scholarly journals Identification and characterization of Coronaviridae genomes from Vietnamese bats and rats based on conserved protein domains

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
My V T Phan ◽  
Tue Ngo Tri ◽  
Pham Hong Anh ◽  
Stephen Baker ◽  
Paul Kellam ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Protein Domains ◽  
Sequencing Data ◽  
Essential Information ◽  
Diagnostic Assays ◽  
Local Diversity ◽  
Virus Surveillance ◽  
Deep Sequencing Technology ◽  
Classification Tool

Abstract The Coronaviridae family of viruses encompasses a group of pathogens with a zoonotic potential as observed from previous outbreaks of the severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus. Accordingly, it seems important to identify and document the coronaviruses in animal reservoirs, many of which are uncharacterized and potentially missed by more standard diagnostic assays. A combination of sensitive deep sequencing technology and computational algorithms is essential for virus surveillance, especially for characterizing novel- or distantly related virus strains. Here, we explore the use of profile Hidden Markov Model-defined Pfam protein domains (Pfam domains) encoded by new sequences as a Coronaviridae sequence classification tool. The encoded domains are used first in a triage to identify potential Coronaviridae sequences and then processed using a Random Forest method to classify the sequences to the Coronaviridae genus level. The application of this algorithm on Coronaviridae genomes assembled from agnostic deep sequencing data from surveillance of bats and rats in Dong Thap province (Vietnam) identified thirty-four Alphacoronavirus and eleven Betacoronavirus genomes. This collection of bat and rat coronaviruses genomes provided essential information on the local diversity of coronaviruses and substantially expanded the number of coronavirus full genomes available from bat and rats and may facilitate further molecular studies on this group of viruses.

scholarly journals Characterization of microRNAs from goat (Capra hircus) by Solexa deep-sequencing technology

2013 ◽  
Vol 12 (2) ◽  
pp. 1951-1961 ◽  
Author(s):  
Y.H. Ling ◽  
J.P. Ding ◽  
X.D. Zhang ◽  
L.J. Wang ◽  
Y.H. Zhang ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Capra Hircus ◽  
Sequencing Technology ◽  
Deep Sequencing Technology ◽  
Solexa Deep Sequencing

scholarly journals Oasis2.0: improved online analysis of small RNA-seq data

2017 ◽  
Author(s):  
Raza-Ur Rahman ◽  
Abhivyakti Gautam ◽  
Jörn Bethune ◽  
Abdul Sattar ◽  
Maksims Fiosins ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Small Rna ◽  
Small Rnas ◽  
Web Application ◽  
The Novel ◽  
Sequencing Data ◽  
Essential Information ◽  
Deep Sequencing Data ◽  
Small Rna Deep Sequencing ◽  
User Friendly

AbstractOasis 2 is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.Availability and Implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at http://oasis.dzne.de

scholarly journals Identification of Novel MicroRNA-Like Molecules Generated from Herpesvirus and Host tRNA Transcripts

2010 ◽  
Vol 84 (19) ◽  
pp. 10344-10353 ◽  
Author(s):  
Tiffany A. Reese ◽  
Jing Xia ◽  
L. Steven Johnson ◽  
Xiang Zhou ◽  
Weixiong Zhang ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Small Rnas ◽  
Northern Blot Analysis ◽  
Sequencing Data ◽  
Viral Genomes ◽  
Murine Gammaherpesvirus ◽  
Deep Sequencing Technology ◽  
Gammaherpesvirus 68 ◽  
Pol Iii ◽  
Murine Gammaherpesvirus 68

ABSTRACT We applied deep sequencing technology to small RNA fractions from cells lytically infected with murine gammaherpesvirus 68 (γHV68) in order to define in detail small RNAs generated from a cluster of tRNA-related polycistronic structures located at the left end of the viral genome. We detected 10 new candidate microRNAs (miRNAs), six of which were confirmed by Northern blot analysis, leaving four as provisional. In addition, we determined that previously identified and annotated viral miRNA molecules were not necessarily represented as the most abundant sequence originating from a transcript. Based on these new small RNAs and previously reported γHV68 miRNAs, we were able to further describe and annotate the distinctive γHV68 tRNA-miRNA structures. We used this deep sequencing data and computational analysis to identify similar structures in the mouse genome and validated that these host structures also give rise to small RNAs. This reveals a possible convergent usage of tRNA/polymerase III (pol III) transcripts to generate small RNAs from both mammalian and viral genomes.

scholarly journals MERIT: a Mutation Error Rate Identification Toolkit for Ultra-deep Sequencing Applications

2017 ◽  
Author(s):  
Mohammad Hadigol ◽  
Hossein Khiabanian
Keyword(s):  
Deep Sequencing ◽  
High Throughput Sequencing ◽  
Clonal Evolution ◽  
False Negative ◽  
Error Rates ◽  
Sequencing Data ◽  
Genomic Context ◽  
Nucleotide Incorporation ◽  
Double Base

AbstractRapid progress in high-throughput sequencing (HTS) has enabled the molecular characterization of mutational landscapes in heterogeneous populations and has improved our understanding of clonal evolution processes. Analyzing the sensitivity of detecting genomic mutations in HTS requires comprehensive profiling of sequencing artifacts. To this end, we introduce MERIT, designed for in-depth quantification of erroneous substitutions and small insertions and deletions, specifically for ultra-deep applications. MERIT incorporates an all-inclusive variant caller and considers genomic context, including the nucleotides immediately at 5′ and 3′, thereby establishing error rates for 96 possible substitutions as well as four singlebase and 16 double-base indels. We apply MERIT to ultra-deep sequencing data (1,300,000×) and show a significant relationship between error rates and genomic contexts. We devise an in silico approach to determine the optimal sequencing depth, where errors occur at rates similar to those of true mutations. Finally, we assess nucleotide-incorporation fidelity of four high-fidelity DNA polymerases in clinically relevant loci, and demonstrate how fixed detection thresholds may result in substantial false positive as well as false negative calls.

scholarly journals Development of a User-Friendly Pipeline for Mutational Analyses of HIV Using Ultra-Accurate Maximum-Depth Sequencing

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1338
Author(s):  
Morgan E. Meissner ◽  
Emily J. Julik ◽  
Jonathan P. Badalamenti ◽  
William G. Arndt ◽  
Lauren J. Mills ◽  
...  
Keyword(s):  
Error Rates ◽  
Maximum Depth ◽  
Sequencing Data ◽  
Background Error ◽  
High Background ◽  
Immunodeficiency Virus ◽  
User Friendly ◽  
Viral Mutagenesis ◽  
Hiv 1

Human immunodeficiency virus type 2 (HIV-2) accumulates fewer mutations during replication than HIV type 1 (HIV-1). Advanced studies of HIV-2 mutagenesis, however, have historically been confounded by high background error rates in traditional next-generation sequencing techniques. In this study, we describe the adaptation of the previously described maximum-depth sequencing (MDS) technique to studies of both HIV-1 and HIV-2 for the ultra-accurate characterization of viral mutagenesis. We also present the development of a user-friendly Galaxy workflow for the bioinformatic analyses of sequencing data generated using the MDS technique, designed to improve replicability and accessibility to molecular virologists. This adapted MDS technique and analysis pipeline were validated by comparisons with previously published analyses of the frequency and spectra of mutations in HIV-1 and HIV-2 and is readily expandable to studies of viral mutation across the genomes of both viruses. Using this novel sequencing pipeline, we observed that the background error rate was reduced 100-fold over standard Illumina error rates, and 10-fold over traditional unique molecular identifier (UMI)-based sequencing. This technical advancement will allow for the exploration of novel and previously unrecognized sources of viral mutagenesis in both HIV-1 and HIV-2, which will expand our understanding of retroviral diversity and evolution.

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