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- Bennett, Alex, 1995, et al.
(författare)
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Syntactic sugars: crafting a regular expression framework for glycan structures
- 2024
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Ingår i: BIOINFORMATICS ADVANCES. - 2635-0041. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation Structural analysis of glycans poses significant challenges in glycobiology due to their complex sequences. Research questions such as analyzing the sequence content of the alpha 1-6 branch in N-glycans, are biologically meaningful yet can be hard to automate.Results Here, we introduce a regular expression system, designed for glycans, feature-complete, and closely aligned with regular expression formatting. We use this to annotate glycan motifs of arbitrary complexity, perform differential expression analysis on designated sequence stretches, or elucidate branch-specific binding specificities of lectins in an automated manner. We are confident that glycan regular expressions will empower computational analyses of these sequences.Availability and implementation Our regular expression framework for glycans is implemented in Python and is incorporated into the open-source glycowork package (version 1.1+).
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- Castresana-Aguirre, Miguel, et al.
(författare)
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PathBIX—a web server for network-based pathway annotation with adaptive null models
- 2021
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Ingår i: Bioinformatics Advances. - : Oxford University Press (OUP). - 2635-0041. ; 1:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation.Results: We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways.
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- de Weerd, Hendrik A., et al.
(författare)
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MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
- 2022
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Ingår i: Bioinformatics Advances. - : Oxford University Press. - 2635-0041. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- MotivationNetwork-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.ResultsWe developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.Availability and implementationMODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
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- Ekdahl, Ludvig, et al.
(författare)
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AliGater : a framework for the development of bioinformatic pipelines for large-scale, high-dimensional cytometry data
- 2023
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Ingår i: Bioinformatics Advances. - 2635-0041. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: AliGater is an open-source framework to accelerate the development of bioinformatic pipelines for the analysis of large-scale, high-dimensional flow cytometry data. AliGater provides a Python package for automatic feature extraction workflows, as well as building blocks to construct analysis pipelines. Results: We illustrate the use of AliGater in a high-resolution flow cytometry-based genome-wide association study on 46 immune cell populations in 14 288 individuals.
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- Kushwaha, Sandeep Kumar, et al.
(författare)
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ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture
- 2021
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Ingår i: Bioinformatics Advances. - : Oxford University Press (OUP). - 2635-0041. ; 1
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Tidskriftsartikel (refereegranskat)abstract
- The discovery of novel resistance genes (R-genes) is an important component in disease resistance breeding. Nevertheless, R-gene identification from wild species and close relatives of plants is not only a difficult but also a cumbersome process. In this study, ResCap, a support vector machine-based high-throughput R-gene prediction and probe generation pipeline has been developed to generate probes from genomic datasets. ResCap contains two integral modules. The first module identifies the R-genes and R-gene like sequences under four categories containing different domains such as TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), Receptor-like kinase (RLK) and Receptor-like proteins (RLPs). The second module generates probes from extracted nucleotide sequences of resistance genes to conduct sequence capture (SeqCap) experiments. For the validation of ResCap pipeline, ResCap generated probes were synthesized and a sequence capture experiment was performed to capture expressed resistance genes among six spring barley genotypes. The developed ResCap pipeline in combination with the performed sequence capture experiment has shown to increase precision of R-gene identification while simultaneously allowing rapid gene validation including non-sequenced plants.
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| 9. |
- Mutsuddy, Arnab, et al.
(författare)
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Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format
- 2023
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Ingår i: Bioinformatics Advances. - : Oxford University Press. - 2635-0041. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Summary: Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks.Availability and implementation: Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).
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| 10. |
- Reder, Gabriel, 1992, et al.
(författare)
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Genesis-DB: a database for autonomous laboratory systems
- 2023
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Ingår i: Bioinformatics Advances. - 2635-0041. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI)-driven laboratory automation - combining robotic labware and autonomous software agents - is a powerful trend in modern biology. We developed Genesis-DB, a database system designed to support AI-driven autonomous laboratories by providing software agents access to large quantities of structured domain information. In addition, we present a new ontology for modeling data and metadata from autonomously performed yeast microchemostat cultivations in the framework of the Genesis robot scientist system. We show an example of how Genesis-DB enables the research life cycle by modeling yeast gene regulation, guiding future hypotheses generation and design of experiments. Genesis-DB supports AI-driven discovery through automated reasoning and its design is portable, generic, and easily extensible to other AI-driven molecular biology laboratory data and beyond.
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| 11. |
- Yan, Yingxiao, 1997, et al.
(författare)
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Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2
- 2024
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Ingår i: Bioinformatics Advances. - 2635-0041. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework. Results: The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.
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