scholarly journals SQMtools: automated processing and visual analysis of ’omics data with R and anvi’o

2020 ◽  
Author(s):  
Fernando Puente-Sánchez ◽  
Natalia García-García ◽  
Javier Tamames
Keyword(s):  
High Throughput Sequencing ◽  
Visual Analysis ◽  
Visual Exploration ◽  
Research Groups ◽  
Sequencing Data ◽  
Dramatic Decrease ◽  
Standard Tool ◽  
Automated Processing ◽  
Analysis Platform ◽  
Analysis Environment

AbstractBackgroundThe dramatic decrease in sequencing costs over the last decade has boosted the adoption of high-throughput sequencing applications as a standard tool for the analysis of environmental microbial communities. Nowadays even small research groups can easily obtain raw sequencing data. After that, however, non-specialists are faced with the double challenge of choosing among an ever-increasing array of analysis methodologies, and navigating the vast amounts of results returned by these approaches.ResultsHere we present a workflow that relies on the SqueezeMeta software for the automated processing of raw reads into annotated contigs and reconstructed genomes (bins). A set of custom scripts seamlessly integrates the output into the anvi’o analysis platform, allowing filtering and visual exploration of the results. Furthermore, we provide a software package with utility functions to expose the SqueezeMeta results to the R analysis environment.ConclusionsAltogether, our workflow allows non-expert users to go from raw sequencing reads to custom plots with only a few powerful, flexible and well-documented commands.

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

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ratanond Koonchanok ◽  
Swapna Vidhur Daulatabad ◽  
Quoseena Mir ◽  
Khairi Reda ◽  
Sarath Chandra Janga
Keyword(s):  
Single Molecule ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Direct Sequencing ◽  
Visual Exploration ◽  
Nanopore Sequencing ◽  
Sequencing Data ◽  
Rna Sequences ◽  
Sequencing Technologies ◽  
Signal Features

Abstract 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.

scholarly journals omicplotR: visualizing omic datasets as compositions

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel J. Giguere ◽  
Jean M. Macklaim ◽  
Brandon Y. Lieng ◽  
Gregory B. Gloor
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Visual Exploration ◽  
Command Line ◽  
Sequencing Data ◽  
Software Packages ◽  
High Throughput Sequencing Data ◽  
Differential Abundance Analysis

Abstract Background Differential abundance analysis is widely used with high-throughput sequencing data to compare gene abundance or expression between groups of samples. Many software packages exist for this purpose, but each uses a unique set of statistical assumptions to solve problems on a case-by-case basis. These software packages are typically difficult to use for researchers without command-line skills, and software that does offer a graphical user interface do not use a compositionally valid method. Results omicplotR facilitates visual exploration of omic datasets for researchers with and without prior scripting knowledge. Reproducible visualizations include principal component analysis, hierarchical clustering, MA plots and effect plots. We demonstrate the functionality of omicplotR using a publicly available metatranscriptome dataset. Conclusions omicplotR provides a graphical user interface to explore sequence count data using generalizable compositional methods, facilitating visualization for investigators without command-line experience.

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

scholarly journals BiSulfite Bolt: A bisulfite sequencing analysis platform

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Colin Farrell ◽  
Michael Thompson ◽  
Anela Tosevska ◽  
Adewale Oyetunde ◽  
Matteo Pellegrini
Keyword(s):  
Data Aggregation ◽  
Bisulfite Sequencing ◽  
Low Complexity ◽  
Sequencing Analysis ◽  
Command Line ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data ◽  
Analysis Platform ◽  
Python Package ◽  
Bisulfite Sequencing Analysis

Abstract Background Bisulfite sequencing is commonly used to measure DNA methylation. Processing bisulfite sequencing data is often challenging owing to the computational demands of mapping a low-complexity, asymmetrical library and the lack of a unified processing toolset to produce an analysis-ready methylation matrix from read alignments. To address these shortcomings, we have developed BiSulfite Bolt (BSBolt), a fast and scalable bisulfite sequencing analysis platform. BSBolt performs a pre-alignment sequencing read assessment step to improve efficiency when handling asymmetrical bisulfite sequencing libraries. Findings We evaluated BSBolt against simulated and real bisulfite sequencing libraries. We found that BSBolt provides accurate and fast bisulfite sequencing alignments and methylation calls. We also compared BSBolt to several existing bisulfite alignment tools and found BSBolt outperforms Bismark, BSSeeker2, BISCUIT, and BWA-Meth based on alignment accuracy and methylation calling accuracy. Conclusion BSBolt offers streamlined processing of bisulfite sequencing data through an integrated toolset that offers support for simulation, alignment, methylation calling, and data aggregation. BSBolt is implemented as a Python package and command line utility for flexibility when building informatics pipelines. BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.

scholarly journals Experimental infection with the hookworm, Necator americanus, is associated with stable gut microbial diversity in human volunteers with relapsing multiple sclerosis

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Timothy P. Jenkins ◽  
David I. Pritchard ◽  
Radu Tanasescu ◽  
Gary Telford ◽  
Marina Papaiakovou ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Experimental Infection ◽  
High Throughput Sequencing ◽  
Alpha Diversity ◽  
Placebo Treatment ◽  
Sequencing Data ◽  
Faecal Microbiota ◽  
Microbial Composition ◽  
Necator Americanus ◽  
Human Volunteers

Abstract Background Helminth-associated changes in gut microbiota composition have been hypothesised to contribute to the immune-suppressive properties of parasitic worms. Multiple sclerosis is an immune-mediated autoimmune disease of the central nervous system whose pathophysiology has been linked to imbalances in gut microbial communities. Results In the present study, we investigated, for the first time, qualitative and quantitative changes in the faecal bacterial composition of human volunteers with remitting multiple sclerosis (RMS) prior to and following experimental infection with the human hookworm, Necator americanus (N+), and following anthelmintic treatment, and compared the findings with data obtained from a cohort of RMS patients subjected to placebo treatment (PBO). Bacterial 16S rRNA high-throughput sequencing data revealed significantly decreased alpha diversity in the faecal microbiota of PBO compared to N+ subjects over the course of the trial; additionally, we observed significant differences in the abundances of several bacterial taxa with putative immune-modulatory functions between study cohorts. Parabacteroides were significantly expanded in the faecal microbiota of N+ individuals for which no clinical and/or radiological relapses were recorded at the end of the trial. Conclusions Overall, our data lend support to the hypothesis of a contributory role of parasite-associated alterations in gut microbial composition to the immune-modulatory properties of hookworm parasites.

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

scholarly journals deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

Sign in / Sign up

Export Citation Format

Share Document