Katarzyna Wreczycka

Katarzyna Wreczycka

AI/ML Scientist. Computational Biology. Multi-Omics.

Portfolio

Research

Interpreting bias in genomics: high-occupancy target regions

We used machine-learning models - elastic-net regression and principal component analysis (PCA) - to investigate genomic regions known as high-occupancy target (HOT) regions. These regions attract unusually high numbers of proteins and are likely technical artifacts of chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

While antibody quality and chromatin interactions are known to affect ChIP-seq reliability, our study found that GC- and CpG-rich sequences, DNA methylation, and RNA:DNA hybrids (R-loops) also contribute to these artifacts across species. This work shows how machine learning can uncover hidden biases in genomic data and improve experimental interpretation.

HOT-region ChIP-seq signal comparison
Unexpected ChIP-seq signals appear in HOT regions even without the target protein (KO ChIP-seq). The barplot shows how often these regions are detected as bound. HOT regions correspond to the top 1% of genomic regions with the highest protein-binding signals (99th percentile).

Publication: Wreczycka et al., Nucleic Acids Research, 2019

Liquid-biopsy epigenetics in disease

DNA methylation biomarkers in acute coronary syndrome

We investigated circulating cell-free DNA (cfDNA) methylation as a non-invasive biomarker for acute coronary syndrome (ACS), based on the principle that damaged tissues release DNA into the bloodstream. Methylation profiles distinguished ACS subtypes and identified cell-type-specific markers that can help trace the tissue origin of cfDNA. Hundreds of markers linked to cardiovascular conditions and inflammation were validated in an independent cohort.

PCA of differentially methylated regions associated with ACS severity
PCA of 254 differentially methylated regions linked to ACS severity using linear models.

Publication: Cuadrat et al., NAR Genomics and Bioinformatics, 2023

DNA methylation profiling in neuroblastoma

In collaboration with Charité Hospital in Berlin, we analyzed primary neuroblastoma tumors and urine-derived cfDNA using bisulfite sequencing and RNA-seq. We identified methylation patterns that distinguish high- and low-risk tumors and linked MYCN-driven methylation changes to disrupted transcription-factor networks, highlighting potential therapeutic targets.

Methylation-based clustering of neuroblastoma patients
Methylation-based clustering of neuroblastoma patients using differentially methylated CpGs.

Open-source software

genomation

genomation is a Bioconductor R package for genomic feature and interval analysis. It provides tools to read BED/GFF files as GRanges, summarize genomic features across regions, create enrichment plots and heatmaps, and annotate regions with exons, introns, or promoters. I was a co-developer and maintainer.

GitHub repository · Developed with the MDC-BIMSB Bioinformatics and Omics Data Science Platform

PiGx

PiGx is a collection of genomics pipelines implemented with Snakemake, Python, and R. Pipelines are configured with a sample sheet and settings file, then generate interactive HTML reports summarizing results. My contribution included co-implementing the PiGx BS-seq pipeline.

GitHub repository · Developed with the MDC-BIMSB Bioinformatics and Omics Data Science Platform

motifActivity

motifActivity is an R package for identifying transcription factors that may explain changes in gene expression or epigenetic marks. It predicts transcription-factor activity from RNA-seq, BS-seq, ChIP-seq, ATAC-seq, and related data, together with DNA motif collections.

GitHub repository · Developed with the MDC-BIMSB Bioinformatics and Omics Data Science Platform

Freelance & industry projects

Endometriosis prediction from scRNA-seq data

I built a single-cell RNA-seq workflow using menstrual-effluent samples to detect endometriosis through endometrium-atlas-based cell mapping and composition profiling. It identifies disease-associated shifts in stromal and immune populations and derives a sample-level similarity score.

  • Processed data with nf-core/scrnaseq for standardized quality control, alignment, and count-matrix generation.
  • Used AWS resources to scale preprocessing, reference mapping, and downstream modeling.
  • Mapped donor cells to an endometrium reference atlas using scArches transfer learning, scvi-tools, and PyTorch.
  • Performed analysis in Python with AnnData, Scanpy, scvi-tools, NumPy, pandas, Matplotlib, and seaborn.
  • Derived a sample-level score to classify donors as control-like or endometriosis-like.
Menstrual-effluent single-cell profiles mapped to an endometrium atlas
Menstrual effluent scRNA-seq profiles were mapped to an endometrium reference atlas to define cell-type composition shifts and generate a sample-level score that distinguishes endometriosis from control donors.

Prioritizing therapeutic targets in clinical trials

Biomarker visualization and survival analysis

We developed interactive visualizations, including oncoprints, to highlight biomarkers in patients with limited treatment options. These summaries reveal genomic alterations and support the identification of potential therapeutic targets. Survival analyses helped assess the clinical relevance of nominated targets in patient cohorts with poor outcomes or few effective therapies.

Example biomarker oncoprintSurvival analysis of clinical biomarkers
Example of biomarker visualization and survival analysis.

Machine learning for target identification

To prioritize therapeutic targets, we applied positive-unlabeled (PU) learning, which is useful when only confirmed targets are labeled. PU classifiers combined gene expression, mutation, and therapy annotations to identify potential targets. We also used autoencoders to uncover hidden patterns and prioritize molecular features without labels.

Positive-unlabeled learning concept
PU learning principle (figure adapted from a blogpost).
Variational autoencoder schematic
Schematic of a Variational Autoencoder (figure adapted from a blogpost).

Multi-omics and Enformer for an Alzheimer’s disease biomarker

In this project, I investigated glial-to-neuron reprogramming through activation of a specific transcription factor (TF) in Alzheimer’s disease.

I integrated multi-omics data—including RNA-seq, ATAC-seq, and H3K4me2/H3K27me3 ChIP-seq—to identify differentially expressed genes and pathways (GO, GSEA), and examine their association with Alzheimer’s disease risk variants (GWAS), regulatory enhancers, DNA motifs, and TF binding sites linked to neuronal differentiation.

Large-scale analyses were executed via Nextflow pipelines on Kubernetes and AWS, ensuring scalable and reproducible processing of NGS datasets.

Using Enformer, I mapped the regulatory landscape around the target transcription factor and identified sequence regions predicted to influence its expression in neurons and glia. Enformer predicts regulatory activity from DNA sequence; gradient-based attribution highlights regions with strong effects on gene expression. I analyzed an approximately 400 kb region, applying cell-type-specific masks and signal smoothing to identify candidate enhancers.

Example RNA-seq analysis
Example RNA-seq analysis.
Motif activity in candidate enhancers
Example DNA motif activity in enhancer regions defined by ATAC-seq and histone-mark ChIP-seq.
ChIP-seq peaks around the target transcription-factor gene
Example ChIP-seq peaks around the target transcription-factor gene.
Enformer model architecture
Enformer’s architecture by Avsec et al., copied from the blog post.
Enformer output tracks for neurons and glia
Example Enformer tracks for neurons and glia.
Candidate enhancer regions identified with Enformer
Candidate enhancer peaks and regions of interest.

IGV web-app feature development

I contributed to the IGV web application, an interactive tool for exploring genomic data (source code). The work used JavaScript and Python and enabled visualization of public and in-house datasets.

  • Enabled dynamic visualization of new in-house genomic datasets.
  • Added highlighting of genomic regions of interest, including variants.
  • Added RefSeq and GENCODE transcript controls to collapse or expand isoforms, extend selected gene isoforms, and adjust track widths.
  • Linked visualized tracks to their source databases.
  • Implemented a command-line tool to generate snapshots of specified genes or regions.
IGV web application showing genomic data tracks
Example IGV web-app view with genomic data tracks.

Proteomic signatures for Crohn’s disease

In this project, I worked with biobank data from IBD Plexus, a resource of the Crohn’s & Colitis Foundation, to explore machine-learning approaches for identifying proteomic signatures to support blood-based testing for Crohn’s disease. I also developed AWS infrastructure to support the associated data workflows.

ROC curves for the Endoscopic Healing Index and fecal calprotectin distinguishing active Crohn’s disease from endoscopic remission
ROC curves for the Endoscopic Healing Index (EHI) and fecal calprotectin (FC) for distinguishing active disease from endoscopic remission. Figure from D’Haens et al., “Development and Validation of a Test to Monitor Endoscopic Activity in Patients With Crohn’s Disease Based on Serum Levels of Proteins,” Gastroenterology (2020).
AWS VPC example with public and private subnets, NAT gateways, and application servers
Reference diagram of a VPC with public and private subnets and NAT gateways. Source: Amazon VPC documentation.