Biology subjects

Benner, P.

Publications and source records attributed to Benner, P..

5 recordsLinked to original sources

bioRxiv2024

Fiora: Local neighborhood-based prediction of compound mass spectra from single fragmentation events

Non-targeted metabolomics holds great promise for advancing precision medicine and facilitating the discovery of novel biomarkers. However, the identification of compounds from tandem mass spectra remains a non-trivial task due to the incomplete nature of spectral reference libraries. Augmenting these libraries with simulated mass spectra can provide the necessary reference to resolve unmatched mass spectra, but remains a difficult undertaking to this day. In this study, we introduce Fiora, an innovative open-source algorithm using graph neural networks to simulate tandem mass spectra in silico. Our objective is to improve fragment intensity prediction with an intricate graph model architecture that facilitates edge prediction, thereby modeling fragment ions as the result of singular bond breaks and their local molecular neighborhood. We evaluate the performance on test data from NIST (2017) and the curated MS-Dial spectral library, as well as compounds from the 2016 and 2022 CASMI challenges. Fiora not only surpasses state-of-the-art fragmentation algorithms, ICEBERG and CFM-ID, in terms of prediction quality, but also predicts additional features, such as retention time and collision cross section. In addition, Fiora demonstrates significant speed improvements through the use of GPUs. This enables rapid (re)scoring of putative compound identifications in non-targeted experiments and facilitates large-scale expansion of spectral reference libraries with accurate spectral predictions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/590551v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@90bf99org.highwire.dtl.DTLVardef@112da33org.highwire.dtl.DTLVardef@b376eeorg.highwire.dtl.DTLVardef@450ee6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Mistle: bringing spectral library predictions to metaproteomics with an efficient search index

MotivationDeep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or used for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. ResultsIn this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with an 8 to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. AvailabilityMistle is freely available on GitHub at https://github.com/BAMeScience/Mistle. Contactthilo.muth@bam.de

bioinformatics↗

bioRxiv2021

Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences

Recent efforts to measure epigenetic marks across a wide variety of different cell types and tissues provide insights into the cell type-specific regulatory landscape. We use this data to study if there exists a correlate of epigenetic signals in the DNA sequence of enhancers and explore with computational methods to what degree such sequence patterns can be used to predict cell type-specific regulatory activity. By constructing classifiers that predict in which tissues enhancers are active, we are able to identify sequence features that might be recognized by the cell in order to regulate gene expression. While classification performances vary greatly between tissues, we show examples where our classifiers correctly predict tissue specific regulation from sequence alone. We also show that many of the informative patterns indeed harbor transcription factor footprints.

bioinformatics↗

bioRxiv2018

CRUP: A comprehensive framework to predict condition-specific regulatory units

We present the software CRUP (Condition-specific Regulatory Units Prediction) to infer from epigenetic marks a list of regulatory units consisting of dynamically changing enhancers with their target genes. The workflow consists of a novel pre-trained enhancer predictor that can be reliably applied across cell lines and species, solely based on histone modification ChIP-seq data. Enhancers are subsequently assigned to different conditions and correlated with gene expression to derive regulatory units. We thoroughly test and then apply CRUP to a rheumatoid arthritis model, identifying enhancer-gene pairs comprising known disease genes as well as new candidate genes.\n\nAvailabilityhttps://github.com/VerenaHeinrich/CRUP

bioinformatics↗

bioRxiv2018

Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise.

The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of dozens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GRs core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed distinct DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise.

systems biology↗