MOTIVATION: In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g. Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions. RESULTS: We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering a weight for each k-mer; i.e. the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes. AVAILABILITY AND IMPLEMENTATION: Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap.

SUMMARY: HLA*LA implements a new graph alignment model for human leukocyte antigen (HLA) type inference, based on the projection of linear alignments onto a variation graph. It enables accurate HLA type inference from whole-genome (99% accuracy) and whole-exome (93% accuracy) Illumina data; from long-read Oxford Nanopore and Pacific Biosciences data (98% accuracy for whole-genome and targeted data) and from genome assemblies. Computational requirements for a typical sample vary between 0.7 and 14 CPU hours per sample. AVAILABILITY AND IMPLEMENTATION: HLA*LA is implemented in C++ and Perl and freely available as a bioconda package or from https://github.com/DiltheyLab/HLA-LA (GPL v3). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long-read sequencing and novel long-range assays have revolutionized de novo genome assembly by automating the reconstruction of reference-quality genomes. In particular, Hi-C sequencing is becoming an economical method for generating chromosome-scale scaffolds. Despite its increasing popularity, there are limited open-source tools available. Errors, particularly inversions and fusions across chromosomes, remain higher than alternate scaffolding technologies. We present a novel open-source Hi-C scaffolder that does not require an a priori estimate of chromosome number and minimizes errors by scaffolding with the assistance of an assembly graph. We demonstrate higher accuracy than the state-of-the-art methods across a variety of Hi-C library preparations and input assembly sizes. The Python and C++ code for our method is openly available at https://github.com/machinegun/SALSA.

UNLABELLED: FX is an RNA-Seq analysis tool, which runs in parallel on cloud computing infrastructure, for the estimation of gene expression levels and genomic variant calling. In the mapping of short RNA-Seq reads, FX uses a transcriptome-based reference primarily, generated from ~160 000 mRNA sequences from RefSeq, UCSC and Ensembl databases. This approach reduces the misalignment of reads originating from splicing junctions. Unmapped reads not aligned on known transcripts are then mapped on the human genome reference. FX allows analysis of RNA-Seq data on cloud computing infrastructures, supporting access through a user-friendly web interface. AVAILABILITY: FX is freely available on the web at (http://fx.gmi.ac.kr), and can be installed on local Hadoop clusters. Guidance for the installation and operation of FX can be found under the 'Documentation' menu on the website. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Graphical layout techniques serve a vital part in systems biology to enhance understanding and visualization of chemical reaction pathways in our body. Metabolic networks have particularly complex binding structures, making its graphical representation challenging to comprehend. For the purpose of legibility, reducing graph complexity in metabolic networks is crucial when working with large number of nodes and edges. This paper introduces a node abstraction algorithm that treats metabolic pathways as hierarchical networks and considers reactions between compound pairs-the equivalent of node pairs in the context of biological networks-as an elastic parameter for reaction compression in an automated way. Substrates and products that locally compose reactions with low connectivity were reduced, and cyclical or hierarchical pathways were aligned according to their structural composition.