Yes. If customers require greater coverage depth for hemoglobin-encoding genes or DMD gene, the probes for the corresponding genes in NBGS Premium Panel v1.0 can be upgraded to the more comprehensive panels in the HGBP Panel v1.0 (Cat #1001961/1001962) and DMD Research Panel v1.0 (Cat #1001891/1001892) to achieve more complete target coverage.

Yes. The NBGS series offers four standard versions—NBGS Mini Panel (39 genes), NBGS Core Panel (83 genes), NBGS Plus Panel (153 genes), and NBGS Premium Panel v1.0 (282 genes)—which can be further personalized according to client requirements. Customization will be based on evaluation of target regions using authoritative variant databases and supplemented with probes at appropriate loci to ensure scientific validity and accuracy of coverage.

Yes. The NBGS bioinformatics software can be tailored to customer-specific panels; clients should provide sequencing data for the customized panel to rebuild analysis baselines.

Yes, the DMD Research Panel v1.0 supports mixed usage with the NEXome-series Panels. The CDS (coding sequence) probes of the DMD Research Panel v1.0 can be separated to avoid overlap with the DMD gene exon probes in the whole-exome panel, thereby eliminating data redundancy.

Fusion genes serve as critical molecular markers for the diagnosis and treatment of hematologic malignancies, making their accurate detection essential. The DNA workflow detects gene fusions by designing probes covering intronic regions; however, due to the large size and high repetitiveness of introns, relying solely on DNA detection poses challenges such as high cost and incomplete coverage, and it cannot confirm the functional impact of fusion events. In contrast, the RNA workflow directly focuses on the transcript level, enabling precise identification of actual fusion events and their partner genes while assessing whether these fusions lead to aberrant transcription. Consequently, the combined use of DNA and RNA workflows provides a “dual safeguard” for fusion gene detection, enhancing both the comprehensiveness and accuracy of the results.

Total RNA-seq requires rRNA depletion and individual reactions per sample, increasing both complexity and cost. Moreover, total RNA-seq exhibits relatively lower sensitivity, particularly in detecting low-frequency fusion events in low-expression samples. In contrast, the DNA panel in NanoHema Panel v2.0 already covers the intronic regions of key genes, allowing users to flexibly add custom intronic regions based on requirements and budget without the need for a separate RNA panel.

NanoHema Panel v2.0 is a comprehensive large panel that covers a wide range of hematologic malignancy-associated gene variants. To meet diverse clinical needs, the panel offers multiple customization options:

•  It can be flexibly split into multiple sub-panels (e.g., for acute myeloid leukemia [AML], lymphoblastic leukemia, T-cell lymphoma, and B-cell lymphoma).

•  Additional target genes can be incorporated based on specific research or diagnostic requirements, enabling the creation of a personalized detection solution.

• Support. Different library preparation approaches can be employed for RNA and DNA viruses. One approach involves the concurrent library preparation of RNA and DNA viruses, followed by subsequent hybridization capture steps. Another approach entails preparing RNA libraries and DNA libraries separately, and then mixing them for hybridization capture. Generally, for RNA libraries, there is no need to perform the step of removing of host ribosomal RNA.
• As shown in Figure, the example demonstrates the capture scenario when RNA libraries containing influenza virus (H10N3) and DNA libraries are hybridized together. A total of 15 libraries are involved, including 11 DNA libraries and 4 RNA libraries. The example library data amount was 100 Mb (totaling 9.3 Gb for all 15 libraries), with host sequences accounting for 58.3%.

Due to significant variations in pathogen content among different samples, if the aim is to achieve similar data amounts for each sample, it’s not feasible to perform multiple-plex hybridization; separate hybridization must be carried out instead. However, as shown in Figure 5, when there are orders of magnitude differences in microbial content among samples, although there are substantial variations in data amounts, low-abundance samples require relatively less data on their own. Moreover, different samples, using the same experimental parameters, exhibit similar PCR duplication rates. If they are subjected to separate hybrid capture and provided with the same sequencing data, low-abundance samples would need an increased number of PCR cycles. The identical information content would only be repeated to form an augmented sequencing data, as post-panel capture sequencing becomes easier to saturate. Based on these considerations, for samples with similar origins, performing the multiple-plex hybridization process is recommended. 

• As shown in Figure 4, the detection limit for samples with varying microbial content is approximately 3 copies. In practical applications, sensitivity is mainly determined by the amount of input for library preparation. It's important to note that the library preparation process is constrained by conversion efficiency, meaning not all copies can be successfully converted.  Similarly, when the copy number is in the single digits, the success rate of multiplex amplicons'  amplification decreases.
• Furthermore, having less than 1 copy does not imply that it cannot be detected. This is due to the fact that when NEX-t Panel v1.0 features multiple probe capture regions for a single species, their copy numbers can accumulate, enhancing the detection sensitivity.