https://www.novareader.co Default RSS Feed en-us Newgen KnowledgeWorks https://www.novareader.co/book/isbn/10.1093/nar/gkab010 Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo ‘singleton’ insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1272 Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkab133 Thousands of new phages have recently been discovered thanks to viral metagenomics. These phages are extremely diverse and their genome sequences often do not resemble any known phages. To appreciate their ecological impact, it is important to determine their bacterial hosts. CRISPR spacers can be used to predict hosts of unknown phages, as spacers represent biological records of past phage–bacteria interactions. However, no guidelines have been established to standardize host prediction based on CRISPR spacers. Additionally, there are no tools that use spacers to perform host predictions on large viral datasets. Here, we developed a set of tools that includes all the necessary steps for predicting the hosts of uncharacterized phages. We created a database of >11 million spacers and a program to execute host predictions on large viral datasets. Our host prediction approach uses biological criteria inspired by how CRISPR–Cas naturally work as adaptive immune systems, which make the results easy to interpret. We evaluated the performance using 9484 phages with known hosts and obtained a recall of 49% and a precision of 69%. We also found that this host prediction method yielded higher performance for phages that infect gut-associated bacteria, suggesting it is well suited for gut-virome characterization.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkab123 CRISPR-Cas9 is an RNA-guided DNA endonuclease involved in bacterial adaptive immunity and widely repurposed for genome editing in human cells, animals and plants. In bacteria, RNA molecules that guide Cas9′s activity derive from foreign DNA fragments that are captured and integrated into the host CRISPR genomic locus by the Cas1-Cas2 CRISPR integrase. How cells generate the specific lengths of DNA required for integrase capture is a central unanswered question of type II-A CRISPR-based adaptive immunity. Here, we show that an integrase supercomplex comprising guide RNA and the proteins Cas1, Cas2, Csn2 and Cas9 generates precisely trimmed 30-base pair DNA molecules required for genome integration. The HNH active site of Cas9 catalyzes exonucleolytic DNA trimming by a mechanism that is independent of the guide RNA sequence. These results show that Cas9 possesses a distinct catalytic capacity for generating immunological memory in prokaryotes.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1237 A significant portion of human cancers are due to viruses integrating into human genomes. Therefore, accurately predicting virus integrations can help uncover the mechanisms that lead to many devastating diseases. Virus integrations can be called by analysing second generation high-throughput sequencing datasets. Unfortunately, existing methods fail to report a significant portion of integrations, while predicting a large number of false positives. We observe that the inaccuracy is caused by incorrect alignment of reads in repetitive regions. False alignments create false positives, while missing alignments create false negatives. This paper proposes SurVirus, an improved virus integration caller that corrects the alignment of reads which are crucial for the discovery of integrations. We use publicly available datasets to show that existing methods predict hundreds of thousands of false positives; SurVirus, on the other hand, is significantly more precise while it also detects many novel integrations previously missed by other tools, most of which are in repetitive regions. We validate a subset of these novel integrations, and find that the majority are correct. Using SurVirus, we find that HPV and HBV integrations are enriched in LINE and Satellite regions which had been overlooked, as well as discover recurrent HBV and HPV breakpoints in human genome-virus fusion transcripts.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1175 Sensitive detection of microsatellite instability (MSI) in tissue or liquid biopsies using next generation sequencing (NGS) has growing prognostic and predictive applications in cancer. However, the complexities of NGS make it cumbersome as compared to established multiplex-PCR detection of MSI. We present a new approach to detect MSI using inter-Alu-PCR followed by targeted NGS, that combines the practical advantages of multiplexed-PCR with the breadth of information provided by NGS. Inter-Alu-PCR employs poly-adenine repeats of variable length present in every Alu element and provides a massively-parallel, rapid approach to capture poly-A-rich genomic fractions within short 80–150bp amplicons generated from adjacent Alu-sequences. A custom-made software analysis tool, MSI-tracer, enables Alu-associated MSI detection from tissue biopsies or MSI-tracing at low-levels in circulating-DNA. MSI-associated indels at somatic-indel frequencies of 0.05–1.5% can be detected depending on the availability of matching normal tissue and the extent of instability. Due to the high Alu copy-number in human genomes, a single inter-Alu-PCR retrieves enough information for identification of MSI-associated-indels from ∼100 pg circulating-DNA, reducing current limits by ∼2-orders of magnitude and equivalent to circulating-DNA obtained from finger-sticks. The combined practical and informational advantages of inter-Alu-PCR make it a powerful tool for identifying tissue-MSI-status or tracing MSI-associated-indels in liquid biopsies.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1152 The gene and cell therapy fields are advancing rapidly, with a potential to treat and cure a wide range of diseases, and lentivirus-based gene transfer agents are the vector of choice for many investigators. Early cases of insertional mutagenesis caused by gammaretroviral vectors highlighted that integration site (IS) analysis was a major safety and quality control checkpoint for lentiviral applications. The methods established to detect lentiviral integrations using next-generation sequencing (NGS) are limited by short read length, inadvertent PCR bias, low yield, or lengthy protocols. Here, we describe a new method to sequence IS using Amplification-free Integration Site sequencing (AFIS-Seq). AFIS-Seq is based on amplification-free, Cas9-mediated enrichment of high-molecular-weight chromosomal DNA suitable for long-range Nanopore MinION sequencing. This accessible and low-cost approach generates long reads enabling IS mapping with high certainty within a single day. We demonstrate proof-of-concept by mapping IS of lentiviral vectors in a variety of cell models and report up to 1600-fold enrichment of the signal. This method can be further extended to sequencing of Cas9-mediated integration of genes and to in vivo analysis of IS. AFIS-Seq uses long-read sequencing to facilitate safety evaluation of preclinical lentiviral vector gene therapies by providing IS analysis with improved confidence.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1138 Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1157 Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms. However, the noise inherent in single-cell RNA-sequencing data severely disturbs the accuracy of cell clustering, marker identification and visualization. We propose that clustering based on feature density profiles can distinguish informative features from noise. We named such strategy as ‘entropy subspace’ separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the ‘entropy subspace’ separation strategy with a consensus clustering method. We demonstrate that ENCORE performs superiorly on cell clustering and generates high-resolution visualization across 12 standard datasets. More importantly, ENCORE enables identification of group markers with biological significance from a hard-to-separate dataset. With the advantages of effective feature selection, improved clustering, accurate marker identification and high-resolution visualization, we present ENCORE to the community as an important tool for scRNA-seq data analysis to study cellular heterogeneity and discover group markers.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1088 Whole-genome mapping technologies have been developed as a complementary tool to provide scaffolds for genome assembly and structural variation analysis (1,2). We recently introduced a novel DNA labeling strategy based on a CRISPR–Cas9 genome editing system, which can target any 20bp sequences. The labeling strategy is specifically useful in targeting repetitive sequences, and sequences not accessible to other labeling methods. In this report, we present customized mapping strategies that extend the applications of CRISPR–Cas9 DNA labeling. We first design a CRISPR–Cas9 labeling strategy to interrogate and differentiate the single allele differences in NGG protospacer adjacent motifs (PAM sequence). Combined with sequence motif labeling, we can pinpoint the single-base differences in highly conserved sequences. In the second strategy, we design mapping patterns across a genome by selecting sets of specific single-guide RNAs (sgRNAs) for labeling multiple loci of a genomic region or a whole genome. By developing and optimizing a single tube synthesis of multiple sgRNAs, we demonstrate the utility of CRISPR–Cas9 mapping with 162 sgRNAs targeting the 2Mb Haemophilus influenzae chromosome. These CRISPR–Cas9 mapping approaches could be particularly useful for applications in defining long-distance haplotypes and pinpointing the breakpoints in large structural variants in complex genomes and microbial mixtures.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1073 A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that ‘escape’ the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1015 Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1137 Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1014 Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach ‘TENET’ to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.

]]> https://www.novareader.co/book/isbn/10.1093/nar/gkaa1052 DNA strand asymmetries can have a major effect on several biological functions, including replication, transcription and transcription factor binding. As such, DNA strand asymmetries and mutational strand bias can provide information about biological function. However, a versatile tool to explore this does not exist. Here, we present Asymmetron, a user-friendly computational tool that performs statistical analysis and visualizations for the evaluation of strand asymmetries. Asymmetron takes as input DNA features provided with strand annotation and outputs strand asymmetries for consecutive occurrences of a single DNA feature or between pairs of features. We illustrate the use of Asymmetron by identifying transcriptional and replicative strand asymmetries of germline structural variant breakpoints. We also show that the orientation of the binding sites of 45% of human transcription factors analyzed have a significant DNA strand bias in transcribed regions, that is also corroborated in ChIP-seq analyses, and is likely associated with transcription. In summary, we provide a novel tool to assess DNA strand asymmetries and show how it can be used to derive new insights across a variety of biological disciplines.

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