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At least 19 records

Human RNome Project draft human RNome sequence of GM12878, B-cell line, obtained by mass-spectrometry sequencing, long-read sequencing and short-read sequencing.

Here we report the first draft of the human RNome sequence, a reference map of RNA chemical modifications in a human B-cell line. RNA carries a diverse repertoire of chemical modifications that regulate gene expression, cellular function, and responses to physiological and pathological cues. Yet, unlike the genome, no reference map of RNA modifications is available for any human cell. To generate this resource, the Human RNome Project Consortium analyzed a shared RNA preparation from the well-characterized GM12878 B-cell line using short-read sequencing, long-read direct RNA sequencing, and mass spectrometry, generating more than 7.1 billion sequencing reads spanning approximately 1.2 trillion nucleotides. The resulting maps of the human RNome reveal that RNA modifications are organized according to function, transcript architecture, and cellular identity. Modifications concentrate at functional centers of ribosomal and transfer RNAs, follow the canonical topology of N6-methyladenosine in coding transcripts, and form coordinated hotspots in immune regulatory genes. This first reference human RNome provides a foundation for understanding how RNA chemistry shapes cellular identity, human disease, and the development of RNA-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗

Saccharomycopsis sp. strain UFMG-CM-Y6632 internal transcribed spacer 1, partial sequence; 5.8S ribosomal RNA gene and internal transcribed spacer 2, complete sequence; and large subunit ribosomal RNA gene, partial sequence

Three yeast isolates were obtained from soil and rotting wood samples collected in an Amazonian rainforest biome in Brazil. Comparison of the intergenic spacer 5.8S region and the D1/D2 domains of the large subunit rRNA gene showed that the isolates represent a novel species of the genus Saccharomycopsis. A tree inferred from the D1/D2 sequences placed the novel species near a subclade containing Saccharomycopsis lassenensis, Saccharomycopsis fermentans, Saccharomycopsis javanensis, Saccharomycopsis babjevae, Saccharomycopsis schoenii and Saccharomycopsis oosterbeekiorum, but with low bootstrap support. In terms of sequence divergence, the novel species had the highest identity in the D1/D2 domains with Saccharomycopsis capsularis, from which it differed by 36 substitutions. In contrast, a phylogenomic analysis based on 1061 single-copy orthologs for a smaller set of Saccharomycopsis species whose whole genome sequences are available indicated that the novel species represented by strain UFMG-CM-Y6991 is phylogenetically closer to Saccharomycopsis fodiens and Saccharomycopsis sp. TF2021a (=Saccharomycopsis phalluae). The novel yeast is homothallic and produces asci with one spheroidal ascospore with an equatorial or subequatorial ledge. The name Saccharomycopsis praedatoria sp. nov. is proposed to accommodate the novel species. The holotype of Saccharomycopsis praedatoria is CBS 16589(T). The MycoBank number is MB849369. S. praedatoria was able to kill cells of Saccharomyces cerevisiae by means of penetration with infection pegs, a trait common to most species of Saccharomycopsis.

Amazonian Forest↗

Pichia sp. strain yHMH446 internal transcribed spacer 1, partial sequence; 5.8S ribosomal RNA gene, complete sequence; and internal transcribed spacer 2, partial sequence

A novel budding yeast species was isolated from a soil sample collected in the United States of America. Phylogenetic analyses of multiple loci and phylogenomic analyses conclusively placed the species within the genus Pichia. Strain yHMH446 falls within a clade that includes Pichia norvegensis, Pichia pseudocactophila, Candida inconspicua, and Pichia cactophila. Whole genome sequence data were analyzed for the presence of genes known to be important for carbon and nitrogen metabolism, and the phenotypic data from the novel species were compared to all Pichia species with publicly available genomes. Across the genus, including the novel species candidate, we found that the inability to use many carbon and nitrogen sources correlated with the absence of metabolic genes. Based on these results, Pichia galeolata sp. nov. is proposed to accommodate yHMH446(T) (=NRRL Y-64187 = CBS 16864). This study shows how integrated taxogenomic analysis can add mechanistic insight to species descriptions.

genome sequence↗

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields—isotope, isotopolog, isotopolog label, labeling approach, and gradient position—and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Simpson, Abigayle↗

Non-target RNA depletion strategy to improve sensitivity of next-generation sequencing for the detection of RNA viruses in poultry

PCR-based assays have become the benchmark for detecting pathogens of poultry and other livestock; however, these techniques are limited in their ability to detect multiple infecting agents, provide limited genetic information on the pathogen, and, for RNA viruses, must be reviewed frequently to assure high sensitivity and specificity. In contrast, untargeted, high-throughput sequencing can rapidly detect all infecting agents in a sample while providing genomic sequence information to allow more in-depth characterization of viruses. Although next-generation sequencing (NGS) offers many advantages, one of its primary limitations is low sensitivity to pathogens given the abundance of host and other non-target sequences in sequencing libraries. We explored methods for improving the sensitivity of NGS to detect respiratory and enteric viruses in poultry from RNA extracts of swab samples. We employed commercial and custom-designed negative enrichment strategies to selectively deplete the most abundant rRNA reads from the host and non-target bacteria; host RNA was diminished from up to 40% of total reads to as low as 3%, and the total number of reads assigned to abundant bacterial classes were reduced greatly. Our treatment resulted in up to a 700-fold increase in the number of viral reads, detection of a greater number of viral agents, and higher average genome coverage for pathogens. Depletion assays added only 2 h to the NGS library preparation workflow. Custom depletion probe design offered significant cost savings (US$7–12 per sample) compared to commercial kits (US$30–50 per sample).

Parris, D. Joshua↗

Next-Generation Sequencing Data from a CUT&RUN Study of R. toruloides IFO0880 Cse4 and Orc1 Binding Sites

Rhodotorula toruloides has been increasingly explored as a host for bioproduction of lipids, fatty acid derivatives and terpenoids. Various genetic tools have been developed, but neither a centromere nor an autonomously replicating sequence (ARS), both necessary elements for stable episomal plasmid maintenance, has yet been reported. In this study, cleavage under targets and release using nuclease (CUT&RUN), a method used for genome-wide mapping of DNA–protein interactions, was used to identify R. toruloides IFO0880 genomic regions associated with the centromeric histone H3 protein Cse4, a marker of centromeric DNA. Fifteen putative centromeres ranging from 8 to 19 kb in length were identified and analyzed, and four were tested for, but did not show, ARS activity. These centromeric sequences contained below average GC content, corresponded to transcriptional cold spots, were primarily nonrepetitive and shared some vestigial transposon-related sequences but otherwise did not show significant sequence conservation. Future efforts to identify an ARS in this yeast can utilize these centromeric DNA sequences to improve the stability of episomal plasmids derived from putative ARS elements.

Genome Engineering↗

The Effects of Herbarium Specimen Characteristics on Short-Read NGS Sequencing Success in Nearly 8000 Specimens: Old, Degraded Samples Have Lower DNA Yields but Consistent Sequencing Success

Phylogenetic datasets are now commonly generated using short-read sequencing technologies unhampered by degraded DNA, such as that often extracted from herbarium specimens. The compatibility of these methods with herbarium specimens has precipitated an increase in broad sampling of herbarium specimens for inclusion in phylogenetic studies. Understanding which sample characteristics are predictive of sequencing success can guide researchers in the selection of tissues and specimens most likely to yield good results. Multiple recent studies have considered the relationship between sample characteristics and DNA yield and sequence capture success. Here we report an analysis of the relationship between sample characteristics and sequencing success for nearly 8,000 herbarium specimens. This study, the largest of its kind, is also the first to include a measure of specimen quality (“greenness”) as a predictor of DNA sequencing success. We found that taxonomic group and source herbarium are strong predictors of both DNA yield and sequencing success and that the most important specimen characteristics for predicting success differ for DNA yield and sequencing: greenness was the strongest predictor of DNA yield, and age was the strongest predictor of proportion-on-target reads recovered. Surprisingly, the relationship between age and proportion-on-target reads is the inverse of expectations; older specimens performed slightly better in our capture-based protocols. We also found that DNA yield itself is not a strong predictor of sequencing success. Most literature on DNA sequencing from herbarium specimens considers specimen selection for optimal DNA extraction success, which we find to be an inappropriate metric for predicting success using next-generation sequencing technologies.

54 ENVIRONMENTAL SCIENCES↗

Genetic time traveling: sequencing old herbarium specimens, including the oldest herbarium specimen sequenced from kingdom Fungi, reveals the population structure of an agriculturally significant rust

Summary Sequencing herbarium specimens can be instrumental in answering ecological, evolutionary, and taxonomic inquiries. We developed a protocol for sequencing herbarium specimens of rust fungi (Pucciniales) and proceeded to sequence specimens ranging from 4 to 211 yr old from five different genera. We then obtained sequences from an economically important biological control agent, Puccinia suaveolens , to highlight the potential of sequencing herbarium specimens in an ecological sense and to evaluate the following hypotheses: (1) The population structure of a plant pathogen changes over time, and (2) introduced pathogens are more diverse in their native range. Our efforts resulted in sequences from 87 herbarium specimens that revealed a high level of diversity with a population structure that exhibited spatial–temporal patterns. The specimens sequenced from Europe showed more diversity than the ones from North America, uncovering an invasion pattern likely related to its European native host in North America. Additionally, to the best of our knowledge, the specimen from France collected in c. 1811 is the oldest herbarium specimen sequenced from kingdom Fungi. In conclusion, sequencing old herbarium specimens is an important tool that can be extrapolated to better understand plant–microbe evolution and to evaluate old type specimens to solidify the taxonomy of plant pathogenic fungi.

Plant Sciences↗

New alignment method for remote protein sequences by the direct use of pairwise sequence correlations and substitutions

Understanding protein sequences and how they relate to the functions of proteins is extremely important. One of the most basic operations in bioinformatics is sequence alignment and usually the first things learned from these are which positions are the most conserved and often these are critical parts of the structure, such as enzyme active site residues. In addition, the contact pairs in a protein usually correspond closely to the correlations between residue positions in the multiple sequence alignment, and these usually change in a systematic and coordinated way, if one position changes then the other member of the pair also changes to compensate. In the present work, these correlated pairs are taken as anchor points for a new type of sequence alignment. The main advantage of the method here is its combining the remote homolog detection from our method PROST with pairwise sequence substitutions in the rigorous method from Kleinjung et al. We show a few examples of some resulting sequence alignments, and how they can lead to improvements in alignments for function, even for a disordered protein.

Jia, Kejue↗

Main-sequence Scatter is Real: The Joint Dependence of Galaxy Clustering on Star Formation and Stellar Mass

We present new measurements of the clustering of stellar-mass-complete samples of ∼40,000 SDSS galaxies at z ∼ 0.03 as a joint function of stellar mass and specific star formation rate (sSFR). Our results confirm what Coil et al. find at z ∼ 0.7: galaxy clustering is a stronger function of sSFR at fixed stellar mass than of stellar mass at fixed sSFR. We also find that galaxies above the star-forming main sequence (SFMS) with higher sSFR are less clustered than galaxies below the SFMS with lower sSFR, at a given stellar mass. A similar trend is present for quiescent galaxies. This confirms that main-sequence scatter, and scatter within the quiescent sequence, is physically connected to the large-scale cosmic density field. We compare the resulting galaxy bias versus sSFR, and relative bias versus sSFR ratio, for different galaxy samples across 0 < z < 1.2 to mock galaxy catalogs based on the empirical galaxy evolution model of Behroozi et al. This model fits PRIMUS and DEEP2 clustering data well at intermediate redshift, but agreement with SDSS is not as strong. We show that increasing the correlation between galaxy SFR and halo accretion rate at z ∼ 0 in the model substantially improves agreement with SDSS data. Mock catalogs suggest that central galaxies contribute substantially to the dependence of clustering on sSFR at a given stellar mass and that the signal is not simply an effect of satellite galaxy fraction differences with sSFR. Our results are highly constraining for galaxy evolution models and show that the stellar-to-halo mass relation depends on sSFR.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation

This dataset accompanies the publication "ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation". This paper introduces a new AI model for protein sequence generation. This dataset contains data related to experiments discussed in the publication. This includes generated sequences and evaluation metrics supporting all unconditional and bias-controlled experiments in the ProtNHF paper.

60 APPLIED LIFE SCIENCES↗

The role of filamentation in activation and DNA sequence specificity of the sequence-specific endonuclease SgrAI

Filament formation by metabolic, biosynthetic, and other enzymes has recently come into focus as a mechanism to fine-tune enzyme activity in the cell. Filamentation is key to the function of SgrAI, a sequence-specific DNA endonuclease that has served as a model system to provide some of the deepest insights into the biophysical characteristics of filamentation and its functional consequences. Structure-function analyses reveal that, in the filamentous state, SgrAI stabilizes an activated enzyme conformation that leads to accelerated DNA cleavage activity and expanded DNA sequence specificity. The latter is thought to be mediated by sequence-specific DNA structure, protein–DNA interactions, and a disorder-to-order transition in the protein, which collectively affect the relative stabilities of the inactive, non-filamentous conformation and the active, filamentous conformation of SgrAI bound to DNA. Full global kinetic modeling of the DNA cleavage pathway reveals a slow, rate-limiting, second-order association rate constant for filament assembly, and simulations of in vivo activity predict that filamentation is superior to non-filamenting mechanisms in ensuring rapid activation and sequestration of SgrAI's DNA cleavage activity on phage DNA and away from the host chromosome. In vivo studies demonstrate the critical requirement for accelerated DNA cleavage by SgrAI in its biological role to safeguard the bacterial host. Collectively, these data have advanced our understanding of how filamentation can regulate enzyme structure and function, while the experimental strategies used for SgrAI can be applied to other enzymatic systems to identify novel functional roles for filamentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning

We propose a deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequence-to-sequence forecasting framework, enabling it to model long-term dependencies and multiscale patterns in both space and time. By sharing weights across spatial and temporal dimensions, WaveCastNet requires significantly fewer parameters than more resource-intensive models such as transformers, resulting in faster inference times. Crucially, WaveCastNet also generalizes better than transformers to rare and critical seismic scenarios, such as high-magnitude earthquakes. Here, we show the ability of the model to predict the intensity and timing of destructive ground motions in real time, using simulated data from the San Francisco Bay Area. Furthermore, we demonstrate its zero-shot capabilities by evaluating WaveCastNet on real earthquake data. Our approach does not require estimating earthquake magnitudes and epicenters, steps that are prone to error in conventional methods, nor does it rely on empirical ground-motion models, which often fail to capture strongly heterogeneous wave propagation effects.

Geophysics↗

Investigating Deposition Sequence During Synthesis of Pd/Al2O3 Catalysts Modified with Organic Monolayers

Modification of supported metal catalysts with self-assembled monolayers (SAMs) has been shown to improve selectivity and turnover frequencies (TOFs) for many catalytic reactions. However, these benefits are often accompanied by a decrease in overall mass activity due to partial blocking of active sites by the monolayers. Therefore, a potential method for increasing site accessibility is to deposit the active metal (e.g., Pd) after deposition of a SAM modifier on the support material. In this work, Pd/Al2O3 catalysts were synthesized using both a conventional, "metal-first" sequence in which the phosphonic acid (PA) SAM was deposited on Pd/Al2O3 and a "SAM-first" technique in which the ligands were deposited onto the support prior to deposition of the metal. Although metal-first catalysts showed a significant increase in TOF during benzyl alcohol HDO, they exhibited only a modest increase in overall mass activity due to a decrease in the number of active sites. Meanwhile, SAM-first catalysts showed similar improvements in TOF and toluene selectivity but exhibited significantly improved HDO rates compared to metal-first catalysts. The rate increase was attributed to reduced site blocking as evidenced by CO chemisorption measurements. Additionally, analysis of catalysts having high Pd loadings using transmission electron microscopy (TEM) showed that SAM-first catalysts generally resulted in lower average particle sizes than traditionally modified and unmodified catalysts, suggesting that precoating the support surface with PAs changes the way that Pd is deposited during incipient wetness, leading here to improved activity.

catalytic reactions↗

A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.

Ye, Kai↗

Sequence Modulates Polypeptoid Hydration Water Structure and Dynamics

We use molecular dynamics simulations to investigate the effect of polypeptoid sequence on the structure and dynamics of its hydration waters. Polypeptoids provide an excellent platform to study small-molecule hydration in disordered polymers, as they can be precisely synthesized with a variety of sidechain chemistries. We examine water behavior near a set of peptoid oligomers in which the number and placement of nonpolar versus polar sidechains are systematically varied. To do this, we leverage a new computational workflow enabling accurate sampling of polypeptoid conformations. We find that the hydration waters are less dense, are more tetrahedral, and have slower dynamics compared to bulk water. The magnitude of these shifts increases with the number of nonpolar groups. Here, we also find that shifts in the water structure and dynamics are strongly correlated, suggesting that experimental insight into the dynamics of hydration water obtained by Overhauser dynamic nuclear polarization (ODNP) also contains information about water structural properties. We then demonstrate the ability of ODNP to probe site-specific dynamics of hydration water near these model peptoid systems.

36 MATERIALS SCIENCE↗