Rustims: An Open-Source Framework for Rapid Development and Processing of timsTOF Data-Dependent Acquisition Data.

Mass spectrometry is essential for analyzing and quantifying biological samples. The timsTOF platform is a prominent commercial tool for this purpose, particularly in bottom-up acquisition scenarios. The additional ion mobility dimension requires more complex data processing, yet most current software solutions for timsTOF raw data are proprietary or closed-source, limiting integration into custom workflows.

The RMaP challenge of predicting RNA modifications by nanopore sequencing.

The field of epitranscriptomics is undergoing a technology-driven revolution. During past decades, RNA modifications like N6-methyladenosine (mA), pseudouridine (ψ), and 5-methylcytosine (mC) became acknowledged for playing critical roles in cellular processes. Direct RNA sequencing by Oxford Nanopore Technologies (ONT) enabled the detection of modifications in native RNA, by detecting noncanonical RNA nucleosides properties in raw data.

MetaTransformer: deep metagenomic sequencing read classification using self-attention models.

Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning as a tool for analyzing the genomic sequences is promising, yielding convincing results in fields such as motif identification and variant calling.

In Silico and In Vitro Identification of P-Glycoprotein Inhibitors from a Library of 375 Phytochemicals.

Cancer therapy with clinically established anticancer drugs is frequently hampered by the development of drug resistance of tumors and severe side effects in normal organs and tissues. The demand for powerful, but less toxic, drugs is high. Phytochemicals represent an important reservoir for drug development and frequently exert less toxicity than synthetic drugs.

Coronavirus Inhibitors Targeting nsp16.

During the past three decades, humans have been confronted with different new coronavirus outbreaks. Since the end of the year 2019, COVID-19 threatens the world as a rapidly spreading infectious disease. For this work, we targeted the non-structural protein 16 (nsp16) as a key protein of SARS-CoV-2, SARS-CoV-1 and MERS-CoV to develop broad-spectrum inhibitors of nsp16.

Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data.

Background: Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions.

NOseq: amplicon sequencing evaluation method for RNA m6A sites after chemical deamination.

Methods for the detection of m6A by RNA-Seq technologies are increasingly sought after. We here present NOseq, a method to detect m6A residues in defined amplicons by virtue of their resistance to chemical deamination, effected by nitrous acid. Partial deamination in NOseq affects all exocyclic amino groups present in nucleobases and thus also changes sequence information.

Polish is quantitatively different on quartzite flakes used on different worked materials.

Metrology has been successfully used in the last decade to quantify use-wear on stone tools. Such techniques have been mostly applied to fine-grained rocks (chert), while studies on coarse-grained raw materials have been relatively infrequent. In this study, confocal microscopy was employed to investigate polished surfaces on a coarse-grained lithology, quartzite.

Deep learning in next-generation sequencing.

Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addressing questions such as variant calling, metagenomic classification and quantification, genomic feature detection, or downstream analysis in larger biological or medical contexts. In addition to classical algorithmic approaches, machine-learning (ML) techniques are often used for such tasks.

CARE: context-aware sequencing read error correction.

Motivation: Error correction is a fundamental pre-processing step in many Next-Generation Sequencing (NGS) pipelines, in particular for de novo genome assembly. However, existing error correction methods either suffer from high false-positive rates since they break reads into independent k-mers or do not scale efficiently to large amounts of sequencing reads and complex genomes.

Results: We present CARE-an alignment-based scalable error correction algorithm for Illumina data using the concept of minhashing.

A big data approach to metagenomics for all-food-sequencing.

Background: All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches.

Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes.

Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA.

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.

The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility.

Many archeologists are skeptical about the capabilities of use-wear analysis to infer on the function of archeological tools, mainly because the method is seen as subjective, not standardized and not reproducible. Quantitative methods in particular have been developed and applied to address these issues. However, the importance of equipment, acquisition and analysis settings remains underestimated.

Disease-relevant signalling-pathways in head and neck cancer: Taspase1's proteolytic activity fine-tunes TFIIA function.

Head and neck cancer (HNC) is the seventh most common malignancy in the world and its prevailing form, the head and neck squamous cell carcinoma (HNSCC), is characterized as aggressive and invasive cancer type. The transcription factor II A (TFIIA), initially described as general regulator of RNA polymerase II-dependent transcription, is part of complex transcriptional networks also controlling mammalian head morphogenesis. Posttranslational cleavage of the TFIIA precursor by the oncologically relevant protease Taspase1 is crucial in this process.

MetaCache: context-aware classification of metagenomic reads using minhashing.

Motivation: Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e.

Next-generation sequencing: big data meets high performance computing.

The progress of next-generation sequencing has a major impact on medical and genomic research. This high-throughput technology can now produce billions of short DNA or RNA fragments in excess of a few terabytes of data in a single run. This leads to massive datasets used by a wide range of applications including personalized cancer treatment and precision medicine.

CoverageAnalyzer (CAn): A Tool for Inspection of Modification Signatures in RNA Sequencing Profiles.

Combination of reverse transcription (RT) and deep sequencing has emerged as a powerful instrument for the detection of RNA modifications, a field that has seen a recent surge in activity because of its importance in gene regulation. Recent studies yielded high-resolution RT signatures of modified ribonucleotides relying on both sequence-dependent mismatch patterns and reverse transcription arrests. Common alignment viewers lack specialized functionality, such as filtering, tailored visualization, image export and differential analysis.

rapidGSEA: Speeding up gene set enrichment analysis on multi-core CPUs and CUDA-enabled GPUs.

Background: Gene Set Enrichment Analysis (GSEA) is a popular method to reveal significant dependencies between predefined sets of gene symbols and observed phenotypes by evaluating the deviation of gene expression values between cases and controls. An established measure of inter-class deviation, the enrichment score, is usually computed using a weighted running sum statistic over the whole set of gene symbols. Due to the lack of analytic expressions the significance of enrichment scores is determined using a non-parametric estimation of their null distribution by permuting the phenotype labels of the probed patients.

Integrated quantitative proteomic and transcriptomic analysis of lung tumor and control tissue: a lung cancer showcase.

Proteomics analysis of paired cancer and control tissue can be applied to investigate pathological processes in tumors. Advancements in data-independent acquisition mass spectrometry allow for highly reproducible quantitative analysis of complex proteomic patterns. Optimized sample preparation workflows enable integrative multi-omics studies from the same tissue specimens.

DrugTargetInspector: An assistance tool for patient treatment stratification.

Cancer is a large class of diseases that are characterized by a common set of features, known as the Hallmarks of cancer. One of these hallmarks is the acquisition of genome instability and mutations. This, combined with high proliferation rates and failure of repair mechanisms, leads to clonal evolution as well as a high genotypic and phenotypic diversity within the tumor.

The reverse transcription signature of N-1-methyladenosine in RNA-Seq is sequence dependent.

The combination of Reverse Transcription (RT) and high-throughput sequencing has emerged as a powerful combination to detect modified nucleotides in RNA via analysis of either abortive RT-products or of the incorporation of mismatched dNTPs into cDNA. Here we simultaneously analyze both parameters in detail with respect to the occurrence of N-1-methyladenosine (m(1)A) in the template RNA. This naturally occurring modification is associated with structural effects, but it is also known as a mediator of antibiotic resistance in ribosomal RNA.