Ahmed Moustafa commited on
Commit
ff4a553
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1 Parent(s): d531b23

Cleaning up

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README.md CHANGED
@@ -19,15 +19,20 @@ size_categories:
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  This dataset contains 16S rRNA gene sequences with hierarchical taxonomic annotations, designed for training and evaluating models like DeepTaxa. It is a processed version of the Greengenes database, widely used in microbiome research.
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  ## Dataset Details
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- - **Files**:
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- - `gg_2024_09_training.fna.gz`: Training sequences (FASTA)
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- - `gg_2024_09_training.tsv.gz`: Training labels (TSV)
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- - `gg_2024_09_testing.fna.gz`: Testing sequences (FASTA)
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- - `gg_2024_09_testing.tsv.gz`: Testing labels (TSV)
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- - `gg_2024_09_training_ids.txt`: Training sequence IDs
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- - `gg_2024_09_testing_ids.txt`: Testing sequence IDs
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- - **Format**: FASTA (sequences), TSV (labels with 7 taxonomic levels)
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- - **Source**: Derived from Greengenes (DeSantis et al., 2006)
 
 
 
 
 
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  ## Usage
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  Use with DeepTaxa:
@@ -57,7 +62,18 @@ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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  ### Modifications
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- This modified version (gg_2024_09) was created by the Systems Genomics Lab for use with DeepTaxa. The modifications include splitting the dataset into training and testing sets and adding ID files.
 
 
 
 
 
 
 
 
 
 
 
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  Copyright (c) 2025, Systems Genomics Lab
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@@ -77,4 +93,5 @@ For the dataset:
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  For DeepTaxa: See [GitHub](https://github.com/systems-genomics-lab/deeptaxa).
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  ## Contact
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- Report issues on [GitHub](https://github.com/systems-genomics-lab/deeptaxa/issues).
 
 
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  This dataset contains 16S rRNA gene sequences with hierarchical taxonomic annotations, designed for training and evaluating models like DeepTaxa. It is a processed version of the Greengenes database, widely used in microbiome research.
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  ## Dataset Details
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+
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+ The dataset includes the following files:
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+
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+ | File Name | Type | Number of Sequences | Size |
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+ |----------------------------------|-----------------------|---------------------|--------------|
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+ | `gg_2024_09_training.fna.gz` | FASTA (sequences) | 277,336 | ~96.4 MB |
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+ | `gg_2024_09_training.tsv.gz` | TSV (taxonomy labels) | 277,336 | ~2.6 MB |
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+ | `gg_2024_09_testing.fna.gz` | FASTA (sequences) | 69,335 | ~24.1 MB |
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+ | `gg_2024_09_testing.tsv.gz` | TSV (taxonomy labels) | 69,335 | ~0.8 MB |
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+ | `gg_2024_09_training_ids.txt` | Text (sequence IDs) | 277,336 | - |
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+ | `gg_2024_09_testing_ids.txt` | Text (sequence IDs) | 69,335 | - |
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+
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+ - **Format**: FASTA for sequences (compressed), TSV for labels with 7 taxonomic levels (compressed).
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+ - **Source**: Derived from Greengenes (DeSantis et al., 2006), downloaded from https://ftp.microbio.me/greengenes_release/2022.10/
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  ## Usage
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  Use with DeepTaxa:
 
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  ### Modifications
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+ This modified version (gg_2024_09) was created by the Systems Genomics Lab for use with DeepTaxa. The Greengenes database, downloaded from https://ftp.microbio.me/greengenes_release/2022.10/, was processed as follows:
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+ 1. The original dataset, containing 23,450,269 sequences, was downloaded.
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+ 2. Missing taxonomic classifications were filled with "Unclassified."
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+ 3. Sequences were filtered to retain only those with at least 300 nucleotides, resulting in 354,023 sequences.
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+ 4. Sequence IDs were extracted, and the taxonomy table was filtered to match these IDs, yielding 346,671 entries (7,352 IDs not found were noted).
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+ 5. A clean ID list was generated from the filtered taxonomy table (346,671 IDs).
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+ 6. Sequences were filtered to match the clean ID list, retaining 346,671 sequences.
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+ 7. The dataset was split into training (277,336 sequences) and testing (69,335 sequences) sets using an 80:20 ratio and a fixed random seed.
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+ 8. Training and testing sequence IDs were extracted (277,336 and 69,335 IDs, respectively).
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+ 9. The taxonomy table was filtered to create separate training (277,336 rows) and testing (69,335 rows) label files.
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+
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+ These steps ensured the dataset was cleaned, filtered, and split for use with DeepTaxa.
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  Copyright (c) 2025, Systems Genomics Lab
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  For DeepTaxa: See [GitHub](https://github.com/systems-genomics-lab/deeptaxa).
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  ## Contact
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+ Report issues on [GitHub](https://github.com/systems-genomics-lab/deeptaxa/issues).
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+
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