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Interpreting FastQC results

how to interpret fastqc results
Although warnings or failures in NGS data analysis should be interpreted with caution, they may not necessarily have a detrimental impact on the overall analysis

In fact, certain warnings might even be anticipated in specific scenarios. For instance, when examining Sequence Duplication Levels, a low number of duplicated sequences is generally expected in genomics studies, but in transcriptomics, a higher number of duplicated sequences is often observed due to the nature of the RNA samples.

Another aspect to consider is over-represented sequences, which is particularly relevant in small RNA libraries where sequences are not randomly fragmented. In such cases, it is natural for the same sequence to be present in a significant proportion of the library, and thus, an over-representation warning should be interpreted in context.


In cases where the read sequences exhibit poor quality for any reason, it becomes essential to consider performing sequence trimming. This process involves the removal of reads with low-quality bases to ensure the data’s integrity and accuracy. By eliminating bad quality reads, the downstream analysis can be significantly improved, as it focuses on more reliable and informative data. Sequence trimming is a critical step in NGS data processing, as it helps mitigate the impact of sequencing errors and other artifacts, leading to more robust and accurate results in subsequent analyses and research interpretations. Use the link attached to read in more details on how to perform trimming on sequence data.

The figure below show sequences before and after trimming:

two plots for the data before and after trimming

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Bioinformatics for Biologists: Analysing and Interpreting Genomics Datasets

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