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AMAPEC
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The machine learning tool AMAPEC, is a predictor of antimicrobial activity for fungal secreted proteins, that aims to assist researchers in the characterization of new effectors.
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Bart Thomma
| - Mesny F, Thomma B (2024) AMAPEC: accurate antimicrobial activity prediction for fungal effector proteins. BioRxiv. doi: doi.org/10.1101/2024.01.04.574150.
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BARVISTA
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A web client for genome-wide spatially resolved gene expression analysis with single-cell resolution in barley.
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Rüdiger Simon, Björn Usadel
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DeepCRE
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DeepCRE is a deep learning model that predicts gene expression and identifies predictive regulatory sequence motifs from gene sequences and RNA-seq data.
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Jędrzej Jakub Szymański
| - Peleke FF, Zumkeller SM, & Szymański J (2024) DeepCRE: Deep deep learning model linking regulatory sequences with gene expression patterns (0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.10822014
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Peleke FF, Zumkeller SM, et int, Szymański J (2024) Deep learning the cis-regulatory code for gene expression in selected model plants. Nat Commun 15(1):3488. doi: 10.1038/s41467-024-47744-0.
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DeepMolecules
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A webserver containing models for predicting substrates for enzymes and transport proteins as well as prediction models for enzyme kinetic parameters.
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Martin Lercher
| - Kroll A, Ranjan S, Engqvist MKM, Lercher MJ (2023) A general model to predict small molecule substrates of enzymes based on machine and deep learning. Nat Commun 14(1):2787. doi: 10.1038/s41467-023-38347-2.
- Kroll A, Ranjan S, Lercher MJ (2024) A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships. PLoS Comput Biol 20(5):e1012100. doi: 10.1371/journal.pcbi.1012100.
- Kroll A, Rousset Y, Hu XP, Liebrand NA, Lercher MJ (2023) Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning. Nat Commun 14(1):4139. doi: 10.1038/s41467-023-39840-4.
- Kroll A, Engqvist MKM, Heckmann D, Lercher MJ (2021) Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLoS Biol 19(10):e3001402. doi: 10.1371/journal.pbio.3001402.
- Kroll A, Niebuhr N, Butler G, Lercher MJ (2023) A general prediction model for substrates of transport proteins. bioRxiv:2023.2010.2031.564943. doi: 10.1101/2023.10.31.564943.
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GMOCU
(Link) |
GMOCU Software for the standardized semi-automated digital documentation, management, and biological risk assessment of genetic parts.
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Uriel Urquiza, Matias Zurbriggen
| - Wagner C, Urquiza-Garcia U, Zurbriggen MD, Beyer HM (2024) GMOCU: Digital Documentation, Management, and Biological Risk Assessment of Genetic Parts. Adv Biol (Weinh) 8(4):e2300529. doi: 10.1002/adbi.202300529.
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Helixer
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A tool for structural annotation of genes from plants, fungi and animals.
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Andreas Weber, Björn Usadel
| - Holst F, Bolger A, Günther C, Maß J, Triesch S, Kindel F, Kiel N, Saadat N, Ebenhöh O, Usadel B, Schwacke R, Bolger M, Weber APM, Denton AK (2023) Helixer–de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model. bioRxiv. doi: 10.1101/2023.02.06.527280.
- Stiehler F, Steinborn M, Scholz S, Dey D, Weber APM, Denton AK (2021) Helixer: cross-species gene annotation of large eukaryotic genomes using deep learning. Bioinformatics 36(22-23):5291-5298. doi: 10.1093/bioinformatics/btaa1044.
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modelbase
(Link) |
modelbase is a Python package that facilitates the in silico implementation of complex biological systems. This package includes various applications and continues to grow. You can use modelbase to develop ordinary differential and simple partial differential equation-based models. In addition, it is quite easy to construct carbon labeling systems that can be used to understand the distribution of isotopomers in metabolic networks.
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Anna Matuszyńska, Oliver Ebenhöh
| - van Aalst M, Ebenhöh O, Matuszyńska A (2021) Constructing and analysing dynamic models with modelbase v1.2.3: a software update. BMC Bioinformatics 22(1):203. doi: 10.1186/s12859-021-04122-7.
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PlantEd Game
(Link) |
PlantEd Game is a whole-plant modeling environment designed to explore plant growth and survival strategies in changing environments, utilizing citizen science and reinforcement learning.
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Jędrzej Jakub Szymański
| - Koch, D., Psaroudakis, D., Weder, J.-N., Cambordia, S., Töpfer, N., & Szymanski, J. (n.d.). PlantEd: Educational Game on Plant Metabolism (1.0) [Computer software]. https://doi.org/10.5281/zenodo.10978202
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WhatsHap polyphase
(Link) |
A tool for reference-based polyploid haplotype phasing from long reads.
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Gunnar Klau
| - Schrinner SD, Mari RS, Ebler J, Rautiainen M, Seillier L, Reimer JJ, Usadel B, Marschall T, Klau GW (2020) Haplotype threading: accurate polyploid phasing from long reads. Genome Biol 21(1):252. doi: 10.1186/s13059-020-02158-1.
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Predmoter
(Link) |
Predmoter predicts promoter and enhancer associated next generation sequencing (NGS) data, Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) and histone (H3K4me3) Chromatin immunoprecipitation DNA-sequencing (ChIP-seq), base-wise for plant species.
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Andreas Weber
| - Kindel F, Triesch S, et int, Denton AK (2024) Predmoter-cross-species prediction of plant promoter and enhancer regions. Bioinform Adv 4(1):vbae074. doi: 10.1093/bioadv/vbae074.
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