DOI: 10.5061/dryad.ksn02v7jg
title: Data from: Deep learning reveals hidden diversity of <em>Synechococcus</em> in the coastal water of China: Novel clades and their ecological insights

description: Synechococcus is ubiquitous and diverse in marine environments and
 contributes significantly to primary productivity in the ocean. The
 genetic diversity of the genus Synechococcus has been extensively explored
 based on the 16S-23S rRNA internal transcribed spacer (ITS) region.
 However, accurate identification of Synechococcus ITS from large
 sequencing datasets is challenging due to the absence of a standardized
 taxonomy and ambiguous clade boundaries. To address these limitations, we
 developed Syn_Tool, a deep learning-based framework integrating a curated
 Synechococcus ITS database for sequence identification, classification,
 and novel clade discovery. Analyzing 1,087,323 ITS sequences from the
 coastal water of China—the largest Synechococcus dataset to date—Syn_Tool
 classified them into 42 clades, including 28 known and 14 newly defined
 clades. Biogeographic analyses revealed a latitudinal diversity gradient
 driven by temperature, with 12 newly defined clades (clades CSII-IV,
 CSVI-XIV) primarily found in estuarine regions where rapid diversification
 may promote the emergence of novel genotypes. This study demonstrates the
 application of deep learning in classifying Synechococcus and
 understanding their ecological roles in dynamic marine ecosystems.
description: # Data from: Deep learning reveals hidden diversity of *Synechococcus* in
 the coastal water of China: Novel clades and their ecological insights
 Dataset DOI:
 [10.5061/dryad.ksn02v7jg](https://doi.org/10.5061/dryad.ksn02v7jg) ##
 Description of the data and file structure This dataset contains three
 main components generated in the study: 1. Supplementary_Data 2.
 ChinaCoastalWater_ITS_Dataset 3. Syn_Tool ### Files and variables ####
 File: Supplementary_Data.zip **Description:**  Data S1–S4 for this study,
 including the list of references for the collected *Synechococcus* ITS
 sequences (Data S1, Supplementary Data 1.csv), the starting dataset for
 *Synechococcus* ITS (Data S2, Supplementary Data 2.csv), the Syn_ITS
 database (Data S3, Supplementary Data 3.csv), and the sampling station
 information (Data S4, Supplementary Data 4.csv). **Supplementary Data
 1.csv** * Title: Title of the reference from which the *Synechococcus* ITS
 sequence was collected * Authors: Reference authors * Year: Publication
 year * Journal: Journal name - DOI: Digital Object Identifier of the
 reference. “n/a” indicates that no DOI is available **Supplementary Data
 2.csv** * Accession_number: NCBI (National Center for Biotechnology
 Information) accession number for each *Synechococcus* Internal
 Transcribed Spacer (ITS) sequence * Label: Custom label for each sequence
 * Clade: Clade assigned in this study * Sequence: ITS DNA sequence *
 Strain_or_Clone: Strain or clone name of the organism from which the
 sequence was obtained * Region: Genomic region covered by the sequence *
 16S: The 16S ribosomal RNA gene * ITS: Internal Transcribed Spacer * 23S:
 The 23S ribosomal RNA gene * Clade_in_origin_reference: Clade assignment
 reported in the original reference. =“--” indicates that no clade
 assignment was provided in the original reference * NCBI_taxonomy: NCBI
 taxonomic classification * Origin_reference: Source reference for the
 sequence **Supplementary Data 3.csv** * Accession_number: NCBI (National
 Center for Biotechnology Information) accession number for
 each *Synechococcus* Internal Transcribed Spacer (ITS) sequence * Label:
 Custom label for each sequence * Clade: Clade assigned in this study *
 Sequence: ITS DNA sequence * Strain_or_Clone: Strain or clone name of the
 organism from which the sequence was obtained * Region: Genomic region
 covered by the sequence * 16S: The 16S ribosomal RNA gene * ITS: Internal
 Transcribed Spacer * 23S: The 23S ribosomal RNA gene *
 Clade_in_Origin_Reference: Clade assignment reported in the original
 reference. “--” indicates that no clade assignment was provided in the
 original reference * NCBI_taxonomy: NCBI taxonomic classification -
 Origin_Reference: Source reference for the sequence **Supplementary Data
 4.csv** * Station: Sampling station ID * Sea Area: Name of the sea area
 where the station is located * Latitude: Latitude of the station in
 decimal degrees * Longitude: Longitude of the station in decimal degrees *
 Bottom Depth [m]: Water depth at the station (meters). “NA” indicate that
 the bottom depth data were not available * Sample Depth [m]: Depth at
 which the sample was collected (meters) * Date [yyyymm]: Sampling date in
 year and month (YYYYMM) #### File: ChinaCoastalWater_ITS_Dataset.zip
 **Description:**  This dataset contains operational taxonomic unit (OTU)
 representative sequences and corresponding abundance information derived
 from internal transcribed spacer (ITS) sequences of *Synechococcus*
 collected from the coastal water of China. **40535_OTU_repseqs.fasta** *
 Representative sequences of all 40,535 OTUs obtained in this study, in
 FASTA format.  * OTUs were generated using a 97% sequence similarity
 threshold **40535_OTU_repseqs_abundance.txt** * Abundance file
 corresponding to the 40,535 OTUs * Column 1 is the sequence ID, matching
 the headers in `40535_OTU_repseqs.fasta` * Column 2 is the sequence
 abundance, representing the number of reads for each OTU
 **22980_OTU_repseqs_Syn.fasta** * Representative sequences of 22,980
 *Synechococcus* ITS OTUs in FASTA format * Sequences were identified using
 the Syn_Tool software **646_OTU_repseqs_Novel_Syn.fasta** * Representative
 sequences of 646 OTUs assigned to 14 newly defined *Synechococcus* clades
 in FASTA format #### File: Syn_Tool.zip **Description:**  This zip file
 contains the Syn_Tool software package developed in this study for
 identifying, classifying, and analyzing *Synechococcus* ITS sequences. It
 includes the following files and folders: **model/**: Models and related
 files used by Syn_Tool * `CNN_model.h5`: Pre-trained CNN (Convolutional
 Neural Network) model for sequence embedding of ITS sequences *
 `CNN_kmer_tokenizer.pkl`: Tokenizer for k-mer representation used by the
 CNN model * `CNN_label_encoder.pkl`: Label encoder for CNN class labels *
 `Transformer_model.h5`: Pre-trained Transformer model for ITS
 classification * `Transformer_BPE_tokenizer.1024.json`: BPE (Byte-Pair
 Encoding) tokenizer used by the Transformer model *
 `Transformer_layers.py`: Python script defining the Transformer model
 layers * `Syn_ITS_database.txt`: *Synechococcus* ITS reference database
 for Syn_Tool * Column 1: Sequence  - Column 2: Clade assignment - Column
 3: NCBI accession number * `Picocyanobacteria_ITS_database.fasta`:
 Picocyanobacteria ITS reference sequences for Syn_Tool -
 `Picocyanobacteria_ITS_database.tax`: Taxonomy file for the
 Picocyanobacteria ITS reference sequences * Column 1: NCBI accession
 number * Column 2: Clade assignment. All entries are “Pico”, representing
 Picocyanobacteria **scripts/**: Scripts for running Syn_Tool *
 `Sequence_QC.batch`: Batch script for performing quality control on input
 ITS sequences before analysis * `Syn_Tool_run.py`: Main Python script for
 running the Syn_Tool workflow **example/**: Example input data *
 `example_abundance.fasta`: Example FASTA file containing representative
 ITS sequences * `example_abundance.txt`: Example OTU abundance file
 corresponding to the sequences **README.md**: Instructions and usage
 guidelines for the software **requirements.txt**: List of Python packages
 and dependencies required to run Syn_Tool ## Code/software # Syn_Tool
 **Syn_Tool** is a deep learning framework designed for analyzing
 *Synechococcus* ITS sequences. It integrates identification,
 classification, and novel clade delineation into a single streamlined
 workflow. With minimal setup, you can process sequence data and abundance
 information to gain insights into *Synechococcus* communities. --- ##
 Installation ### Requirements * **Python Version**: Python 3.8 or later *
 **Dependencies**: Listed in `requirements.txt` ### Steps 1. Clone the
 repository: ```bash git clone https://github.com/Aldred-Wang/Syn_Tool.git
 cd Syn_Tool ``` 2. Install dependencies: ```bash pip install -r
 requirements.txt ``` --- ## Usage Before running the tool, ensure that the
 following files are placed inside the `model` folder: * `Syn_Tool_run.py`
 * `input_sequences.fasta` * `input_abundance.txt` Run the following
 command to start the tool: ```bash python Syn_Tool_run.py -fa
 input_sequences.fasta -a input_abundance.txt ``` **Parameters** * `-fa` or
 `--fasta`: Path to the input FASTA file. * `-a` or `--abundance`: Path to
 the abundance file. --- ## Example Here’s an example of how to use
 Syn_Tool: ```bash python Syn_Tool_run.py -fa example_sequences.fasta -a
 example_abundance.txt ``` --- ## Output After execution, the following
 files will be generated: * `Syn.fasta`: Output file from the
 identification module containing *Synechococcus* ITS sequences in FASTA
 format. * `Syn_df.csv`: Output file from the identification module
 containing *Synechococcus* ITS sequences in CSV format. *
 `combined_df.csv`: Output file from the classification module containing
 feature vectors of *Synechococcus* ITS sequences extracted by the CNN
 model. * `result.csv`: Output file from the novel clade delineation module
 providing information about *Synechococcus* novel clades. *
 `Syn_Tool_final_result.txt`: Final classification file generated by
 Syn_Tool. ---