Project description:We present MultiEditR, the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from in Sanger sequencing traces, the accuracy and the precision of this approach has yet to be evaluated against gold-standards next-generation sequencing methods. Through a comprehensive comparison to RNA-seq and amplicon based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing.
Project description:Total RNA was extracted from zebrafish embryos from the SAT (Sanger AB Tubingen) strain. The RNA was DNase treated. The 3' ends of fragmented RNA was pulled down using polyT oligos attached to magnetic beads, reverse transcribed, made into Illumina libraries and sequenced using IlluminaHiSeq paired-end sequencing. Protocol: Total RNA was extracted and DNase treated. Fragmented RNA was enriched for the 3 ends by pull down using a polyT oligo attached to magnetic beads. An RNA oligo comprising part of the Illumina adapter 2 was ligated to the 5 end of the captured RNA and the RNA was eluted from the beads. Reverse transcription was primed with an anchored polyT oligo with part of Illumina adapter 1 at the 5 end followed by 12 random bases, then an 8 base indexing tag, then CG and 14 T bases. An Illumina library with full adapter sequence was produced by PCR. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
Project description:Total RNA was extracted from zebrafish embryos from the SAT (Sanger AB Tbingen) strain. The RNA was DNase treated. Stranded RNAseq libraries were constructed using the Illumina TruSeq Stranded RNA protocol after treatmant with Ribozero.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
Project description:Microbiome sequencing model is a Named Entity Recognition (NER) model that identifies and annotates microbiome nucleic acid sequencing method or platform in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sequencing metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
