Learning Strategies For Successful Crowd Navigation

Teaching autonomous mobile robots to successfully navigate human crowds is a challenging task. Not only does it require planning, but it requires maintaining social norms which may differ from one context to another. Here we focus on crowd navigation, using a neural network to learn specific strategies in-situ with a robot. This allows us to take into account human behavior and reactions toward a real robot as well as learn strategies that are specific to various scenarios in that context. A CNN takes a top-down image of the scene as input and outputs the next action for the robot to take in terms of speed and angle. Here we present the method, experimental results, and quantitatively evaluate our approach.Comment: 8 page

Lantern: a semi-automated pipeline and repository for annotating lncRNAs with ontologies

Digitized for IUPUI ScholarWorks inclusion in 2021.With advancements in in omics technologies, the range of biological processes long non-coding RNAs (lncRNAs) are involved in is expanding extensively [1,2). The accelerating rate of evidence discovery of lncRNAs' role in various critical biochemical, cellular, and physiological processes is necessitating a robust platform of lncRNA annotation resources. Available resources with lncRNA ontology annotations are rare: despite a plethora of resources for annotating genes, and an extensive body of lncRNA literature. Here, we present a lncRNA annotation extractor and repository (Lantern), was developed using PubMed's abstract retrieval engine and NCBO's recommender annotation system [3]. Between 1-150 abstracts were extracted per lncRNA, which were subsequently used for extracting annotations with respect to each ontology by querying NCBO's recommender system via Application Programming Interface (API). To evaluate the quality of annotations in Lantern, benchmarking analysis was performed by deploying Lantern's pipeline over 182 lncRNAs from lncRNAdb [4) and compared the extracted annotations against annotations mapped onto the lncRNAdb's manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8 based on manual verification of ontology annotations for 50 lncRNAs. Additionally, lncRNAs were also annotated with multiple omics information, like: RBP-interaction, tissue specific expression, protein c-expression, coding potential, sub-cellular localization and SNPs for around 11000 lncRNAs; retrieved and analyzed by robust NGS tools and pipelines. The extracted annotations for 11000 lncRNAs are available at http://www.iupui.edu/~sysbio/lantern/

SARS-CoV-2 contributes to altering the post-transcriptional regulatory networks across human tissues by sponging RNA binding proteins and micro-RNAs

The outbreak of a novel coronavirus SARS-CoV2 responsible for COVID-19 pandemic has caused worldwide public health emergency. Due to the constantly evolving nature of the coronaviruses, SARS-CoV-2 mediated alteration on post-transcriptional gene regulation across human tissues remains elusive. In this study, we systematically dissected the crosstalk and dysregulation of human post-transcriptional regulatory networks governed by RNA binding proteins (RBPs) and micro-RNAs (miRs), due to SARS-CoV-2 infection. We uncovered that 13 out of 29 SARS-CoV- 2 encoded proteins directly interact with 51 human RBPs of which majority of them were abundantly expressed in gonadal tissues and immune cells. We further performed functional analysis of differentially expressed genes in mock treated versus SARS-CoV-2 infected lung cells that revealed an enrichment for immune response, cytokine mediated signaling, and metabolism associated genes. This study also characterized the alternative splicing events in SARS-CoV-2 infected cells compared to control demonstrating that skipped exons and mutually exclusive exons were the most abundant events that potentially contributed to differential outcomes in response to viral infection. Motif enrichment analysis on the RNA genomic sequence of SARS-CoV-2 clearly revealed an enrichment for RBPs such as SRSFs, PCBPs, ELAVs and HNRNPs illustrating the sponging of RBPs by SARS-CoV-2 genome. Similar analysis to study the interactions of miRs with SARS-CoV-2 revealed the potential for several miRs to be sponged, suggesting that these interactions may contribute to altered pos-transcriptional regulation across human tissues. Given the need to understand the interactions of SARS-CoV-2 with key pos-transcriptional regulators in the human genome, this study provides a systematic analysis to dissect the role of dysregulated post-transcriptional regulatory networks controlled by RBPs and miRs, across tissues types during SARS-CoV2 infection.This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R01GM123314 (SCJ). We also thank the lab members for their valuable suggestions and supporting dataset required for completion of this project

Long Non-Coding RNA Expression Levels Modulate Cell-Type-Specific Splicing Patterns by Altering Their Interaction Landscape with RNA-Binding Proteins

Recent developments in our understanding of the interactions between long non-coding RNAs (lncRNAs) and cellular components have improved treatment approaches for various human diseases including cancer, vascular diseases, and neurological diseases. Although investigation of specific lncRNAs revealed their role in the metabolism of cellular RNA, our understanding of their contribution to post-transcriptional regulation is relatively limited. In this study, we explore the role of lncRNAs in modulating alternative splicing and their impact on downstream protein-RNA interaction networks. Analysis of alternative splicing events across 39 lncRNA knockdown and wildtype RNA-sequencing datasets from three human cell lines-HeLa (cervical cancer), K562 (myeloid leukemia), and U87 (glioblastoma)-resulted in the high-confidence (false discovery rate (fdr) < 0.01) identification of 11,630 skipped exon events and 5895 retained intron events, implicating 759 genes to be impacted at the post-transcriptional level due to the loss of lncRNAs. We observed that a majority of the alternatively spliced genes in a lncRNA knockdown were specific to the cell type. In tandem, the functions annotated to the genes affected by alternative splicing across each lncRNA knockdown also displayed cell-type specificity. To understand the mechanism behind this cell-type-specific alternative splicing pattern, we analyzed RNA-binding protein (RBP)-RNA interaction profiles across the spliced regions in order to observe cell-type-specific alternative splice event RBP binding preference. Despite limited RBP binding data across cell lines, alternatively spliced events detected in lncRNA perturbation experiments were associated with RBPs binding in proximal intron-exon junctions in a cell-type-specific manner. The cellular functions affected by alternative splicing were also affected in a cell-type-specific manner. Based on the RBP binding profiles in HeLa and K562 cells, we hypothesize that several lncRNAs are likely to exhibit a sponge effect in disease contexts, resulting in the functional disruption of RBPs and their downstream functions. We propose that such lncRNA sponges can extensively rewire post-transcriptional gene regulatory networks by altering the protein-RNA interaction landscape in a cell-type-specific manner

Penguin: A tool for predicting pseudouridine sites in direct RNA nanopore sequencing data

Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and also has an importance in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies enable direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge this technology has not been used to identify RNA Pseudouridine sites. To this end, in this paper, we address this limitation by introducing a tool called Penguin that integrates several developed machine learning (ML) models (i.e., predictors) to identify RNA Pseudouridine sites in Nanopore direct RNA sequencing reads. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, is able to predict whether the signal is modified by the presence of Pseudouridine sites. We have included various predictors in Penguin including Support vector machine (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets show that Penguin is able to identify Pseudouridine sites with a high accuracy of 93.38% and 92.61% using SVM in random split testing and independent validation testing respectively. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature that achieved an accuracy of 76.0 at most with an independent validation testing. A GitHub of the tool is accessible at https://github.com/Janga-Lab/Pengui

Lantern: an integrative repository of functional annotations for lncRNAs in the human genome

Background: With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare. Results: We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed's abstract retrieval engine and NCBO's recommender annotation system. Lantern's annotations were benchmarked against lncRNAdb's manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform. Conclusions: Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern

Systems biology approach to obtain significant modules of immune therapy and colorectal cancer

poster abstractColorectal cancer (CRC) is the second leading cause of cancer death in the United States. There has been a lot of research around genes influencing CRC, despite its extensive understanding on the genetic perspective and the emergence of drugs targeting these genes, the tumor progression could be hardly mitigated. However, immune therapy has recently been observed to be effective in CRC treatment and diagnosis. This study focuses on developing a statistically validated multi-feature analytical approach to identify immuno-oncology targets. The features considered in this study were gene expression, DNA methylation, concepts from literature and immuno-cancer pathways. The network algorithm will identify the potentially relevant immuno-oncology modules of CRC. For the study level-3 data (7.2 gigabytes) of gene expression and DNA methylation was obtained from The Cancer Genome Atlas. Around 13000 genes were identified to be significant from the gene expression data analysis and 19000 genes significant in DNA methylation data. The CRC and Immuno-oncology concepts were manually annotated from 50 peer reviewed articles. The output of the preliminary analysis could predict 95 concepts annotated to the 1587 significant genes and were integrated into the network. The top rank concepts in terms of genes associated were ‘apoptosis’, ‘transforming growth factor’, ‘protein arginine methyltransferase’, ‘carcinoembryonic antigen’ and ‘methyl binding protein’. The gene annotated with highest number of concepts was ‘PRMT5’, ‘CSF2’, ‘CFLAR’ and ‘MLH1’. These genes were observed in the literature as targets of CRC

Role of SARS-CoV-2 in Altering the RNA-Binding Protein and miRNA-Directed Post-Transcriptional Regulatory Networks in Humans

The outbreak of a novel coronavirus SARS-CoV-2 responsible for the COVID-19 pandemic has caused a worldwide public health emergency. Due to the constantly evolving nature of the coronaviruses, SARS-CoV-2-mediated alterations on post-transcriptional gene regulations across human tissues remain elusive. In this study, we analyzed publicly available genomic datasets to systematically dissect the crosstalk and dysregulation of the human post-transcriptional regulatory networks governed by RNA-binding proteins (RBPs) and micro-RNAs (miRs) due to SARS-CoV-2 infection. We uncovered that 13 out of 29 SARS-CoV-2-encoded proteins directly interacted with 51 human RBPs, of which the majority of them were abundantly expressed in gonadal tissues and immune cells. We further performed a functional analysis of differentially expressed genes in mock-treated versus SARS-CoV-2-infected lung cells that revealed enrichment for the immune response, cytokine-mediated signaling, and metabolism-associated genes. This study also characterized the alternative splicing events in SARS-CoV-2-infected cells compared to the control, demonstrating that skipped exons and mutually exclusive exons were the most abundant events that potentially contributed to differential outcomes in response to the viral infection. A motif enrichment analysis on the RNA genomic sequence of SARS-CoV-2 clearly revealed the enrichment for RBPs such as SRSFs, PCBPs, ELAVs, and HNRNPs, suggesting the sponging of RBPs by the SARS-CoV-2 genome. A similar analysis to study the interactions of miRs with SARS-CoV-2 revealed functionally important miRs that were highly expressed in immune cells, suggesting that these interactions may contribute to the progression of the viral infection and modulate the host immune response across other human tissues. Given the need to understand the interactions of SARS-CoV-2 with key post-transcriptional regulators in the human genome, this study provided a systematic computational analysis to dissect the role of dysregulated post-transcriptional regulatory networks controlled by RBPs and miRs across tissue types during a SARS-CoV-2 infection

Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

Background: Direct-sequencing technologies, such as Oxford Nanopore's, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result: Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions: Sequoia's interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia

A long non‐coding RNA (Lrap) modulates brain gene expression and levels of alcohol consumption in rats

LncRNAs are important regulators of quantitative and qualitative features of the transcriptome. We have used QTL and other statistical analyses to identify a gene coexpression module associated with alcohol consumption. The "hub gene" of this module, Lrap (Long non-coding RNA for alcohol preference), was an unannotated transcript resembling a lncRNA. We used partial correlation analyses to establish that Lrap is a major contributor to the integrity of the coexpression module. Using CRISPR/Cas9 technology, we disrupted an exon of Lrap in Wistar rats. Measures of alcohol consumption in wild type, heterozygous and knockout rats showed that disruption of Lrap produced increases in alcohol consumption/alcohol preference. The disruption of Lrap also produced changes in expression of over 700 other transcripts. Furthermore, it became apparent that Lrap may have a function in alternative splicing of the affected transcripts. The GO category of "Response to Ethanol" emerged as one of the top candidates in an enrichment analysis of the differentially expressed transcripts. We validate the role of Lrap as a mediator of alcohol consumption by rats, and also implicate Lrap as a modifier of the expression and splicing of a large number of brain transcripts. A defined subset of these transcripts significantly impacts alcohol consumption by rats (and possibly humans). Our work shows the pleiotropic nature of non-coding elements of the genome, the power of network analysis in identifying the critical elements influencing phenotypes, and the fact that not all changes produced by genetic editing are critical for the concomitant changes in phenotype