LncRNA sequencing is a high-throughput technique used to identify and quantify long non-coding RNAs, which are crucial for regulating gene expression but do not encode proteins. This method is pivotal in uncovering the roles of lncRNAs in various diseases, such as cancer and neurological disorders, facilitating the discovery of new therapeutic targets and biomarkers.

Work flow

Technical Parameters

Sequencing range	lncRNA+circRNA+mRNA+miRNA
Sequencing strategy	NGS PE150
Sequencing throughput	12Gb
Data quality	Fastq files, Q30≥85%
Data analysis	Standard analysis
TAT	Standard: 35 WD

Applications

Gene Regulation Studies	Cancer ResearchVariants	Drug Development
Disease Mechanism Research	Biomarker Discovery	Comparative Genomics and Evolutionary Studies

Bioinformatics Analysis

1.Data Quality Control: Removal of adapter contamination sequences and low-quality reads from the raw data.
2.Reference Genome Alignment: Statistical results of alignments, display of alignment region distribution, display of reads distribution on chromosomes.
3.Transcript Assembly and Quantitative Analysis: Transcript assembly and statistics, expression quantification (FPKM), comparison of expression levels between groups, sample correlation analysis.
4.lncRNA Screening and Identification: Statistics of lncRNA screening, classification statistics of lncRNAs.
5.Comparison of lncRNA and mRNA Features: Analysis of transcript length, number of exons, ORF length, and expression level comparisons.
6.Differential Expression Analysis: Statistics of differentially expressed transcripts, volcano plots, cluster heatmaps, and Venn diagrams for differentially expressed lncRNAs and mRNAs.
7.lncRNA Target Gene Prediction Analysis: Prediction and statistics of cis and trans target genes of lncRNAs.
8.Functional Enrichment Analysis: GO and KEGG enrichment analysis of target genes of lncRNAs and corresponding genes of mRNAs.
9.Differential Gene Protein Interaction Network Analysis.
10.Differential Alternative Splicing Analysis and Statistics.
11.Fusion Gene Analysis: Detection and annotation statistics of fusion genes, display of fusion gene chromosomal distribution.
12.Variant Detection and Statistics.

Advantages

High Sensitivity and Specificity
Comprehensive Coverage
Scalability and Flexibility