Spatially resolved transcriptomic analysis methods are rapidly maturing, with both academic and commercially available technology solutions able to generate high quality data at varying resolutions. Different platforms (e.g. 10X Visium, Slide-seq and Stereo-seq) have their own protocols and customised analysis pipelines, which presents challenges when the goal is to obtain uniformly pre-processed data for benchmarking or for further downstream analysis using established tools. The current lack of open-source solutions that can deal with sequencing-based spatial transcriptomics (sST) data from different platforms motivated our development of the stPipe R package. stPipe provides a comprehensive and modular analysis pipeline that handles the following aspects of analysis: (i) data preprocessing from raw fastq files to obtain a spatially resolved feature count matrix; (ii) collection of appropriate quality control metrics during pre-processing to ensure unwanted artefacts can be removed; and (iii) adoption of standardised data storage containers to allow results to be easily passed on to downstream analysis packages for different goals (including clustering, cell-cell communication, etc). stPipe builds upon functionality in the scPipe package to allow in-depth exploration of the strengths and weaknesses of different sST technologies, guide analytical method selection and lead to the development of new and improved data processing pipelines. stPipe is available as an R package from GitHub and will be submitted to Bioconductor in a future release. We are able to show the stPipe provides paralleled performance with vendor provided softwares such as spaceranger and allows for comprehensive options for downstream analysis.