plastid differs from other packages in its design goals. Namely:
its intended audience includes both bench and computational biologists. We tried to make it easy to use, and wrote lots of Tutorials
- uses Read mapping functions to extract the biology of interest from read alignments – e.g. in the case of ribosome profiling, a ribosomal P-site, in DMS-seq, sites of nucleotide modification, et c. – and turn these into quantitative data, usually
numpy arraysof counts at each nucleotide position in a transcript.
- encapsulates multi-segment features, such as spliced transcripts, as single objects. This facilitates many common tasks, such as converting coordinates between genome and feature-centric spaces.
It separates data from its representation on disk by providing consistent interfaces to many of the various file formats, found in the wild.
Where to go next¶
- Those new to sequencing and/or bioinformatics, and those who are ribosome profiling should start with Getting started, and then continue to the Tour and Tutorials. The
description of command-line scriptsmay also be helpful.
- Advanced users might be more interested in a quick Tour of the primary data structures and the module documentation.