Deep learning in integrating spatial transcriptomics with other modalities.

Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.

DP-ID: Interleaving and Denoising to Improve the Quality of DNA Storage Image.

In the field of storing images into DNA, the code tables and universal error correction codes have the potential to mitigate the effect of base errors to a certain extent. However, they prove to be ineffective in dealing with indels (insertion and deletion errors), resulting in a decline in information density and the quality of reconstructed image. This paper proposes a novel encoding and decoding method named DP-ID for storing images into DNA that improves information density and the quality of reconstructed image.

Advances and Challenges in Random Access Techniques for In Vitro DNA Data Storage.

With digital transformation and the general application of new technologies, data storage is facing new challenges with the demand for high-density loading of massive information. In response, DNA storage technology has emerged as a promising research direction. Efficient and reliable data retrieval is critical for DNA storage, and the development of random access technology plays a key role in its practicality and reliability.

High-Risk Sequence Prediction Model in DNA Storage: The LQSF Method.

Traditional DNA storage technologies rely on passive filtering methods for error correction during synthesis and sequencing, which result in redundancy and inadequate error correction. Addressing this, the Low Quality Sequence Filter (LQSF) was introduced, an innovative method employing deep learning models to predict high-risk sequences. The LQSF approach leverages a classification model trained on error-prone sequences, enabling efficient pre-sequencing filtration of low-quality sequences and reducing time and resources in subsequent stages.

Effect of Different Staining Methods on Brain Cryosections.

Staining frozen sections is often required to distinguish cell types for spatial transcriptomic studies of the brain. The impact of the staining methods on the RNA integrity of the cells becomes one of the limitations of spatial transcriptome technology with microdissection. However, there is a lack of systematic comparisons of different staining modalities for the pretreatment of frozen sections of brain tissue as well as their effects on transcriptome sequencing results.

SNP-based and haplotype-based genome-wide association on drug dependence in Han Chinese.

Background: Drug addiction is a serious problem worldwide and is influenced by genetic factors. The present study aimed to investigate the association between genetics and drug addiction among Han Chinese.

Methods: A total of 1000 Chinese users of illicit drugs and 9693 healthy controls were enrolled and underwent single nucleotide polymorphism (SNP)-based and haplotype-based association analyses via whole-genome genotyping.

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning.

Objective: Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE.

Deciphering temporal gene expression dynamics in multiple coral species exposed to heat stress: Implications for predicting resilience.

Coral reefs are facing unprecedented threats due to global climate change, particularly elevated sea surface temperatures causing coral bleaching. Understanding coral responses at the molecular level is crucial for predicting their resilience and developing effective conservation strategies. In this study, we conducted a comprehensive gene expression analysis of four coral species to investigate their long-term molecular response to heat stress.

Comparative transcriptome analysis reveals deep molecular landscapes in stony coral clade.

Coral reefs, among the most invaluable ecosystems in the world, face escalating threats from climate change and anthropogenic activities. To decipher the genetic underpinnings of coral adaptation and resilience, we undertook comprehensive transcriptome profiling of two emblematic coral species, and , leveraging PacBio Iso-Seq technology. These species were strategically selected for their ecological significance and their taxonomic proximity within the Anthozoa class.

Spatial Transcriptomic Analysis Reveals Regional Transcript Changes in Early and Late Stages of rd1 Model Mice with Retinitis Pigmentosa.

Retinitis pigmentosa (RP) is the leading cause of inherited blindness with a genetically heterogeneous disorder. Currently, there is no effective treatment that can protect vision for those with RP. In recent decades, the rd1 mouse has been used to study the pathological mechanisms of RP.

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud.

MineProt: a stand-alone server for structural proteome curation.

AlphaFold-like systems are rapidly expanding the scale of proteome structuring, and MineProt provides an effective solution for custom curation of these novel high-throughput data. It enables researchers to build their own server in simple steps, run almost out-of-the-box scripts to annotate and curate their proteins, analyze their data via a user-friendly online interface, and utilize plugins to extend the functionality of server. It is expected to support researcher productivity and facilitate data sharing in the new era of structural proteomics.

Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data.

Motivation: Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown.

Magnetic Bead Spherical Nucleic Acid Microstructure for Reliable DNA Preservation and Repeated DNA Reading.

DNA is an attractive medium for long-term data storage because of its density, ease of copying, sustainability, and longevity. Recent advances have focused on the development of new encoding algorithms, automation, and sequencing technologies. Despite progress in these subareas, the most challenging hurdle in the deployment of DNA storage remains the reliability of preservation and the repeatability of reading.

Polyp-Canal Reconstruction Reveals Evolution Toward Complexity in Corals.

Modern scleractinian corals are classified into robust, complex, and basal clades through comparative molecular studies. However, only few morphological or biological criteria can systematically determine the evolutionary trajectories of these major scleractinian coral clades. Here, we obtained the structural information of 21 scleractinian coral species representing robust and complex clades: High-resolution micro-computed tomography was used to reconstruct the polyp-canal systems in their colonies and to visualize the dynamic polyp growth processes.

Nanopore sensors for single molecular protein detection: Research progress based on computer simulations.

As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label-free and high-throughput protein sequencing.

Chimera: The spoiler in multiple displacement amplification.

Multiple displacement amplification (MDA) based on isothermal random priming and high fidelity phi29 DNA polymerase-mediated processive extension has revolutionized the field of whole genome amplification by enabling the amplification of minute amounts of DNA, such as from a single cell, generating vast amounts of DNA with high genome coverage. Despite its advantages, MDA has its own challenges, one of the grandest being the formation of chimeric sequences (chimeras), which presents in all MDA products and seriously disturbs the downstream analysis. In this review, we provide a comprehensive overview of current research on MDA chimeras.

A molecular dynamics investigation of DNA polymerase and its complex with a DNA substrate using a solid-state nanopore biosensor.

Proteins have a small volume difference by the diversity of amino acids, which make protein detection and identification a great challenge. Solid-state nanopore as label-free biosensors has attracted attention with high sensitivity. In this work, we investigated the DNA polymerase before and after combining it with a DNA substrate on a solid-state nanopore through molecular dynamics.

Utilizing an artificial intelligence system to build the digital structural proteome of reef-building corals.

Background: Reef-building corals play an important role in the marine ecosystem, and analyzing their proteomes from a structural perspective will exert positive effects on exploring their biology. Here we integrated mass spectrometry with newly published ColabFold to obtain digital structural proteomes of dominant reef-building corals.

Results: Of the 8,382 homologous proteins in Acropora muricata, Montipora foliosa, and Pocillopora verrucosa identified, 8,166 received predicted structures after about 4,060 GPU hours of computation.

The Mechanism of Overflow Amplitude in Nanopore Experiments and Its Application in Molecule Detection.

The investigation of abnormal experimental phenomena observed in nanopore research improves our understanding of nanopores. In this article, we report and explore the unusual phenomenon that the amplitude of current blockage decreases beyond zero baseline (overflow amplitudes), which was observed in the translocation behavior of 100 bp double-stranded DNA molecules through SiN nanopores. In our experiments, the overflow amplitude decreases with the increase of salt concentration and also decreases when the dwell time is shortened as the normalized amplitude of the overflow current showed a reduction with the increase of voltage.