Lorenz Bastian
@lbastianmd.bsky.social
translational leukemia research // ALL 🧬 disease classification // crossroads of B cell development and leukemogenesis // Co-Head of Functional Genomics of Acute Leukemia Lab @ UKSH, Kiel 🌊, Germany and PI @ catchall-kfo5010.com // mountain 🥾 enthusiast
Big shoutout to the whole group who made #IntegrateALL possible - especially @alinamh.bsky.social, Thomas Beder and Claudia Baldus at www.catchall-kfo5010.com
CATCH ALL KFO 5010
Within the new Clinical Research Unit (CRU) “CATCH ALL – towards a cure for all adults and children with Acute Lymphoblastic Leukemia (ALL)”, funded by the German Research Foundation (DFG)…
www.catchall-kfo5010.com
September 28, 2025 at 8:34 AM
Big shoutout to the whole group who made #IntegrateALL possible - especially @alinamh.bsky.social, Thomas Beder and Claudia Baldus at www.catchall-kfo5010.com
🔗 Get the pipeline here:
github.com/NadineWolgas...
We welcome your feedback, issues & pull requests — and are happy to help you get started!🚀
#OpenSource #Bioinformatics #AcuteLymphoblasticLeukemia
github.com/NadineWolgas...
We welcome your feedback, issues & pull requests — and are happy to help you get started!🚀
#OpenSource #Bioinformatics #AcuteLymphoblasticLeukemia
GitHub - NadineWolgast/IntegrateALL: Snakemake diagnostic Pipline for B-ALL classification
Snakemake diagnostic Pipline for B-ALL classification - NadineWolgast/IntegrateALL
github.com
September 28, 2025 at 8:24 AM
🔗 Get the pipeline here:
github.com/NadineWolgas...
We welcome your feedback, issues & pull requests — and are happy to help you get started!🚀
#OpenSource #Bioinformatics #AcuteLymphoblasticLeukemia
github.com/NadineWolgas...
We welcome your feedback, issues & pull requests — and are happy to help you get started!🚀
#OpenSource #Bioinformatics #AcuteLymphoblasticLeukemia
In n=31/1210 B-ALL cases (2.6%), #IntegrateALL identified drivers from two different subtypes in one sample! One gene expression signature dominated in most cases, supporting unsupervised screening for drivers.
September 28, 2025 at 8:24 AM
In n=31/1210 B-ALL cases (2.6%), #IntegrateALL identified drivers from two different subtypes in one sample! One gene expression signature dominated in most cases, supporting unsupervised screening for drivers.
Analysis with our ICC/WHO-HAEM5 based rule-set yielded ~80% unambiguous subtype definitions based on driver calls and corresponding gene expression signatures, which was validated across independent cohorts. - Many thanks to collaborators @ www.mll.com!
September 28, 2025 at 8:24 AM
Analysis with our ICC/WHO-HAEM5 based rule-set yielded ~80% unambiguous subtype definitions based on driver calls and corresponding gene expression signatures, which was validated across independent cohorts. - Many thanks to collaborators @ www.mll.com!
With #IntegrateALL, we extract from RNA-seq:
- Expression counts
- B-ALL subtypes via #ALLCatchR
- Driver gene fusions
- SNVs
- Virtual karyotypes
For classifying #RNASeqCNV B-ALL karyotypes, we built #KaryALL, a machine-learning classifier reaching 0.98 accuracy!
- Expression counts
- B-ALL subtypes via #ALLCatchR
- Driver gene fusions
- SNVs
- Virtual karyotypes
For classifying #RNASeqCNV B-ALL karyotypes, we built #KaryALL, a machine-learning classifier reaching 0.98 accuracy!
September 28, 2025 at 8:24 AM
With #IntegrateALL, we extract from RNA-seq:
- Expression counts
- B-ALL subtypes via #ALLCatchR
- Driver gene fusions
- SNVs
- Virtual karyotypes
For classifying #RNASeqCNV B-ALL karyotypes, we built #KaryALL, a machine-learning classifier reaching 0.98 accuracy!
- Expression counts
- B-ALL subtypes via #ALLCatchR
- Driver gene fusions
- SNVs
- Virtual karyotypes
For classifying #RNASeqCNV B-ALL karyotypes, we built #KaryALL, a machine-learning classifier reaching 0.98 accuracy!