DNA has been pursued as a compelling medium for digital data storage during the past decade. While large-scale data storage and random access have been achieved in artificial DNA, the synthesis cost keeps hindering DNA data storage from popularizing into daily life. In this study, we proposed a more efficient paradigm for digital data compressing to DNA, while excluding arbitrary sequence constraints. Both standalone neural networks and pre-trained language models were used to extract the intrinsic patterns of data, and generated probabilistic portrayal, which was then transformed into constraint-free nucleotide sequences with a hierarchical finite state machine. Utilizing these methods, a 12%-26% improvement of compression ratio was realized for various data, which directly translated to up to 26% reduction in DNA synthesis cost. Combined with the progress in DNA synthesis, our methods are expected to facilitate the realization of practical DNA data storage. <<<

AbstractPerceptual decisions should depend on sensory evidence. However, such decisions are also influenced by past choices and outcomes. These choice history biases may reflect advantageous strategies to exploit temporal regularities of natural environments. However, it is unclear whether and how observers can adapt their choice history biases to different temporal regularities, to exploit the multitude of temporal correlations that exist in nature. Here, we show that mice adapt their perceptual choice history biases to different temporal regularities. This adaptation is well captured by a normative reinforcement learning algorithm with multi-trial belief states, comprising both current trial sensory and previous trial memory states. We demonstrate that striatal dopamine tracks predictions of the model and behavior, pointing towards the involvement of dopamine in forming adaptive history biases. Our results reveal the adaptive nature of perceptual choice history biases, and shed light on their underlying computational principles and neural implementation. <<<

AbstractSusceptibility artifacts (SAs), which are inevitable for modern diffusion brain MR images with single-shot echo planar imaging (EPI) protocols in wide large-scale neuroimaging datasets, severely hamper the accurate detection of the human brain white matter structure. While several conventional and deep-learning based distortion correction methods have been proposed, the correction quality and model generality of these approaches are still limited. Here, we proposed the SACNet, a flexible SAs correction (SAC) framework for brain diffusion MR images of various phase-encoding EPI protocols based on an unsupervised learning-based registration convolutional neural network. This method could generate smooth diffeomorphic warps with optional neuroanatomy guidance to correct both geometric and intensity distortions of SAs. By employing near 2000 brain scans covering neonatal, child, adult and traveling participants, our SACNet consistently demonstrates state-of-the-art correction performance and effectively eliminates SAs-related multicenter effects compared with existing SAC methods. To facilitate the development of standard SAC tools for future neuroimaging studies, we also created easy-to-use command lines incorporating containerization techniques for quick user deployment. <<<

AbstractThere has been considerable recent progress in designing new proteins using deep learning methods1–9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including<i>de novo</i>binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RF<i>diffusion</i>), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RF<i>diffusion</i>enables the design of diverse, complex, functional proteins from simple molecular specifications. <<<

AbstractFor both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output — a challenge that is known as<i>credit assignment</i>. How the brain solves credit assignment is a key question in neuroscience, and also of significant importance for artificial intelligence. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. However, it has been questioned whether it is possible for the brain to implement backpropagation and learning in the brain may actually be more efficient and effective than backpropagation. Here, we set out a fundamentally different principle on credit assignment, called<i>prospective configuration</i>. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms, and (3) reproduces surprising patterns of neural activity and behaviour observed in diverse human and animal learning experiments. Our findings establish a new foundation for learning beyond backpropagation, for both understanding biological learning and building artificial intelligence. <<<

AbstractNo sooner is an experience over than its neural memory representation begins to be strengthened and transformed through the process of memory replay. Using fMRI, we examined how memory strength manipulated through repetition during encoding modulates post-encoding replay in humans. Results revealed that repetition did not increase replay frequency in the hippocampus. However, replay in cortical regions and hippocampal-cortical coordinated replay were significantly enhanced for repeated events, suggesting that repetition accelerates the consolidation process. Interestingly, we found that replay frequency in both hippocampus and cortex modulated behavioral success on an immediate associative recognition test for the weakly encoded information, indicating a significant role for post-encoding replay in rescuing once-presented events. Together, our findings highlight the relationships of replay to stabilizing weak memories and accelerating cortical consolidation for strong memories. <<<

AbstractRecent human electrophysiology work has uncovered the presence of high frequency oscillatory events, termed ripples, during awake behavior. This prior work focuses on ripples in the medial temporal lobe (MTL) during memory retrieval. Few studies, however, investigate ripples during item encoding. Many studies have found neural activity during encoding that predicts later recall, termed subsequent memory effects (SMEs), but it is unclear if ripples during encoding also predict subsequent recall. Detecting ripples in 124 neurosurgical participants performing an episodic memory task, we find insignificant ripple SMEs in any MTL region, even as these regions exhibit robust high frequency activity (HFA) SMEs. Instead, hippocampal ripples increase during encoding of items leading to recall of temporally or semantically associated items, a phenomenon known as clustering. This subsequent clustering effect (SCE) arises specifically when hippocampal ripples occur during both encoding and retrieval, suggesting that ripples mediate the encoding and future reinstatement of episodic memories. <<<

AbstractPremature birth increases the risk of developing neurocognitive and neurobe-havioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned <i>in vivo.</i> This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images. <<<

AbstractIn this paper, we present a deep learning method, DDMReg, for accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. DDMReg is a novel method that uses joint whole-brain and tract-specific information for dMRI registration. Based on the successful VoxelMorph framework for image registration, we propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. DDMReg is an unsupervised method for deformable registration between pairs of dMRI datasets: it does not require nonlinearly pre-registered training data or the corresponding deformation fields as ground truth. We perform comparisons with four state-of-the-art registration methods on multiple independently acquired datasets from different populations (including teenagers, young and elderly adults) and different imaging protocols and scanners. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance compared to the state-of-the-art methods. Importantly, we demonstrate successful generalization of DDMReg to dMRI data from different populations with varying ages and acquired using different acquisition protocols and different scanners. <<<

Motivation: Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another, and vice versa. <br>Results: To understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with an arbitrary distribution of reading frames. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias, and the distribution of selection biases (dN/dS) in the respective reading frames. <br>Availability and implementation: Our simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License (GPL) version 3, and is available at https://github.com/PoonLab/HexSE. <<<

Expansion of a single repetitive DNA sequence, termed a tandem repeat (TR), is known to cause more than 50 diseases. However, repeat expansions are often not explored beyond neurological and neurodegenerative disorders. In some cancers, mutations accumulate in short tracts of TRs (STRs), a phenomenon termed microsatellite instability (MSI); however larger repeat expansions have not been systematically analyzed in cancer. Here, we identified TR expansions in 2,622 cancer genomes, spanning 29 cancer types. In 7 cancer types, we found 160 recurrent repeat expansions (rREs); most of these (155/160) were subtype specific. We found that rREs were non-uniformly distributed in the genome with an enrichment near candidate cis-regulatory elements, suggesting a role in gene regulation. One rRE located near a regulatory element in the first intron of UGT2B7 was detected in 34% of renal cell carcinoma samples and was validated by long-read DNA sequencing. Moreover, targeting cells harboring this rRE with a rationally designed, sequence-specific DNA binder led to a dose-dependent decrease in cell proliferation. Overall, our results demonstrate that rREs are an important but unexplored source of genetic variation in human cancers, and we provide a comprehensive catalog for further study. <<<

Aneuploidy is a frequent feature of human tumors. Germline mutations leading to aneuploidy are very rare in humans, and their tumor-promoting properties are mostly unknown at the molecular level. We report here novel germline biallelic mutations in <i>MAD1L1</i>, the gene encoding the Spindle Assembly Checkpoint (SAC) protein MAD1, in a 36-year-old female with a dozen of neoplasias, including five malignant tumors. Functional studies in peripheral blood cells demonstrated lack of full-length protein and deficient SAC response, resulting in ∼30-40% of aneuploid cells as detected by cytogenetic and single-cell (sc) DNA analysis. scRNA-seq analysis of patient blood cells identified mitochondrial stress accompanied by systemic inflammation with enhanced interferon and NFkB signaling. The inference of chromosomal aberrations from scRNA-seq analysis detected inflammatory signals both in aneuploid and euploid cells, suggesting a non-cell autonomous response to aneuploidy. In addition to random aneuploidies, <i>MAD1L1</i> mutations resulted in specific clonal expansions of γδ T-cells with chromosome 18 gains and enhanced cytotoxic profile, as well as intermediate B-cells with chromosome 12 gains and transcriptomic signatures characteristic of chronic lymphocytic leukemia cells. These data point to <i>MAD1L1</i> mutations as the cause of a new aneuploidy syndrome with systemic inflammation and unprecedented tumor susceptibility. <<<

BackgroundThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3’-untranslated region (3’-UTR) of mRNA produces transcripts with shorter or longer 3’-UTR. Often, 3’-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3’-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3’-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3’-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3’-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations.MethodsAPA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3’-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3’-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3’-UTR annotation and read coverage on the 3’-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user’s manual are freely available at https://github.com/compbiolabucf/APA-Scan.ResultAPA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3’-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3’-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3’ -UTR APA events and improve genome annotation.ConclusionAPA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3’-UTR APA events. The pipeline integrates both RNA-seq and 3’-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots. <<<

AbstractThe nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts, and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus. <<<

Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) to accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan proteins and fast-evolving proteins like antibodies, and a protein typically folds in a natural setting from its primary amino acid sequence into its three-dimensional structure, suggesting that evolutionary information and MSAs should not be necessary to predict a protein's folded form. Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures. OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution. Our study fills a much-needed structure prediction gap and brings us a step closer to understanding protein folding in nature. <<<

We introduce Dual Coordinate VCF (DVCF), a file format that records genomic variants against two different reference genomes simultaneously and is fully compliant with the current VCF specification. As implemented in the Genozip platform, DVCF enables bioinformatics pipelines to seamlessly operate across two coordinate systems by leveraging the system most advantageous to each pipeline step, simplifying bioinformatics workflows and reducing file generation and associated data storage burden. Moreover, our benchmarking of Genozip DVCF shows that it produces more complete, less erroneous, and less biased translations across coordinate systems than two widely used alternative tools (i.e., LiftoverVcf and CrossMap). <<<

Single-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects. A multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard Maximum Mean Discrepancy (MMD) measure. The Pareto front between conservation of biological variation and batch effect removal is a valuable tool for researchers in computational biology. Our results demonstrate the efficacy of applying Pareto MTL to estimate the Pareto front in conjunction with applying MINE to measure the batch effect. <<<

The Human Pangenome Reference Consortium (HPRC) presents a first draft human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence and are more than 99% accurate at the structural and base-pair levels. Based on alignments of the assemblies, we generated a draft pangenome that captures known variants and haplotypes, reveals novel alleles at structurally complex loci, and adds 119 million base pairs of euchromatic polymorphic sequence and 1,529 gene duplications relative to the existing reference, GRCh38. Roughly 90 million of the additional base pairs derive from structural variation. Using our draft pangenome to analyze short-read data reduces errors when discovering small variants by 34% and boosts the detected structural variants per haplotype by 104% compared to GRCh38-based workflows, and by 34% compared to using previous diversity sets of genome assemblies. <<<

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a software tool called PaliDIS that recognises insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying this software to 266 human metagenomes identifies 11,681 unique insertion sequences. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events of clinically relevant antimicrobial resistance genes between classes of bacteria. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences. <<<

Here, we leveraged a large neoadjuvant PD-1 blockade trial in patients with hepatocellular carcinoma (HCC) to search for correlates of response to immune checkpoint blockade (ICB) within T cell-rich tumors. We show that ICB response correlated with the clonal expansion of intratumoral CXCL13+ CH25H+ IL-21+ PD-1+ CD4 T helper cells (CXCL13+ Th) and Granzyme K+ PD-1+ effector-like CD8 T cells, whereas terminally exhausted CD39hi TOXhi PD-1hi CD8 T cells dominated in non-responders. Strikingly, most T cell receptor (TCR) clones that expanded post-treatment were found in pre-treatment biopsies. Notably, PD-1+ TCF-1+ progenitor-like CD8 T cells were present in tumors of responders and non-responders and shared clones mainly with effector-like cells in responders or terminally differentiated cells in non-responders, suggesting that local CD8 T cell differentiation occurs upon ICB. We found that these progenitor CD8 T cells interact with CXCL13+ Th cells within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or "mregDC". Receptor-ligand analysis revealed unique interactions within these triads that may promote the differentiation of progenitor CD8 T cells into effector-like cells upon ICB. These results suggest that discrete intratumoral niches that include mregDC and CXCL13+ Th cells control the differentiation of tumor-specific progenitor CD8 T cell clones in patients treated with ICB. <<<