BGP2_F1_R1

Modern life sciences generate data at a scale and complexity that fundamentally depend on scalable computing infrastructures and rigorous research data management. From assembling individual genomes, exploring complex microbial communities to building graph-based pangenomes, researchers today face both unprecedented opportunities and significant methodological challenges, not only in analysis, but also in how data and workflows are stored, shared, and sustained.

Information about the three days in which data were analyzed.

Genomics Metagenomics Pangenomics

Recent survey of anaerobic digesters to uncover distinct microbiome signatures, finding the order Clostridia MBA03 (a subgroup of Limnochordia) to be highly abundant. Follow‑up analyses showed that Limnochordia genomes are resilient and metabolically versatile, e.g. capable of fermenting sugars and amino acids across varied reactor conditions, suggesting they stabilize biogas reactions. Phylogenetic analysis revealed 5 distinct families (DTU010, DTU012, B1SED10-159, JAAvYFS01, SLO01) encompassing ...

Data from 8 different biogas plants operating under different conditions and fed differently collected and sequenced.

• Data in 1: 8 Samples (as described in samples_metadata.tsv)
• Data out 1: 8 sets of pairs of fastq files (forward & reverse), as described in run_metadata.tsv, located in wednesday→data

Data from 8 different biogas plants operating under different conditions and fed differently collected and sequenced.

• Data in 1: 8 Samples (as described in samples_metadata.tsv)
• Data out 1: 8 sets of pairs of fastq files (forward & reverse), as described in run_metadata.tsv, located in wednesday→data

DESeq2 edgeR log2FoldChange > 1 Padj < 0.05

Metagenomic Assay

Analysis Data metadata

The sample type of the submission.yml