Metagenome analyses!

This is a repository in progress!

These are scripts I use to analyze shotgun metagenomes. These scripts can be adapted for amplicon reads, single end reads, etc.

Currently I am using a high performance computing cluster that uses a Slurm manager.

Trimming adapters & indexes from reads

• bbduk_loop.sh

Normalize read depth of coverage

• bbnorm_loop.sh

Merging reads

• bbmerge_loop.sh

Read Error Correction

• Spades_Mgm_Error_Correction.sh

Metagenome Assembly

• Spades_mgm_Assembly.sh
  • can be used for non-metagenomic genome assembly by changing metaspades.py to spades.py and removing the --meta argument
• megahit_loop_hpcc.sh (for running on Slurm cluster system)
• megahit_loop_local.sh (for running locally)
  • both megahit scripts could be used for non-metagenomic genome assembly, but please refer to their manuals for available arguments & syntax.

• Currently metaSPADES can only handle one library at a time, whereas megahit can do co-assembly

Map Reads to Assembly

• bwa_map_to_contigs.sh
  • creates SAM file which is then converted to BAM file with Samtools
  • After indexing and sorting the BAM file, this script uses Samtools to create stats about alignment + mapping

Contig Binning with MetaBAT2

• metaBAT_contig_binning_loop_hpcc.sh (for running on Slurm cluster system)
• metaBAT_contig_binning_loop.sh (for running locally)

Check Assembly Quality

• Checkm_QA_MAGs.sh
  • Check genome completeness, contamination & basic taxonomy
• metaQUAST_Compare_mgm_Assemblies.sh
  • Compare genome assemblies, completeness, contamination, & basic taxonomy
• Parse_QA_Results.sh
  • separate genome bins based on completeness and contamination percentages
• Examine_MGM_Quality.R
  • visually examine genome bins based on completeness, contamination, and other stats (based on CheckM output)

Gene Prediction & Annotation

• Taxonomic Annotation
  • TBD
• Functional Annotation
  • TBD

Reconstruct SSU rRNA genes from metagenome short reads

• PhyloFlash_16S_ID.sh (for running on cluster system)
  • uses metagenome short reads to assemble into possible SSU rRNA genes (16S, 18S) for taxnomic identification