piscem-infer

Crates.iopiscem-infer
lib.rspiscem-infer
version0.6.0
sourcesrc
created_at2023-09-30 03:34:45.762963
updated_at2024-02-26 20:10:21.750635
descriptionA flexible tool to perform target quantification from bulk-sequencing data
homepagehttps://piscem-infer.readthedocs.io/
repositoryhttps://github.com/COMBINE-lab/piscem-infer/
max_upload_size
id988361
size99,509
Rob Patro (rob-p)

documentation

README

piscem-infer

What is piscem-infer?

piscem-infer is a tool to consume bulk-RAD files (produced by piscem or piscem-cpp) and to produce from them abundance estimates of the targets against which the reads were mapped. For example, a basic RNA-seq pipeline could consist of mapping the reads against the transcriptome using piscem, and then quantifying transcript abundance using piscem-infer. Likewise, one could use the pair of tools on metagenomic index and metagenomic sequencing reads to perform taxonomic abundance estimation. The main goal of piscem-infer is to separate the statistical inference algorithms and code from the code that performs indexing and mapping. This allows faster and more agile development of new improvements to the inference method, as well as eases the maintenance burden.

The piscem-infer program is written in rust, which makes it easy for end-users to compile, and which also makes it easy for us to deploy without the need for end-users to compile it (it's easy to create statically-linked, pre-compiled executables, and to put the package on bioconda). At the same time, this gives us access to the safety guarantees of rust, making the code easier to develop and maintain with confidence while retaining the efficiency of a high-performance, statically-typed, compiled language.

While piscem-infer is in active development, it is already very usable! We encourage folks who are curious to try it out, to open Github issues for any questions or feature requests, and to provide feedback on how you'd like to see this next "evolution" of (bulk sequencing) abundance estimation tools evolve!

Commit count: 106

cargo fmt