mirna Target Prediction and Pare Validationmentioning

confidence: 99%

An Atlas of Soybean Small RNAs Identifies Phased siRNAs from Hundreds of Coding Genes

et al. 2014

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Small RNAs are ubiquitous, versatile repressors and include (1) microRNAs (miRNAs), processed from mRNA forming stem-loops; and (2) small interfering RNAs (siRNAs), the latter derived in plants by a process typically requiring an RNAdependent RNA polymerase. We constructed and analyzed an expression atlas of soybean (Glycine max) small RNAs, identifying over 500 loci generating 21-nucleotide phased siRNAs (phasiRNAs; from PHAS loci), of which 483 overlapped annotated protein-coding genes. Via the integration of miRNAs with parallel analysis of RNA end (PARE) data, 20 miRNA triggers of 127 PHAS loci were detected. The primary class of PHAS loci (208 or 41% of the total) corresponded to NB-LRR genes; some of these small RNAs preferentially accumulate in nodules. Among the PHAS loci, novel representatives of TAS3 and noncanonical phasing patterns were also observed. A noncoding PHAS locus, triggered by miR4392, accumulated preferentially in anthers; the phasiRNAs are predicted to target transposable elements, with their peak abundance during soybean reproductive development. Thus, phasiRNAs show tremendous diversity in dicots. We identified novel miRNAs and assessed the veracity of soybean miRNAs registered in miRBase, substantially improving the soybean miRNA annotation, facilitating an improvement of miRBase annotations and identifying at high stringency novel miRNAs and their targets.

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sPARTA: a parallelized pipeline for integrated analysis of plant miRNA and cleaved mRNA data sets, including new miRNA target-identification software

Cited by 77 publications

References 43 publications

Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

Reproductive phasiRNAs in grasses are compositionally distinct from other classes of small RNAs

Literature review of baseline information to support the risk assessment of RNAi‐based GM plants

An Atlas of Soybean Small RNAs Identifies Phased siRNAs from Hundreds of Coding Genes

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