Institute for Computational Cancer Biology | Groups

Schwarzlab

What we do

Developing robust and reliable open-source software tools tackling some of the toughest challenges in cancer research and computational biology is key to our work. For an overview, see below or

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Molecular prognostic index for central nervous system lymphomas

MOP-C

MOP-C is a prognostic model for risk stratification of central nervous system lymphomas developed by the Borchmann Lab in collaboration with the Schwarzlab at the ICCB. 

The MOP-C machine learning based classifier computes a score for the risk of relapse for central nervous system lymphoma patients. It thereby integrates clinical risk factors, radiographic responses and peripheral residual disease measured by circulating tumor DNA.

MOP-C is available as a Shiny application: https://www.mop-c.com/ and available on Bitbucket at: https://bitbucket.org/schwarzlab/mopc.

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Reconstructing cancer phylogenies from copy-number alterations

MEDICC2

MEDICC2 is the leading software tool and algorithm for reconstructing cancer evolution from haplotype-specific (or total) somatic copy-number alterations (SCNAs). MEDICC2 is based on a minimum evolution criterion. It measures the distance between any pair of SCNA profiles as the minimum number of whole-genome doubling events, and gains or losses of arbitrary length, needed to transform one genome into another.

MEDICC2

  • Infers the tree topology that explains the data best according to the above criterion.
  • Reconstructs the unsampled ancestral genomes at the internal nodes of the phylogenetic tree.
  • Determines and extracts the number of evolutionary events along each branch of the tree.

MEDICC2 is available on Bitbucket at https://bitbucket.org/schwarzlab/medicc2.

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Phasing of somatic copy-number alterations

Refphase

Refphase implements multi-region reference phasing for assigning somatic copy-number alterations (SCNAs) to their parental haplotype-of-origin. Refphase was the algorithm with which Mirrored Subclonal Allelic Imbalance (MSAI) events were discovered in the TRACERx-100 cohort (see here), and which allowed us to identify continuous structural and parallel evolution across human cancers (see here).

Refphase

  • Assigns SCNAs to their parental haplotype-of-origin, revealing parallel evolution and MSAI in multi-region sequencing scenarios.
  • Analyses multi-region tumour samples and stratifies individual copy-number segments relative to ploidy into concrete event classes.
  • Allows systematic comparison of different sample subgroups (e.g. metastases vs primaries) to identify genetic commonalities and differences.

Refphase is available on Bitbucket at https://bitbucket.org/schwarzlab/refphase.

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Simulating cancer evolution of one billion cells with spatial constraints

SMITH

SMITH is a novel tool and algorithm for simulating cancer evolution including random mutations with varying fitness effects. SMITH is based on the classic branching model of cancer which it extends with "confinement", a new mechanism which simulates spatial constraints by limiting clonal growth based on its size. 

SMITH

  • Simulates cell growth and cancer evolution for tumours of realistic sizes with more than one billion cells.
  • Implements the confinement mechanism which simulates spatial constraints.
  • Recreates the rich clonal dynamics only known from explicitely spatial tumour growth models.

SMITH is available on Bitbucket at https://bitbucket.org/schwarzlab/smith.

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Fish plots for Python

pyFish

PyFish is a Python library for creating beautiful Fish (or Muller) plots in Python, which display the clonal dynamics of a tumour cell population, i.e. the size of subclones as they arise over time during cancer evolution.

PyFish

  • Creates Fish plots from user input data, which can be smoothed or displayed raw.
  • Is highly customisable with user-defined rendering parameters and color schemes.
  • Allows for high-resolution export of figures in publication quality.

PyFish is available on Bitbucket at https://bitbucket.org/schwarzlab/pyfish.

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Incidence-based haplotype estimation and reconstruction from GAM

GAMIBHEAR

GAMIBHEAR leverages Genome Architecture Mapping (GAM) data to reconstruct chromosome-spanning haplotypes with high accuracy. GAM is a novel experimental technique based on cryosectioning of cell nuclei followed by sequencing to determine chromatin contacts without ligation.

GAMIBHEAR

  • Leverages local haplotype fidelity in GAM data to construct a haplotype graph from the co-occurence of SNPs in GAM nuclear slices.
  • Formulates the haplotype reconstruction problem as a minimum error correction problem and solves it through efficient heuristics.
  • Outperforms Hi-C based haplotype reconstruction in completeness and contiguity, while maintaining similar phasing correctness.

GAMIBHEAR is available on Bitbucket at https://bitbucket.org/schwarzlab/gamibhear.

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Visualisation and explorative analysis of multiple-sequence alignments

ALVIS

ALVIS implements Sequence Bundles, a visualisation technique of sequence motifs that maintains horizontal dependencies between adjacent sites in a multiple-sequence alignment (MSA). 

ALVIS

  • Provides an interactive graphical user interface to explore large MSAs and visualises them using Sequence Bundles.
  • Leverages Fisher score embedding of sequences using a Profile Hidden-Markov-Model to enable quantitative analyses.
  • Aligns phylogenetic trees to the alignment to allow identification of phylogenetic signals in MSAs.

ALVIS is available on Bitbucket at https://bitbucket.org/schwarzlab/alvis.

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