scholarly journals Denoising DNA deep sequencing data—high-throughput sequencing errors and their correction

2015 ◽  
Vol 17 (1) ◽  
pp. 154-179 ◽  
Author(s):  
David Laehnemann ◽  
Arndt Borkhardt ◽  
Alice Carolyn McHardy
Keyword(s):  
High Throughput ◽  
Deep Sequencing ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Deep Sequencing Data

scholarly journals Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasemin Guenay-Greunke ◽  
David A. Bohan ◽  
Michael Traugott ◽  
Corinna Wallinger
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Cost Effective ◽  
Amplicon Sequencing ◽  
Sequencing Depth ◽  
Sequencing Error ◽  
Sequencing Data ◽  
Large Sample ◽  
Sequencing Errors ◽  
Plant Feeding

AbstractHigh-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.

scholarly journals Binnacle: Using Scaffolds to Improve the Contiguity and Quality of Metagenomic Bins

2021 ◽  
Vol 12 ◽  
Author(s):  
Harihara Subrahmaniam Muralidharan ◽  
Nidhi Shah ◽  
Jacquelyn S. Meisel ◽  
Mihai Pop
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Mobile Elements ◽  
Shotgun Sequencing ◽  
Strain Level ◽  
Level Variation ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Complete Genomes

High-throughput sequencing has revolutionized the field of microbiology, however, reconstructing complete genomes of organisms from whole metagenomic shotgun sequencing data remains a challenge. Recovered genomes are often highly fragmented, due to uneven abundances of organisms, repeats within and across genomes, sequencing errors, and strain-level variation. To address the fragmented nature of metagenomic assemblies, scientists rely on a process called binning, which clusters together contigs inferred to originate from the same organism. Existing binning algorithms use oligonucleotide frequencies and contig abundance (coverage) within and across samples to group together contigs from the same organism. However, these algorithms often miss short contigs and contigs from regions with unusual coverage or DNA composition characteristics, such as mobile elements. Here, we propose that information from assembly graphs can assist current strategies for metagenomic binning. We use MetaCarvel, a metagenomic scaffolding tool, to construct assembly graphs where contigs are nodes and edges are inferred based on paired-end reads. We developed a tool, Binnacle, that extracts information from the assembly graphs and clusters scaffolds into comprehensive bins. Binnacle also provides wrapper scripts to integrate with existing binning methods. The Binnacle pipeline can be found on GitHub (https://github.com/marbl/binnacle). We show that binning graph-based scaffolds, rather than contigs, improves the contiguity and quality of the resulting bins, and captures a broader set of the genes of the organisms being reconstructed.

scholarly journals vcfView: An Extensible Data Visualization and Quality Assurance Platform for Integrated Somatic Variant Analysis

2020 ◽  
Vol 19 ◽  
pp. 117693512097237
Author(s):  
Brian O’Sullivan ◽  
Cathal Seoighe
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Somatic Mutations ◽  
Driver Mutations ◽  
Sequencing Data ◽  
Data Set ◽  
Therapeutic Implications ◽  
Cancer Driver ◽  
Sequencing Errors ◽  
The Status

Motivation: Somatic mutations can have critical prognostic and therapeutic implications for cancer patients. Although targeted methods are often used to assay specific cancer driver mutations, high throughput sequencing is frequently applied to discover novel driver mutations and to determine the status of less-frequent driver mutations. The task of recovering somatic mutations from these data is nontrivial as somatic mutations must be distinguished from germline variants, sequencing errors, and other artefacts. Consequently, bioinformatics pipelines for recovery of somatic mutations from high throughput sequencing typically involve a large number of analytical choices in the form of quality filters. Results: We present vcfView, an interactive tool designed to support the evaluation of somatic mutation calls from cancer sequencing data. The tool takes as input a single variant call format (VCF) file and enables researchers to explore the impacts of analytical choices on the mutant allele frequency spectrum, on mutational signatures and on annotated somatic variants in genes of interest. It allows variants that have failed variant caller filters to be re-examined to improve sensitivity or guide the design of future experiments. It is extensible, allowing other algorithms to be incorporated easily. Availability: The shiny application can be downloaded from GitHub ( https://github.com/BrianOSullivanGit/vcfView ). All data processing is performed within R to ensure platform independence. The app has been tested on RStudio, version 1.1.456, with base R 3.6.2 and Shiny 1.4.0. A vignette based on a publicly available data set is also available on GitHub.

scholarly journals GenomeGems: evaluation of genetic variability from deep sequencing data

2012 ◽  
Vol 5 (1) ◽  
pp. 338
Author(s):  
Sharon Ben-Zvi ◽  
Adi Givati ◽  
Noam Shomron
Keyword(s):  
Genetic Variability ◽  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data

Considering DNA damage when interpreting mtDNA heteroplasmy in deep sequencing data

2017 ◽  
Vol 26 ◽  
pp. 1-11 ◽  
Author(s):  
Molly M. Rathbun ◽  
Jennifer A. McElhoe ◽  
Walther Parson ◽  
Mitchell M. Holland
Keyword(s):  
Dna Damage ◽  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

scholarly journals Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data

Biology ◽  
2012 ◽  
Vol 1 (2) ◽  
pp. 297-310 ◽  
Author(s):  
Xiaozeng Yang ◽  
Lei Li
Keyword(s):  
Deep Sequencing ◽  
Sequencing Data ◽  
Deep Sequencing Data
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