Bork, P. | bioRxiv Science

bioRxiv2018

NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

NGLess is a domain specific language for describing next-generation sequence processing pipelines. It was developed with the goal of enabling user-friendly computational reproducibility.\n\nUsing this framework, we developed NG-meta-profiler, a fast profiler for metagenomes which performs sequence preprocessing, mapping to bundled databases, filtering of the mapping results, and profiling (taxonomic and functional). It is significantly faster than either MOCAT2 or htseq-count and (as it builds on NGLess) its results are perfectly reproducible. These pipelines can easily be customized and extended with other tools.\n\nNGLess and NG-meta-profiler are open source software (under the liberal MIT licence) and can be downloaded from http://ngless.embl.de or installed through bioconda.

bioinformatics↗

bioRxiv2018

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗