Biology subjects

Zhang, F.

Publications and source records attributed to Zhang, F..

At least 253 records · Page 14Linked to original sources

bioRxiv2022

Allele-specific gene editing approach for vision loss restoration in RHO-associated Retinitis Pigmentosa

Mutant RHO is the most frequent genetic cause of autosomal dominant retinitis pigmentosa. Here, we developed an allele-specific gene editing therapeutic drug to selectively target the human T17M RHO mutant allele while leaving the wild-type RHO allele intact for the first time. We identified a Staphylococcus aureus Cas9 (SaCas9) guide RNA that was highly active and specific to the human T17M RHO allele. In vitro experiments using HEK293T cells and patient-specific induced pluripotent stem cells (iPSCs) demonstrated active nuclease activity and high specificity. Subretinal delivery of a single adeno-associated virus serotype 2/8 packaging SaCas9 and sgRNA to the retinas of the RHO humanized mice showed that this therapeutic drug targeted the mutant allele selectively, thereby downregulating the mutant RHO mRNA expression. Administration of this therapeutic drug resulted in a long-term (up to 11 months after treatment) improvement of retinal function and preservation of photoreceptors in the mutant humanized heterozygous mice. Our study demonstrated a dose-dependent therapeutic effect in vivo. Unwanted off-target effects were not observed at the whole-genome sequencing level. Our study provides strong support for the further development of this effective therapeutic drug to treat RHO-T17M associated autosomal dominant retinitis pigmentosa (adRP), also offers a generalizable framework for developing gene editing medicine. Furthermore, our success in retoring the vision loss in the suffering RHO humanized mice verifies the feasibility of allele-specific CRISPR/Cas9-based medicines for other autosomal dominant inherited retinal dystrophies.

genetics↗

bioRxiv2022

West Nile Virus spread in Europe - phylogeographic pattern analysis and key drivers

Spread and emergence of West Nile virus (WNV) in Europe have been very different from those observed in North America. Here, we describe key drivers by combining viral genome sequences with epidemiological data and possible factors of spread into phylodynamic models. WNV in Europe has greater lineage diversity than other regions of the world, suggesting repeated introductions and local amplification. Among the six lineages found in Europe, WNV-2a is predominant, has spread to at least 14 countries and evolved into two major co-circulating clusters (A and B). Both of these seem to originate from regions of Central Europe. Viruses of Cluster A emerged earlier and have spread towards the west of Europe with higher genetic diversity. Amongst multiple drivers, high agriculture activities were associated with both spread direction and velocity. Our study suggests future surveillance activities should be strengthened in Central Europe and Southeast European countries, and enhanced monitoring should be targeted to areas with high agriculture activities.

evolutionary biology↗

bioRxiv2022

Accurate de novo prediction of RNA 3D structure with transformer network

RNA 3D structure prediction remains challenging though after years of efforts. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, a novel deep learning-based approach to de novo prediction of RNA 3D structure. Like trRosetta, the trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and full-atom 3D structure folding by energy minimization with constraints from the predicted geometries. We benchmarked trRosettaRNA on two independent datasets. The results show that trRosettaRNA outperforms other conventional methods by a large margin. For example, on 25 targets from the RNA-Puzzles experiments, the mean RMSD of the models predicted by trRosettaRNA is 5.5 [A], compared with 10.5 [A] from the state-of-the-art human group (i.e., Das). Further comparisons with two recently released deep learning-based methods (i.e., DeepFoldRNA and RoseTTAFoldNA) show that all three methods have similar accuracy. However, trRosettaRNA yields more accurate and physically more realistic side-chain atoms than DeepFoldRNA and RoseTTAFoldNA. Finally, we apply trRosettaRNA to predict the structures for the Rfam families that do not have known structures. Analysis shows that for 263 families, the predicted structure models are estimated to be accurate with RMSD < 4 [A]. The trRosettaRNA server and the package are available at: https://yanglab.nankai.edu.cn/trRosettaRNA/.

bioinformatics↗

bioRxiv2022

Research on protein structure prediction and folding based on novel remote homologs recognition

Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we developed a new method, PAthreader, which identifies remote homologous structures based on the three-track alignment of distance profiles and structure profiles originated from PDB and AlphaFold DB by deep learning. Based on the identified templates, we further enhanced state-of-the-art modelling method and explored protein folding pathways based on the residue frequency distribution of homologs and the secondary structure. The results show that the average accuracy of templates identified by PAthreader is 11.6% higher than those of HHsearch on 551 nonredundant proteins. In terms of structure modelling, PAthreader improves the performance of AlphaFold2 and ranks first in CAMEO blind test for the last three months. Furthermore, we explored protein folding pathways for 37 proteins. The results are almost consistent with biological experiments for 7 proteins, and the remaining 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures.

bioinformatics↗

bioRxiv2022

An Extensible Vector Toolkit and Parts Library for Advanced Engineering of Plant Genomes

Plant biotechnology is rife with new advances in transformation and genome engineering techniques. A common requirement for delivery and coordinated expression in plant cells, however, places the design and assembly of transformation constructs at a crucial juncture as desired reagent suites grow more complex. Modular cloning principles have simplified some aspects of vector design, yet many important components remain unavailable or poorly adapted for rapid implementation in biotechnology research. Here, we describe a universal Golden Gate cloning toolkit for vector construction. The toolkit chassis is compatible with the widely accepted Phytobrick standard for genetic parts, and supports assembly of arbitrarily complex T-DNAs through improved capacity, positional flexibility, and extensibility in comparison to extant kits. We also provision a substantial library of newly adapted Phytobricks, including regulatory elements for monocot and dicot gene expression, and coding sequences for genes of interest such as reporters, developmental regulators, and site-specific recombinases. Finally, we use a series of dual luciferase assays to measure contributions to expression from promoters, terminators, and from cross-cassette interactions attributable to enhancer elements in certain promoters. Taken together, these publicly available cloning resources can greatly accelerate the testing and deployment of new tools for plant engineering.

plant biology↗

bioRxiv2022

Capsid-mediated chromatin state of the AAV vector genome controls host species range

Recombinant adeno-associated viral vectors (rAAVs) are among the most commonly used vehicles for in vivo based gene therapies. However, it is hard to predict which AAV capsid will provide the most robust expression in human subjects due to the observed discordance in vector-mediated transduction between species. We used a primate specific capsid, AAV-LK03, and demonstrated that the limitation of this capsid towards transduction of mouse cells was unrelated to cell entry and nuclear transport but rather due to depleted histone H3 chemical modifications related to active transcription, namely H3K4me3 and H3K27ac, on the vector DNA itself. A single-amino acid insertion into the AAV-LK03 capsid enabled efficient transduction and the accumulation of active histone marks on the vector chromatin in mouse without compromising transduction efficiency in human cells. Our study suggests that the capsid protein itself is involved in determining the chromatin status of the vector genome, most likely during the process of uncoating. Programming viral chromatin states by capsid design may enable facile DNA transduction between vector and host species.

molecular biology↗

bioRxiv2022

COVIDpro: Database for mining protein dysregulation in patients with COVID-19

BackgroundThe ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers. MethodsWe built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers. FindingsWe built and maintain a web server COVIDpro (https://www.guomics.com/covidPro/) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80. InterpretationCOVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients. FundingNational Key R&D Program of China: Key PDPM technologies (2021YFA1301602, 2021YFA1301601, 2021YFA1301603), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04), National Natural Science Foundation of China (81972492) and National Science Fund for Young Scholars (21904107), National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS-P41 GM103504) Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAlthough an increasing number of therapies against COVID-19 are being developed, they are still insufficient, especially with the rise of new variants of concern. This is partially due to our incomplete understanding of the diseases mechanisms. As data have been collected worldwide, several questions are now worth addressing via meta-analyses. Most COVID-19 drugs function by targeting or affecting proteins. Effectiveness and resistance to therapeutics can be effectively assessed via protein measurements. Empowered by mass spectrometry-based proteomics, protein expression has been characterized in a variety of patient specimens, including body fluids (e.g., serum, plasma, urea) and tissue (i.e., formalin-fixed and paraffin-embedded (FFPE)). We expert-curated proteomic expression data from COVID-19 patients published before May 2022, from the largest proteomic data repository ProteomeXhange as well as from literature search engines. Using this resource, a COVID-19 proteome meta-analysis could provide useful insights into the mechanisms of the disease and identify new potential drug targets. Added value of this studyWe integrated many published datasets from patients with COVID-19 from 11 nations, with over 3000 patients and more than 5434 proteome measurements. We collected these datasets in an online database, and generated a toolbox to easily explore, analyze, and visualize the data. Next, we used the database and its associated toolbox to identify new proteins of diagnostic and therapeutic value for COVID-19 treatment. In particular, we identified a set of significantly dysregulated proteins for distinguishing severe from non-severe patients using serum samples. Implications of all the available evidenceCOVIDpro will support the navigation and analysis of patterns of dysregulated proteins in various COVID-19 clinical specimens for identification and verification of protein biomarkers and potential therapeutic targets.

systems biology↗

bioRxiv2022

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationship between brain age and multimorbidity. To answer these questions, our study investigates whether elevated biological age and accelerated brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States (MIDUS) Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using convolutional neural networks and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity and presents the first effort to test whether sex moderates the relationship between these surrogate aging biomarkers and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of the accumulation of physical and mental health conditions, which may contribute to new strategies to improve the treatment of multimorbidity and detection of at-risk individuals.

neuroscience↗

bioRxiv2022

The molecular mechanism of chloramphenicol and thiamphenicol resistance mediated by a novel oxidase CmO in Sphingomonadaceae

Antibiotic resistance mediated by bacterial enzyme inactivation plays a mysterious and crucial role for antibiotic degradation and selection pressure reduction in the environment. The enzymatic inactivation of the antibiotic chloramphenicol (CAP) involves nitro reduction, amide bond hydrolysis and acetylation modification. However, the molecular mechanism of enzymatic oxidation of CAP remains unknown. Here, a novel oxidase gene cmO was identified and confirmed biochemically to catalyze the resistance process through the oxidative inactivation at the side chain C-3 position of CAP and thiamphenicol (TAP) in Sphingomonadaceae. The oxidase CmO is highly conservative in Sphingomonadaceae and shares the highest amino acid homology of 41.05% with the biochemically identified glucose methanol choline (GMC) oxidoreductases. Molecular docking and site-directed mutagenesis analyses demonstrated that CAP was anchored inside the protein pocket of CmO with the hydrogen bonding of key residues glycine (G)99, asparagine (N)518, methionine (M)474 and tyrosine (Y)380. CAP sensitivity test demonstrated that the acetyltransferase and CmO showed higher resistance to CAP as compared with the amide bond-hydrolyzing esterase and nitroreductase. This study provides a better theoretical basis and a novel diagnostic gene for understanding and assessing the fate and resistance risk of CAP and TAP in the environment. ImportanceRising levels of antibiotic resistance undermines ecological and human health as a result of indiscriminate usage of antibiotics. Various resistance mechanisms have been revealed, for instance genes encoding proteins that degrade antibiotics, yet requiring further exploration. In this study, we reported a novel gene encoding an oxidase involved in the inactivation of typical amphenicol antibiotics (chloramphenicol and thiamphenicol), and the molecular mechanism was elucidated. The observation provides novel data to understand capabilities of bacteria to tackle antibiotic stress and suggests complex function of enzymes in the context of antibiotic resistance development and antibiotics removal. The reported gene can be further employed as an indicator to monitor amphenicols fate in the environment, benefiting the risk assessment in this era of antibiotic resistance.

microbiology↗

bioRxiv2022

Single-nuclei transcriptomics of mammalian prion diseases identifies dynamic gene signatures shared between species

Mammalian prion diseases are fatal and transmissible neurological conditions caused by the propagation of prions, self-replicating multimeric assemblies of misfolded forms of host cellular prion protein (PrP). The most common human form of the disease, sporadic Creutzfeldt-Jakob disease (sCJD), typically presents as a rapidly progressive dementia and has no effective treatments. Prion diseases are transmissible to laboratory rodents affording unprecedented opportunities to understand neurodegeneration in its evolving stages. Murine models are especially useful in prion research as they develop bona fide prion disease and recapitulate all biochemical and neuropathological hallmarks of human prion disease. Despite extensive studies investigating the changes in transcriptional profiles in prion diseases the mechanisms by which prion diseases induce cellular toxicity, including changes in gene expression profiles are yet to be fully characterized. This is at least in part because confounding effects related to brain cellular heterogeneity have not been resolved. Here, we took advantage of the recent developments in single-cell technologies and performed an unbiased whole-transcriptome single-nucleus transcriptomic analysis in prion disease.

neuroscience↗

bioRxiv2022

Construction of epithelial-mesenchymal transition related miRNAs signatures as prognostic biomarkers in gastric cancer patients

AimTo identify the potential post-healing EMT related miRNAs associated with lymph node metastatic gastric cancer (LNMGC). MethodsBoth RNA expression and clinical medical data were obtained from the TCGA dataset. We performed differential expression and normalization analysis of miRNAs. Cox linear regression model confirmed the differentially expressed miRNAs (DEmiRNAs) and clinical medical parameters related to overall survival (OS). The role of target genes of DEmiRNAs was determined according to the role enrichment analysis. ResultsWe obtained a total of 7531 DEmRNAs and 267 DEmRNAs, of which 185 DEmRNAs were down-regulated and 82 DEmRNAs were up-regulated. We randomly divided the LMNGC cases (n=291) into a training group (n=207) and a test group (n=84). The results showed that a total of 103, 11, 13 and 83 overlapping genes were associated with hsa-mir-141-3p, hsa-mir-4664-3p, hsa-mir-125b-5p and hsa-mir-7-5p, respectively. Kaplan-Meier determined that these four miRNAs can effectively distinguish high-risk and low-risk groups, and have a good indicator role (all p<0.05). Multivariate cox regression analysis also showed that EMT-related miRNA predictive model and lymph node metastasis were both prognostic risk factors (all p<0.05). The ROC curve showed that this feature had high accuracy (AUC>0.7, p<0.05). In addition, KEGG analysis showed that EMT-related pathways were mainly enriched in HIF-1 signaling pathway and focal adhesion. ConclusionsOur study demonstrated that EMT-related miRNAs could serve as independent prognostic markers in pN1-3 GC patients.

cancer biology↗

bioRxiv2022

Dear-DIAXMBD: deep autoencoder for data-independent acquisition proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition (DDA) data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides, and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

bioinformatics↗

bioRxiv2022

Human anti-ACE2 monoclonal antibodies as pan-sarbecovirus prophylactic agents

Human monoclonal antibodies from convalescent individuals that target the SARS-CoV-2 spike protein have been deployed as therapeutics against SARS-CoV-2. However, nearly all of these antibodies have been rendered obsolete by SARS-CoV-2 variants that evolved to resist similar, naturally occurring antibodies. Here, we describe the development of human monoclonal antibodies that bind the ACE2 receptor rather than the viral spike protein. These antibodies block infection by all ACE2 binding sarbecoviruses, including emergent SARS-CoV-2 variants. Structural and biochemical analyses revealed that the antibodies target an ACE2 epitope that engages SARS-CoV-2 spike. Importantly, the antibodies do not inhibit ACE2 enzymatic activity, nor do they induce ACE depletion from cell surfaces. The antibodies exhibit favorable pharmacology and protect human ACE2 knock-in mice against SARS-CoV-2 infection. Such antibodies should be useful prophylactic and treatment agents against any current and future SARS-CoV-2 variants, as well as ACE2-binding sarbecoviruses that might emerge as future pandemic threats.

microbiology↗

bioRxiv2022

Single-cell Atlas of Human Fetal Cerebellum Decoding Medulloblastoma Origin and Oncogenesis

Medulloblastoma (MB) is the most common malignant childhood brain tumor1,2, yet the origin of the most aggressive subgroup-3 form remains elusive, impeding development of effective targeted treatments. Previous analyses of mouse cerebella3,4 or human counterparts from frozen tissue nuclei5 have not fully defined the compositional heterogeneity of MBs. Here, we undertook an unprecedented single-cell profiling of freshly-isolated human fetal cerebella to establish a reference map delineating hierarchical cellular states in MBs. We identified a unique transitional cerebellar progenitor connecting neural stem cells to neuronal lineages in developing fetal cerebella. Intersectional analysis revealed that the transitional progenitors were enriched in aggressive MB subgroups, including group-3 and metastatic tumors. Single-cell multi-omics revealed underlying regulatory networks in the transitional progenitor populations, including transcriptional determinants HNRNPH1 and SOX11, which are correlated with clinical prognosis in group-3 MBs. Genomic and Hi-C profiling identified de novo long-range chromatin-loops juxtaposing HNRNPH1/SOX11-targeted super-enhancers to cis-regulatory elements of MYC, an oncogenic driver for group-3 MBs. Targeting the transitional progenitor regulators inhibited MYC expression and MYC-driven group-3 MB growth. Our integrated single-cell atlases of human fetal cerebella and MBs reveal potential cell populations predisposed to transformation and regulatory circuitries underlying tumor cell states and oncogenesis, highlighting hitherto unrecognized transitional progenitor intermediates predictive of disease prognosis and potential therapeutic vulnerabilities.

cancer biology↗

bioRxiv2022

Multimodal electrocorticogram active electrode array based on zinc oxide-thin film transistors

Active electrocorticogram (ECoG) electrodes can amplify the weak electrophysiological signals and improve the anti-interference ability, but the traditional active electrodes are so opaque that cannot realize photoelectric collaborative observation. Here an active and fully-transparent ECoG array based on zinc oxide-thin film transistors (ZnO-TFTs) was developed as the local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array was up to 85% which is superior to previous reported active electrode array. Various electrical characterizations demonstrated its ability of electrophysiological signal recording, with a higher signal-to-noise ratio of 19.9 dB compared to the Au grid one (13.2 dB). The high transparency of ZnO-TFT electrode array allowed the collecting electrophysiological signals under direct light stimulation on optogenetic mice brain concurrently. The ECoG array could also work under 7-Tesla magnetic resonance imaging to record local brain signal without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.

bioengineering↗

bioRxiv2022

Multi-Domain Characterization and Rapid Detection Technology for Cocaine and Methamphetamine Acute Abuse based on EEG

Multi-domain characterization and a new fast detection method for acute illegal psychostimulants abuse detection based on waking-state EEG of mice is proposed in this paper. To get corftical electroencephalogram (EEG), three groups of mice were injected with cocaine (Coca), methamphetamine (Meth), and saline (Sali) respectively following the experimental paradigm of drug abuse. The EEG features were extracted out by multi-domain views, including time, frequency, complexity, dynamics, and independent domains to obtain acute drug abuse effects on the brain. New strategy combing time domain with frequency domain is developed as multi-domain input and by means of dimension transformation approach preserving temporal information, the performance of drug abuse detection is greatly improved with deep learning models of ResNet50. Results show that comparing to support vector machine (SVM), k nearest neighbor (kNN), random forest (RF), and long short-term memory fully convolutional networks (LSTM-FCN), ResNet50 based on our proposed multi-domain features has best F1-score of 95.10%. This promising method provides a low-cost, fast, and widely assisted technology for psychostimulants abuse identification.

bioinformatics↗

bioRxiv2022

Comprehensive analysis identifies histone 2A ubiquitination as a marker for aging and pan-cancer prognosis

Ubiquitination is a post-translational modification (PTM) that induces protein degradation or function alteration and plays crucial roles in aging and cancer. Previous ubiquitinomes of aging mainly focused on how ubiquitination changes in drosophila and other lower animals, but how ubiquitination changes during the aging of higher animals and what causes these changes remain unclear. Here, we profiled whole-life ubiquitinome data of mouse brain, heart, liver, muscle, and spleen, and integratively analyzed the ubiquitinome data with RNA sequencing data. The results showed that the ubiquitination of protein, especially histone 2A (H2A), changed intensely during aging due to the regulated expression of E3 ligases (E3s) and deubiquitylating enzymes (DUBs). Then we developed two distinct H2A E3s/DUBs expression subtypes with different prognosis, DNA damage response (DDR), and tumor microenvironment cell infiltration degrees based on an unsupervised method in pan-cancer. In conclusion, our study provided temporal resolution ubiquitinome data of mouse aging and revealed the vital role of H2A ubiquitination in aging and tumor progression.

cancer biology↗

bioRxiv2022

A toolkit to handle T-less alignment for U insertion/deletion RNA editing

SummaryIn the mitochondrial genome of kinetoplastids, the targeted insertion and deletion of uridine in cryptic mRNA is essential to generate functional protein-coding mRNAs, but such process leads to a highly hetergenous and complex RNA population. Few tools were developped to perform alignment based on a T depleted strategy for such mRNA population, however to our knowledge no downstream analysis tool is available so far to allow easy data processing and interpretation. Here, we fill this gap with the T-less-Alignment-Toolkit, which provides useful functions such as duplication removal with UMI, alignment strandness analysis, statistical summary and visualization. Availability and ImplementationScripts and toolkits can be downloaded at https://gitee.com/Zhanglab/tless-alignment-toolkit

bioinformatics↗