README.md

Trace Microbial Sequences & NGS Instruments

Research: To see if there is a relationship between the metadata and the contamination present in the resulting sequencing data.

Python Environment

requirements.txt

Use at your Discretion. Provided as reference to reproduce working Environment.

Data

Assumption 1 : Public Study Accession ID name begins with "PRJ"

data
├── bam_files
|   ├── PRJ######
|       └──  <Run Accession>.bam
├── csv_files
|   ├── PRJ######
|       └── PRJ######_<Run Accession>.csv
├── sam_files
|   ├── PRJ######
|       └── <Run Accession>.sam
└── PRJ######
    ├── <Run Accession>_1.fastq
    └── <Run Accession>_2.fastq

Scripts

run_download_data.ipynb

Description

Notebook to download FASTQ Files from The European Nucleotide Archive (ENA)

Dependencies

Inputs

Public Study Accession ID ( Typically in Form PRJ##### )

Outputs

FASTQ files Unpacked into $PWD/< Public Study Accession ID >/*.fastq

Known Issues

Sometimes on dual read, the 2nd read will try to download twice ( does not affect storage)

run_HISAT2.ipynb

Description

Notebook to process FASTQ files through HISAT2 to produce .sam files

Dependencies

HISAT2

SAMTOOLS

Genome Reference Consortium Human Build 38

Inputs:

Directory Path

FASTQ files in $PWD/< Public Study Accession ID >/< Run Accession >.fastq data structure

Note: Single Reads typically store as .fastq while Dual reads will be stored as _1.fastq and _2.fastq

Outputs

SAM file in .sam format Files are stored as $PWD/sam_files/< Public Study Accession ID >/.sam data structure

Support

The Genome Reference Consortium

run_SAM_to_BAM.ipynb

Description

Notebook to Convert Sequence Alignment and Map (SAM) file to Binary Alignment and Map (BAM) File. Additionally, will sort BAM file during conversion

Dependencies

SAMTOOLS

Inputs:

$PWD/sam_files/< Public Study Accession ID >/.sam data structure

Outputs

BAM file in .bam format Files are stored as $PWD/bam_files/< Public Study Accession ID >/.bam data structure

run_etl_BAM_to_pd.ipynb

Description

Processing Pipeline

Dependencies

capstoneUtils.py : Script containing additional processing functions

Inputs:

Directory in structure $PWD/bam_files/< Public Study Accession ID >/.bam data structure Directory in structure $PWD/csv_files/< Public Study Accession ID >_.csv data structure

Outputs

Run Directory structure

runs
└── K_< K >
    ├── images
    |   └── *.png
    ├── models
    |   └── lda_model_Ntopic< Number of Topics>_K<K>*
    ├── cluster_sample_K< K >.csv
    ├── crosstab_K< K >.csv
    ├── filtered_crosstab_K< K >.csv
    ├── K< K >_library.txt
    ├── kmer_df_K< K >.csv
    ├── objs_K< K >.pkl
    ├── sizes.csv
    ├── test.csv
    └── train.csv

File Descriptions

• cluster_sample_K< K >.csv
  • 100000 Rows of K-Mer Length Sequences and Estimated Topic
• crosstab_K< K >.csv
  • Cross Tab in the Format K-Mer Length Sequence Rows and < Public Study Accession ID > Columns pre Chi-Squared Filtering
• filtered_crosstab_K< K >.csv
  • Cross Tab in the Format K-Mer Length Sequence Rows and < Public Study Accession ID > Columns post Chi-Squared Filtering
• K< K >_library.txt
  • List K-Mer Length Sequences derived from Training Set Post Chi-Squared Filtering
• kmer_df_K< K >.csv
  • K-Mer Length Sequences derived from Training Set. Used to generate model
• objs_K< K >.pkl
  • Pickle file in format K, kmer_dictionary. kmer_dictionary format is: {< Public Study Accession ID and Run Accession> : K-Mer Length Sequences as List }
• sizes.csv
  • File to show Raw Data number of Sequences and Pandas Data Frame Size. Useful to estimate RAM and Hard Disk Space Requirements
• test.csv
  • Test Set of Sequences after initial import of Raw Data
• train.csv
  • Training Set of Sequences after initial import of Raw Data

Optional Files

run_chi_squared.ipynb

run_classification.ipynb

run_optimize.ipynb