Plant genomes provide information on biosynthetic pathways involved in the production of industrially relevant compounds. Genome size estimates are essential for the initiation of genome projects. The genome size of rooibos (Aspalathus linearis species complex) was estimated using DAPI flow cytometry and k-mer analyses. For flow cytometry, a suitable nuclei isolation buffer, plant tissue and a transport medium for rooibos ecotype samples collected from distant locations were identified. When using radicles from commercial rooibos seedlings, Woody Plant Buffer and Vicia faba as an internal standard, the flow cytometry-estimated genome size of rooibos was 1.24 ± 0.01 Gbp. The estimates for eight wild rooibos growth types did not deviate significantly from this value. K-mer analysis was performed using Illumina paired-end sequencing data from one commercial rooibos genotype. For biocomputational estimation of the genome size, four k-mer analysis methods were investigated: A standard formula and three popular programs (BBNorm, GenomeScope, and FindGSE). GenomeScope estimates were strongly affected by parameter settings, specifically CovMax. When using the complete k-mer frequency histogram (up to 9 × 10⁵), the programs did not deviate significantly, estimating an average rooibos genome size of 1.03 ± 0.04 Gbp. Differences between the flow cytometry and biocomputational estimates are discussed.

Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to 742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibody-based therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions. Our models unlock a plethora of opportunities across different scientific domains, such as direct protein sequencing, immunopeptidomics, and exploration of the dark proteome.

Aspalathin, the main polyphenol of rooibos (Aspalathus linearis), is associated with diverse health promoting properties of the tea. During fermentation, aspalathin is oxidized and concentrations are significantly reduced. Standardized methods for quality control of rooibos products do not investigate aspalathin, since current techniques of aspalathin detection require expensive equipment and expertise. Here, we describe a simple and fast thin-layer chromatography (TLC) method that can reproducibly visualize aspalathin in rooibos herbal tea and plant extracts at a limit of detection (LOD) equal to 178.7 ng and a limit of quantification (LOQ) equal to 541.6 ng. Aspalathin is a rare compound, so far only found in A. linearis and its (rare) sister species A. pendula. Therefore, aspalathin could serve as a marker compound for authentication and quality control of rooibos products, and the described TLC method represents a cost-effective approach for high-throughput screening of plant and herbal tea extracts.

Rooibos (Aspalathus linearis), widely known as a herbal tea, is endemic to the Cape Floristic Region of South Africa (SA). It produces a wide range of phenolic compounds that have been associated with diverse health promoting properties of the plant. The species comprises several growth forms that differ in their morphology and biochemical composition, only one of which is cultivated and used commercially. Here, we established methodologies for non-invasive transcriptome research of wild-growing South African plant species, including (1) harvesting and transport of plant material suitable for RNA sequencing; (2) inexpensive, high-throughput biochemical sample screening; (3) extraction of high-quality RNA from recalcitrant, polysaccharide- and polyphenol rich plant material; and (4) biocomputational analysis of Illumina sequencing data, together with the evaluation of programs for transcriptome assembly (Trinity, IDBA-Trans, SOAPdenovo-Trans, CLC), protein prediction, as well as functional and taxonomic transcript annotation. In the process, we established a biochemically characterized sample pool from 44 distinct rooibos ecotypes (1–5 harvests) and generated four in-depth annotated transcriptomes (each comprising on average ≈86,000 transcripts) from rooibos plants that represent distinct growth forms and differ in their biochemical profiles. These resources will serve future rooibos research and plant breeding endeavours.