scholarly journals The discrepancy among single nucleotide variants detected by DNA and RNA high throughput sequencing data

BMC Genomics ◽  
2017 ◽  
Vol 18 (S6) ◽  
Author(s):  
Yan Guo ◽  
Shilin Zhao ◽  
Quanhu Sheng ◽  
David C Samuels ◽  
Yu Shyr
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Dna And Rna ◽  
High Throughput Sequencing Data

scholarly journals seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1466 ◽  
Author(s):  
Erik Fasterius ◽  
Cristina Al-Khalili Szigyarto
Keyword(s):  
Genetic Variation ◽  
Liver Cancer ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Ease Of Use ◽  
Sequencing Data ◽  
Dna And Rna ◽  
High Throughput Sequencing Data ◽  
Wide Range

High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, demonstrating that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%.

scholarly journals A Systematic Evaluation of High-Throughput Sequencing Approaches to Identify Low-Frequency Single Nucleotide Variants in Viral Populations

Viruses ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1187
Author(s):  
David J. King ◽  
Graham Freimanis ◽  
Lidia Lasecka-Dykes ◽  
Amin Asfor ◽  
Paolo Ribeca ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Low Frequency ◽  
Illumina Miseq ◽  
Read Length ◽  
Systematic Evaluation ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Dna And Rna

High-throughput sequencing such as those provided by Illumina are an efficient way to understand sequence variation within viral populations. However, challenges exist in distinguishing process-introduced error from biological variance, which significantly impacts our ability to identify sub-consensus single-nucleotide variants (SNVs). Here we have taken a systematic approach to evaluate laboratory and bioinformatic pipelines to accurately identify low-frequency SNVs in viral populations. Artificial DNA and RNA “populations” were created by introducing known SNVs at predetermined frequencies into template nucleic acid before being sequenced on an Illumina MiSeq platform. These were used to assess the effects of abundance and starting input material type, technical replicates, read length and quality, short-read aligner, and percentage frequency thresholds on the ability to accurately call variants. Analyses revealed that the abundance and type of input nucleic acid had the greatest impact on the accuracy of SNV calling as measured by a micro-averaged Matthews correlation coefficient score, with DNA and high RNA inputs (107 copies) allowing for variants to be called at a 0.2% frequency. Reduced input RNA (105 copies) required more technical replicates to maintain accuracy, while low RNA inputs (103 copies) suffered from consensus-level errors. Base errors identified at specific motifs identified in all technical replicates were also identified which can be excluded to further increase SNV calling accuracy. These findings indicate that samples with low RNA inputs should be excluded for SNV calling and reinforce the importance of optimising the technical and bioinformatics steps in pipelines that are used to accurately identify sequence variants.

scholarly journals seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data

F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1466 ◽  
Author(s):  
Erik Fasterius ◽  
Cristina Al-Khalili Szigyarto
Keyword(s):  
Genetic Variation ◽  
Liver Cancer ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Ease Of Use ◽  
Sequencing Data ◽  
Dna And Rna ◽  
High Throughput Sequencing Data ◽  
Wide Range

High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, corroborating the original authors' conclusions that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%. SeqCAT is an open source software under a MIT licence available at https://bioconductor.org/packages/release/bioc/html/seqCAT.html.

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 Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Genomics ◽  
2017 ◽  
Vol 109 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Yan Guo ◽  
Yulin Dai ◽  
Hui Yu ◽  
Shilin Zhao ◽  
David C. Samuels ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Data Analysis

scholarly journals HTSeq - A Python framework to work with high-throughput sequencing data

2014 ◽  
Author(s):  
Simon Anders ◽  
Paul Theodor Pyl ◽  
Wolfgang Huber
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rapid Development ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Standard Work ◽  
Data Formats ◽  
High Throughput Sequencing Data ◽  
Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

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