Plain-language summaries of new research papers in bioinformatics, computational biology and life sciences.
Researchers from Stockholm University, Sweden, have developed a novel bioinformatics pipeline to predict the structure of large protein molecules, such as the nuclear pore or the 26S proteasome complex, using AlphaFold and Monte Carlo Tree Search. A protein complex is a group of two or more polypeptide chains associated together. These biological entities play a…
Drug repurposing has been strategically used to modulate the usage of pre-existing drugs. The virtual screening tool DrugRep facilitates computational screening using both receptor and ligand-based methods. Being freely available, it provides the unstinted potential for repurposing and investing in current pharmacology. Historically, introducing novel drugs bears challenges in terms of exploring and introducing them…
Researchers from Pennsylvania State University have put forth a novel machine-learning approach to help predict Autism Spectrum Disorder in children at a very young age, between 18 to 30 months. Such early detection can vastly help both the patients and the clinicians to improve treatment outcomes. The authors define Autism Spectrum Disorder (ASD) as a…
Witnessing the most wrecking pandemic in a decade, COVID-19 visibly tested the potential of healthcare advancements. A collaborative team of researchers addressed earlier loopholes in research by systematically identifying protein interaction contacts between humans and the sars-cov-2 proteome. The team successfully evaluated the ‘contactome theory’, associating the host genome with morbidity and the COVID-19 community…
Researchers from the University of Oslo have developed a novel genotyper named KAGE for detecting SNPs, short insertions, and deletions. KAGE uses an alignment-free word-based approach to genotype variants in a shorter duration, higher accuracy, and less memory. Genotyping refers to the process of detecting the genotype (genetic makeup) of an individual using biological techniques…
Throughout the years, dissecting gene regulation has been a major challenge for researchers. Using high-throughput sequencing, understanding chromatin landscapes and conformations has set a new era in genomic medicine. Researchers from Shanghai, China, have presented DLoopCaller – a deep learning approach to predict chromatin architecture to identify gene regulation down to transcription factor. Chromatin is…
Researchers from Tartu University have put forth a semi-supervised machine learning framework to detect invasive ductal carcinoma using a small amount of labeled breast cancer histological data. Breast cancer is one of the most common cancers. According to the world health organization (WHO), nearly 2.3 million women worldwide have been affected with breast cancer, with…
Deep learning extensively affects studying genetics and the breakthrough research associated with them. The study focuses on understanding the building of complex models rather than complex datasets using model interpretations in Explainable Artificial Intelligence (xAI). This helps in understanding the utility of complex models to decipher biological datasets. Artificial intelligence-based deep learning models are known…
Researchers from Cambridge University have reported the presence of nuclear-mitochondrial segments (NUMT) in a sample population of 66,083 people with 12,509 cancer samples. Whole genome sequencing (WGS) of the samples observed de novo NUMT of one in every 10^4 births and one in every 10^3 cancers, indicating that DNA translocation is a continuing process. Endosymbiosis…
The novel clustering algorithm HAL-x provides insights into single-cell clustering, bridging the biomedicine sciences for healthcare purposes. With its specificity and sensitivity, it allows the generation of multiple-clustering for in-depth analysis of high-dimensional data. The future directions of algorithms are extremely promising in understanding disease, disease progression, and drug discovery. Throughout the years, clustering has…
Scientists from A*STAR’s Genome Institute of Singapore (GIS) discovered that carbapenemase-producing Enterobacteriaceae (CPE) could hide out among gut bacteria in asymptomatic humans. Long-term research to understand microbiome ecology within the population suggests visible shifts in microbial pathways assisting in the colonization of Enterobacteriaceae favoring CPE (Carbapenemase-producing Enterobacteriaceae ) infections. The study provides imperative insight into…
New RNA transcripts have been discovered as a result of the growing amount of transcriptomic data. Bioinformatic tool developers have had difficulty separating long non-coding RNA (lncRNA) from protein-coding messenger RNA (mRNA). Currently, tools implementing deep learning architectures have been developed to accomplish this task, but their potential to discover sequence features and their interactions…
Recent advancements in trajectory inferences have unfolded a new side of bioinformatics, i.e., single-cell analysis. The research group from the University of Northern Carolina School of Medicine benchmark study addressed gaps in capturing temporal changes in a single-cell state. Interpretation of dynamic phenotypes using ten datasets spanning different biological environments at three distinct temporal stages….
A new powerful machine learning tool, ‘SigProfilerExtractor‘ has identified a link between bladder cancer and tobacco smoking. The study was led by researchers at the University of California San Diego School of Medicine and published in the journal Cell Genomics. The study may help researchers determine what environmental factors could cause cancer in certain patients, such…
Researchers from Jilin University have constructed a machine learning-based model that reduces confounders’ effects to identify potential driver genes involved in cancer initiation and progression. Cancer is one of the deadliest diseases worldwide. According to the World Health Organization (WHO), cancer has claimed the lives of nearly 10 million people in 2020. Mutations in the…
Microbial network analysis is an acceptable approach to investigating microbiome and metagenomic datasets and finding insights into a complex ecosystem. Ye Deng et al. introduced an integrated pipeline, iNAP, for network construction and visualization to help researchers understand microbial ecology without prior knowledge of bioinformatics or statistical programming skills. Understanding and deducing information from microbial…
Antimicrobial resistance (AMR) may be reservoired in intensive livestock farms, posing a threat to surrounding communities. The gut microbiome of livestock, workers, and their households, as well as microbial communities in carcasses and soil, were investigated using shotgun metagenome sequencing, machine learning (ML), and culture-based methods at a poultry farm in China. In this study,…
Scientists from the University of Michigan employed hypergraph theory, utilizing long sequence reads to map genome-wide multi-ways to identify chromatin architecture within the human genome. Understanding how a cell is organized could help biologists develop treatments that halt cancer and other diseases. The kinetics of high-order chromatin composition is known to play a role in…
Predicting protein structure has been revolutionized by machine learning over the past two years. A similar breakthrough in protein design has now been reported in the journal Science. The researchers from the Baker lab at the University of Washington demonstrate that machine learning makes the design of proteins much more accurate and faster with a…
In order to perform an extensive benchmarking of the known tools for differential abundance analysis, the researchers from the University of Padova used a simulator of microbiome data to create a simulation framework based on microbiome data. This is the first step towards the development of a robust, reliable, and fair simulation framework that will…
Scientists from the University of Michigan developed an open-source program, DeepFold, to quickly construct accurate protein structure models from deep learning-based potentials. In spite of recent progress in protein structure prediction, it is still necessary to improve the accuracy of models for proteins lacking sequence and/or structure homologs. The DeepFold program uses multi-task deep residual…
Researchers from the Indian Institute of Technology, Madras, present iCOMIC, a tool for quickly analyzing genomic data. With iCOMIC, users can analyze whole genome and exome sequencing data as well as RNA-Seq data using several pre-configured workflows. Developed as an open-source, standalone tool characterized by a Python-based GUI and automated bioinformatics pipelines for DNA-Seq and…
Life is based on individual cells. Landscape dynamics across species have not been compared despite extensive efforts to characterize cellular heterogeneity. A large number of cell landscapes were mapped for mice, zebrafish, and Drosophila at multiple life stages using single-cell RNA sequencing (scRNA-seq). With over 2.6 million single cells integrated, the construction of a cross-species…
A toolshed for synthetic biology, metabolic engineering, and industrial biotechnology is available through the Galaxy-SynBioCAD portal. Using the tools and workflows available on the portal, one can create libraries of strains that produce the desired chemical targets, spanning a comprehensive metabolic pathway design and engineering process from selecting strains and targets, designing DNA parts to…
It is highly likely that the COVID-19 pandemic will last longer due to the continuous evolution of SARS-CoV-2 and the emergence of variants that are resistant to vaccines and neutralizing antibodies. The ACE2 receptor-binding domain (RBD), a prime target site for neutralizing antibodies, plays a significant role in the selection and emergence of SARS-CoV-2 variants….
T-cell epitopes are predicted based on amino acid sequences of proteins from different MHC alleles using multiple immunoinformatic tools. Many of these tools produce hundreds of potential peptide candidates, but they are either non-graphical or unfriendly to non-programmers. Epitope-Evaluator is a web application developed in the Shiny/R framework for analyzing predicted T-cell epitopes interactively. A…
Understanding cellular architecture requires understanding the diversity and complexity of protein localization. The present study aims to introduce cytoself, a deep-learning approach that allows self-supervised profiling and clustering of protein localization. A self-supervised training scheme is leveraged by Cytoself, which requires no preexisting knowledge or categories. Using images of 1,311 endogenously labeled proteins from the…
Scientists at the University of California, Irvine, provide new insights into “parasitic genes” or transposable elements. Despite accounting for more than half of human DNA, there is still much to learn about these elements. The new perspective could one day aid in the fight against cancer and aging-related diseases. The transposable element (TE) is an…
Scientists from MIT developed a device resembling a Wi-Fi router that employs a neural network to detect the presence and severity of the most rapidly growing neurological disease in the world, Parkinson’s disease. Parkinson’s disease (PD) affects more than 1 million Americans as of 2020, causing $52 billion in economic costs each year. Drugs have…
A research team led by the professors at MIT and the University of Arizona has developed an experiment-theory platform for programming bacterial systems to grow into arbitrary two-dimensional target structures using genetically engineered bacteria and mathematical modeling. Multicellular systems, such as bacterial biofilms and human organs form interfaces (or boundaries) between different cell collectives to…
Scientists from the Central South University, China, developed a novel Tumor Treating Fields ‘TTFields’ system named “CL-301A” and showcase its efficacy in the suppression of glioblastoma cell proliferation and induction of cell apoptosis. One of the most dangerous types of brain cancer is glioblastoma. The most recent method of treating glioblastoma (GBM) is called tumor…
Researchers at the Broad Institute of MIT and Harvard have developed Genebass, a free online browser that will assist other researchers and clinicians studying specific genes in linking rare genetic variants with disease phenotypes. Genebass represents a genetic analysis of nearly 400,000 people from the UK Biobank and may assist researchers in identifying potential novel therapeutic…
Universitas Gadjah Mada, Indonesia, scientists developed a low-cost, non-invasive method to rapidly sniff out COVID-19 by using an electronic nose named “GeNose C19”. The method of RT-qPCR, which stands for a reverse transcription-quantitative polymerase chain reaction, has been widely utilized to identify the SARS-CoV-2 virus that causes COVID-19. However, clinicians frequently prefer to use a…
Scientists from The City University of New York present a protocol for collecting and analyzing data for KBG syndrome that emphasizes the use of videoconference and artificial intelligence (AI). Based on this information, the authors make recommendations for diagnostic and treatment approaches for KBG syndrome. The study reports 25 previously unpublished individuals from 22 families…
A tumor is a complex tissue of cancerous cells associated with a heterogeneous cellular microenvironment. Using single-cell sequencing, tumor cells can be molecularly characterized. Identifying tumor cells within single-cell or spatial sequencing experiments is particularly challenging due to cell annotation, which assigns cell type or state to each sequenced cell. Ikarus is a machine learning…
The University of Cincinnati scientists introduce iLINCS, an integrative web-based platform for analyzing OMICS data and markers of cellular disturbances. The user-friendly interfaces of iLINCS allow for the execution of sophisticated omics signature analyses, mechanism of action analysis, and signature-driven drug repositioning. Only a few systems exist that combine several omics data types, bioinformatics tools,…
Scientists present an interpretable, loosely guided, multimodal deep learning strategy for cancer prognosis, which also investigates the use of multimodal interpretability to describe how WSI and molecular characteristics affect risk. The objective prognostic models that can be created from histology pictures have shown promise in the quickly evolving field of computational pathology. Most prognostic models,…
Researchers at Meta AI developed an artificial intelligence (AI) model that can precisely predict entire atomic protein structures from a single sequence of a protein. The use of language model representations to replace the requirement for explicit homologous sequences distinguishes ESMFold from AlphaFold2. As a result of this simplification, ESMFold is significantly faster than the…
The Chinese Academy of Sciences researchers introduced a dual-plasmid editing system that enhances DNA digital storage. The study strongly encourages the use of DNA-based information technology by demonstrating a digital-to-biological information processing approach for highly effective data storage, amplification, and rewriting. In recent years, DNA has gained increasing attention as an information storage medium. A…
The development of new mechanisms for antibiotics is essential to combating antimicrobial resistance. With its unique chemical scaffold, Teixobactin is a new class of antibiotics that lacks detectable resistance. A precursor of peptidoglycan, lipid II, is targeted by Teixobactin. Teixobactin binds specifically to the sugar moiety of lipid II at its C-terminal enduracididine headgroup, whereas…
Scientists from the University of Florida, US, and the Research Centre for Natural Science, Hungary, developed a new frame selection algorithm, “ECS-MeDiv,” as an application of the extended continuous similarity indices to describe the conformational diversity in a set of structures. A fundamental approach to molecular modeling and computational design for the investigation of the…
Since the first appearance of SARS-CoV-2, a number of in silico methods have been employed to discover possible drugs that can target the viral life cycle. Scientists from National Research Centre, Egypt, targeted Endoribonuclease, which is one of the crucial non-structural proteins since it is responsible for processing viral RNA to escape detection by the…
Scientists perform the whole-transcriptome RNA sequencing and informatics of diabetes-induced sarcopenia model of db/db mice to illustrate the specific function of novel lncRNA Gm20743. Type 2 diabetes mellitus raises the risk of sarcopenia, which is characterized by decreased muscle mass, strength, and function, although the specific process is yet unknown. RNA whole-transcriptome sequencing and informatics…
Scientists from the University of Bayreuth, Germany, introduced ProtGPT2, a language model for protein design trained on the protein space that generates de novo protein sequences following the principles of natural ones. ProtGPT2-sequences predicted using AlphaFold result in well-folded, non-idealized structures. ProtGPT2 generates sequences in a fraction of seconds and has open source access. Novel…
Scientists from the University of Edinburgh developed an open-source platform, ‘Graphia’, for the graph-based visualization and analysis of the high dimensional data being produced from research on cells, genes, proteins, and metabolites. A brand-new visual analytics platform called Graphia was developed especially for network-based analysis of enormous and complicated data, such as the vast volumes…
Scientists from University College London demonstrate the use of various cutting-edge artificial neural networks to analyze images of bacterial microscopy. The approach will help future researchers to explore the prospective deep learning (DL) applications and employ pre-trained models in their research on a designated platform. Microbiology, the study of microorganisms and their community, is a…
Scientists unleash the potential of digital pathology data by training computer-aided diagnosis models and making use of convolutional neural networks without human annotations. The cancer diagnostics model developed in the study for empowering digital pathology limits the human effort to a minimum. Deep learning algorithms’ improved performance and the digitization of healthcare operations are opening…
The American Association for Cancer Research (AACR) Project GENIE serves as a worldwide precision medicine knowledge base with ever-increasing implications for clinical decision-making as well as connects cancer researchers from around the world. The AACR Project GENIE is known to be an international pan-cancer registry with the objective to illuminate cancer research and clinical consideration…
Using read pairs with several SARS-CoV-2 variant-specific signature mutations as a reliable signal of low-frequency variants, scientists develop the bioinformatics technique known as COJAC (Co-Occurrence adjusted Analysis and Calling). The genomic sequencing, in conjunction with COJAC, can offer population-level estimates of the prevalence and fitness of emerging variants from wastewater samples earlier and on the basis…
MIT researchers have developed a geometric deep-learning model, EquiBind, which is 1,200 times faster than one of the fastest computational molecular docking models currently in use, QuickVina2-W, for effective binding of drug-like substances to proteins. One of the main challenges in drug discovery is to predict the binding properties of a drug-like chemical to a…
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