Biotechnology papers

December 5, 2016
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Its raining RNA-seq papers at Nature Biotechnology. Like Nature Genetics moved Nature GWAS, Nature Biotechnology went Nature RNA-seq :-) Nature BioTechnology record simply posted 5 RNA-seq documents in its advanced web publication. The 5 RNA-seq documents covers some earliest pens concerns like “concordance of RNA-seq and microarray data”, interesting old concerns like “RNA-seq normalization” plus some brand-new interesting concerns like “how does RNA-seq measures up across several sequencing technologies, such lllumina and PacBio?” All of them appear to be interesting read, looking to review them and write some articles onto it quickly.

  1. Detecting and correcting organized difference in large-scale RNA sequencing information, Sheng Li et. al.
  2. Multi-platform evaluation of transcriptome profiling using RNA-seq within the ABRF next-generation sequencing research, Sheng Li et al.
  3. Davide Risso, John Ngai, Terence P Speed, and Sandrine Dudoit
  4. Charles Wang
  5. A thorough assessment of RNA-seq reliability, reproducibility and information content because of the Sequencing quality-control Consortium EQC/MAQC-IIwe Consortium

Here you will find the abstracts regarding the five documents.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript variety, Charles Wang

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene appearance has not been rigorously examined utilizing a selection of chemical therapy problems. Right here we utilize an extensive study design to generate Illumina RNA-seq and Affymetrix microarray information from exact same liver samples of rats subjected in triplicate to varying quantities of perturbation by 27 chemical compounds representing several modes of activity (MOAs). The cross-platform concordance with regards to differentially expressed genetics (DEGs) or enriched paths is linearly correlated with therapy effect size (R2?0.8). Also, the concordance can also be impacted by transcript variety and biological complexity of MOA. RNA-seq outperforms microarray (93% versus 75percent) in DEG verification as evaluated by quantitative PCR, with all the gain due mainly to its improved accuracy for low-abundance transcripts. None the less, classifiers to predict MOAs perform similarly whenever created making use of data from either platform. For that reason, the endpoint examined and its own biological complexity, transcript variety additionally the genomic application are very important elements in transcriptomic research as well as clinical and regulatory decision making.

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