FERTBase

FERTBase is a comprehensive knowledgebase combining reproductive genetics, single-cell and spatial transcriptomics information to facilitate the understanding of genetic defacts affecting huamn reproducution.

1. Introduction

Infertility and reproductive failure are one of the key clinical and biological challenges for humans of the modern era, especially for those residing in industrialized societies. These challenges involve both maternal factors, paternal factors, and failures in early embryonic development. Although clinical genetics studies in the past several decades have reported hundreds of genes contributing to clinical infertility phenotypes, the mechanisms linking to genetic alternations underlying reproductive cell types, developmental stages, and clinical outcomes are not fully explored. Here we present FERTBase, a comprehensive, expert-curated database that integrates clinical fertility genetics data with single-cell and spatial transcriptomics from the male and female reproductive system, as well as early embryonic development from zygote formation to postfertilization week 9 as well as model organism experimental evidence for fertility impairments.

2. Search

FERTBase provides a friendly search engine for users to fetch useful data. You can search the database using gene symbol or Entrez Gene ID.

Further, ontology-based searching methods for genes and phenotypeic categories are also provided.

3. Data Presentation

3.1. Home Page

This figure shows an example of the search result. The total number of fertility mutations matching your searching criteria is shown on the top of the page, following the detailed information for each mutation. Many of the terms shown are linked to a detailed information page. The gene id links to the related gene page in FERTBase. The Entrez Gene ID and Gene name link to the NCBI gene page. The mutation and location term link to genome browser. The category term links to category page. User can also export the search result to a .CSV table.

3.2. Gene Page

Detailed information is provided in the gene page, which are classified into three sections.

3.2.1. Basic Information

Basic Information section provides information on the specific gene including Gene Symbol, Entrez Gene ID, relative location, gene name and alternative names, gene IDs (OMIM, HGNC, and Ensembl), as well as brief description of the gene and what fertility phenotpyic category it belongs to.

3.2.2. Variants Detail

Variants Detail section provides information of all the fertility-related variants on this gene. This include RefSeq IDs (if present), mutaiton description (with genomic coordinates), variant description on cDNA, amino acid change, and related publication PMID.

3.2.3. Expression Visualizations

This tab provides visualizations of single-cell and spatial transcriptomics data of the related genes based on re-analysis described in sections below. We first present the expression of the gene of interest in male and female reproductive system and during gamete development, as well as very early embyonic development from zygote formation to implementation.

Additionally, we provided the visualization of the expression of these genes in a recently published spatial transcriptomics dataset for early embryonic development from week 4 to week 20 in both male and female fetuses.

3.3. Search Result Page For Gene

The gene search result page provides the detailed information of all fertility variants related to the gene of interest, user can download the search result in a .csv file.

3.4. Gene List Page

Gene list page directly all the fertility genes in our database.

3.5. Category List Page

This page provides a list of all phenotypic categories in our database.

4. Browser

4.1 Genome Browser

Our Genome Browser provides a fast, interactive genome visualization for users. Fertility defact mutations are shown in one track, along with other genetic and epigenetic tracks. Search by both of coordinates and gene symbols are enabled.

4.2 Gene Browser

Users can browse all the genes and their related information

4.3 Category Browser

Users can browser based on different infertility categories for genes and mutations.

5. Submission System

If you have reliable knowledge on human fertility defact mutations that are not included in this database, you are welcomed to submit that to us. We will appreciate for your submission and examine the submitted information as soon as possible.

6. Statistics Page

This page provides the real-time statistics and visualizations of total data recorded in FERTBase include number of genes, publications, and categories.

7. Tutroials

A copy of this tutorial page is also available on the FERTBase website.

9. Team

Changhong Yang, PhD

Muhammad Zubair, PhD

Xincen Xi, MD

Sai Babu Patarlapalli

Ian Alfred Graham

Xiaoxu Yang, PhD (PI)

9. Cite FERTBase

Changhong Yang*, Muhammad Zubair*, Sai Babu Patarlapalli, Xincen Xi, Ian Alfred Graham, Xichen Nie, Sixian Wu, Xiaoxu Yang#. FERTBase: A comprehensive knowledge base for genetic variations affecting fertility and early embryonic development.

10. Fertility Analysis Repository

10.1. Overview

This repository contains analysis workflows for male, female, and embryonic datasets. It includes Python scripts for gene-level analysis, a methods overview document, environment files for reproducibility, and input gene lists used in the analysis.

All three analysis scripts follow a shared Scanpy-based workflow that performs quality control, normalization, dimensionality reduction, clustering, cell-type annotation, and gene-level visualization.

10.2. Repository contents

• Data/Genelist.csv
  Input gene list used for analysis.

• Docs/methods_overview.md
  Overview of the shared analysis workflow, dataset notes, and software used.

• Scripts/male_gene_analysis.py
  Male dataset analysis script. This workflow uses an .h5ad input file and a CSV gene list, applies Scanpy-based QC and clustering, and generates annotated output objects and plots.

• Scripts/female_gene_analysis.py
  Female dataset analysis script. This workflow uses a female .h5ad input file and Genelist.csv, then applies the same general analysis framework to the female dataset.

• Scripts/embryonic_gene_analysis.py
  Embryonic dataset analysis script. This workflow uses an .h5ad input file and Genelist.csv, then applies the same general analysis framework to the embryonic dataset.

• environment.yml
  Conda environment definition for reproducing the analysis environment.

• requirements.txt
  Python package requirements for pip-based installation.

• Scripts/Slurm.sh
  Example server submission script used to run the analysis on a compute environment.

10.3. Dataset sources

Male datasets

The male analysis was performed using integrated single-cell datasets from GEO: GSE254315, GSE182786, GSE215754, and GSE120508. Only the germline component of these datasets was retained for downstream analysis.

Female datasets

The female analysis was performed using GSE232601(Adult)

Embryonic datasets

The embryonic analysis was performed using curated embryonic datasets obtained from the Petropoulos & Lanner Labs dataset portal:

https://petropoulos-lanner-labs.clintec.ki.se/dataset.download.html

10.4. Input files required

The analysis scripts require AnnData input objects and gene list CSV files.

Male workflow

• Input_Dataset/<file_name>.h5ad
• Data/Genelist.csv

The male script also expects adata.var["gene_id"] to contain Ensembl gene IDs, which are mapped to gene symbols before plotting and output generation.

Female workflow

• Input/<file_name>.h5ad
• Data/Genelist.csv

Embryonic workflow

• Input/<file_name>.h5ad
• Data/Genelist.csv

In each case, the gene list CSV should contain a column named Gene, and genes are processed iteratively through the pipeline.

10.5. Analysis workflow

Across all three groups, the scripts follow a common workflow:

1. Compute mitochondrial and per-cell QC metrics.
2. Filter cells by gene count and mitochondrial fraction.
3. Normalize counts and apply log transformation when needed.
4. Identify highly variable genes.
5. Perform PCA, construct a neighbor graph, run UMAP, and cluster cells using Leiden.
6. Identify gene-positive cells for each target gene.
7. Detect clusters enriched for gene-positive cells.
8. Perform marker analysis on enriched clusters.
9. Score curated marker panels and assign cluster-level cell-type labels.
10. Generate summary plots and write annotated output objects.

10.6. Outputs

For each target gene, the scripts generate outputs such as:

• cluster-level gene-positive statistics in CSV format
• enriched-cluster marker tables
• cell-type label summaries
• cell-type composition summaries for gene-positive cells
• UMAP-based PDF plots
• annotated .h5ad output objects containing updated metadata and labels

10.7. Reproducibility

You can recreate the software environment using either Conda or pip.

10.8. Notes

• Ensure all required .h5ad input files are placed in the expected input directories before running the scripts.
• Confirm that Genelist.csv includes a Gene column.
• Update input/output paths in the scripts if your local directory structure differs from the examples above.

11. Early embyronic sptial transcriptomics analyses

11.1. Overview

This repository provides a visualization workflow for the human embryonic Stereo-seq dataset published by Pan et al. (Nature, 2026).

The original dataset contains 13 human embryos, 77 sagittal sections, including 7 male and 6 female embryos spanning Carnegie stages (CS) 12–23.

This repository is designed for visualization of the published processed data. It generates standardized spatial cell type maps and spatial gene expression maps from the provided .h5ad files, with optional section orientation adjustment and flexible sample organization.

11.2. Repository contents

Genelist.csv

Example gene list for spatial gene expression visualization.

Target genes can be supplied either by

• --genes GeneA,GeneB,GeneC
• --gene-file Data/Genelist.csv

metadata.tsv

Metadata describing each embryo, including

• Carnegie stage
• embryo identifier
• biological sex

sample_list.tsv

Optional sample configuration file.

This file specifies

• selected embryo sections
• rotation angle
• horizontal flipping

to generate standardized figure layouts across all sections.

11.3. Scripts/embryonic_gene_visualization.py

Main visualization workflow.

The script

• loads processed Stereo-seq .h5ad files
• generates spatial cell type maps
• optionally rotates and horizontally flips individual sections
• automatically arranges embryo sections according to developmental stage
• exports publication-quality figures in SVG, PDF, or PNG format

The script supports four visualization modes through the --sex argument:

Mode	Description
all	Visualize all embryo sections together in developmental order (Carnegie stage → embryo → section), regardless of sex.
male	Visualize only male embryos.
female	Visualize only female embryos.
split	Visualize male and female embryos separately, with each group independently ordered by developmental stage.

11.4. Data Availability

This repository does not redistribute the original Stereo-seq dataset.

The processed .h5ad files should be downloaded from the original publication:

Pan J. et al.

Spatiotemporal transcriptome atlas of human embryos after gastrulation

Nature (2026)

https://doi.org/10.1038/s41586-026-10545-0

After downloading, place all .h5ad files into the Input/ directory before running the visualization workflow.

Dataset summary

• 13 human embryos
• 77 sagittal sections
• Carnegie stages CS12–CS23

11.5. Required Input Files

The workflow requires

Stereo-seq datasets

Processed .h5ad files downloaded from the original publication.

Gene input

Target genes can be supplied in either of the following ways

--genes AARS2,ABCC6

or

--gene-file Data/Genelist.csv

Metadata

--sex-tsv Data/metadata.tsv

Optional sample configuration

--sample-list Data/sample_list.tsv

11.6. Example Usage

python Scripts/ embryonic_gene_visualization.py \
    --root Input \
    --outdir Output \
    --sex-tsv Data/metadata.tsv \
    --sample-list Data/sample_list.tsv \
    --sex split \
    --gene-file Data/Genelist.csv

11.7. Output

The workflow generates

• spatial cell type maps
• spatial gene expression maps for each target gene
• multi-panel figures containing all embryo sections