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. This was not an easy task however as nanopore technology is novel and many of the available pipelines are tailored to illumina sequencing. Generally though, a basic transcriptomics pipeline looks like the following: Alignment to a reference genome, gene counting and differential gene expression [1]. However a couple of data processing steps were needed for the nanopore data beforehand such as demultiplexing and adapter trimming.

Demultiplexing and Adapter Trimming

Gene Counting

Result

Validating our Transcriptomics Pipeline

The transcriptomics pipeline was tried out and validated using read files available from the internet. The files consisted of two datasets of E. Coli (triplicates) cultured in regular LB and a sugar solution respectively.

The results after searching for the genes in the NCBI database showed that the most expressed gene from the sugar-cultured E. Coli was shown to be involved in a type of sugar system, proving that the pipeline was indeed working.

Analyzing Our Own Sequencing Data

Table 1: The first few genes as a result of the differential gene expression analysis seen in Figure 6 together with their promotor sequence and function in the organism.

Gene ID	Gene name	Promotor sequence	Function	Fold change
ER3413_45 ER3413_70 ER3413_87 ER3413_126 ER3413_173	apaG leuA murG panD frr	ggcaccatgcagggtcactacgaaatgatcgatgaaa ttgacatccgtttttgtatccagtaactctaaaagc - tagacactaaacaaaaatcgggcaatactgcgtga ttacccgtaatatgtttaatcagggctatacttagcac	protein associated with Co2+ and Mg2+ efflux 2-isopropylmalate synthase N-acetylglucosaminyl transferase putative inner membrane protein inner membrane protein, UPF0118 family	0.40 0.40 0.40 0.40 0.40

The resuts from our runs unfortunately did not produce as good results as seen above. Due to the major issues with sequencing and actually generating enough data, it can be seen in figure 6 what kind of effects it had. Judging by the adjusted p-values it is clear that even though the genes can indeed be identified as seen in Table 1 the statistical significance is extremely uncertain (the minimal accepted threshold is an adjusted p-value of <= 0.05). Any up-or down regulation of fold-change of interest was not able to be identified either. Looking at these errors it can be assumed that no major change in fold-change as well as low significancy is due to simply not enough data being generated from the prior sequencing step. Because of these facts no gene could be identified as a possible candidate for our reporter system.

References

[1] Galaxyproject, 2018. Reference-based RNA-Seq data analysis https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html Date of visit 2018-10-15