Dataset Information

Evaluation of Commercial Next-Generation Sequencing Bioinformatics Software Solutions.

ABSTRACT: Next-generation sequencing (NGS) diagnostics continue to expand rapidly in clinical medicine. An ever-expanding menu of molecular biomarkers is deemed important for diagnostic, prognostic, and therapeutic assessment in patients. The increasing role of NGS in the clinic is driven mainly by the falling costs of sequencing. However, the data-intensive nature of NGS makes bioinformatic analysis a major challenge to many clinical laboratories. Critically needed NGS bioinformatics personnel are hard to recruit and retain in small- to mid-size clinical laboratories. Also, NGS software often lacks the scalability necessary for expanded clinical laboratory testing volumes. Commercial software solutions aim to bridge the bioinformatics barrier via turnkey informatics solutions tailored specifically for the clinical workplace. Yet, there has been no systematic assessment of these software solutions thus far. This article presents an end-to-end vendor evaluation experience of commercial NGS bioinformatics solutions. Six different commercial vendor solutions were assessed systematically. Key metrics of NGS software evaluation to aid in the robust assessment of software solutions are described. Comprehensive feedback, provided by the TriCore Reference Laboratories molecular pathology team, enabled the final vendor selection. Many key lessons were learned during the software evaluation process, which are described herein. This article aims to provide a detailed road map for small- to mid-size clinical laboratories interested in evaluating commercial bioinformatics solutions available in the marketplace.

SUBMITTER: Gullapalli RR

PROVIDER: S-EPMC7031678 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Evaluation of Commercial Next-Generation Sequencing Bioinformatics Software Solutions.

Gullapalli Rama R RR

The Journal of molecular diagnostics : JMD 20191118 2

Next-generation sequencing (NGS) diagnostics continue to expand rapidly in clinical medicine. An ever-expanding menu of molecular biomarkers is deemed important for diagnostic, prognostic, and therapeutic assessment in patients. The increasing role of NGS in the clinic is driven mainly by the falling costs of sequencing. However, the data-intensive nature of NGS makes bioinformatic analysis a major challenge to many clinical laboratories. Critically needed NGS bioinformatics personnel are hard t ...[more]

PMID: 31751676

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Project description:MicroRNAs (miRNAs) are key post-transcriptional regulators that affect protein translation by targeting mRNAs. Their role in disease etiology and toxicity are well recognized. Given the rapid advancement of next-generation sequencing techniques, miRNA profiling has been increasingly conducted with RNA-seq, namely miRNA-seq. Analysis of miRNA-seq data requires several steps: (1) mapping the reads to miRBase, (2) considering mismatches during the hairpin alignment (windowing), and (3) counting the reads (quantification). The choice made in each step with respect to the parameter settings could affect miRNA quantification, differentially expressed miRNAs (DEMs) detection and novel miRNA identification. Furthermore, these parameters do not act in isolation and their joint effects impact miRNA-seq results and interpretation. In toxicogenomics, the variation associated with parameter setting should not overpower the treatment effect (such as the dose/time-dependent effect). In this study, four commonly used miRNA-seq analysis tools (i.e., miRDeep2, miRExpress, miRNAkey, sRNAbench) were comparatively evaluated with a standard toxicogenomics study design. We tested 30 different parameter settings on miRNA-seq data generated from thioacetamide-treated rat liver samples for three dose levels across four time points, followed by four normalization options. Because both miRExpress and miRNAkey yielded larger variation than that of the treatment effects across multiple parameter settings, our analyses mainly focused on the side-by-side comparison between miRDeep2 and sRNAbench. While the number of miRNAs detected by miRDeep2 was almost the subset of those detected by sRNAbench, the number of DEMs identified by both tools was comparable under the same parameter settings and normalization method. Change in the number of nucleotides out of the mature sequence in the hairpin alignment (window option) yielded the largest variation for miRNA quantification and DEMs detection. However, such a variation is relatively small compared to the treatment effect when the study focused on DEMs that are more critical to interpret the toxicological effect. While the normalization methods introduced a large variation in DEMs, toxic behavior of thioacetamide showed consistency in the trend of time-dose responses. Overall, miRDeep2 was found to be preferable over other choices when the window option allowed up to three nucleotides from both ends.

Project description:BACKGROUND:When compared to Sanger sequencing technology, next-generation sequencing (NGS) technologies are hindered by shorter sequence read length, higher base-call error rate, non-uniform coverage, and platform-specific sequencing artifacts. These characteristics lower the quality of their downstream analyses, e.g. de novo and reference-based assembly, by introducing sequencing artifacts and errors that may contribute to incorrect interpretation of data. Although many tools have been developed for quality control and pre-processing of NGS data, none of them provide flexible and comprehensive trimming options in conjunction with parallel processing to expedite pre-processing of large NGS datasets. METHODS:We developed ngsShoRT (next-generation sequencing Short Reads Trimmer), a flexible and comprehensive open-source software package written in Perl that provides a set of algorithms commonly used for pre-processing NGS short read sequences. We compared the features and performance of ngsShoRT with existing tools: CutAdapt, NGS QC Toolkit and Trimmomatic. We also compared the effects of using pre-processed short read sequences generated by different algorithms on de novo and reference-based assembly for three different genomes: Caenorhabditis elegans, Saccharomyces cerevisiae S288c, and Escherichia coli O157 H7. RESULTS:Several combinations of ngsShoRT algorithms were tested on publicly available Illumina GA II, HiSeq 2000, and MiSeq eukaryotic and bacteria genomic short read sequences with the focus on removing sequencing artifacts and low-quality reads and/or bases. Our results show that across three organisms and three sequencing platforms, trimming improved the mean quality scores of trimmed sequences. Using trimmed sequences for de novo and reference-based assembly improved assembly quality as well as assembler performance. In general, ngsShoRT outperformed comparable trimming tools in terms of trimming speed and improvement of de novo and reference-based assembly as measured by assembly contiguity and correctness. CONCLUSIONS:Trimming of short read sequences can improve the quality of de novo and reference-based assembly and assembler performance. The parallel processing capability of ngsShoRT reduces trimming time and improves the memory efficiency when dealing with large datasets. We recommend combining sequencing artifacts removal, and quality score based read filtering and base trimming as the most consistent method for improving sequence quality and downstream assemblies. ngsShoRT source code, user guide and tutorial are available at http://research.bioinformatics.udel.edu/genomics/ngsShoRT/. ngsShoRT can be incorporated as a pre-processing step in genome and transcriptome assembly projects.

Dataset Information

Evaluation of Commercial Next-Generation Sequencing Bioinformatics Software Solutions.

Publications

Evaluation of Commercial Next-Generation Sequencing Bioinformatics Software Solutions.

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