超多重PCR設計
Ultra-multiplex PCR Primer Sets Design
透過強大的生物資訊計算，設計標靶次世代定序技術(targeted Next Generation Sequencing, tNGS) 使用超多重PCR(Ultra-multiplex PCR)來進行檢測，配合各種定序平台，用於一次檢測數百乃至上千個的目標序列(Target regions)。 在有限的檢體量實驗成本下，增加樣本中目標序列資訊、提高專一性及檢出靈敏度，大幅減少定序數據量，從而實現性能及成本的雙重優化。
收集物種參考基因組
（refseq/nr/CARD等資料庫）
引物的飽和設計
每個參考基因組全基因組進行引物的飽和設計
設計引物篩選
引物長度、Tm值、GC含量、引物間3‘端互補情況等
擴增產物篩選
擴增子長度、位置分佈、Tm值、GC含量、序列特異性、株型覆蓋度等
Panel測試
二聚體測試、非特異性擴增測試、靈敏度測試等
康健客製化 超多重PCR
| 常規PCR | 多重PCR | 超多重PCR |
|---|---|---|
| 待擴增樣品模版（DNA/RNA） | 待擴增樣品模版（DNA/RNA） | 待擴增樣品模版（DNA/RNA） |
| 一對特異性引物（Primers） | 多對特異性引物（Primers） | 成百上千對特異性引物（Primers） |
| DNA聚合酶 (polymerase) | DNA聚合酶 (polymerase) | DNA聚合酶 (polymerase) |
| 擴增底物dNTPs | 擴增底物dNTPs | 擴增底物dNTPs |
| 含有Mg2+的反應緩衝液 | 含有Mg2+的反應緩衝液 | 含有Mg2+的反應緩衝液 |
| 1種 目標片段產物 |
多種 目標片段產物 |
成百上千種 目標片段產物 |
| 跑膠/一代定序/QPCR | 多重QPCR/飛行質譜 | 高通量定序 |
生物資訊分析
| Application | Level | Item |
|---|---|---|
| Metagenome 16S | A | Data QC and aligned(OTU to species level) |
| B | Alpha-diversity Heatmap Beta-diversity |
|
| C | (With sample grouping information) Significant difference analysis LEfSe Network of co-occurrence and counteraction Pathway enrichment |
|
| Metagenome shotgun | A | 1. Data QC and MAG assembly 2. MAG annotation and abundance calculation |
| B | Alpha-diversity Heatmap Beta-diversity |
|
| C | (With sample grouping information) Significant difference analysis LEfSe Network of co-occurrence and counteraction Pathway enrichment |
|
| RNA-Seq (Quantification) | A | 1 Data filtering includes removing adaptors, contamination and low-quality reads from raw reads; 2 Assessment of sequencing (Statistics of raw reads, Sequencing saturation analysis, Analysis of the distribution of reads on reference gene); 3 Gene expression analysis 4 Differential gene expression analysis (two or more samples should be provided); 5 Gene ontology enrichment analysis of DEGs;; 6 Pathway enrichment analysis of DEGs; |
| B | Protein-protein interaction network analysis |
|
| Eukaryotic Small RNA sequencing |
A | 1 Remove adaptors, low quality tags as well as contaminants to get clean reads; 2 Summarize the length distribution of small RNA; 3 Identify rRNAs, tRNAs, snRNAs, etc. by aligning to Rfam and Genbank databases; 4 Identify known miRNAs by aligning to designated part of miRBase Annotate small RNAs into several categories based on priority; 5 Expression analysis of known miRNAs 6 Differential expression analysis of known miRNA (two or more samples should be provided) |
| B | 1 Predict novel miRNAs and their secondary structures 2 Target genes prediction of novel known miRNA and miRNA; 3 GO annotation and KEGG pathway analysis of known miRNA and novel miRNA Target genes; 4 Differential expression analysis of novel miRNA (two or more samples should be provided) 5 Target genes prediction of differential miRNA (the sequence of coding genes should be provided, two or more samples should be provided); 6 GO annotation and KEGG pathway analysis of differential miRNA Target genes (two or more samples should be provided) |
|
| Human whole genome resequencing | A | 1 Data filtering (removing adaptors, contamination, and low-quality reads from raw reads) 2 Assessment of sequencing quality, including data production statistics, sequencing depth distribution and coverage uniformity 3 Align reads to the human reference genome 4 SNP calling 5 SNP annotation (annotate each SNP with dbSNP database, 1000 Genomes Project database, ClinVar, etc.) 6 InDel calling 7 InDel annotation (annotate each InDel with dbSNP database, 1000 Genomes Project database, etc) |
| Whole Exome Sequencing (Human) |
A | 1 Data filtering (removing adaptors, contamination, and low-quality reads from raw reads) 2 Assessment of sequencing quality, including data production statistics, sequencing depth distribution and coverage uniformity 3 Align reads to the human reference genome 4 SNP calling 5 SNP annotation (annotate each SNP with dbSNP database, 1000 Genomes Project database, ClinVar, etc.) 6 InDel calling 7 InDel annotation (annotate each InDel with dbSNP database, 1000 Genomes Project database, ClinVar, etc) |
| Genome de novo assembly Animal Genome |
A | Standard Bioinformatics Analysis 1 Data filtering 1.1 Filtering adapters and low quality data; 1.2 Data production and quality control. 2 Genome assembly 2.1 Assembly 2.2 Analysis of GC-Depth distribution 2.3 Analysis of GC-Content distribution 2.4 Analysis of sequence depth 3 Annotation 3.1 Repeat annotation 3.2 Gene prediction (ORF prediction) 3.3 Gene function Annotation 3.4 Non-coding RNA annotation (tRNA, snoRNA, etc) |
| Bacterial re-sequencing |
A | 1 Data Statistics 1.1 Filtering adapters and low quality data; 1.2 Data production and quality control. 2 Summary of assembly 3 Genome Component 3.1 Gene component; 3.2 Repeat Sequence; 3.3 Non-codingRNA; 4 Gene function 4.1 Gene functional annotation (GO, KEGG, COG, etc); 4.2 Pathway annotation |
| B | 1 Comparative genomic analysis 1.1 Structural Variation (Synteny analysis); 1.2 Evolution analysis |
|
| Bacterial sequencing (single strain) |
A | 1 Data Statistics 1.1 Filtering adapters and low quality data; 1.2 Data production and quality control. 2 Summary of assembly 3 Genome Component 3.1 Gene component; 3.2 Repeat Sequence; 3.3 Non-codingRNA; 4 Gene function 4.1 Gene functional annotation (GO, KEGG, COG, etc); 4.2 Pathway annotation |
| B | 1 Comparative genomic analysis 1.1 Structural Variation (Synteny analysis); 1.2 Evolution analysis |
科研分析的範例報告
16S Anaysis
GenomeAssembly_nanopore Analysis
dRNA_ Nanopore analysis