Systematic assessment of long-read RNA-seq methods for transcript identification and quantification.

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Long-insert sequence capture detects high copy numbers in a defence-related beta-glucosidase gene ßglu-1 with large variations in white spruce but not Norway spruce.

Conifers are long-lived and slow-evolving, thus requiring effective defences against their fast-evolving insect natural enemies. The copy number variation (CNV) of two key acetophenone biosynthesis genes Ugt5/Ugt5b and ßglu-1 may provide a plausible mechanism underlying the constitutively variable defence in white spruce (Picea glauca) against its primary defoliator, spruce budworm. This study develops a long-insert sequence capture probe set (Picea_hung_p1.0) for quantifying copy number of ßglu-1-like, Ugt5-like genes and single-copy genes on 38 Norway spruce (Picea abies) and 40 P. glauca individuals from eight and nine provenances across Europe and North America respectively. We developed local assemblies (Piabi_c1.0 and Pigla_c.1.0), full-length transcriptomes (PIAB_v1 and PIGL_v1), and gene models to characterise the diversity of ßglu-1 and Ugt5 genes. We observed very large copy numbers of ßglu-1, with up to 381 copies in a single P. glauca individual. We observed among-provenance CNV of ßglu-1 in P. glauca but not P. abies. Ugt5b was predominantly single-copy in both species. This study generates critical hypotheses for testing the emergence and mechanism of extreme CNV, the dosage effect on phenotype, and the varying copy number of genes with the same pathway. We demonstrate new approaches to overcome experimental challenges in genomic research in conifer defences.

ntEmbd: Deep learning embedding for nucleotide sequences.

Enabled by the explosion of data and substantial increase in computational power, deep learning has transformed fields such as computer vision and natural language processing (NLP) and it has become a successful method to be applied to many transcriptomic analysis tasks. A core advantage of deep learning is its inherent capability to incorporate feature computation within the machine learning models. This results in a comprehensive and machine-readable representation of sequences, facilitating the downstream classification and clustering tasks. Compared to machine translation problems in NLP, feature embedding is particularly challenging for transcriptomic studies as the sequences are string of thousands of nucleotides in length, which make the long-term dependencies between features from different parts of the sequence even more difficult to capture. This highlights the need for nucleotide sequence embedding methods that are capable of learning input sequence features implicitly. Here we introduce ntEmbd, a deep learning embedding tool that captures dependencies between different features of the sequences and learns a latent representation for given nucleotide sequences. We further provide two sample use cases, describing how learned RNA features can be used in downstream analysis. The first use case demonstrates ntEmbd's utility in classifying coding and noncoding RNA benchmarked against existing tools, and the second one explores the utility of learned representations in identifying adapter sequences in nanopore RNA-seq reads. The tool as well as the trained models are freely available on GitHub at https://github.com/bcgsc/ntEmbd.

A comprehensive tandem repeat catalog of the human genome.

With the increasing availability of long-read sequencing data, high-quality human genome assemblies, and software for fully characterizing tandem repeats, genome-wide genotyping of tandem repeat loci on a population scale becomes more feasible. Such efforts not only expand our knowledge of the tandem repeat landscape in the human genome but also enhance our ability to differentiate pathogenic tandem repeat mutations from benign polymorphisms. To this end, we analyzed 272 genomes assembled using datasets from three public initiatives that employed different long-read sequencing technologies. Here, we report a catalog of over 18 million tandem repeat loci, many of which were previously unannotated. Some of these loci are highly polymorphic, and many of them reside within coding sequences.

Transcriptomic profiling of Rana [Lithobates] catesbeiana back skin during natural and thyroid hormone-induced metamorphosis under different temperature regimes with particular emphasis on innate immune system components.

As amphibians undergo thyroid hormone (TH)-dependent metamorphosis from an aquatic tadpole to the terrestrial frog, their innate immune system must adapt to the new environment. Skin is a primary line of defense, yet this organ undergoes extensive remodelling during metamorphosis and how it responds to TH is poorly understood. Temperature modulation, which regulates metamorphic timing, is a unique way to uncover early TH-induced transcriptomic events. Metamorphosis of premetamorphic tadpoles is induced by exogenous TH administration at 24 °C but is paused at 5 °C. However, at 5 °C a "molecular memory" of TH exposure is retained that results in an accelerated metamorphosis upon shifting to 24 °C. We used RNA-sequencing to identify changes in Rana (Lithobates) catesbeiana back skin gene expression during natural and TH-induced metamorphosis. During natural metamorphosis, significant differential expression (DE) was observed in >6500 transcripts including classic TH-responsive transcripts (thrb and thibz), heat shock proteins, and innate immune system components: keratins, mucins, and antimicrobial peptides (AMPs). Premetamorphic tadpoles maintained at 5 °C showed 83 DE transcripts within 48 h after TH administration, including thibz which has previously been identified as a molecular memory component in other tissues. Over 3600 DE transcripts were detected in TH-treated tadpoles at 24 °C or when tadpoles held at 5 °C were shifted to 24 °C. Gene ontology (GO) terms related to transcription, RNA metabolic processes, and translation were enriched in both datasets and immune related GO terms were observed in the temperature-modulated experiment. Our findings have implications on survival as climate change affects amphibia worldwide.

Conifers Concentrate Large Numbers of NLR Immune Receptor Genes on One Chromosome.

Nucleotide-binding domain and leucine-rich repeat (NLR) immune receptor genes form a major line of defense in plants, acting in both pathogen recognition and resistance machinery activation. NLRs are reported to form large gene clusters in limber pine (Pinus flexilis), but it is unknown how widespread this genomic architecture may be among the extant species of conifers (Pinophyta). We used comparative genomic analyses to assess patterns in the abundance, diversity, and genomic distribution of NLR genes. Chromosome-level whole genome assemblies and high-density linkage maps in the Pinaceae, Cupressaceae, Taxaceae, and other gymnosperms were scanned for NLR genes using existing and customized pipelines. The discovered genes were mapped across chromosomes and linkage groups and analyzed phylogenetically for evolutionary history. Conifer genomes are characterized by dense clusters of NLR genes, highly localized on one chromosome. These clusters are rich in TNL-encoding genes, which seem to have formed through multiple tandem duplication events. In contrast to angiosperms and nonconiferous gymnosperms, genomic clustering of NLR genes is ubiquitous in conifers. NLR-dense genomic regions are likely to influence a large part of the plant's resistance, informing our understanding of adaptation to biotic stress and the development of genetic resources through breeding.

De novo synthetic antimicrobial peptide design with a recurrent neural network.

Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of host defense peptides, are emerging as promising candidates to address this need. The potential sequence space of amino acids is combinatorially vast, making it possible to extend the current arsenal of antimicrobial agents with a practically infinite number of new peptide-based candidates. However, mining naturally occurring AMPs, whether directly by wet lab screening methods or aided by bioinformatics prediction tools, has its theoretical limit regarding the number of samples or genomic/transcriptomic resources researchers have access to. Further, manually designing novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods are gaining interest as a high-throughput solution to the problem. Here, we introduce AMPd-Up, a recurrent neural network based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through antimicrobial susceptibility testing revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities.