Skip to content

Latest commit

 

History

History
128 lines (112 loc) · 6.54 KB

README.md

File metadata and controls

128 lines (112 loc) · 6.54 KB
Raw

Project Coprolite Viromes

The scripts in this repository comprise the bioinformatic pipeline used to collect and analyze publicly available human gut metagenomic sequence data. Samples in this project were from modern-industrial, modern-non-industrial, and paleofecal samples, known as coprolites. The pipeline can be run in a Linux environment. Statistical analysis and visualization were performed in an .Rmd file.

Overview of Pipeline

The pipeline follows 6 major steps:

  1. Download and Assembly
  2. Viral Gene Annotation
  3. Predictions of Viral Characteristics
  4. Bacterial Gene Annotation on Viruses
  5. Align Phage Proteins to Databases
    • Virulence Factors
    • Carbohydrate Active Enzymes Files that govern these steps can be found in the pipeline directory, distinguished by the file name template {number}_{name}.sh

Each major step is associated with an auxiliary step to extract additional data needed for analysis. These auxiliary files are distinguished by the file name template {number}a_{name}.sh, and can be run once the major step file has run.

The one exception to this rule is step (3.). The auxiliary file calls on "sub-auxiliary" data wrangling steps, distinguished by the file name prefix 3b_{number}.... Running 3a_... will complete all necessary sub-auxiliary steps, but they could be directly run in any order if desired.

Description of Pipeline Files and Directories in main

The step(s) each directory is associated with are provided in "[]" at the end of the subsection header.

./run_step.sh [General/All]

run_step.sh is used to run samples from all origins through a given pipeline step, whether major of auxilliary. The pipeline step to be run is designated as the one and only argument for this script. Here are the usage instructions for run_step.sh.

./run_step.sh pipeline/<script>.sh

./run_ori.sh [General/All]

run_ori.sh is used to submit sections of the pipeline in the pipeline directory on the UCLA Hoffman2 Cluster as a job array. run.sh requires specifying the "origin" of samples (-o). Valid origins are:

  • Industrial samples: ind-DNK ind-ESP ind-USA
  • Non-industrial samples: pre-FJI pre-MDG pre-MEX pre-PER pre-TZA
  • Ancient samples: pal-AWC pal-BEL pal-BMS pal-ENG pal-ZAF pal-ZAP
  • Misc: test all In particular, the "all" origin is used when submitting auxiliary steps, since these steps are not specific to an origin. However, auxilliary steps should still be done through run_step.sh. Here are the usage instructions for run_ori.sh.
./run.sh -p <project_directory> -d <data_per_core> -c <cores> -s <script> -o <sample_origin>

./samples/ [General/All]

The samples directory contains copies of all samples across all origin categories. A copy of this directory exists in the directory where files are \generated by this pipeline. That is the crucial copy for this pipeline's function.

./general_bash_functions/ [General/All]

The general_bash_functions directory contains general functions and configure.sh.

./pipeline/ [General/All]

The pipeline directory contains the 5 main steps in the pipeline that each sample must individually go through. It also contains the auxiliary steps that can be completed once all samples have completed the associated major step. Here are the naming conventions again:

  • Major step: all sample origins run individually
    • ./pipeline/{number}_{name}.sh
  • Auxiliary step: all sample origins run together with -o all
    • ./pipeline/{number}a_{name}.sh
  • Sub-auxiliar functions for step (3.) in ./pipeline/3a_supplement
    • ./pipeline/3a_supplement/{number}b_{number}_{name}.sh

./download/ [1]

The download directory contains scripts that use prefetch and fasterq-dump from the SRA-Toolkit to download all accession runs for a given sample. Adapter trimming and quality control are performed with the tool Trim Galore. Further host removal was performed with the tool Bowtie2, by aligning trimmed reads to a reference human genome (GRCH38_noalt_as) and retaining unaligned reads for further processing. For paired reads, both reads must align to the reference genome for that pair to be discarded. If only one read of the pair aligns, both reads will be retained for further processing.

./megahit_assembly/ [1]

The megahit_assembly directory contains scripts that use MEGAHIT to perform de novo metagenomic assembly of reads after download and quality control. Scripts involved in library preparation are also found here, as many scripts had multiple accession runs that contained both paired and unpaired reads.

./prokka_annotations/ [2,3,4]

The prokka_annotations directory contains scripts that use Prokka for genome annotation. Scripts that identify which assembled contigs contain a viral gene annotation are also found here.

./phabox_predictions/ [3]

The phabox_predictions directory contains scripts that use the tools under PhaBox to perform phage contig identification, viral lifestyle prediction, viral taxonomic family prediction, and host prediction.

'./hmm_alignment/' [5]

The hmm_alignment directory contains scripts that align predicted phage proteins to data bases with HMMER. The 2 data bases in question are:

  • Virulence Factor (VF)
    • Liu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 2022 Jan 7;50(D1):D912-D917. doi: 10.1093/nar/gkab1107. PMID: 34850947; PMCID: PMC8728188.
  • Carbohydrate Active Enzymes (CAZY)
    • Elodie Drula, Marie-Line Garron, Suzan Dogan, Vincent Lombard, Bernard Henrissat, Nicolas Terrapon, The carbohydrate-active enzyme database: functions and literature, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D571–D577, https://doi.org/10.1093/nar/gkab1045. An HMM profile of clustered CAZY proteins was already provided. Construction of an HMM profile of clusted VF proteins was performed by clustering VF by their VFID (designation made by VF Data Base). Predicted phage proteins were generated by annotating phage contigs for bacterial genes in step (3.).

./data_wrangling/ [All Auxiliary Steps]

The data_wrangling directory contains scripts that collect raw counts and meta information after a major step is completed. These are the scripts used by the auxiliary steps of the pipeline.

Overview of Statistical Analysis

The analysis directory holds all files used to analyze the outputs of the pipeline. The file used to perform statistical tests, learning methods, and create visuals is analysis/analysis.Rmd.