Biology subjects

Nabi, A.

Publications and source records attributed to Nabi, A..

4 recordsLinked to original sources

bioRxiv2020

ace2 expression is higher in intestines and liver while being tightly regulated in development and disease in zebrafish

Human Angiotensin I Converting Enzyme 2 (ACE2) that acts as a receptor for SARS-CoV-2 entry is highly expressed in human type II pneumocytes and enterocytes and similarly in other mammals and zebrafish (Danio rerio). The zebrafish genome has a highly conserved, one-to-one ortholog of ACE2, i.e., ace2, whose expression profile however has not yet been studied during development or in pathologies relevant to COVID-19. Herein, we identified significant development-, tissue- and gender-specific modulations in ace2 expression based on meta-analysis of zebrafish Affymetrix transcriptomics datasets (ndatasets=107, GPL1319 in GEO database). Co-expression network analysis of ace2 revealed distinct positively correlated (carboxypeptidase activity and fibrin clot formation), and negatively correlated (cilia biogenesis/transport and chromatin modifications) STRING network modules. Using additional transcriptomics datasets, we showed zebrafish embryos before 3 days post fertilization (dpf) exhibited low levels of ace2 that increased significantly until 4 dpf implicating a role for ace2 in organogenesis. Re-analysis of RNA-seq datasets from zebrafish adult tissues demonstrated ace2 was expressed highly in intestines, variably in liver, and at lower levels in other organs. In addition, zebrafish females and males showed significant dimorphism in their age-dependent expression of ace2, and between ovary and testis where the latter had higher levels. Moreover, we demonstrated ace2 expression was significantly modulated under different physiological and pathological conditions associated with development, diet, infection, and inflammation. Our findings implicate a novel translational role for zebrafish ace2 in differentiation and pathologies predominantly found in intestines and liver, in which the effects of SARS-CoV-2 could be detrimental.

molecular biology↗

bioRxiv2020

Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification

Long non-coding RNAs (lncRNAs) are the largest class of non-coding RNAs (ncRNAs). However, recent experimental evidence has shown that some lncRNAs contain small open reading frames (sORFs) that are translated into functional micropeptides. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (ribo-seq) experiments, which are expensive and cell-type dependent. In addition, while very accurate machine learning models have been trained to distinguish between coding and non-coding sequences, little attention has been paid to the increasing evidence about the incorrect ground-truth labels of some lncRNAs in the underlying training datasets. We present a framework that leverages deep learning models training dynamics to determine whether a given lncRNA transcript is misannotated. Our models achieve AUC scores > 91% and AUPR > 93% in classifying non-coding vs. coding sequences while allowing us to identify possible misannotated lncRNAs present in the dataset. Our results overlap significantly with a set of experimentally validated misannotated lncRNAs as well as with coding sORFs within lncRNAs found by a ribo-seq dataset. The general framework applied here offers promising potential for use in curating datasets used for training coding potential predictors and assisting experimental efforts in characterizing the hidden proteome encoded by misannotated lncRNAs. Source code is available at https://github.com/nabiafshan/DetectingMisannotatedLncRNAs.

genomics↗

bioRxiv2019

NoRCE: Non-coding RNA Sets Cis Enrichment Tool

SummaryWhile some non-coding RNAs (ncRNAs) are assigned to critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic spatial proximity can lead to a functional association. Based on this idea, we present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. NoRCE allows incorporating other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information. NoRCE repository provides several data, such as cell-line specific TAD boundaries, functional gene sets, and expression data for coding and ncRNAs specific to cancer for the analysis. Additionally, users can utilize their custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. NoRCE is a platform-independent, user-friendly, comprehensive R package that could be used to gain insight into the functional importance of a list of any type of interesting ncRNAs. Users can run the pipeline in a single function; also, the tool offers flexibility to conduct the users preferred analysis in a single base and design their pipeline. It is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

bioinformatics↗

bioRxiv2019

High accuracy DNA sequencing on a small, scalable platform via electrical detection of single base incorporations

High throughput DNA sequencing technologies have undergone tremendous development over the past decade. Although optical detection-based sequencing has constituted the majority of data output, it requires a large capital investment and aggregation of samples to achieve optimal cost per sample. We have developed a novel electronic detection-based platform capable of accurately detecting single base incorporations. The GenapSys technology with its electronic detection modality allows the system to be compact, accessible, and affordable. We demonstrate the performance of the system by sequencing several different microbial genomes with varying GC content. The platform is capable of generating up to 2 Gb of high-quality nucleic acid sequence in a single run. We routinely generate sequence data that exceeds 99% raw accuracy with read lengths of up to 175 bp. Average quality scores remain above Q30 (99.9% raw sequencing accuracy) beyond 150 bp, with more than 85% of total bases at or above Q30. The utility of the platform is highlighted by targeted sequencing of the human genome. We show high concordance of SNP detection on the human NA12878 HapMap cell line with data generated on the Illumina sequencing platform. In addition, we sequenced a targeted panel of cancer-associated genes in a well characterized reference standard. With multiple library preparation approaches on this sample, we were able to identify low frequency mutations at expected allele frequencies.

genomics↗