Role of an interdependent Wnt, GSK3-ß/ß-catenin and HB-EGF/EGFR mechanism in arsenic-induced hippocampal neurotoxicity in adult mice.

We previously reported the neurotoxic effects of arsenic in the hippocampus. Here, we explored the involvement of Wnt pathway, which contributes to neuronal functions. Administering environmentally relevant arsenic concentrations to postnatal day-60 (PND60) mice demonstrated a dose-dependent increase in hippocampal Wnt3a and its components, Frizzled, phospho-LRP6, Dishevelled and Axin1 at PND90 and PND120. However, p-GSK3-ß(Ser9) and ß-catenin levels although elevated at PND90, decreased at PND120. Additionally, treatment with Wnt-inhibitor, rDkk1, reduced p-GSK3-ß(Ser9) and ß-catenin at PND90, but failed to affect their levels at PND120, indicating a time-dependent link with Wnt. To explore other underlying factors, we assessed epidermal growth factor receptor (EGFR) pathway, which interacts with GSK3-ß and appears relevant to neuronal functions. We primarily found that arsenic reduced hippocampal phosphorylated-EGFR and its ligand, Heparin-binding EGF-like growth factor (HB-EGF), at both PND90 and PND120. Moreover, treatment with HB-EGF rescued p-GSK3-ß(Ser9) and ß-catenin levels at PND120, suggesting their HB-EGF/EGFR-dependent regulation at this time point. Additionally, rDkk1, LiCl (GSK3-ß-activity inhibitor), or ß-catenin protein treatments induced a time-dependent recovery in HB-EGF, indicating potential inter-dependent mechanism between hippocampal Wnt/ß-catenin and HB-EGF/EGFR following arsenic exposure. Fluorescence immunolabeling then validated these findings in hippocampal neurons. Further exploration of hippocampal neuronal survival and apoptosis demonstrated that treatment with rDkk1, LiCl, ß-catenin and HB-EGF improved Nissl staining and NeuN levels, and reduced cleaved-caspase-3 levels in arsenic-treated mice. Supportively, we detected improved Y-Maze and Passive Avoidance performances for learning-memory functions in these mice. Overall, our study provides novel insights into Wnt/ß-catenin and HB-EGF/EGFR pathway interaction in arsenic-induced hippocampal neurotoxicity.

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurements that only paint a macroscopic picture, gene measurements at the cell level aid researchers in studying different tissues and organs at various stages. However, accurate clustering methods for such high-dimensional data remain exiguous and a persistent challenge in this domain. Of late, several methods and techniques have been promulgated to address this issue. In this article, we propose a novel framework for clustering large-scale single-cell data and subsequently identifying the rare-cell sub-populations. To handle such sparse, high-dimensional data, we leverage PaCMAP (Pairwise Controlled Manifold Approximation), a feature extraction algorithm that preserves both the local and the global structures of the data and Gaussian Mixture Model to cluster single-cell data. Subsequently, we exploit Edited Nearest Neighbours sampling and Isolation Forest/One-class Support Vector Machine to identify rare-cell sub-populations. The performance of the proposed method is validated using the publicly available datasets with varying degrees of cell types and rare-cell sub-populations. On several benchmark datasets, the proposed method outperforms the existing state-of-the-art methods. The proposed method successfully identifies cell types that constitute populations ranging from 0.1 to 8% with F1-scores of 0.91 0.09. The source code is available at https://github.com/scrab017/RarPG.

IV-GNN : interval valued data handling using graph neural network.

Interval-valued data is an effective way to represent complex information where uncertainty, inaccuracy etc. are involved in the data space and they are worthy of taking into account. Interval analysis together with neural network has proven to work well on Euclidean data. However, in real-life scenarios, data follows a much more complex structure and is often represented as graphs, which is non-Euclidean in nature. Graph Neural Network is a powerful tool to handle graph like data with countable feature space. So, there is a research gap between the interval-valued data handling approaches and existing GNN model. No model in GNN literature can handle a graph with interval-valued features and, on the other hand, Multi Layer Perceptron (MLP) based on interval mathematics can not process the same due to non-Euclidean structure behind the graph. This article proposes an Interval-Valued Graph Neural Network, a novel GNN model where, for the first time, we relax the restriction of the feature space being countable without compromising the time complexity of the best performing GNN model in the literature. Our model is much more general than existing models as any countable set is always a subset of the universal set ℝ n , which is uncountable. Here, to deal with interval-valued feature vectors, we propose a new aggregation scheme of intervals and show its expressive power to capture different interval structures. We validate our theoretical findings about our model for graph classification task by comparing its performance with those of the state-of-the-art models on several benchmark and synthetic network datasets.

Circulating Interleukin-8 Dynamics Parallels Disease Course and Is Linked to Clinical Outcomes in Severe COVID-19.

Severe COVID-19 frequently features a systemic deluge of cytokines. Circulating cytokines that can stratify risks are useful for more effective triage and management. Here, we ran a machine-learning algorithm on a dataset of 36 plasma cytokines in a cohort of severe COVID-19 to identify cytokine/s useful for describing the dynamic clinical state in multiple regression analysis. We performed RNA-sequencing of circulating blood cells collected at different time-points. From a Bayesian Information Criterion analysis, a combination of interleukin-8 (IL-8), Eotaxin, and Interferon-γ (IFNγ) was found to be significantly linked to blood oxygenation over seven days. Individually testing the cytokines in receiver operator characteristics analyses identified IL-8 as a strong stratifier for clinical outcomes. Circulating IL-8 dynamics paralleled disease course. We also revealed key transitions in immune transcriptome in patients stratified for circulating IL-8 at three time-points. The study identifies plasma IL-8 as a key pathogenic cytokine linking systemic hyper-inflammation to the clinical outcomes in COVID-19.

Hypothyroidism induces motor deficit via altered cerebellar HB-EGF/EGFR and autophagy.

Thyroid hormones (TH) are vital for brain functions, while TH deficiency, i.e. hypothyroidism, induces neurological impairment in children and adults. Cerebellar neuronal apoptosis and motor deficits are crucial events in hypothyroidism; however, the underlying mechanism is less-known. Using a methimazole-treated hypothyroidism rat model, we investigated cerebellar autophagy, growth factor, and apoptotic mechanisms that participate in motor functions. We first identified that methimazole up-regulated cerebellar autophagy, marked by enhanced LC3B-II, Beclin-1, ATG7, ATG5-12, p-AMPKα/AMPKα, and p62 degradation as well as reduced p-AKT/AKT, p-mTOR/mTOR, and p-ULK1/ULK1 in developing and young adult rats. We probed upstream effectors of this abnormal autophagy and detected a methimazole-induced reduction in cerebellar phospho-epidermal growth factor receptor (p-EGFR)/EGFR and heparin-binding EGF-like growth factor (HB-EGF). Here, while a thyroxine-induced TH replenishment alleviated autophagy process and restored HB-EGF/EGFR, HB-EGF treatment regulated AKT-mTOR and autophagy signaling in the cerebellum. Moreover, neurons of the rat cerebellum demonstrated this reduced HB-EGF-dependent increased autophagy in hypothyroidism. We further checked whether the above events were related to cerebellar neuronal apoptosis and motor functions. We detected that comparable to thyroxine, treatment with HB-EGF or autophagy inhibitor, 3-MA, reduced methimazole-induced decrease in Nissl staining and increase in c-Caspase-3 and TUNEL-+ve apoptotic count of cerebellar neurons. Additionally, 3-MA, HB-EGF, and thyroxine attenuated the methimazole-induced diminution in riding time on rota-rod and grip strength for the motor performance of rats. Overall, our study enlightens HB-EGF/EGFR-dependent autophagy mechanism as a key to cerebellar neuronal loss and functional impairments in developmental hypothyroidism, which may be inhibited by HB-EGF and 3-MA treatments, like thyroxine.

Deep variational graph autoencoders for novel host-directed therapy options against COVID-19.

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

In silico strategies to identify protein-protein interaction modulator in cell-to-cell transmission of SARS CoV2.

RNA sequence data from SARS CoV2 patients helps to construct a gene network related to this disease. A detailed analysis of the human host response to SARS CoV2 with expression profiling by high-throughput sequencing has been accomplished with primary human lung epithelial cell lines. Using this data, the clustered gene annotation and gene network construction are performed with the help of the String database. Among the four clusters identified, only 1 with 44 genes could be annotated. Interestingly, this corresponded to basal cells with p = 1.37e - 05, which is relevant for respiratory tract infection. Functional enrichment analysis of genes present in the gene network has been completed using the String database and the Network Analyst tool. Among three types of cell-cell communication, only the anchoring junction between the basal cell membrane and the basal lamina in the host cell is involved in the virus transmission. In this junction point, a hemidesmosome structure plays a vital role in virus spread from one cell to basal lamina in the respiratory tract. In this protein complex structure, different integrin protein molecules of the host cell are used to promote the spread of virus infection into the extracellular matrix. So, small molecular blockers of different anchoring junction proteins, such as integrin alpha 3, integrin beta 1, can provide efficient protection against this deadly viral disease. ORF8 from SARS CoV2 virus can interact with both integrin proteins of human host. By using molecular docking technique, a ternary complex of these three proteins is modelled. Several oligopeptides are predicted as modulators for this ternary complex. In silico analysis of these modulators is very important to develop novel therapeutics for the treatment of SARS CoV2.

Arsenic Induces GSK3ß-Dependent p-Tau, Neuronal Apoptosis, and Cognitive Impairment via an Interdependent Hippocampal ERα and IL-1/IL-1R1 Mechanism in Female Rats.

Arsenic is an environmental contaminant with potential neurotoxicity. We previously reported that arsenic promoted hippocampal neuronal apoptosis, inducing cognitive loss. Here, we correlated it with tau pathology. We observed that environmentally relevant arsenic exposure increased tau phosphorylation and the principal tau kinase, glycogen synthase kinase-3 beta (GSK3ß), in the female rat hippocampal neurons. We detected the same in primary hippocampal neurons. Because a regulated estrogen receptor (ER) level and inflammation contributed to normal hippocampal functions, we examined their levels following arsenic exposure. Our ER screening data revealed that arsenic down-regulated hippocampal neuronal ERα. We also detected an up-regulated hippocampal interleukin-1 (IL-1) and its receptor, IL-1R1. Further, co-treating arsenic with the ERα agonist, 4,4',4″-(4-Propyl-[1H]-pyrazole-1,3,5-triyl)trisphenol (PPT), or IL-1R antagonist (IL-1Ra) resulted in reduced GSK3ß and p-tau, indicating involvement of decreased ERα and increased IL-1/IL-1R1 in tau hyperphosphorylation. We then checked whether ERα and IL-1/IL-1R1 had linkage, and detected that although PPT reduced IL-1 and IL-1R1, the IL-1Ra restored ERα, suggesting their arsenic-induced interdependence. We finally correlated this pathway with apoptosis and cognition. We observed that PPT, IL-1Ra and the GSK3ß inhibitor, LiCl, reduced hippocampal neuronal cleaved caspase-3 and TUNEL+ve apoptotic count, and decreased the number of errors during learning and increased the saving memory for Y-Maze test and retention performance for Passive avoidance test in arsenic-treated rats. Thus, our study reveals a novel mechanism of arsenic-induced GSK3ß-dependent tau pathology via interdependent ERα and IL-1/IL-1R1 signaling. It also envisages the protective role of ERα agonist and IL-1 inhibitor against arsenic-induced neurotoxicity.

JID 'Long run effects of Covid-19 on developing countries' Special Issue Editorial Introduction.

The developing world has experienced unprecedented effects of the Covid-19 pandemic. The devastating effects of this major crisis are felt in all possible spheres of the developing world and with a serious impact on social and economic development in developing countries. The spread of Covid-19, which has brought the world to a near standstill, has given rise to the question on the socioeconomic effects of the pandemic. The special issue on Covid-19 at JID aims to bring together contemporary research on several aspects of how the devastating effects of the pandemic have panned out in different spheres of life, particularly, in the developing world. This special issue has 10 papers with a particular emphasis on evidence of the impact of the Covid-19 pandemic in Sub-Saharan Africa and South America. The volume documents studies on the effects of the pandemic at the macro-level, for economy wide effects, the impact of the pandemic on firms and on its effects on households.

Topological Analysis on Multi-scenario Graphs: Applications Toward Discerning Variability in SARS-CoV-2 and Topic Similarity in Research.

A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysis. We focus on two different application areas: analyzing geographical variations in SARS-CoV-2 and studying topic similarity in citation patterns.

LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data.

A fundamental problem of downstream analysis of scRNA-seq data is the unavailability of enough cell samples compare to the feature size. This is mostly due to the budgetary constraint of single cell experiments or simply because of the small number of available patient samples. Here, we present an improved version of generative adversarial network (GAN) called LSH-GAN to address this issue by producing new realistic cell samples. We update the training procedure of the generator of GAN using locality sensitive hashing which speeds up the sample generation, thus maintains the feasibility of applying the standard procedures of downstream analysis. LSH-GAN outperforms the benchmarks for realistic generation of quality cell samples. Experimental results show that generated samples of LSH-GAN improves the performance of the downstream analysis such as feature (gene) selection and cell clustering. Overall, LSH-GAN therefore addressed the key challenges of small sample scRNA-seq data analysis.

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell-cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space.

Arsenic Induces Differential Neurotoxicity in Male, Female, and E2-Deficient Females: Comparative Effects on Hippocampal Neurons and Cognition in Adult Rats.

We earlier reported that arsenic induced hippocampal neuronal loss, causing cognitive dysfunctions in male rats. This neuronal damage mechanism involved an altered bone morphogenetic protein (BMP2)/Smad and brain-derived neurotrophic factor (BDNF)/TrkB signaling. Susceptibility to toxicants is often sex-dependent, and hence we studied the comparative effects of arsenic in adult male and female rats. We observed that a lower dose of arsenic reduced learning-memory ability, examined through passive avoidance and Y-maze tests, in male but not female rats. Again, male rats exhibited greater learning-memory loss at a higher dose of arsenic. Supporting this, arsenic-treated male rats demonstrated larger reduction in the hippocampal NeuN and %-surviving neurons, together with increased apoptosis and altered BMP2/Smad and BDNF/TrkB pathways compared to their female counterparts. Since the primary female hormone, estrogen (E2), regulates normal brain functions, we next probed whether endogenous E2 levels in females offered resistance against arsenic-induced neurotoxicity. We used ovariectomized (OVX) rat as the model for E2 deficiency. We primarily identified that OVX itself induced hippocampal neuronal damage and cognitive decline, involving an increased BMP2/Smad and reduced BDNF/TrkB. Further, these effects appeared greater in arsenic + OVX compared to arsenic + sham (ovary intact) or OVX rats alone. The OVX-induced adverse effects were significantly reduced by E2 treatment. Overall, our study suggests that adult males could be more susceptible than females to arsenic-induced neurotoxicity. It also indicates that endogenous E2 regulates hippocampal BMP and BDNF signaling and restrains arsenic-induced neuronal dysfunctions in females, which may be inhibited in E2-deficient conditions, such as menopause or ovarian failure.

sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data.

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single-cell data are susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF [robust entropy based feature (gene) selection method], aiming to leverage the advantages of $R{\prime}{e}nyi$ and $Tsallis$ entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter ($q$), $R{\prime}{e}nyi$ and $Tsallis$ entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features/genes can able to determine the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data. Availability: The sc-REnF is available at https://github.com/Snehalikalall/sc-REnF.

A Novel Graph Topology-Based GO-Similarity Measure for Signature Detection From Multi-Omics Data and its Application to Other Problems.

Large scale multi-omics data analysis and signature prediction have been a topic of interest in the last two decades. While various traditional clustering/correlation-based methods have been proposed, but the overall prediction is not always satisfactory. To solve these challenges, in this article, we propose a new approach by leveraging the Gene Ontology (GO)similarity combined with multiomics data. In this article, a new GO similarity measure, ModSchlicker, is proposed and the effectiveness of the proposed measure along with other standardized measures are reviewed while using various graph topology-based Information Content (IC)values of GO-term. The proposed measure is deployed to PPI prediction. Furthermore, by involving GO similarity, we propose a new framework for stronger disease-based gene signature detection from the multi-omics data. For the first objective, we predict interaction from various benchmark PPI datasets of Yeast and Human species. For the latter, the gene expression and methylation profiles are used to identify Differentially Expressed and Methylated (DEM)genes. Thereafter, the GO similarity score along with a statistical method are used to determine the potential gene signature. Interestingly, the proposed method produces a better performance ( 0.9 avg. accuracy and 0.95 AUC)as compared to the other existing related methods during the classification of the participating features (genes)of the signature. Moreover, the proposed method is highly useful in other prediction/classification problems for any kind of large scale omics data.

RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We formulate an objective function by adding l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop is able to annotate the unknown cells with high accuracy.

Role of Neuron and Glia in Alzheimer's Disease and Associated Vascular Dysfunction.

Amyloidogenicity and vascular dysfunction are the key players in the pathogenesis of Alzheimer's disease (AD), involving dysregulated cellular interactions. An intricate balance between neurons, astrocytes, microglia, oligodendrocytes and vascular cells sustains the normal neuronal circuits. Conversely, cerebrovascular diseases overlap neuropathologically with AD, and glial dyshomeostasis promotes AD-associated neurodegenerative cascade. While pathological hallmarks of AD primarily include amyloid-ß (Aß) plaques and neurofibrillary tangles, microvascular disorders, altered cerebral blood flow (CBF), and blood-brain barrier (BBB) permeability induce neuronal loss and synaptic atrophy. Accordingly, microglia-mediated inflammation and astrogliosis disrupt the homeostasis of the neuro-vascular unit and stimulate infiltration of circulating leukocytes into the brain. Large-scale genetic and epidemiological studies demonstrate a critical role of cellular crosstalk for altered immune response, metabolism, and vasculature in AD. The glia associated genetic risk factors include APOE, TREM2, CD33, PGRN, CR1, and NLRP3, which correlate with the deposition and altered phagocytosis of Aß. Moreover, aging-dependent downregulation of astrocyte and microglial Aß-degrading enzymes limits the neurotrophic and neurogenic role of glial cells and inhibits lysosomal degradation and clearance of Aß. Microglial cells secrete IGF-1, and neurons show a reduced responsiveness to the neurotrophic IGF-1R/IRS-2/PI3K signaling pathway, generating amyloidogenic and vascular dyshomeostasis in AD. Glial signals connect to neural stem cells, and a shift in glial phenotype over the AD trajectory even affects adult neurogenesis and the neurovascular niche. Overall, the current review informs about the interaction of neuronal and glial cell types in AD pathogenesis and its critical association with cerebrovascular dysfunction.

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data.

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.

S-conLSH: alignment-free gapped mapping of noisy long reads.

BACKGROUND: The advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate. RESULTS: We present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the recently developed method lordFAST. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing. CONCLUSIONS: S-conLSH is one of the first alignment-free reference genome mapping tools achieving a high level of sensitivity. The spaced-context is especially suitable for extracting distant similarities. The variable-length spaced-seeds or patterns add flexibility to the proposed algorithm by introducing gapped mapping of the noisy long reads. Therefore, S-conLSH may be considered as a prominent direction towards alignment-free sequence analysis.