The Microbe Directory (TMD) is a comprehensive database of annotations for microbial species collating features such as gram-stain, capsid-symmetry, resistance to antibiotics and more. This work presents a significant improvement to the original Microbe Directory (2018). This update adds 68,852 taxa, many new annotation features, an interface for the statistical analysis of microbiomes based on TMD features, and presents a portal for the broad community to add or correct entries. This update also adds curated lists of gene annotations which are useful for characterizing microbial genomes. Much of the new data in TMD is sourced from a set of databases and independent studies collating these data into a single quality controlled and curated source. This will allow researchers and clinicians to have easier access to microbial data and provide for the possibility of serendipitous discovery of otherwise unexpected trends.

Background: De novo assemblies are critical for capturing the genetic composition of complex samples. Linked-read sequencing techniques such as 10x Genomics' Linked-Reads, UST's TELL-Seq, Loop Genomics' LoopSeq, and BGI's Long Fragment Read combines 30 barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. The application of linked-read sequencing to genome assembly has demonstrated that barcoding-based technologies balance the ffs between long-range linkage, per-base coverage, and costs. Linkedreads come with their own challenges, chief among them the association of multiple long fragments with the same 30 barcode. The lack of a unique correspondence between a long fragment and a barcode, in conjunction with low sequencing depth, confounds the assignment of linkage between short-reads. Results: We introduce Ariadne, a novel linked-read deconvolution algorithm based on assembly graphs, that can be used to extract single-species read-sets from a large linked-read dataset. Ariadne deconvolution of linked-read clouds increases the proportion of read clouds containing only reads from a single fragment by up to 37.5-fold. Using these enhanced read clouds in de novo assembly significantly improves assembly contiguity and the size of the largest aligned blocks in comparison to the non-deconvolved read clouds. Integrating barcode deconvolution tools, such as Ariadne, into the postprocessing pipeline for linked-read technologies increases the quality of de novo assembly for complex populations, such as microbiomes. Ariadne is intuitive, computationally efficient, and scalable to other large-scale linked-read problems, such as human genome phasing.