RNA-seq analysis - Quality Control

Quality control of RNA-seq data is essential

The basics of quality control

As any technology, RNA-seq has its limitations. Those limitations arise because of biases in nucleotide composition, library preparation issues, PCR biases - all of which influence the analysis. Before any analysis or sequence alignment is done, we perform a read-level analysis of your data. We examine the distribution of quality scores along the sequences, the duplication rate (i.e. how many sequences are identical), nucleotide composition biases, and %GC content. We also deliver post-alignment quality metrics, for example number of aligned reads, uniquely aligned reads, reads mapping in specific genomic elements (such as ribosomal RNA genes) as well as the coverage along the gene body.

Why quality control is important

The biological accuracy of the data is our focus. We want to ensure the success and reliability of the data for our customers. There is no precise reference for what passes as good quality, because different assays have different requirements. We have broad experience from many sequencing platforms and protocols. We also give quality assessment based on biological and technical replicates. Technical replicates are useful for assessing background noise, differences in sequencing chemistry as well as batch effects.

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Transcriptomics (e.g. RNA-seq, microarray)
Epigenomics (e.g. ChIP-seq)
Genomics (e.g. genome assembly and annotation)
Metagenomics (e.g. 16S rRNA)
Proteomics (e.g. Mass spectrometry)

Bioinformatics/Big data consulting
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