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Jens Lagergren

Researcher at Royal Institute of Technology

Publications -  102
Citations -  5049

Jens Lagergren is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Tree (data structure) & Phylogenetic tree. The author has an hindex of 37, co-authored 88 publications receiving 4552 citations. Previous affiliations of Jens Lagergren include Science for Life Laboratory & SERC Reliability Corporation.

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DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

TL;DR: DeepMP as mentioned in this paper is a CNN-based model that takes information from Nanopore signals and base-calling errors to detect whether a given motif in a read is methylated or not, and it introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells.
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Motif Yggdrasil: sampling sequence motifs from a tree mixture model.

TL;DR: The Motif Yggdrasil sampler is described; the first Gibbs sampler based on a general tree that uses unaligned sequences and it is shown that the MY model improves the modeling of difficult motif instances and that the use of the tree achieves a substantial increase in nucleotide level correlation coefficient.
Posted ContentDOI

Spatial transcriptomics of T and B cell receptors uncovers lymphocyte clonal dynamics in human tissue

TL;DR: Spatial transcriptomics of VDJ sequences (Spatial VDJ) as mentioned in this paper was developed to map immunoglobulin and TR antigen receptors in human tissue sections, which can capture lymphocyte spatial clonal architecture across tissues, which could have important therapeutic implications.
Journal ArticleDOI

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data.

TL;DR: DeepMP as discussed by the authors is a CNN-based model that takes information from Nanopore signals and base-calling errors to detect whether a given motif in a read is methylated or not, and it introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells.
Journal ArticleDOI

Fast and general tests of genetic interaction for genome-wide association studies

TL;DR: Computationally efficient tests of interaction for the complete family of generalized linear models (GLMs) are presented and it is shown that jointly testing the full set of interaction parameters yields superior power and control of false positive rate.