Biology subjects

Mandoiu, I.

Publications and source records attributed to Mandoiu, I..

4 recordsLinked to original sources

bioRxiv2021

SC1: A Tool for Interactive Web-Based Single Cell RNA-Seq Data Analysis

Single cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. Here, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on Term-Frequency Inverse-Document-Frequency (TF-IDF) scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single cell omics data modalities like TCR-Seq and supports several single cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.

bioinformatics↗

bioRxiv2019

TreeFix-TP: Phylogenetic Error-Correction for Infectious Disease Transmission Network Inference

BackgroundMany existing methods for estimation of infectious disease transmission networks use a phylogeny of the infecting strains as the basis for transmission network inference, and accurate network inference relies on accuracy of this underlying evolutionary history. However, phylogenetic reconstruction can be highly error prone and more sophisticated methods can fail to scale to larger outbreaks, negatively impacting downstream transmission network inference. Additionally, there are no currently available methods which are able to use within-host diversity to improve phylogenetic reconstruction.\n\nResultsWe introduce a new method, TreeFix-TP, for accurate and scalable reconstruction of transmission phylogenies based on an error-correction framework. Our method uses intra-host strain diversity and host information to balance a parsimonious evaluation of the implied transmission network with statistical hypothesis testing on sequence data likelihood. The reconstructed tree minimizes the number of required disease transmissions while being as well supported by sequence data as the maximum likelihood phylogeny. We use a simulation framework for viral transmission and evolution to demonstrate that TreeFix-TP improves phylogenetic accuracy and downstream transmission network accuracy. We also use real data from ten HCV outbreaks and demonstrate how error-correction improves source detection.\n\nConclusionsOur results show that using TreeFix-TP can lead to significant improvement in transmission phylogeny inference and that its performance is robust to variations in transmission and evolutionary parameters. Our experiments also demonstrate the importance of sampling multiple strain sequences from each infected host for accurate transmission network inference. TreeFix-TP is freely available open-source from https://compbio.engr.uconn.edu/software/treefix-tp/.

bioinformatics↗

bioRxiv2018

Locality Sensitive Imputation for Single-Cell RNA-Seq Data

One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this paper we study some existing scRNA-Seq imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq datasets with varying per cell sequencing depth.

bioinformatics↗

bioRxiv2017

CircMarker: A Fast and Accurate Algorithm for Circular RNA Detection

While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3 and 5 and forms the socalled circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility in some diseases. within these few years, several experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circRNA detection have been developed as well, these tools may miss many circular RNA. Also, existing tools are slow for large data because those tools often depend on reads mapping. In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create k-mer table for circular RNA detection. Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets. CircMarker can be downloaded from https://github.com/lxwgcool/CircMarker.

bioinformatics↗