Several improvements in reporting

• Moved the lists of columns of the final quality report to external files
• No more report on % of reads/contigs/ORFs, only quantifications
• Reporting now reaches Binning and Gene expression analysis

New tools and integration into bioconda

New tools, same functionalities

• reCOGnizer now a standalone tool for domain-based functional annotation
• UPIMAPI now a standalone tool for retrieving information from UniProt using its API
• KEGGCharter now a standalone tool for mapping genomic potential and gene expression information into KEGG metabolic pathways
• Allowed to easily solve hella lot of bugs (and publish a little more :D)

Integration into Bioconda

• Some adaptations in the main scripts
• meta.yaml hopefully well configured
• MOSCA will now have the new tools as dependencies

MOSCA 1.0

Updated for changes in conda repositories.

MOSCA 1.0

Dockerfile was fixed, and MOSCA's image is now fully functional again!

MOSCA 1.0

Same tool, new features
Meta-Omics Software for Community Analysis was previsously published as a pipeline for integrated metagenomics (MG) and metatranscriptomics (MT) data analysis. This new version includes the integration of a metaproteomics (MP) workflow for raw data analysis and binning for contigs resulting from assembly, and improvements on the assemly and annotation steps.

Integration of metaproteomics: Two workflows have been developed for the handling of MP spectra, both for label-free quantification and analysis. While this feature is not complete, it already allows for identification and quantification of proteins, using the results from the MG workflow as a reference database.

Integration of binning: contigs resulting from MG assembly can now be binned into contigs. In the next few weeks, CheckM will be integrated for quality check of bins.

Improvement of assembly: assembly was performed with one MG sample at a time. This made sense in the scenario where each MG sample came from a different microbial community, but if some samples come from the same community, it is advisable to perform assembly with all data together. It is now possible to perform 3 different assembly strategies in MOSCA: "unique" for performing assembly as previously, one assembly for MG sample; "all" for performing assembly with all MG data together, use it only if the samples come from very similar communities; "samples", which requires the "--mg-samples" to specify which samples go together for each assembly/annotation.

Improvement of annotation: domain annotation with PSI-BLAST and COG database is now possible within MOSCA. This type of annotation is focused on functional classification by identifying domains specific to metabolic and/or structural functions.