Metabolite Measurement: Pitfalls to Avoid and Practices to Follow.

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Physics-supervised deep learning-based optimization (PSDLO) with accuracy and efficiency.

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

Internal feedback in the cortical perception-action loop enables fast and accurate behavior.

Animals move smoothly and reliably in unpredictable environments. Models of sensorimotor control, drawing on control theory, have assumed that sensory information from the environment leads to actions, which then act back on the environment, creating a single, unidirectional perception-action loop. However, the sensorimotor loop contains internal delays in sensory and motor pathways, which can lead to unstable control. We show here that these delays can be compensated by internal feedback signals that flow backward, from motor toward sensory areas. This internal feedback is ubiquitous in neural sensorimotor systems, and we show how internal feedback compensates internal delays. This is accomplished by filtering out self-generated and other predictable changes so that unpredicted, actionable information can be rapidly transmitted toward action by the fastest components, effectively compressing the sensory input to more efficiently use feedforward pathways: Tracts of fast, giant neurons necessarily convey less accurate signals than tracts with many smaller neurons, but they are crucial for fast and accurate behavior. We use a mathematically tractable control model to show that internal feedback has an indispensable role in achieving state estimation, localization of function (how different parts of the cortex control different parts of the body), and attention, all of which are crucial for effective sensorimotor control. This control model can explain anatomical, physiological, and behavioral observations, including motor signals in the visual cortex, heterogeneous kinetics of sensory receptors, and the presence of giant cells in the cortex of humans as well as internal feedback patterns and unexplained heterogeneity in neural systems.

Genotype error biases trio-based estimates of haplotype phase accuracy.

Haplotypes can be estimated from unphased genotype data via statistical methods. When parent-offspring trios are available for inferring the true phase from Mendelian inheritance rules, the accuracy of statistical phasing is usually measured by the switch error rate, which is the proportion of pairs of consecutive heterozygotes that are incorrectly phased. We present a method for estimating the genotype error rate from parent-offspring trios and a method for estimating the bias that occurs in the observed switch error rate as a result of genotype error. We apply these methods to 485,301 genotyped UK Biobank samples that include 898 White British trios and to 38,387 sequenced TOPMed samples that include 217 African Caribbean trios and 669 European American trios. We show that genotype error inflates the observed switch error rate and that the relative bias increases with sample size. For the UK Biobank White British trios, the observed switch error rate in the trio offspring is 2.4 times larger than the estimated true switch error rate (1.4 × 10-3 vs 5.8 × 10-4. We propose an alternate definition of phase error that counts two consecutive switch errors as a single error because back-to-back switch errors arise when a single heterozygote is incorrectly phased with respect to the surrounding heterozygotes. With this definition, we estimate that the average distance between phase errors is 64 megabases in the UK Biobank White British individuals.

High-accuracy protein model quality assessment using attention graph neural networks.

Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Test accuracy of rapid diagnostic tests and reverse-transcription polymerase chain reaction against virus isolation in cell culture for assessing SARS-CoV-2 infectivity: Systematic review and meta-analysis.

We aimed to assess the performance of Ag-RDT and RT-qPCR with regard to detecting infectious SARS-CoV-2 in cell cultures, as their diagnostic test accuracy (DTA) compared to virus isolation remains largely unknown. We searched three databases up to 15 December 2021 for DTA studies. The bivariate model was used to synthesise the estimates. Risk of bias was assessed using QUADAS-2/C. Twenty studies (2605 respiratory samples) using cell culture and at least one molecular test were identified. All studies were at high or unclear risk of bias in at least one domain. Three comparative DTA studies reported results on Ag-RDT and RT-qPCR against cell culture. Two studies evaluated RT-qPCR against cell culture only. Fifteen studies evaluated Ag-RDT against cell culture as reference standard in RT-qPCR-positive samples. For Ag-RDT, summary sensitivity was 93% (95% CI 78; 98%) and specificity 87% (95% CI 70; 95%). For RT-qPCR, summary sensitivity (continuity-corrected) was 98% (95% CI 95; 99%) and specificity 45% (95% CI 28; 63%). In studies relying on RT-qPCR-positive subsamples (n = 15), the summary sensitivity of Ag-RDT was 93% (95% CI 92; 93%) and specificity 63% (95% CI 63; 63%). Ag-RDT show moderately high sensitivity, detecting most but not all samples demonstrated to be infectious based on virus isolation. Although RT-qPCR exhibits high sensitivity across studies, its low specificity to indicate infectivity raises the question of its general superiority in all clinical settings. Study findings should be interpreted with caution due to the risk of bias, heterogeneity and the imperfect reference standard for infectivity.

The history and conceptual framework of assays and screens.

Since the 16th century, assays and screens have been essential for scientific investigation. However, most methods could be significantly improved, especially in accuracy, scalability, and often lack adequate comparisons to negative controls. There is a lack of consistency in distinguishing assays, in which accuracy is the main goal, from screens, in which scalability is prioritized over accuracy. We dissected and modernized the original definitions of assays and screens based upon recent developments and the conceptual framework of the original definitions. All methods have three components: design/measurement, performance, and interpretation. We propose a model of method development in which reproducible observations become new methods, initially assessed by sensitivity. Further development can proceed along a path to either screens or assays. The screen path focuses on scalability first, but can later prioritize analysis of negatives. Alternatively, the assay path first compares results to negative controls, assessing specificity and accuracy, later adding scalability. Both pathways converge on a high-accuracy and throughput (HAT) assay, like next generation sequencing, which we suggest should be the ultimate goal of all testing methods. Our model will help scientists better select among available methods, as well as improve existing methods, expanding their impact on science.

Assessment of Advanced Diagnostic Bronchoscopy Outcomes for Peripheral Lung Lesions: A Delphi Consensus Definition of Diagnostic Yield and Recommendations for Patient-centered Study Designs. An Official American Thoracic Society/American College of Chest Physicians Research Statement.

Background: Advanced diagnostic bronchoscopy targeting the lung periphery has developed at an accelerated pace over the last two decades, whereas evidence to support introduction of innovative technologies has been variable and deficient. A major gap relates to variable reporting of diagnostic yield, in addition to limited comparative studies. Objectives: To develop a research framework to standardize the evaluation of advanced diagnostic bronchoscopy techniques for peripheral lung lesions. Specifically, we aimed for consensus on a robust definition of diagnostic yield, and we propose potential study designs at various stages of technology development. Methods: Panel members were selected for their diverse expertise. Workgroup meetings were conducted in virtual or hybrid format. The cochairs subsequently developed summary statements, with voting proceeding according to a modified Delphi process. The statement was cosponsored by the American Thoracic Society and the American College of Chest Physicians. Results: Consensus was reached on 15 statements on the definition of diagnostic outcomes and study designs. A strict definition of diagnostic yield should be used, and studies should be reported according to the STARD (Standards for Reporting Diagnostic Accuracy Studies) guidelines. Clinical or radiographic follow-up may be incorporated into the reference standard definition but should not be used to calculate diagnostic yield from the procedural encounter. Methodologically robust comparative studies, with incorporation of patient-reported outcomes, are needed to adequately assess and validate minimally invasive diagnostic technologies targeting the lung periphery. Conclusions: This American Thoracic Society/American College of Chest Physicians statement aims to provide a research framework that allows greater standardization of device validation efforts through clearly defined diagnostic outcomes and robust study designs. High-quality studies, both industry and publicly funded, can support subsequent health economic analyses and guide implementation decisions in various healthcare settings.

The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale's 18th problem.

Deep learning (DL) has had unprecedented success and is now entering scientific computing with full force. However, current DL methods typically suffer from instability, even when universal approximation properties guarantee the existence of stable neural networks (NNs). We address this paradox by demonstrating basic well-conditioned problems in scientific computing where one can prove the existence of NNs with great approximation qualities; however, there does not exist any algorithm, even randomized, that can train (or compute) such a NN. For any positive integers K>2 and L, there are cases where simultaneously 1) no randomized training algorithm can compute a NN correct to K digits with probability greater than 1/2; 2) there exists a deterministic training algorithm that computes a NN with K ­1 correct digits, but any such (even randomized) algorithm needs arbitrarily many training data; and 3) there exists a deterministic training algorithm that computes a NN with K ­2 correct digits using no more than L training samples. These results imply a classification theory describing conditions under which (stable) NNs with a given accuracy can be computed by an algorithm. We begin this theory by establishing sufficient conditions for the existence of algorithms that compute stable NNs in inverse problems. We introduce fast iterative restarted networks (FIRENETs), which we both prove and numerically verify are stable. Moreover, we prove that only O(|log (ϵ)|) layers are needed for an ϵ-accurate solution to the inverse problem.

Bone-Targeted Supramolecular Nanoagonist Assembled by Accurate Ratiometric Herbal-Derived Therapeutics for Osteoporosis Reversal.

Developing novel strategies for defeating osteoporosis has become a world-wide challenge with the aging of the population. In this work, novel supramolecular nanoagonists (NAs), constructed from alkaloids and phenolic acids, emerge as a carrier-free nanotherapy for efficacious osteoporosis treatment. These precision nanoagonists are formed through the self-assembly of berberine (BER) and chlorogenic acid (CGA), utilizing noncovalent electrostatic, π-π, and hydrophobic interactions. This assembly results in a 100% drug loading capacity and stable nanostructure. Furthermore, the resulting weights and proportions of CGA and BER within the NAs are meticulously controlled with strong consistency when the CGA/BER assembly feed ratio is altered from 1:1 to 1:4. As anticipated, our NAs themselves could passively target osteoporotic bone tissues following prolonged blood circulation, modulate Wnt signaling, regulate osteogenic differentiation, and ameliorate bone loss in ovariectomy-induced osteoporotic mice. We hope this work will open a new strategy to design efficient herbal-derived Wnt NAs for dealing with intractable osteoporosis.

Effects of Stimulus Timing on the Acquisition of an Olfactory Working Memory Task in Head-Fixed Mice.

Acquisition of a behavioral task is influenced by many factors. The relative timing of stimuli is such a factor and is especially relevant for tasks relying on short-term memory, like working memory paradigms, because of the constant evolution and decay of neuronal activity evoked by stimuli. Here, we assess two aspects of stimulus timing on the acquisition of an olfactory delayed nonmatch-to-sample (DNMS) task. We demonstrate that head-fixed male mice learn to perform the task more quickly when the initial training uses a shorter sample-test odor delay without detectable loss of generalizability. Unexpectedly, we observed a slower task acquisition when the odor-reward interval was shorter. The effect of early reward timing was accompanied by a shortening of reaction times and more frequent sporadic licking. Analysis of this result using a drift-diffusion model indicated that a primary consequence of early reward delivery is a lowered threshold to act, or a lower decision bound. Because an accurate performance with a lower decision bound requires greater discriminability in the sensory representations, this may underlie the slower learning rate with early reward arrival. Together, our results reflect the possible effects of stimulus timing on stimulus encoding and its consequence on the acquisition of a complex task.SIGNIFICANCE STATEMENT This study describes how head-fixed mice acquire a working memory task (olfactory delayed nonmatch-to-sample task). We simplified and optimized the stimulus timing, allowing robust and efficient training of head-fixed mice. Unexpectedly, we found that early reward timing leads to slower learning. Analysis of this data using a computational model (drift-diffusion model) revealed that the reward timing affects the behavioral threshold, or how quickly animals respond to a stimulus. But, to still be accurate with early reaction times, the sensory representation needs to become even more refined. This may explain the slower learning rate with early reward timing.

Dopamine Alters the Effect of Brain Stimulation on Decision-Making.

Noninvasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), show promise in treating a range of psychiatric and neurologic conditions. However, optimization of such applications requires a better understanding of how tDCS alters cognition and behavior. Existing evidence implicates dopamine in tDCS alterations of brain activity and plasticity; however, there is as yet no causal evidence for a role of dopamine in tDCS effects on cognition and behavior. Here, in a preregistered, double-blinded study, we examined how pharmacologically manipulating dopamine altered the effect of tDCS on the speed-accuracy trade-off, which taps ubiquitous strategic operations. Cathodal tDCS was delivered over the left prefrontal cortex and the superior medial frontal cortex before participants (N = 62, 24 males, 38 females) completed a dot-motion task, making judgments on the direction of a field of moving dots under instructions to emphasize speed, accuracy, or both. We leveraged computational modeling to uncover how our interventions altered latent decisional processes driving the speed-accuracy trade-off. We show that dopamine in combination with tDCS (but not tDCS alone nor dopamine alone) not only impaired decision accuracy but also impaired discriminability, which suggests that these manipulations altered the encoding or representation of discriminative evidence. This is, to the best of our knowledge, the first direct evidence implicating dopamine in the way tDCS affects cognition and behavior.SIGNIFICANCE STATEMENT tDCS can improve cognitive and behavioral impairments in clinical conditions; however, a better understanding of its mechanisms is required to optimize future clinical applications. Here, using a pharmacological approach to manipulate brain dopamine levels in healthy adults, we demonstrate a role for dopamine in the effects of tDCS in the speed-accuracy trade-off, a strategic cognitive process ubiquitous in many contexts. In doing so, we provide direct evidence implicating dopamine in the way tDCS affects cognition and behavior.

Individual Differences in Decision Strategy Relate to Neurochemical Excitability and Cortical Thickness.

The speed-accuracy trade-off (SAT), whereby faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during the performance of almost all daily tasks. To date, computational modeling has implicated the latent decision variable of response caution (thresholds), the amount of evidence required for a decision to be made, in the SAT. Previous imaging has associated frontal regions, notably the left prefrontal cortex and the presupplementary motor area (pre-SMA), with the setting of such caution levels. In addition, causal brain stimulation studies, using transcranial direct current stimulation (tDCS), have indicated that while both of these regions are involved in the SAT, their role appears to be dissociable. tDCS efficacy to impact decision-making processes has previously been linked with neurochemical concentrations and cortical thickness of stimulated regions. However, to date, it is unknown whether these neurophysiological measures predict individual differences in the SAT, and brain stimulation effects on the SAT. Using ultra-high field (7T) imaging, here we report that instruction-based adjustments in caution are associated with both neurochemical excitability (the balance between GABA+ and glutamate) and cortical thickness across a range of frontal regions in both sexes. In addition, cortical thickness, but not neurochemical concentrations, was associated with the efficacy of left prefrontal and superior medial frontal cortex (SMFC) stimulation to modulate performance. Overall, our findings elucidate key neurophysiological predictors, frontal neural excitation, of individual differences in latent psychological processes and the efficacy of stimulation to modulate these.SIGNIFICANCE STATEMENT The speed-accuracy trade-off (SAT), faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during most daily tasks. The SAT is often investigated by explicitly instructing participants to prioritize speed or accuracy when responding to stimuli. Using ultra-high field (7T) magnetic resonance imaging (MRI), we found that individual differences in the extent to which participants adjust their decision strategies with instruction related to neurochemical excitability (ratio of GABA+ to glutamate) and cortical thickness in the frontal cortex. Moreover, brain stimulation to the left prefrontal cortex and the superior medial frontal cortex (SMFC) modulated performance, with the efficacy specifically related to cortical thickness. This work sheds new light on the neurophysiological basis of decision strategies and brain stimulation.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Bed rest impairs the vestibular control of balance.

Prolonged bed rest impairs standing balance but the underlying mechanisms are uncertain. Previous research suggests strength loss is not the cause, leaving impaired sensorimotor control as an alternative. Here we examine vestibular control of posture in 18 male volunteers before and after 60 days of bed rest. Stochastic vestibular stimulation (SVS) was used to evoke sway responses before, 1 and 6 days after bed rest under different head yaw orientations. The directional accuracy and precision of these responses were calculated from ground reaction force vectors. Bed rest caused up to 63% increases in spontaneous standing sway and 31% reductions in leg strength, changes which were uncorrelated. The increase in sway was exacerbated when the eyes were closed. Mean directions of SVS-evoked sway responses were unaffected, being directed towards the anodal ear and rotating in line with head orientation in the same way before and after bed rest. However, individual trial analysis revealed 25%-30% increases in directional variability, which were significantly correlated with the increase in spontaneous sway (r = 0.48-0.71; P ≤ 0.044) and were still elevated on day 6 post-bed rest. This reveals that individual sway responses may be inappropriately oriented, a finding masked by the averaging process. Our results confirm that impaired balance following prolonged bedrest is not related to loss of strength. Rather, they demonstrate that the sensorimotor transformation process which converts vestibular feedback into appropriately directed balance responses is impaired. KEY POINTS: Prolonged inactivity impairs balance but previous research suggests this is not caused by loss of strength. Here we investigated vestibular control of balance before and after 60 days of bed rest using electrical vestibular stimulation (EVS) to evoke sway responses. Spontaneous sway significantly increased and muscle strength reduced following bed rest, but, in keeping with previous research, these two effects were not correlated. While the overall accuracy of EVS-evoked sway responses was unaffected, their directional variability significantly increased following bed rest, and this was correlated with the increases in spontaneous sway. We have shown that the ability to transform head-centred vestibular feedback into an appropriately directed body sway response is negatively affected by prolonged inactivity; this may contribute to the impaired balance commonly observed following bed rest.

Compared Performance of the 2023 Duke-International Society for Cardiovascular Infectious Diseases, 2000 Modified Duke, and 2015 European Society of Cardiology Criteria for the Diagnosis of Infective Endocarditis in a French Multicenter Prospective Cohort.

BACKGROUND: The 2023 Duke-International Society for Cardiovascular Diseases (ISCVID) criteria for infective endocarditis (IE) were proposed as an updated diagnostic classification of IE. Using an open prospective multicenter cohort of patients treated for IE, we compared the performance of these new criteria to that of the 2000 Modified Duke and 2015 European Society of Cardiology (ESC) criteria. METHODS: Cases of patients treated for IE between January 2017 and October 2022 were adjudicated as certain IE or not. Each case was also categorized as either definite or possible/rejected within each classification. Sensitivity, specificity, and accuracy were estimated with 95% confidence intervals. RESULTS: Of the 1194 patients analyzed (mean age, 66.1 years; 71.2% males), 414 (34.7%) had a prosthetic valve and 284 (23.8%) had a cardiac implanted electronic device (CIED); 946 (79.2%) were adjudicated as certain IE; 978 (81.9%), 997 (83.5%), and 1057 (88.5%) were classified as definite IE in the 2000 modified Duke, 2015 ESC, and 2023 Duke-ISCVID criteria, respectively. The sensitivity of each set of criteria was 93.2% (95% confidence interval [CI], 91.6-94.8), 95.0% (95% CI, 93.7-96.4), and 97.6% (95% CI, 96.6-98.6), respectively (P < .001 for all 2-by-2 comparisons). Corresponding specificity rates were 61.3% (95% CI, 55.2-67.4), 60.5% (95% CI, 54.4-66.6), and 46.0% (95% CI, 39.8-52.2), respectively. In patients without CIED, sensitivity rates were 94.8% (95% CI, 93.2-96.4), 96.5% (95% CI, 95.1-97.8), and 97.7% (95% CI, 96.6-98.8); specificity rates were 59.0% (95% CI, 51.6-66.3), 56.6% (95% CI, 49.3-64.0), and 53.8% (95% CI, 46.3-61.2), respectively. CONCLUSIONS: Overall, the 2023 Duke-ISCVID criteria had a significantly higher sensitivity but a significantly lower specificity compared with older criteria. This decreased specificity was mainly attributable to patients with CIED.

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing.

BACKGROUND: Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts, while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been expanding the potential of dRNA-seq, its sequencing accuracy and error patterns remain understudied. RESULTS: We present the first comprehensive evaluation of sequencing accuracy and characterisation of systematic errors in dRNA-seq data from diverse organisms and synthetic in vitro transcribed RNAs. We found that for sequencing kits SQK-RNA001 and SQK-RNA002, the median read accuracy ranged from 87% to 92% across species, and deletions significantly outnumbered mismatches and insertions. Due to their high abundance in the transcriptome, heteropolymers and short homopolymers were the major contributors to the overall sequencing errors. We also observed systematic biases across all species at the levels of single nucleotides and motifs. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines and adenines. By examining raw signal data, we identified the underlying signal-level features potentially associated with the error patterns and their dependency on sequence contexts. While read quality scores can be used to approximate error rates at base and read levels, failure to detect DNA adapters may be a source of errors and data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (basecalling) artefacts. Lastly, we generated dRNA-seq data using the latest SQK-RNA004 sequencing kit released at the end of 2023 and found that although the overall read accuracy increased, the systematic errors remain largely identical compared to the previous kits. CONCLUSIONS: As the first systematic investigation of dRNA-seq errors, this study offers a comprehensive overview of reproducible error patterns across diverse datasets, identifies potential signal-level insufficiency, and lays the foundation for error correction methods.

Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats.

The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.

BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. RESULTS: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. CONCLUSIONS: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.