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Find video protocols related to scientific articles indexed in Pubmed.
Snow surface microbiome on the High Antarctic Plateau (DOME C).
PLoS ONE
PUBLISHED: 01-01-2014
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The cryosphere is an integral part of the global climate system and one of the major habitable ecosystems of Earth's biosphere. These permanently frozen environments harbor diverse, viable and metabolically active microbial populations that represent almost all the major phylogenetic groups. In this study, we investigated the microbial diversity in the surface snow surrounding the Concordia Research Station on the High Antarctic Plateau through a polyphasic approach, including direct prokaryotic quantification by flow cytometry and catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH), and phylogenetic identification by 16S RNA gene clone library sequencing and 454 16S amplicon pyrosequencing. Although the microbial abundance was low (<10(3) cells/ml of snowmelt), concordant results were obtained with the different techniques. The microbial community was mainly composed of members of the Alpha-proteobacteria class (e.g. Kiloniellaceae and Rhodobacteraceae), which is one of the most well-represented bacterial groups in marine habitats, Bacteroidetes (e.g. Cryomorphaceae and Flavobacteriaceae) and Cyanobacteria. Based on our results, polar microorganisms could not only be considered as deposited airborne particles, but as an active component of the snowpack ecology of the High Antarctic Plateau.
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MysiRNA-designer: a workflow for efficient siRNA design.
PLoS ONE
PUBLISHED: 06-12-2011
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The design of small interfering RNA (siRNA) is a multi factorial problem that has gained the attention of many researchers in the area of therapeutic and functional genomics. MysiRNA score was previously introduced that improves the correlation of siRNA activity prediction considering state of the art algorithms. In this paper, a new program, MysiRNA-Designer, is described which integrates several factors in an automated work-flow considering mRNA transcripts variations, siRNA and mRNA target accessibility, and both near-perfect and partial off-target matches. It also features the MysiRNA score, a highly ranked correlated siRNA efficacy prediction score for ranking the designed siRNAs, in addition to top scoring models Biopredsi, DISR, Thermocomposition21 and i-Score, and integrates them in a unique siRNA score-filtration technique. This multi-score filtration layer filters siRNA that passes the 90% thresholds calculated from experimental dataset features. MysiRNA-Designer takes an accession, finds conserved regions among its transcript space, finds accessible regions within the mRNA, designs all possible siRNAs for these regions, filters them based on multi-scores thresholds, and then performs SNP and off-target filtration. These strict selection criteria were tested against human genes in which at least one active siRNA was designed from 95.7% of total genes. In addition, when tested against an experimental dataset, MysiRNA-Designer was found capable of rejecting 98% of the false positive siRNAs, showing superiority over three state of the art siRNA design programs. MysiRNA is a freely accessible (Microsoft Windows based) desktop application that can be used to design siRNA with a high accuracy and specificity. We believe that MysiRNA-Designer has the potential to play an important role in this area.
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MysiRNA: improving siRNA efficacy prediction using a machine-learning model combining multi-tools and whole stacking energy (?G).
J Biomed Inform
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The investigation of small interfering RNA (siRNA) and its posttranscriptional gene-regulation has become an extremely important research topic, both for fundamental reasons and for potential longer-term therapeutic benefits. Several factors affect the functionality of siRNA including positional preferences, target accessibility and other thermodynamic features. State of the art tools aim to optimize the selection of target siRNAs by identifying those that may have high experimental inhibition. Such tools implement artificial neural network models as Biopredsi and ThermoComposition21, and linear regression models as DSIR, i-Score and Scales, among others. However, all these models have limitations in performance. In this work, a neural-network trained new siRNA scoring/efficacy prediction model was developed based on combining two existing scoring algorithms (ThermoComposition21 and i-Score), together with the whole stacking energy (?G), in a multi-layer artificial neural network. These three parameters were chosen after a comparative combinatorial study between five well known tools. Our developed model, MysiRNA was trained on 2431 siRNA records and tested using three further datasets. MysiRNA was compared with 11 alternative existing scoring tools in an evaluation study to assess the predicted and experimental siRNA efficiency where it achieved the highest performance both in terms of correlation coefficient (R(2)=0.600) and receiver operating characteristics analysis (AUC=0.808), improving the prediction accuracy by up to 18% with respect to sensitivity and specificity of the best available tools. MysiRNA is a novel, freely accessible model capable of predicting siRNA inhibition efficiency with improved specificity and sensitivity. This multiclassifier approach could help improve the performance of prediction in several bioinformatics areas. MysiRNA model, part of MysiRNA-Designer package [1], is expected to play a key role in siRNA selection and evaluation.
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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.