Rob Egan

TL;DR: MetaBAT as mentioned in this paper integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning, and automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs.

TL;DR: To characterize biomass-degrading genes and genomes, this work sequenced and analyzed 268 gigabases of metagenomic DNA from microbes adherent to plant fiber incubated in cow rumen and identified 27,755 putative carbohydrate-active genes and expressed 90 candidate proteins, of which 57% were enzymatically active against cellulosic substrates.

TL;DR: The first algorithm for the identification of modified nucleotides without the need for prior training data is presented along with the open source software implementation, nanoraw, which accurately assigns contiguous raw nanopore signal to genomic positions, enabling novel data visualization and increasing power and accuracy for the discovery of covalently modified bases in native DNA.

TL;DR: The ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

TL;DR: HipMer is presented, the first high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code, and significantly improves scalability of parallel k-mer analysis for complex repetitive genomes that exhibit skewed frequency distributions.