Real – time quantitative polymerase chain reaction (qPCR) is a highly powerful molecular biology technique with the following advantages and disadvantages:

Detection of Low – Copy – Number Genes
qPCR is capable of detecting nucleic acids at extremely low levels. For instance, in the early stages of a viral infection, when the number of virus copies in the body is very low, it can accurately detect the presence of viral genes. In the early diagnosis of some diseases, such as the early screening of COVID – 19, this high sensitivity helps to detect the pathogen in a timely manner, thus winning precious time for the treatment of patients.
Detection of Trace Samples
It can be used to test very small amounts of samples. For example, in forensic identification, from trace biological samples (such as hair, bloodstains, etc.) obtained from a crime scene, even if the DNA content in the sample is extremely low, qPCR may still obtain sufficient information for identification.

Design of Primers and Probes
By carefully designing primers and probes (when using the fluorescent probe method), qPCR can specifically recognize the target gene sequence. For example, in the complex genomic background, it can accurately distinguish and amplify specific disease – causing genes. When detecting genes related to certain monogenic genetic diseases, it only targets the mutated disease – causing genes for detection without being interfered by other normal gene sequences.
Reduction of False – Positive Results
Compared with traditional PCR techniques, the specificity of qPCR helps to reduce false – positive results caused by non – specific amplification products. Especially when using the fluorescent probe method, such as the TaqMan probe, its specific fluorescent signal generation mechanism ensures that fluorescence is only generated when the target gene is amplified, thereby improving the accuracy of the detection.

Real – Time Monitoring and Quantitative Analysis
qPCR can monitor the change of fluorescent signals in real – time during the PCR reaction. Through methods such as the standard curve method or the comparative Ct value method (e.g., the 2 – ΔΔCt method), accurate quantification of the target gene can be achieved. In gene expression research, it can precisely measure the differences in gene expression levels among different samples or under different conditions. For example, in the study of the effect of drugs on gene expression in cells, it can accurately determine whether the gene expression is up – regulated or down – regulated and the degree of change.
Wide Dynamic Range
It can detect changes in the target gene over a wide concentration range. For example, when detecting the microbial load, whether it is a high – concentration bacterial or viral infection or a low – concentration latent infection, qPCR can provide reliable quantitative results, and its dynamic range can span several orders of magnitude.

Short Experimental Cycle
Compared with traditional gene detection methods, such as gene cloning and sequencing, the experimental cycle of qPCR is significantly shortened. Generally, a complete qPCR experiment can be completed within a few hours, while traditional methods may take days or even weeks. For example, in the detection of infectious disease pathogens, rapid test results can be obtained, enabling timely prevention and control measures.
High – Throughput Detection Capability
It can simultaneously detect multiple samples and multiple target genes. Modern qPCR instruments are usually equipped with multiple reaction wells, allowing a large number of samples to be run in a single experiment. In large – scale gene screening projects, such as the detection of multiple oncogenes in a large number of tumor samples, this high – throughput feature can greatly improve the detection efficiency.

Expensive Instrumentation
The real – time quantitative PCR instrument is a high – precision device, and its price is usually high. Its complex optical system, precise temperature control module, and advanced data – processing software all increase the cost of the instrument. For some small laboratories or researchers with limited research funds, purchasing and maintaining such an instrument may bring financial pressure.
High – Cost Reagents
The reagents used in qPCR, especially fluorescent – labeled primers, probes, and high – quality enzymes, are relatively expensive. Moreover, in order to ensure the accuracy of the results during quantitative analysis, standardized reagents and standards are usually required, which further increases the experimental cost. For example, in some long – term gene expression research projects, the frequent use of these reagents will significantly increase the experimental costs.

High Requirements for Sample Quality and Processing
The quality of the sample has a great impact on the qPCR results. Factors such as the purity, integrity, and concentration of nucleic acids in the sample need to be strictly controlled. For example, when extracting RNA, if the sample is contaminated by RNase, it will lead to RNA degradation, affecting the test results. And different types of samples (such as tissues, blood, cells, etc.) require different processing methods, and the processing process is relatively complex.
Prone to Contamination during the Experiment
Since qPCR is a highly sensitive technique, the experimental process is easily contaminated by external DNA or RNA. Even a very small amount of contaminating nucleic acid may produce false – positive results during the amplification process. For example, in the laboratory environment, nucleic acid particles in aerosols may enter the reaction system. Therefore, strict laboratory operation procedures and a clean experimental environment are required to avoid contamination.

Professional Knowledge Required for Data Interpretation
The data generated by qPCR involves multiple parameters such as fluorescent signal intensity, Ct value, and standard curve. The interpretation of these data requires certain knowledge of molecular biology and statistics. For example, when determining whether the difference in gene expression is statistically significant, appropriate statistical analysis such as t – test or analysis of variance is required. For those unfamiliar with this knowledge, data interpretation may be difficult.
Complex Quantification Methods and Models
In addition to simple relative and absolute quantification methods, in some special research scenarios, such as the study of multi – gene interactions, more complex quantification methods and mathematical models may be required. The application of these methods and models requires in – depth understanding and practical experience; otherwise, incorrect data analysis results are likely to occur.