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1. An updated and extended version of the Melastomataceae probe set for target capture

   https://doi.org/10.1002/aps3.11564  

   Dagallier, Léo‐Paul_M J; Michelangeli, Fabián A (January 2024, Applications in Plant Sciences)

   Abstract PremiseA probe set was previously designed to target 384 nuclear loci in the Melastomataceae family; however, when trying to use it, we encountered several practical and conceptual problems, such as the presence of sequences in reverse complement, intronic regions with stop codons, and other issues. This raised concerns regarding the use of this probe set for sequence recovery in Melastomataceae. MethodsIn order to correct these issues, we cleaned the Melastomataceae probe set, extended it with additional sequences, and compared its performance with the original version. ResultsThe final probe set targets 396 putative nuclear loci represented by 6009 template sequences. The probe set has been made available, along with details on the cleaning process, for reproducibility. We show that the new probe set performs better than the original version in terms of sequence recovery. DiscussionThis updated, extended, and cleaned probe set will improve the availability of phylogenomic resources across the Melastomataceae family. It is fully compatible with sequence recovery and extraction pipelines. The cleaning process can also be applied to any plant‐targeting probe set that would need to be cleaned or updated if new genomic resources for the targeted taxa become available. 

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2. Seeding with minimized subsequence

   https://doi.org/10.1093/bioinformatics/btad218  

   Li, Xiang; Shi, Qian; Chen, Ke; Shao, Mingfu (June 2023, Bioinformatics)

   Abstract MotivationModern methods for computation-intensive tasks in sequence analysis (e.g. read mapping, sequence alignment, genome assembly, etc.) often first transform each sequence into a list of short, regular-length seeds so that compact data structures and efficient algorithms can be employed to handle the ever-growing large-scale data. Seeding methods using kmers (substrings of length k) have gained tremendous success in processing sequencing data with low mutation/error rates. However, they are much less effective for sequencing data with high error rates as kmers cannot tolerate errors. ResultsWe propose SubseqHash, a strategy that uses subsequences, rather than substrings, as seeds. Formally, SubseqHash maps a string of length n to its smallest subsequence of length k, k < n, according to a given order overall length-k strings. Finding the smallest subsequence of a string by enumeration is impractical as the number of subsequences grows exponentially. To overcome this barrier, we propose a novel algorithmic framework that consists of a specifically designed order (termed ABC order) and an algorithm that computes the minimized subsequence under an ABC order in polynomial time. We first show that the ABC order exhibits the desired property and the probability of hash collision using the ABC order is close to the Jaccard index. We then show that SubseqHash overwhelmingly outperforms the substring-based seeding methods in producing high-quality seed-matches for three critical applications: read mapping, sequence alignment, and overlap detection. SubseqHash presents a major algorithmic breakthrough for tackling the high error rates and we expect it to be widely adapted for long-reads analysis. Availability and implementationSubseqHash is freely available at https://github.com/Shao-Group/subseqhash. 

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3. Annotation-free prediction of microbial dioxygen utilization

   https://doi.org/10.1128/msystems.00763-24  

   Flamholz, Avi I; Goldford, Joshua E; Richter, Philippa A; Larsson, Elin M; Jinich, Adrian; Fischer, Woodward W; Newman, Dianne K (October 2024, mSystems)

   Greening, Chris (Ed.)

   ABSTRACT Aerobes require dioxygen (O₂) to grow; anaerobes do not. However, nearly all microbes—aerobes, anaerobes, and facultative organisms alike—express enzymes whose substrates include O₂, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O₂utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O₂-utilizing enzymes, for example. These effects permit high-quality prediction of O₂utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content—e.g., triplets of amino acids—perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O₂gradient in the Black Sea, we found quantitative correspondence between local chemistry (O₂:sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or “sense,” pivotal features of the chemical environment using DNA sequencing data.IMPORTANCEWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology—an organism’s capacity to engage metabolically with its environment—may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O₂levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O₂and other key facets of complex natural settings. 

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4. RNA design via structure-aware multifrontier ensemble optimization

   https://doi.org/10.1093/bioinformatics/btad252  

   Zhou, Tianshuo; Dai, Ning; Li, Sizhen; Ward, Max; Mathews, David H; Huang, Liang (June 2023, Bioinformatics)

   Abstract MotivationRNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. ResultsWe propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. Availability and implementationOur source code and data used in this article is available at https://github.com/shanry/SAMFEO. 

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5. CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov–Arnold network

   https://doi.org/10.1093/bioinformatics/btaf124  

   Pratyush, Pawel; Carrier, Callen; Pokharel, Suresh; Ismail, Hamid D; Chaudhari, Meenal; KC, Dukka B (March 2025, Bioinformatics)

   Gao, Xin (Ed.)

   Abstract MotivationThe mapping from codon to amino acid is surjective due to codon degeneracy, suggesting that codon space might harbor higher information content. Embeddings from the codon language model have recently demonstrated success in various protein downstream tasks. However, predictive models for residue-level tasks such as phosphorylation sites, arguably the most studied Post-Translational Modification (PTM), and PTM sites prediction in general, have predominantly relied on representations in amino acid space. ResultsWe introduce a novel approach for predicting phosphorylation sites by utilizing codon-level information through embeddings from the codon adaptation language model (CaLM), trained on protein-coding DNA sequences. Protein sequences are first reverse-translated into reliable coding sequences by mapping UniProt sequences to their corresponding NCBI reference sequences and extracting the exact coding sequences from their GenBank format using a dynamic programming-based global pairwise alignment. The resulting coding sequences are encoded using the CaLM encoder to generate codon-aware embeddings, which are subsequently integrated with amino acid-aware embeddings obtained from a protein language model, through an early fusion strategy. Next, a window-level representation of the site of interest, retaining the full sequence context, is constructed from the fused embeddings. A ConvBiGRU network extracts feature maps that capture spatiotemporal correlations between proximal residues within the window. This is followed by a prediction head based on a Kolmogorov-Arnold network (KAN) using the derivative of gaussian wavelet transform to generate the inference for the site. The overall model, dubbed CaLMPhosKAN, performs better than the existing approaches across multiple datasets. Availability and implementationCaLMPhosKAN is publicly available at https://github.com/KCLabMTU/CaLMPhosKAN. 

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