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Find video protocols related to scientific articles indexed in Pubmed.
Macroecological and macroevolutionary patterns of leaf herbivory across vascular plants.
Proc. Biol. Sci.
PUBLISHED: 05-30-2014
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The consumption of plants by animals underlies important evolutionary and ecological processes in nature. Arthropod herbivory evolved approximately 415 Ma and the ensuing coevolution between plants and herbivores is credited with generating much of the macroscopic diversity on the Earth. In contemporary ecosystems, herbivory provides the major conduit of energy from primary producers to consumers. Here, we show that when averaged across all major lineages of vascular plants, herbivores consume 5.3% of the leaf tissue produced annually by plants, whereas previous estimates are up to 3.8× higher. This result suggests that for many plant species, leaf herbivory may play a smaller role in energy and nutrient flow than currently thought. Comparative analyses of a diverse global sample of 1058 species across 2085 populations reveal that models of stabilizing selection best describe rates of leaf consumption, and that rates vary substantially within and among major plant lineages. A key determinant of this variation is plant growth form, where woody plant species experience 64% higher leaf herbivory than non-woody plants. Higher leaf herbivory in woody species supports a key prediction of the plant apparency theory. Our study provides insight into how a long history of coevolution has shaped the ecological and evolutionary relationships between plants and herbivores.
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Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes.
PLoS ONE
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Next-generation sequencing plays a central role in the characterization and quantification of transcriptomes. Although numerous metrics are purported to quantify the quality of RNA, there have been no large-scale empirical evaluations of the major determinants of sequencing success. We used a combination of existing and newly developed methods to isolate total RNA from 1115 samples from 695 plant species in 324 families, which represents >900 million years of phylogenetic diversity from green algae through flowering plants, including many plants of economic importance. We then sequenced 629 of these samples on Illumina GAIIx and HiSeq platforms and performed a large comparative analysis to identify predictors of RNA quality and the diversity of putative genes (scaffolds) expressed within samples. Tissue types (e.g., leaf vs. flower) varied in RNA quality, sequencing depth and the number of scaffolds. Tissue age also influenced RNA quality but not the number of scaffolds ? 1000 bp. Overall, 36% of the variation in the number of scaffolds was explained by metrics of RNA integrity (RIN score), RNA purity (OD 260/230), sequencing platform (GAIIx vs HiSeq) and the amount of total RNA used for sequencing. However, our results show that the most commonly used measures of RNA quality (e.g., RIN) are weak predictors of the number of scaffolds because Illumina sequencing is robust to variation in RNA quality. These results provide novel insight into the methods that are most important in isolating high quality RNA for sequencing and assembling plant transcriptomes. The methods and recommendations provided here could increase the efficiency and decrease the cost of RNA sequencing for individual labs and genome centers.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.