HGTree v2.0: a comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method

Choi, Youngseok; Ahn, Sojin; Park, Myeongkyu; Lee, SaetByeol; Cho, Seoae; Kim, Heebal

doi:10.1093/nar/gkac929

Cited by 6 publications

(5 citation statements)

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“…Although codon usage diversity is well-understood and has led to the development of optimisation tools, the variability in regulatory sequences has been comparatively less explored and lacks similar computational resources. To exemplify the differences in regulatory sequences both within and across species, we chose four orthologous clusters as reference points, utilising HGTree v2.0 for this purpose 18 . The clusters selected come from the datasets acquired from the public repositories and encompass a mix of conserved genes, as well as hosts that belong to either the same genus or multiple genera.…”

Section: Resultsmentioning

confidence: 99%

“…To exemplify the differences in regulatory sequences both within and across species, we chose four orthologous clusters as reference points, utilising HGTree v2.0 for this purpose 18 . The clusters selected come from the datasets acquired from the public repositories and encompass a mix of conserved genes, as well as hosts that belong to either the same genus or multiple genera.…”

Section: /19mentioning

confidence: 99%

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From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

Dahiya,

Bakken,

Fages-Lartaud

et al. 2023

Preprint

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Synthetic biology currently operates under a framework dominated by trial-and-error approaches, which hinders the effective engineering of organisms and the expansion of large-scale biomanufacturing. Motivated by the success of computational designs in areas like architecture and aeronautics, we aspire to transition to a more efficient and predictive methodology in synthetic biology. In this study, we report a DNA Design Platform that relies on the predictive power of Transformer-based deep learning architectures. The platform transforms the conventional paradigms in synthetic biology by enabling the context-sensitive and host-specific engineering of 5′ regulatory elements—promoters and 5′ untranslated regions (UTRs)—along with an array of codon-optimised coding sequence (CDS) variants. This allows us to generate context-sensitive 5′ regulatory sequences and CDSs, achieving an unparalleled level of specificity and adaptability in different target hosts. With context-aware design, we significantly broaden the range of possible gene expression profiles and phenotypic outcomes, substantially reducing the need for laborious high-throughput screening efforts. Our context-aware, AI-driven design strategy marks a significant advancement in synthetic biology, offering a scalable and refined approach for gene expression optimisation across a diverse range of expression hosts. In summary, this study represents a substantial leap forward in the field, utilising deep learning models to transform the conventional design, build, test, learn-cycle into a more efficient and predictive framework.

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Section: Resultsmentioning

confidence: 99%

Section: /19mentioning

confidence: 99%

From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

Dahiya,

Bakken,

Fages-Lartaud

et al. 2023

Preprint

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“…Alternatively, HGT can be detected through comparing genomes and identifying discrepancies between gene and species evolutionary history. These comparative genomics approaches include the detection of nearly identical sequences in genomes from different species 11 – 16 or the more computationally intensive modelling of gene evolution through processes such as gene duplication, transfer or loss 3 , 4 , 17 – 19 . The next-generation sequencing revolution has enabled HGT detection through comparative genomics approaches by enabling an abundance of publicly available, high-quality prokaryotic genomes in curated databases such as proGenomes 20 .…”

Section: Mainmentioning

confidence: 99%

A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer

Dmitrijeva,

Tackmann,

Matias Rodrigues

et al. 2024

Nat Ecol Evol

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Horizontal gene transfer, the exchange of genetic material through means other than reproduction, is a fundamental force in prokaryotic genome evolution. Genomic persistence of horizontally transferred genes has been shown to be influenced by both ecological and evolutionary factors. However, there is limited availability of ecological information about species other than the habitats from which they were isolated, which has prevented a deeper exploration of ecological contributions to horizontal gene transfer. Here we focus on transfers detected through comparison of individual gene trees to the species tree, assessing the distribution of gene-exchanging prokaryotes across over a million environmental sequencing samples. By analysing detected horizontal gene transfer events, we show distinct functional profiles for recent versus old events. Although most genes transferred are part of the accessory genome, genes transferred earlier in evolution tend to be more ubiquitous within present-day species. We find that co-occurring, interacting and high-abundance species tend to exchange more genes. Finally, we show that host-associated specialist species are most likely to exchange genes with other host-associated specialist species, whereas species found across different habitats have similar gene exchange rates irrespective of their preferred habitat. Our study covers an unprecedented scale of integrated horizontal gene transfer and environmental information, highlighting broad eco-evolutionary trends.

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“…Another new database TEDD ( 74 ) looks at temporal (co-) expression and chromatin accessibility during development in model organisms while Lineage Landscape ( 75 ) explores similar themes, additionally covering epigenomics data. Finally, interesting perspectives on genome evolution are offered by the returning HGTree ( 76 ), now covering horizontal gene transfer in eight times as many prokaryotic species, and the new DupScan ( 77 ) which provides detailed insights into whole genome duplications in vertebrates.…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Rigden

Fernández

2023

Nucleic Acids Research

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The 2023 Nucleic Acids Research Database Issue contains 178 papers ranging across biology and related fields. There are 90 papers reporting on new databases and 82 updates from resources previously published in the Issue. Six more papers are updates from databases most recently published elsewhere. Major nucleic acid databases reporting updates include Genbank, ENA, ChIPBase, JASPAR, mirDIP and the Issue's first Breakthrough Article, NACDDB for Circular Dichroism data. Updates from BMRB and RCSB cover experimental protein structural data while AlphaFold 2 computational structure predictions feature widely. STRING and REBASE are stand-out updates in the signalling and enzymes section. Immunology-related databases include CEDAR, the second Breakthrough Article, for cancer epitopes and receptors alongside returning IPD-IMGT/HLA and the new PGG.MHC. Genomics-related resources include Ensembl, GWAS Central and UCSC Genome Browser. Major returning databases for drugs and their targets include Open Targets, DrugCentral, CTD and Pubchem. The EMPIAR image archive appears in the Issue for the first time. The entire database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, revisiting 463 entries, adding 92 new resources and eliminating 96 discontinued URLs so bringing the current total to 1764 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

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HGTree v2.0: a comprehensive database update for horizontal gene transfer (HGT) events detected by the tree-reconciliation method

Cited by 6 publications

References 50 publications

From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

From Context to Code: Rational De Novo DNA Design and Predicting Cross-Species DNA Functionality Using Deep Learning Transformer Models

A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

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