A perfect bacterial genome assembly is one where the assembled sequence is an exact match for the organism's genome-each replicon sequence is complete and contains no errors. While this has been difficult to achieve in the past, improvements in long-read sequencing, assemblers, and polishers have brought perfect assemblies within reach. Here, we describe our recommended approach for assembling a bacterial genome to perfection using a combination of Oxford Nanopore Technologies long reads and Illumina short reads: Trycycler long-read assembly, Medaka long-read polishing, Polypolish short-read polishing, followed by other short-read polishing tools and manual curation. We also discuss potential pitfalls one might encounter when assembling challenging genomes, and we provide an online tutorial with sample data (github.com/rrwick/perfect-bacterial-genome-tutorial).

Long-read-only bacterial genome assemblies usually contain residual errors, most commonly homopolymer-length errors. Short-read polishing tools can use short reads to fix these errors, but most rely on short-read alignment which is unreliable in repeat regions. Errors in such regions are therefore challenging to fix and often remain after short-read polishing. Here we introduce Polypolish, a new short-read polisher which uses all-per-read alignments to repair errors in repeat sequences that other polishers cannot. Polypolish performed well in benchmarking tests using both simulated and real reads, and it almost never introduced errors during polishing. The best results were achieved by using Polypolish in combination with other short-read polishers.

SUMMARY: A common goal of microbiome studies is the elucidation of community composition and member interactions using counts of taxonomic units extracted from sequence data. Inference of interaction networks from sparse and compositional data requires specialized statistical approaches. A popular solution is SparCC, however its performance limits the calculation of interaction networks for very high-dimensional datasets. Here we introduce FastSpar, an efficient and parallelizable implementation of the SparCC algorithm which rapidly infers correlation networks and calculates P-values using an unbiased estimator. We further demonstrate that FastSpar reduces network inference wall time by 2-3 orders of magnitude compared to SparCC. AVAILABILITY AND IMPLEMENTATION: FastSpar source code, precompiled binaries and platform packages are freely available on GitHub: github.com/scwatts/FastSpar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barcode, and current demultiplexing tools fail to classify many reads. Here we present Deepbinner, a tool for Oxford Nanopore demultiplexing that uses a deep neural network to classify reads based on the raw electrical read signal. This 'signal-space' approach allows for greater accuracy than existing 'base-space' tools (Albacore and Porechop) for which signals must first be converted to DNA base calls, itself a complex problem that can introduce noise into the barcode sequence. To assess Deepbinner and existing tools, we performed multiplex sequencing on 12 amplicons chosen for their distinguishability. This allowed us to establish a ground truth classification for each read based on internal sequence alone. Deepbinner had the lowest rate of unclassified reads (7.8%) and the highest demultiplexing precision (98.5% of classified reads were correctly assigned). It can be used alone (to maximise the number of classified reads) or in conjunction with other demultiplexers (to maximise precision and minimise false positive classifications). We also found cross-sample chimeric reads (0.3%) and evidence of barcode switching (0.3%) in our dataset, which likely arise during library preparation and may be detrimental for quantitative studies that use multiplexing. Deepbinner is open source (GPLv3) and available at https://github.com/rrwick/Deepbinner.

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

UNLABELLED: Although de novo assembly graphs contain assembled contigs (nodes), the connections between those contigs (edges) are difficult for users to access. Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualizing assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colors and extracting sequences. BLAST searches can be performed within the Bandage graphical user interface and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analyzing de novo assemblies that are not possible through investigation of contigs alone. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows. A full feature list and screenshots are available at http://rrwick.github.io/Bandage. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Here, we present a method for estimating the frequencies of SNP alleles present within pooled samples of DNA using high-throughput short-read sequencing. The method was tested on real data from six strains of the highly monomorphic pathogen Salmonella Paratyphi A, sequenced individually and in a pool. A variety of read mapping and quality-weighting procedures were tested to determine the optimal parameters, which afforded > or =80% sensitivity of SNP detection and strong correlation with true SNP frequency at poolwide read depth of 40x, declining only slightly at read depths 20-40x. AVAILABILITY: The method was implemented in Perl and relies on the opensource software Maq for read mapping and SNP calling. The Perl script is freely available from ftp://ftp.sanger.ac.uk/pub/pathogens/pools/.