**High-throughput sequencing**
The sequencing protocol was based on the dideoxy sequencing method, first described by Sanger et al. in 1977. To ensure that each clone insert could be sequenced from both ends, every plasmid template DNA plate required two 384-well cycle sequencing plates. The sequencing reaction used Big Dye Terminator chemistry version 3.1 (Applied Biosystems), along with standard M13 or commonly used forward and reverse primers. This process was automated using a Biomek FX pipetting station, which handled the precise aliquoting of the template sample and mixing it with the reaction solution. The reaction mixture included dideoxynucleotides, fluorescently labeled nucleotides, Taq DNA polymerase, sequence primers, and a buffer to support the reaction.
Each template and plate was equipped with barcodes, allowing for accurate tracking via the Biomek FX system to prevent any errors during sample and reaction transfer. The amplification step, typically ranging from 30 to 40 cycles, was carried out using MJ Research Tetrads or the 9700 Thermal Cycler (Applied Biosystems). After the reaction, the product was efficiently precipitated at room temperature using isopropanol, and then stored or resuspended in water at 4°C. If the sequencing instrument was functioning properly, a sample film would be automatically generated after scanning the barcode of the reaction plate. The plate was then transferred to an ABIPrism 3700 or Applied Biosystems 3730xi DNA Analyzer for electrophoresis. These systems can run up to 8 runs per day on the ABIPrism 3700 and 12 on the 3730xi, with a setup time of less than one hour.
High-throughput sequencing systems, which process large volumes of data in parallel, typically require automated management through a Laboratory Information Management System (LIMS). At TIGR, this system includes a comprehensive suite of software tools, from early-stage sequencing to end-of-sequence tracking of library construction. Once processed, the data is stored in a Sybase relational database, linking all information collected throughout the genome sequencing process. This allows users to trace back the data stream in multiple ways, from annotated genes to original sequencing trace files. The system also features client/server applications for sample management, data entry, library handling, and sequence processing. Over time, it has evolved and stabilized through continuous improvements, incorporating new laboratory methods, instruments, and software. These integrated tools include automated vector removal, identification and masking of repetitive elements, detection of contaminated clones, and tracking of template information.
A user-friendly interface enables daily monitoring of template and sequence quality, ensuring that any issues are quickly identified and resolved. Quality control (QC) and quality assessment (QA) teams work together to apply consistent standards, inspect reagents, and monitor template quality, as well as detect deviations from normal performance ranges. They also oversee data quality, conduct audits, identify areas for improvement, and develop standard operating procedures to ensure consistency and technical accuracy.
**Top 5 Challenges for High-throughput Sequencing**
As a rapidly advancing technology in the medical field, gene sequencing has gained significant recognition in clinical practice in recent years and is now being applied across various domains. Especially since the concept of precision medicine emerged, gene sequencing has become a key tool, helping to solve many previously unknown challenges in personalized treatment.
Today, the gene sequencing industry has reached a certain level of maturity, with numerous companies entering the market in different forms. However, behind the rapid growth lies a number of unresolved technical challenges. In an article published by *GEN*, Dr. Shawn C. Baker highlighted the difficulties and obstacles facing the field. The article was compiled by the AIHealth column of Leifeng Network.
Over the past decade, high-throughput sequencing technology has made remarkable progress, with a dramatic increase in sequencing capacity and a significant drop in cost—often by several orders of magnitude. There are now over 10,000 sequencing instruments worldwide.
Major platform companies have focused on improving the usability of their systems. For example, Illumina's latest desktop systems, such as the NextSeq, MiSeq, and MiniSeq, use preloaded kits to reduce manual operations and boot-up times. While Illumina's systems have influenced the design of other platforms, like the Ion Torrent, the latter’s latest system, the Ion S5, aims to simplify the entire workflow—from library preparation to data generation.
Despite these advancements, some may mistakenly believe that all challenges in gene sequencing have been solved. However, the real difficulties are just beginning, and many challenges remain ahead.
**Sample Quality**
One of the most critical and often overlooked aspects is sample quality. Even though test platforms are usually calibrated, real-world samples often face unexpected challenges. One of the most commonly used sample types in human gene sequencing is FFPE (formalin-fixed paraffin-embedded). Their widespread use is due to their abundance—estimated to be over 10 billion globally. FFPE samples are widely used in clinical settings and will continue to grow in number.
In addition to their broad application, FFPE samples often contain rich phenotypic information, such as treatment history and clinical data. However, the fixation and storage processes can cause significant DNA damage. Dr. Hans G. Thormar, CEO of BioCule, noted that after evaluating more than 1,000 samples on their QC platform, they observed a wide range of DNA damage, including cross-linking, single-stranded breaks, and polymerization. Such damage, if not addressed, can negatively impact downstream applications, leading to failed library construction or inaccurate results. Therefore, assessing sample quality at the start of a project is crucial.
**Sequencing Library**
While major sequencing companies have reduced the cost of raw sequence generation, building sequencing libraries remains expensive. For human gene sequencing, library construction costs around $50 per sample, which is relatively small compared to overall costs. However, in other applications like bacterial genome sequencing or low-depth RNA sequencing, library costs can make up a large portion of the total budget.
Some groups have explored in-house solutions to reduce costs, but commercial development has been limited. One notable advancement is the 10X Genomics Chromiumâ„¢ system, which allows hundreds to tens of thousands of samples to be processed in parallel using a bead-based approach. Dr. Serge Saxonov, CEO of 10X Genomics, believes that single-cell RNA sequencing is the future of gene expression analysis and that his platform will lead the way in this area.
**Long Reads vs. Short Reads**
Illumina’s dominance in the sequencing market means most data comes from short reads. While short reads are ideal for detecting SNPs and counting RNA transcripts, they fall short in complex regions like highly repetitive sequences or long-chain structures. Long-read platforms like Pacific Biosciences’ RSII and Sequel, or Oxford Nanopore’s MinION, can produce reads up to 100 kb, offering better resolution for challenging genomic regions.
Dr. Charles Gasser, a professor at UC Davis, praised the success of long-read assembly, especially when combined with high-fidelity short reads. This hybrid approach allows smaller labs to generate usable assemblies from new genomes. Preparing DNA for long reads requires new methods, as traditional techniques aren’t optimized for ultralong fragments. Suppliers now offer specialized kits to isolate DNA fragments over 100 kb, which must be mastered to maximize long-read yield.
An alternative to true long reads is linked reads, such as those used by 10X Genomics. Each long DNA segment contains a unique barcode for each short read, enabling reconstruction of long haplotype blocks. While short reads are fast and accurate, they capture only a fraction of the genetic information, which is mostly encoded in long chains.
**Data Analysis**
Handling the massive data generated by sequencing is a major challenge. A single 30X human genome sample can result in a BAM file of about 90 GB, while a medium-sized project with 100 samples could reach 9 TB. An Illumina HiSeq X can generate over 130 TB of data annually, making storage a pressing issue.
BAM files can be converted into VCF files, which contain only variant information. While VCF files are smaller and easier to work with, the original BAM files must still be retained for future reference. With over 3,000 analysis tools available, researchers face the challenge of selecting the best option for their specific needs.
**Clinical Interpretation and Reimbursement**
Interpreting sequencing variations for clinical use remains a significant hurdle. A single exon can contain 10,000–20,000 mutations, and whole-genome samples can produce over 3 million. Classification systems, such as those developed by the American Society of Medical Genetics and Genomics, help categorize variants, but inconsistencies persist across laboratories.
Reimbursement for NGS-based testing is also problematic. While some labs provide interpretation services, they are rarely reimbursed by insurance. Dr. Jennifer Friedman from Rady Children’s Genome Medicine Institute emphasized that interpreting results is valuable but currently unsupported financially. As a result, patient sample analysis is often treated as a research project rather than a clinical service.
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