Biology subjects

Kovaka, S.

Publications and source records attributed to Kovaka, S..

2 recordsLinked to original sources

bioRxiv2020

Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED

ReadUntil sequencing allows nanopore devices to selectively eject individual reads from the pore in real-time. This could enable purely computational targeted sequencing, however most mapping methods require basecalling, which is computationally intensive. Here we present UNCALLED (github.com/skovaka/UNCALLED), an open-source mapper that rapidly matches streaming nanopore current signals to a reference sequence. UNCALLED probabilistically considers k-mers that the signal could represent, and then prunes the candidates based on the reference encoded within an FM-index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species by 4.46 fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6x coverage using one MinION flowcell, enabling accurate detection of SNPs, indels, structural variants (SVs), and methylation in these genes. Twice as many SVs were detected compared to 50x coverage Illumina sequencing, all verified by whole-genome nanopore and PacBio HiFi sequencing.

genomics↗

bioRxiv2019

Transcriptome assembly from long-read RNA-seq alignments with StringTie2

RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new computational methods to handle the high error rate of long-read sequencing technology, which previous assemblers could not tolerate. It also offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of assemblies. On 33 short-read datasets from humans and two plant species, StringTie2 is 47.3% more precise and 3.9% more sensitive than Scallop. On multiple long read datasets, StringTie2 on average correctly assembles 8.3 and 2.6 times as many transcripts as FLAIR and Traphlor, respectively, with substantially higher precision. StringTie2 is also faster and has a smaller memory footprint than all comparable tools.

genomics↗