Sunbeam is a pipeline written in snakemake that simplifies and automates many of the steps in metagenomic sequencing analysis. It uses conda to manage dependencies, so it doesn't have pre-existing dependencies or admin privileges, and can be deployed on most Linux workstations and clusters.

Sunbeam currently automates the following tasks:

• Quality control, including adaptor trimming, host read removal, and quality filtering;
• Taxonomic assignment of reads to databases using Kraken;
• Assembly of reads into contigs using Megahit;
• Contig annotation using BLAST[n/p/x];
• Mapping of reads to target genomes; and
• ORF prediction using Prodigal.

Sunbeam was designed to be modular and extensible. Some extensions have been built for:

• IGV for viewing read alignments
• KrakenHLL, an alternate read classifier
• Kaiju, a read classifier that uses BWA rather than kmers
• Anvi'o, a downstream analysis pipeline that does lots of stuff!

More extensions can be found at the extension page: https://www.sunbeam-labs.org/

To get started, see our documentation!

• Add implicit dependencies (samtools and bcftools) to environment file to make them explicit

• Increment Snakemake version requirement for compatibility with recent conda
• Specify earlier megahit version to ensure compatbility with existing assembly behavior
• Integration test improvements

• Start a project using resources directly from the SRA using sunbeam init --data_acc [SRA ###]. For more information, see the docs
• New extension website: https://www.sunbeam-labs.org/
• Improved documentation
• Numerous bugfixes and optimizations

• Minor bugfixes

• Low-complexity reads are now removed by default rather than masked
• Bug fixes related to single-end sequencing experiments
• Documentation updates

• Reports include number of filtered reads per host, rather than in aggregate
• Static binary dependency for komplexity for easier deployment
• Remove max length filter for contigs

• First stable release!
• Support for single-end sequencing experiments
• Low-complexity read masking via komplexity
• Support for extensions
• Documentation on ReadTheDocs.io
• Better assembler (megahit)
• Better ORF finder (prodigal)
• Can remove reads from any number of host/contaminant genomes
• Semantic versioning checks
• Integration tests and continuous deployment

• Erik Clarke (@eclarke)
• Chunyu Zhao (@zhaoc1)
• Jesse Connell (@ressy)
• Louis Taylor (@louiejtaylor)
• Kyle Bittinger (@kylebittinger)