• There is a growing backlog of sequence data resulting from the large amount of time, computing power, and expertise required to complete the data processing.
  • At LC Sciences, we have rapid, standardized workflows in place for filtering, normalizing, alignment and statistical analysis of these large, often complex data sets.
  • LC Sciences’ decade of experience in transcriptomics analysis empowers our bioinformatics services capabilities and ensures fast and reliable results.

  1. Small RNA Sequencing Data Analysis
  2. Degradome Sequencing Data Analysis
  3. Digital Gene Expression Sequencing Data Analysis
  4. RNA-Seq (mRNA) Data Analysis
  5. Total RNA-Seq (Whole Transcriptome) Sequencing Data Analysis

Next-Gen DNA Sequencer

Next-Gen DNA Sequencer

Next-Gen DNA Sequencer

Next-Gen DNA Sequencer

We can deliver fully analyzed datasets in 3-5 business days.

Your data can be transferred to us via a secure ftp server.

  1. Alignment, classification, & functional annotation of all mapped reads
  2. Biostatistical analysis – expression analysis, multi-parameter data analysis, length distribution, transcript copy number comparisons, etc
  3. Prediction of novel transcripts
  4. GO and KEGG annotation and enrichment analysis
  5. Statistics and annotation of SNPs/Indels
  6. Alternative splicing analysis

Sample Data

  1. Alignment, classification, & functional annotation of all mapped reads
  2. Biostatistical analysis – expression analysis, multi-parameter data analysis, length distribution, transcript copy number comparisons, etc
  3. Prediction of novel transcripts
  4. GO and KEGG annotation and enrichment analysis
  5. Statistics and annotation of SNPs/Indels
  6. Alternative splicing analysis

Sample Data

  1. Alignment, classification, & functional annotation of all mapped reads
  2. Biostatistical analysis – expression analysis, multi-parameter data analysis, length distribution, transcript copy number comparisons, etc
  3. Conservation profile of the identified miRNAs analysis
  4. Pre-miRNA clusters
  5. miRNA target GO and KEGG enrichment analysis
  6. Optional miRNA target prediction

Sample Data

  1. Overview of sequencing reads
  2. Statistics of miRNA targets
  3. Statistics of mRNA targeted by miRNA
  4. T-plots and density of degradome
  5. GO and KEGG functional analysis
  6. Clusters of orthologous groups (COG) enrichment analysis

Sample Data

  1. Complete mRNA analysis – see Poly(A) RNA-seq
  2. lncRNA profiling – expression analysis, length distribution, etc
  3. Candidate novel lncRNAs
  4. Statistics of different types of class codes (j, i, o, u, x)
  5. Statistics of different CPC (coding potential caculator) and CNCI (coding non-coding index) scores
  6. Localization of lncRNAs on genome
  7. Basic property comparison of lncRNA and mRNA
  8. Differential expression of lncRNAs
  9. Target gene prediction and functional analysis of lncRNAs

Sample Data

  1. Complete mRNA analysis – see Poly(A) RNA-seq
  2. Complete lncRNA analysis – see lncRNA-seq
  3. circRNA profiling – QC, mapping statistics, etc
  4. Localization of circRNAs on genome
  5. Statistics of different circRNA types
  6. Differential expression of circRNAs
  7. Interaction with  miRNA and functional analysis of circRNAs

Sample Data

Additional Custom Bioinformatics Analysis – If you have additional, more complex bioinformatics needs such as functional gene information mining or comparative genomic analysis, please contact us to learn how we can help get you the results you need to keep your research moving ahead