In vivo RNA sequencing was utilized to identify key molecular mechanisms that are responsible for the Salmonella enterica serovar Typhimurium (STm) persistent infection. Our research team optimized an oral mouse infection model with STm to be able to study the acute, and the chronic infection mechanisms of the pathogen. The transcriptomes of STm of acute and persistent infections were compared within host tissue sites. Relying on the output of the analyses we performed additional mice experiments to gain more transcriptomics data to confirm our findings. I received the additional sequencing output (NGI Delivery: delivery06200, delivery06264) at the beginning of August 2022. After the data arrived, I intensively used SNIC 2021/22-663 and the prolongation of it SNIC 2022/22-849 small compute project. Thanks to the transcriptomics data we identified several target genes for further experiments and analyses such as mice infection with certain genes’ knockout. I am expecting new sequencing data during the autumn of this year (probably November of 2023) so I need to run the pipeline on it as I used previously. I combined all of our transcriptomics data including those with low coverage because I am optimizing a method using an autoencoder to be able to retrieve biologically meaningful data from the zero-inflated dataset. We need to add to the whole dataset that we will receive soon and test the autoencoder with a larger dataset. Therefore, I would like to prolong my access to the small computing project (SNIC 2022/22-849) and storage (SNIC 2022-23-446). I will contribute to our group efforts however I will not be affiliated; therefore, I would like to request that in the prolonged project, the PI will be Leyden Fernandez (leyden.fernandez@umu.se) instead of me.