Project description:We reported a Concanavalin A-based Barcoding Strategy (CASB) for single-cell and single-nucleus sample multiplexing, which could be followed by different single-cell sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. Besides sample multiplexing, the CASB could further improve data quality through doublet identification.
Project description:We reported a Concanavalin A-based Barcoding Strategy (CASB) for single-cell and single-nucleus sample multiplexing, which could be followed by different single-cell sequencing techniques. The method involves minimal sample processing, thereby preserving intact transcriptomic or epigenomic patterns. Besides sample multiplexing, the CASB could further improve data quality through doublet identification.
Project description:Microbiome sample-material model is a Named Entity Recognition (NER) model that identifies and annotates the material of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sample-material 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
Project description:To minimize the distortion of genetic signal by system noise, we have explored the latter in an archive of hybridizations in which no genetic signal is expected. This archive is obtained by comparative genomic hybridization (CGH) of a reference sample in one channel to the same sample in the other channel, which we have termed ‘self-self’ data. We show that these self-self hybridizations trap a variety of system noise inherent in sample-reference (test) data. Through singular value decomposition (SVD) of self-self data, we are able to determine the principal components of system noise. Assuming simple linear models of noise generation, we present evidence that the linear correction of test data with self-self data—which we call system normalization—reduces local and long-range correlations as well as improves signal-to-noise metrics, yet does not introduce detectable spurious signal.
