Sequencing Reviews: How to Evaluate and Choose a Sequencing Platform

Choosing a sequencing platform requires a structured comparison of throughput, read length, accuracy, cost per sample, and the specific biological question you need to answer. This article provides a framework for evaluating short-read and long-read platforms, including emerging systems from Roche, Ultima, and Element, with a scoring matrix and vendor questions to guide purchasing decisions for laboratory students, technicians, researchers, and diagnostic professionals.

At a Glance

The table below summarizes the main platform categories and their typical strengths for common applications. Use this as a starting point before applying the detailed evaluation framework in later sections.

Defining Your Sequencing Requirements Before Platform Selection

The first step in platform evaluation is a written specification of what your project demands. Without this document, vendor comparisons become marketing exercises instead of technical assessments. Start with the biological question, then translate that into sequencing parameters.

For whole genome sequencing of bacteria, the key parameters are genome size, expected coverage depth, and whether you need complete circular assemblies. A study comparing the Illumina HiSeq 2000 with the BGI DNA nanoball platform for bacterial genome assembly found that both platforms produced assemblies with similar completeness for most metrics, though the DNB platform showed fewer N bases in high GC genomes while HiSeq assemblies had higher N50 values. The choice between these platforms for bacterial work depends on whether you prioritize contiguity or completeness in specific genomic contexts.

For human or animal genome studies, the decision between short-read and long-read platforms affects which variant classes you can detect. A benchmarking study comparing sequencing technologies for human genetic variant detection found that short-read sequencing combined with DRAGEN achieved high accuracy for single nucleotide variants and indels in well-mapped regions but showed reduced sensitivity for structural variant detection. Long-read platforms demonstrated clear advantages in detecting structural variants and resolving small variants in difficult genomic regions. Coverage analysis in that study indicated that long-read sequencing reached accuracy saturation between 20x and 45x coverage, while short-read sequencing required more than 60x coverage to approach comparable accuracy for some variant classes.

For transcriptome studies, the choice of library preparation kit can influence results as much as the platform itself. A comparison of three mRNA sequencing library kits for the Illumina platform found that the quality and quantity of sequencing data were strongly influenced by the type of library kit used. Researchers should select a library construction kit according to the goal and resources of their experiments, since kits from different manufacturers have distinct advantages and disadvantages.

For metagenomic studies, platform choice introduces systematic bias. A study assessing the effect of library preparation kits and sequencing platforms on microbiome characterization found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation or sample freezing. This finding means that comparisons of data generated on different platforms should be performed cautiously, and standardization of sample processing is key to generating comparable data within a study.

Core Principles of Sequencing Platform Evaluation

Throughput and Scalability

Throughput determines how many samples you can process in a given time and directly affects cost per sample. High-throughput platforms such as Illumina NovaSeq and MGI DNBSEQ systems can produce hundreds of gigabases per run, making them suitable for large population studies or deep coverage projects. The comparison between Illumina NovaSeq 6000 and MGI MGISEQ-2000 and DNBSEQ-T7 for whole genome sequencing of lung cancer tissues found that all three platforms showed high-quality scores and deep coverages, with MGISEQ-2000 most concordant with NovaSeq 6000 for germline variants and DNBSEQ-T7 most concordant for somatic variants.

For diagnostic laboratories, throughput must match clinical demand. A platform that is too large creates batching delays, while a platform that is too small increases per-sample costs. Consider your sample volume projections for the next three years, beyond your current workload. The rapid evolution of sequencing technologies since 2005 has produced platforms that generate over 100 times more data than capillary sequencers based on the Sanger method, and the pace of change continues.

Read Length and Its Consequences

Read length determines what genomic features you can resolve. Short reads of 150 base pairs are excellent for detecting single nucleotide variants and small indels in unique regions of the genome. They struggle with repetitive regions, structural variants, and phasing of alleles across long distances.

Long reads from PacBio and Oxford Nanopore platforms can span repetitive elements and produce contiguous assemblies. A study generating chromosome-scale reference genomes for the tetraploid forage crop sainfoin used PacBio HiFi, Oxford Nanopore, Illumina short read, and Hi-C data to assemble a 2.36 Gb genome resolving all 28 pseudochromosomes. This multi-platform approach demonstrates how different read lengths contribute to a complete assembly.

For RNA sequencing, long reads enable full-length transcript isoform quantification. A study developing lr-kallisto for long-read transcriptome quantification showed that fast and accurate quantification of long-read data is possible using Oxford Nanopore data, and that quantification is improved by exome capture. Short-read RNA-seq provides accurate gene-level counts but cannot definitively resolve isoform structures.

Accuracy and Error Profiles

Accuracy is not a single number. Different platforms have different error profiles, and these profiles matter for different applications. Short-read platforms generally achieve high per-base accuracy, often exceeding Q30 scores for the majority of bases. Long-read platforms have improved substantially with newer chemistries.

A comparison of direct RNA sequencing of Orthoavulavirus javaense using two different chemistries on the Oxford Nanopore MinION platform found that the newer SQK-RNA004 chemistry and flow cells, paired with super accurate base calling, improved read quality and length compared to the previous SQK-RNA002 chemistry. This finding illustrates that accuracy improvements come through both chemistry and software updates.

For clinical applications, accuracy requirements are stringent. A study comparing MGISEQ-2000 and NovaSeq6000 for targeted bisulfite sequencing of DNA methylation found that MGISEQ-2000 yielded data with similar quality to NovaSeq6000, with high consistency in methylation levels and comparable analytic sensitivity. The clinical model performance using MGISEQ-2000 data was highly consistent with that of NovaSeq6000 data.

Cost Structure

Cost per sample includes more than the sequencing run price. Library preparation reagents, consumables, labor, bioinformatics analysis, data storage, and instrument maintenance all contribute to the true cost. A comparison of library construction kits for mRNA sequencing found that alternative reagents and protocols that are relatively cost effective are accessible, but the kits from various manufacturers have advantages and disadvantages.

For whole genome sequencing, the cost difference between platforms can be substantial. The MGI platforms were launched with promises of high-quality sequencing data at lower prices than Illumina instruments. The comparison study of NovaSeq 6000, MGISEQ-2000, and DNBSEQ-T7 supported the potential applicability of the MGI platforms in actual genome analysis fields.

When evaluating cost, request a complete cost per sample from vendors that includes library preparation, sequencing, and primary analysis. Ask about volume discounts and whether the quoted price includes all consumables or only the flow cell and reagents.

Practical Workflow for Platform Comparison

Step 1: Define Your Application and Success Metrics

Write down the specific biological question and the data quality metrics that would constitute success. For a diagnostic assay, this might include sensitivity and specificity thresholds for detecting specific variants. For a research project, it might include assembly contiguity or the number of full-length isoforms detected.

The Laboratory Quality Management System Handbook from the World Health Organization provides guidance on establishing quality metrics for laboratory processes. While this handbook is not sequencing-specific, its framework for quality assurance applies to sequencing workflows, including documentation, validation, and continuous improvement.

Step 2: Identify Candidate Platforms

Based on your application requirements, identify two to four platforms that could meet your needs. Include at least one short-read and one long-read option if your application could benefit from either approach. For emerging platforms from Roche, Ultima, and Element, request demonstration data and peer-reviewed publications that validate performance claims.

Step 3: Request Demonstration Data

Ask vendors for raw sequencing data from samples similar to yours. If you work with bacterial genomes, request bacterial sequencing data. If you work with human clinical samples, request human data with known variant calls. Analyze this data yourself using your own bioinformatics pipeline to verify that the platform produces data compatible with your analysis tools.

Step 4: Run a Pilot Study

If possible, sequence a small set of your own samples on each candidate platform. Use the same DNA or RNA extracts for all platforms to control for sample variation. Include replicates to assess reproducibility. A pilot study of 5 to 10 samples can reveal practical issues that vendor demonstrations will not show, including library preparation failure rates, run failures, and data analysis compatibility.

Step 5: Evaluate Total Cost of Ownership

Calculate the total cost of ownership over a five-year period, including instrument purchase or rental, service contracts, consumables, personnel time, and bioinformatics infrastructure. Compare this across platforms using your projected sample volumes. The lowest cost per sample may not be the lowest total cost if the platform requires more hands-on time or additional bioinformatics support.

Step 6: Assess Vendor Support and Community

Evaluate the vendor's technical support responsiveness, the size of the user community, and the availability of bioinformatics tools for the platform. A platform with a smaller user community may have fewer troubleshooting resources and less mature analysis software. The NCBI Literature Resources can help you assess the volume of published research using each platform.

Options and Tradeoffs Across Platform Types

Short-Read Platforms

Illumina platforms remain the most widely used for next generation sequencing. The comparison of MGI and Illumina platforms for whole genome sequencing found that Illumina systems are the major sequencing platform in the worldwide market. Illumina offers a range of instruments from benchtop models to production-scale systems, allowing laboratories to match instrument capacity to workload.

MGI platforms using DNA nanoball technology offer an alternative to Illumina. The comparison of DNBSEQ and Illumina HiSeq 2000 for bacterial genome assembly found that DNBSEQ platforms would be a valid substitute for HiSeq 2000 for bacterial genome sequencing. The study noted that genome assemblies based on BGISEQ-500 sequencing exhibited higher completeness and fewer N bases in high GC genomes, while HiSeq 2000 assemblies exhibited higher N50 values.

For targeted bisulfite sequencing, the comparison of MGISEQ-2000 and NovaSeq6000 found that MGISEQ-2000 demonstrated similar data quality, consistency of methylation levels, comparable analytic sensitivity, and matching clinical performance. This supports the use of MGI platforms for clinical methylation assays.

Long-Read Platforms

PacBio platforms use single molecule real-time sequencing to produce long reads with high accuracy in the HiFi mode. The benchmarking study of sequencing technologies for human genetic variant detection found that PacBio Revio with DeepVariant achieved the highest single nucleotide variant and indel accuracy genome-wide among long-read pipelines.

Oxford Nanopore platforms offer real-time sequencing with portable options like the MinION. The direct RNA sequencing study of Orthoavulavirus javaense demonstrated that Nanopore sequencing can capture and sequence viral RNA directly without reverse transcription and amplification, enabling near full-length viral RNA genome sequencing. The study noted that additional improvements in direct RNA sequencing are needed before widespread adaptation for rapid field sequencing.

For metagenomic diagnosis of lower respiratory tract infections, a comparative meta-analysis found that Illumina consistently produced superior genome coverage and higher per-base accuracy, while Nanopore demonstrated faster turnaround times, greater flexibility in pathogen detection, and superior sensitivity for Mycobacterium species. The average sensitivity was similar for Illumina and Nanopore across studies, but specificity varied substantially for both platforms.

Emerging Platforms

Roche, Ultima, and Element are developing sequencing platforms that may offer cost or performance advantages. Published benchmarking data for these platforms is limited compared to Illumina, MGI, PacBio, and Oxford Nanopore. When evaluating these platforms, request peer-reviewed publications and independent validation studies. The NCBI Literature Resources can help you search for current publications on these platforms.

The Assay Guidance Manual from the National Center for Advancing Translational Sciences provides a framework for assay development and validation that applies to evaluating new sequencing platforms. This manual emphasizes the importance of characterizing assay performance characteristics including accuracy, precision, sensitivity, and specificity before implementation.

Hybrid Approaches

Combining short-read and long-read data can produce results that neither platform achieves alone. A study reporting new hybrid sequencing data for Vreelandella genomes combined PacBio long reads with Illumina short reads to generate circular and complete genomes. The resequencing of these genomes provided updated high-quality datasets for understanding the metabolism of this genus.

For complex plant genomes, the sainfoin reference genome study used PacBio HiFi, Oxford Nanopore, Illumina short read, and Hi-C data to achieve a haplotype-resolved, chromosome-scale assembly. This multi-platform approach resolved all 28 pseudochromosomes with high contiguity and gene completeness.

Hybrid approaches increase cost and complexity but may be necessary for applications requiring complete assemblies or comprehensive variant detection. Consider whether your application truly requires the additional data or whether a single platform can meet your needs.

Observations and Measurements for Platform Validation

Quality Metrics to Track

Establish a standard set of quality metrics for every sequencing run and track these metrics over time. Key metrics include:

• Yield in gigabases or terabases per run
• Q30 score percentage, representing bases with accuracy of 99.9 percent or higher
• Mean coverage depth and uniformity of coverage
• Duplicate rate, representing the proportion of reads that are PCR or optical duplicates
• GC bias, representing the relationship between GC content and coverage
• Error rate, determined by sequencing a known reference sample

The Bioanalytical Method Validation Guidance from the U.S. Food and Drug Administration provides principles for validating analytical methods that apply to sequencing-based assays. This guidance emphasizes the importance of demonstrating accuracy, precision, selectivity, sensitivity, and reproducibility.

Reference Samples

Sequence a well-characterized reference sample on each platform you evaluate. For human sequencing, reference materials with known variant calls are available. For bacterial sequencing, use a type strain with a published reference genome. For metagenomic studies, use a mock community with known composition.

The comparison of sequencing platforms for metagenomic characterization found that library preparation and sequencing platform had systematic effects on the inferred microbial community composition. Using a mock community with known composition allows you to quantify these biases and determine whether they are acceptable for your application.

Reproducibility Testing

Sequence the same sample multiple times on the same platform to assess run-to-run reproducibility. Sequence the same sample on different platforms to assess cross-platform concordance. The whole genome sequencing comparison between NovaSeq 6000, MGISEQ-2000, and DNBSEQ-T7 found high concordance between platforms for germline and somatic variants, supporting the interchangeability of these platforms for clinical applications.

For methylation sequencing, the comparison of MGISEQ-2000 and NovaSeq6000 found that methylation levels measured by MGISEQ-2000 demonstrated high consistency with NovaSeq6000. The study also tested the clinical model performance with reduced sequencing depth and found matching robustness, supporting the use of MGI platforms for non-invasive early cancer detection.

Records and Documentation for Platform Decisions

Instrument Logs

Maintain a log for each sequencing instrument that records run dates, sample identifiers, library preparation methods, reagent lot numbers, quality metrics, and any anomalies or failures. This log becomes essential for troubleshooting and for demonstrating data quality in publications or regulatory submissions.

The Laboratory Quality Management System Handbook from the World Health Organization emphasizes the importance of documentation for laboratory quality assurance. Accurate records allow you to trace any data quality issue back to specific reagents, protocols, or instrument conditions.

Validation Reports

When you validate a new platform for your laboratory, document the validation study design, results, and conclusions. Include the reference samples used, the quality metrics achieved, and the acceptance criteria that were met or not met. This validation report serves as evidence that the platform is suitable for your intended use.

The Bioanalytical Method Validation Guidance from the U.S. Food and Drug Administration provides a framework for validation studies that can be adapted to sequencing platforms. This guidance emphasizes the importance of validating methods under the conditions in which they will be used.

Cost Tracking

Track the actual cost per sample for each platform, including all consumables, labor, and overhead. Compare these actual costs to vendor estimates. Cost tracking over time reveals trends in reagent prices and identifies opportunities for cost reduction through batching or protocol optimization.

Common Failure Patterns in Platform Evaluation

Overvaluing Published Specifications

Vendor specifications describe performance under optimal conditions. Real-world performance depends on sample quality, library preparation, and bioinformatics analysis. A platform that performs well on vendor demonstration data may perform differently on your samples. Always validate with your own samples before committing to a platform.

Ignoring Bioinformatics Compatibility

The sequencing platform is only one part of the workflow. Your bioinformatics pipeline must be able to process the data output. Some platforms produce data formats that require specific analysis tools. The benchmarking study of sequencing technologies for human genetic variant detection found that variant calling pipelines varied in performance across platforms, with different callers performing best for different platforms.

Underestimating Hands-On Time

The cost per sample quoted by vendors often assumes optimal workflow efficiency. Library preparation, quality control, and data analysis require hands-on time that varies by platform and by the experience of the laboratory staff. The comparison of library construction kits for mRNA sequencing found that kits varied in cost, experimental time, and data output, with the choice of kit affecting the quality and quantity of sequencing data.

Focusing Only on Per-Base Cost

Per-base cost is an important metric but does not capture the full picture. A platform with a lower per-base cost may require more coverage to achieve the same accuracy, resulting in a higher cost per sample. The benchmarking study of sequencing technologies for human genetic variant detection found that short-read sequencing required more than 60x coverage to approach the accuracy that long-read sequencing achieved at 20x to 45x coverage.

Neglecting Long-Term Support

Sequencing platforms require ongoing support, including reagent supply, instrument maintenance, and software updates. Consider the vendor's track record of supporting older instruments and the availability of alternative reagent suppliers. A platform that is discontinued or poorly supported can strand your laboratory with unusable data.

Limitations of Current Evidence

Limited Head-to-Head Comparisons

Direct comparisons of all available platforms using the same samples are rare. Most published studies compare two or three platforms, and the results may not generalize to other platforms or applications. The comparative meta-analysis of long-read and short-read sequencing for lower respiratory tract infections found that risk of bias was frequently high or unclear in the included studies, limiting the robustness of pooled estimates.

Rapid Technology Evolution

Sequencing platforms evolve quickly, with new chemistries and instruments released frequently. Published comparisons may describe platforms that have already been superseded. The direct RNA sequencing study of Orthoavulavirus javaense compared two chemistries on the Oxford Nanopore MinION platform and found that the newer chemistry improved read quality and length, illustrating the pace of improvement.

Application-Specific Performance

A platform that performs well for one application may perform poorly for another. The choice of sequencing method has a strong effect on which species are detected in soil microbiome studies, with different methods disagreeing substantially on alpha diversity estimates, rare taxon detection, and the relative abundances of entire phyla. The review of soil microbiome sequencing methods argued that method choice should be framed as an important part of study design, with the biases of the chosen method acknowledged and controlled where possible.

Emerging Platform Data Gaps

For emerging platforms from Roche, Ultima, and Element, published benchmarking data is limited. The NCBI Literature Resources can help you search for current publications, but you may need to rely on vendor-provided data and your own validation studies. The Assay Guidance Manual from the National Center for Advancing Translational Sciences provides a framework for characterizing assay performance that can be applied to new platforms.

Safety and Regulatory Context

Biosafety Considerations

Sequencing workflows involve handling biological samples that may contain pathogens. The Laboratory Biosafety Manual from the World Health Organization provides guidance on biosafety practices for laboratories handling infectious materials. Follow appropriate biosafety practices for sample collection, nucleic acid extraction, and library preparation, including the use of personal protective equipment and biological safety cabinets where indicated.

Data Privacy and Security

Sequencing data from human samples contains sensitive genetic information. Ensure that your laboratory has appropriate data security measures in place, including access controls, encryption, and secure data storage. Follow applicable regulations for the handling of genetic data in your jurisdiction.

Diagnostic Validation Requirements

If you are using sequencing for diagnostic purposes, the platform and assay must meet regulatory requirements for diagnostic tests. The Bioanalytical Method Validation Guidance from the U.S. Food and Drug Administration provides principles for validating analytical methods that apply to sequencing-based diagnostics. The Laboratory Quality Management System Handbook from the World Health Organization provides a framework for laboratory quality assurance that includes validation, quality control, and documentation requirements.

Professional Escalation Criteria

When to Consult a Bioinformatics Specialist

If your bioinformatics pipeline produces unexpected results, such as low mapping rates, high duplicate rates, or unusual variant calls, consult a bioinformatics specialist before making platform decisions. Data analysis issues can sometimes be resolved through pipeline optimization instead of platform changes.

When to Consult a Vendor Application Specialist

If you encounter persistent quality issues with a platform, such as low yield, high error rates, or run failures, consult the vendor's application specialist. Document the issues with specific quality metrics and run logs to facilitate troubleshooting.

When to Consult a Regulatory Specialist

If you are developing a sequencing-based diagnostic test, consult a regulatory specialist early in the platform evaluation process. Regulatory requirements may influence platform choice, validation study design, and documentation requirements.

When to Escalate to Laboratory Management

If platform evaluation reveals significant cost overruns, quality issues, or workflow inefficiencies, escalate these findings to laboratory management. Platform decisions have long-term financial and operational implications that require management input.

Frequently Asked Questions

How do I compare sequencing platforms when vendor specifications differ in what they measure?

Request raw data from each vendor and analyze it yourself using your own quality metrics and bioinformatics pipeline. Vendor specifications may use different definitions of accuracy, yield, or quality scores. Standardize your evaluation by applying the same analysis to data from all platforms.

What is the minimum coverage depth needed for reliable variant detection?

Coverage requirements depend on the application and the platform. A benchmarking study of human genetic variant detection found that long-read sequencing reached accuracy saturation between 20x and 45x coverage, while short-read sequencing required more than 60x coverage to approach comparable accuracy for some variant classes. For your specific application, determine coverage requirements through validation studies using reference samples with known variants.

Can I use the same bioinformatics pipeline for data from different sequencing platforms?

Some analysis tools are platform-specific, while others can process data from multiple platforms. The benchmarking study of human genetic variant detection found that different variant callers performed best for different platforms. Validate your pipeline with data from each platform you are considering.

How do emerging platforms from Roche, Ultima, and Element compare to established platforms?

Published benchmarking data for these platforms is limited. Request demonstration data and peer-reviewed publications from the vendors, and run your own pilot studies if possible. The NCBI Literature Resources can help you search for current publications on these platforms.

What is the role of library preparation in platform comparison?

Library preparation can introduce bias that affects sequencing results. A study of metagenomic microbiome characterization found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. A comparison of mRNA sequencing library kits found that the quality and quantity of sequencing data were strongly influenced by the type of library kit used. Include library preparation in your platform evaluation.

How do I determine whether I need long-read or short-read sequencing?

Consider the genomic features you need to resolve. Short reads are sufficient for single nucleotide variants and small indels in unique regions. Long reads are needed for structural variants, repetitive regions, and full-length transcript isoforms. A benchmarking study of human genetic variant detection found that short-read sequencing showed reduced sensitivity for structural variant detection, while long-read platforms demonstrated clear advantages in detecting structural variants.

What quality metrics should I track for ongoing platform monitoring?

Track yield, Q30 score percentage, mean coverage depth, coverage uniformity, duplicate rate, GC bias, and error rate for every sequencing run. Maintain instrument logs that record run dates, sample identifiers, reagent lot numbers, and any anomalies. The Laboratory Quality Management System Handbook from the World Health Organization provides a framework for quality assurance that applies to sequencing workflows.

How do I validate a sequencing platform for clinical diagnostic use?

Follow the principles in the Bioanalytical Method Validation Guidance from the U.S. Food and Drug Administration, which emphasizes demonstrating accuracy, precision, selectivity, sensitivity, and reproducibility. Use reference samples with known variants, test reproducibility across runs, and document all validation results. Consult a regulatory specialist for requirements specific to your jurisdiction and application.

Related Diagnostic Guides

• High-Throughput Multiplexed Serological Assays for Emerging Zoonotic Viruses in Livestock Using Bead-Based Flow Cytometric Technology
• Genomic Sequencing Technologies in Veterinary Medicine
• High-Throughput Multiplex RT-qPCR Panel for Differential Detection of Emerging Porcine Coronaviruses in Oral Fluids and Fecal Samples
• CRISPR-Cas13-Based Direct Detection of Porcine Reproductive and Respiratory Syndrome Virus in Oral Fluids: A Field-Deployable Molecular Platform
• Metagenomic Sequencing for Aquatic Viral Pathogens

References and Further Reading

• Laboratory Quality Management System Handbook. World Health Organization.
• Laboratory Biosafety Manual. World Health Organization.
• Assay Guidance Manual. National Center for Advancing Translational Sciences.
• Bioanalytical Method Validation Guidance. U.S. Food and Drug Administration.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Sequencing technologies and genome sequencing.. Journal of applied genetics, 2011.
• A single-cell survey of cellular hierarchy in acute myeloid leukemia.. Journal of hematology & oncology, 2020.
• Comparison of library construction kits for mRNA sequencing in the Illumina platform.. Genes & genomics, 2019.
• Comparison between MGI and Illumina sequencing platforms for whole genome sequencing.. Genes & genomics, 2021.
• Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes.. Microbiology spectrum, 2022.
• Cross-platform comparisons for targeted bisulfite sequencing of MGISEQ-2000 and NovaSeq6000.. Clinical epigenetics, 2023.
• Comparison of direct RNA sequencing of Orthoavulavirus javaense using two different chemistries on the MinION platform.. Journal of virological methods, 2025.
• Comparison of the DNBSEQ platform and Illumina HiSeq 2000 for bacterial genome assembly.. Scientific reports, 2024.
• New hybrid sequencing data of &lt,i&gt,Vreelandella sulfidaeris&lt,/i&gt, DSM 15722&lt,sup&gt,T&lt,/sup&gt, and &lt,i&gt,Vreelandella aquamarina&lt,/i&gt, DSM 15723 provide circular and complete genomes.. 2026.
• A chromosome-scale reference genome and integrative transcriptome provide insight into tissue- and stress-specific responses in tetraploid sainfoin (Onobrychis viciifolia).. 2026.
• Choosing Between Short-Read 16S, Full-Length ONT 16S, and Long-Read Shotgun Metagenomics for Soil Microbiome Studies: A Critical Review of the Benchmarking Evidence.. 2026.
• Benchmarking of sequencing technologies defines optimal strategies for genetic variants detection in a human genome.. 2026.
• Comparative Meta-Analysis of Long-Read and Short-Read Sequencing for Metagenomic Profiling of the Lower Respiratory Tract Infections.. 2025.
• Long-read sequencing transcriptome quantification with lr-kallisto.. 2025.
• Comparative Evaluation of Sequencing Technologies for Detecting Antimicrobial Resistance in Bloodstream Infections.. 2025.
• Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics. BMC Genomics, 2023.
• A Study of Next Generation Sequencing Data, Workflow, Application and Platform Comparison. Iop Conference Series Materials Science and Engineering, 2018.
• Performance comparison between rapid sequencing platforms for ultra-low coverage sequencing strategy. Plos One, 2014.
• Comparison of capture-based mtDNA sequencing performance between MGI and illumina sequencing platforms in various sample types. BMC Genomics, 2024.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.