Bioinformatics

After a succesfull sequencing has been performed and you’re left with raw data containing millions and millions (and millions) of lines of base sequences, all of this needs to be processed and interpreted. This is where the interdisciplinary field of bioinformatics comes in. A vast range of software tools are available, tailored to different kinds of analysis as well as being unique to the different sequencing methods being used.

Most of the tools we used were available through the free website Usegalaxy.org which as well let us do the processing on their servers. Because we also made use of nanopore sequencing, tailored tools used for the MinION data were available from their community hub which could be run from a terminal window.

Experiment

We decided to create our bioinformatics pipeline from scratch. Generally, a basic transcriptomics pipeline looks like the following: Alignment to a reference genome, gene counting and differential gene expression. However a couple of data processing steps were needed for the nanopore data beforehand such as demultiplexing and adapter trimming.

Demultiplexing and adapter trimming

Because the sequencing itself runs pooled samples containing both the barcoded cultured- and control-group samples, the data produced needs to be demultiplexed i.e separated into files containing the reads from respective groups. Because the barcodes used to fingerprint each group is made up of its own base sequence, this also had to be removed or ”trimmed” from the data, leaving us with the pure mRNA sequences. This was achieved using a free nanopore community tool called porechop.

Genome alignment

The base sequences needs to be aligned to the reference genome of the sequenced species in question for the downstream data analysis. This is important because we want to know where each sequence actually lies in the genome and which genes they correspond to. Genome alignment was done using another community tool called minimap2.

Gene counting

Gene counting basically means that you count how many times each mRNA sequence (aligned over a gene from the previous step) occurs. This in turn directly correlates to the amount of up- or down-regulation of that particular gene. A lot of different tools were available for gene counting but ”featureCounts” was chosen through galaxy.