Martin Kircher

Professor of Regulatory Genomics

University of Lübeck

Germany

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Martin Kircher is a Professor of Regulatory Genomics at Universität zu Lübeck in Germany. His research focuses on regulatory genomics, with recent articles exploring topics such as genome-wide pathogenicity prediction, massively parallel reporter assays, and transcriptional regulatory elements. He has also contributed to advancements in liquid biopsy data analysis and machine learning applications in gene prioritization.

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2025

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2021

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2020

Collaborators (6)

Robert Krencik

Associate Professor

UNITED STATES

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Martin Atta Mensah

Berlin Institute of Health at Charité - Universitätsmedizin Berlin

GERMANY

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Lusiné Nazaretyan

Charité - Universitätsmedizin Berlin

GERMANY

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Jay Shendure

Professor

University of Washington

UNITED STATES

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Michael R Speicher

Medical University of Graz

AUSTRIA

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Jill Johnsen

Associate Professor

University of Washington

UNITED STATES

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Martin Kircher

Research Interests

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Articles (20)

varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction

Using individual barcodes to increase quantification power of massively parallel reporter assays

Massively parallel characterization of transcriptional regulatory elements.

Massively parallel jumping assay decodes Alu retrotransposition activity.

Massively parallel reporter assays and mouse transgenic assays provide correlated and complementary information about neuronal enhancer activity.

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

cfDNA UniFlow: a unified preprocessing pipeline for cell-free DNA data from liquid biopsies

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data.

STIGMA: Single-cell tissue-specific gene prioritization using machine learning.

Predicting the pathogenicity of missense variants using features derived from AlphaFold2

Aberrant phase separation and nucleolar dysfunction in rare genetic diseases

Machine learning dissection of human accelerated regions in primate neurodevelopment.

GCparagon: evaluating and correcting GC biases in cell-free DNA at the fragment level

The Regulatory Mendelian Mutation score for GRCh38

Results of genetic analysis of 11 341 participants enrolled in the My Life, Our Future hemophilia genotyping initiative in the United States

A framework to score the effects of structural variants in health and disease

Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.

Systematic assays and resources for the functional annotation of non-coding variants

CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

The impact of different negative training data on regulatory sequence predictions

Collaborators (6)

Robert Krencik

Martin Atta Mensah

Lusiné Nazaretyan

Jay Shendure

Michael R Speicher

Jill Johnsen

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