scholarly journals HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Renesh Bedre ◽  
Carlos Avila ◽  
Kranthi Mandadi
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Science Research ◽  
Control Analysis ◽  
Sequencing Data ◽  
Quality Control Analysis ◽  
High Throughput Sequencing Data ◽  
One Step ◽  
Automated Quality Control

AbstractUse of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Here, we developed HTSQualC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSQualC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSQualC for conducting batch analysis of HTS datasets with 322 samples with an average ~ 1 M (paired end) sequence reads per sample. HTSQualC accomplished the QC analysis in ~ 3 h in distributed mode and ~ 31 h in shared mode, thus underscoring its utility and robust performance. In addition to command-line execution, we integrated HTSQualC into the free, open-source, CyVerse cyberinfrastructure resource as a GUI interface, for wider access to experimental biologists who have limited computational resources and/or programming abilities.

scholarly journals HTSeqQC: A Flexible and One-Step Quality Control Software for High-throughput Sequence Data Analysis

2020 ◽  
Author(s):  
Renesh Bedre ◽  
Carlos Avila ◽  
Kranthi Mandadi
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Supplementary Information ◽  
Supplementary File ◽  
Control Analysis ◽  
Link Type ◽  
Quality Control Analysis ◽  
One Step

AbstractMotivationUse of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly.ResultsHere, we developed HTSeqQC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSeqQC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSeqQC for conducting batch analysis of HTS datasets with 322 sample datasets with an average ∼ 1M (paired end) sequence reads per sample. HTSeqQC accomplished the QC analysis in ∼3 hours in distributed mode and ∼31 hours in shared mode, thus underscoring its utility and robust performance.Availability and implementationHTSeqQC software, Docker image and Nextflow template are available for download at https://github.com/reneshbedre/HTSeqQC and graphical user interface (GUI) is available at CyVerse Discovery Environment (DE) (https://cyverse.org/). Documentation available at https://reneshbedre.github.io/blog/htseqqc.html and https://cyverse-htseqqc-cyverse-tutorial.readthedocs-hosted.com/en/latest/ (for CyVerse).ContactKranthi Mandadi ([email protected])Supplementary informationSupplementary information provided in Supplementary File 1.

scholarly journals PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13890 ◽  
Author(s):  
Changjin Hong ◽  
Solaiappan Manimaran ◽  
William Evan Johnson
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Performance ◽  
High Throughput Sequencing ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Computationally Efficient ◽  
High Throughput Sequencing Data ◽  
Downstream Analysis

Quality control and read preprocessing are critical steps in the analysis of data sets generated from high-throughput genomic screens. In the most extreme cases, improper preprocessing can negatively affect downstream analyses and may lead to incorrect biological conclusions. Here, we present PathoQC, a streamlined toolkit that seamlessly combines the benefits of several popular quality control software approaches for preprocessing next-generation sequencing data. PathoQC provides a variety of quality control options appropriate for most high-throughput sequencing applications. PathoQC is primarily developed as a module in the PathoScope software suite for metagenomic analysis. However, PathoQC is also available as an open-source Python module that can run as a stand-alone application or can be easily integrated into any bioinformatics workflow. PathoQC achieves high performance by supporting parallel computation and is an effective tool that removes technical sequencing artifacts and facilitates robust downstream analysis. The PathoQC software package is available at http://sourceforge.net/projects/PathoScope/ .

scholarly journals AlmostSignificant: simplifying quality control of high-throughput sequencing data

2016 ◽  
Vol 32 (24) ◽  
pp. 3850-3851 ◽  
Author(s):  
Joseph Ward ◽  
Christian Cole ◽  
Melanie Febrer ◽  
Geoffrey J. Barton
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput 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 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
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