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Scientists have developed scalable genetic screening for regulatory circuits using compressed Perturb-seq, according to a study published in Nature Biotechnology. Another study published in bioRxiv reveals genome-transcriptome correlations in cancer through high-throughput single-nucleus hybrid sequencing. Bayesian inference is being used for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data. Researchers have also developed GNorm2, an improved gene name recognition and normalization system. Additionally, a study published in Nature Communications shows that single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects. Lastly, a comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination, as reported in Nature Methods.
A comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination | Nature Methods https://t.co/tM2pHOq2oi
Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects | Nature Communications https://t.co/gV6AJNajme #Bioinformatics https://t.co/qQ1wjrZXVI
GNorm2: an improved gene name recognition and normalization system https://t.co/mNmbG8RU7V https://t.co/B7JbapLUif
GRAIGH: Gene Regulation accessibility integrating GeneHancer database https://t.co/MNO4Qe1GhW https://t.co/Yu8gKch7iG
High-throughput single-nucleus hybrid sequencing reveals genome-transcriptome correlations in cancer | bioRxiv https://t.co/RFLufQB4ka
Scalable genetic screening for regulatory circuits using compressed Perturb-seq | Nature Biotechnology https://t.co/9PhS1QDzF8 #genomics https://t.co/lT4zO6ghhP
Interesting new method for single-nucleus DNA and RNA by Li et al. from the labs of Dan Levy and Mike Wigler @CSHL. https://t.co/ujGHTu6YvC
Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data https://t.co/M9ifIHj8os #biorxiv_bioinfo
High-throughput single-nucleus hybrid sequencing reveals genome-transcriptome correlations in cancer https://t.co/fGZZHll9FZ https://t.co/0cgvkLU1j5