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101 to 110 of 112 Results

01_De-novo_assemblies_from_SRA.rar Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome RAR Archive - 299.1 MB - MD5: ae7edef5e9e9fd788d95cbcd7cb30374 For the genomes downloaded from SRA database (raw data; without assembled version available), assembly was performed using SPAdes Genome Assembler software v.3.15.4 (Bankevich et al. 2012), using default parameters.
02_Genome_Annotation_Files.rar Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome RAR Archive - 1.2 GB - MD5: 7cd4ab27f7ed6e9c56b22b118fa8cb37 The 661 assembled genomes were annotated using AUGUSTUS software v.3.4.0 (Stanke and Morgenstern 2005), considering 16 different pre-trained models, chosen as belonging to the Ascomycota phyla (11) or the Basidiomycota phyla (5): Ascomycota – S. cerevisiae S288c, C. albicans, Mey...
03_Proteomes.rar Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome RAR Archive - 889.4 MB - MD5: 90a5a5170220edf89555cc8702e0ee97 A consensus proteome database was prepared by considering the 530 complete genomes (from 134 species) that passed the quality control.
04_Consensus_KOs.rar Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome RAR Archive - 12.2 MB - MD5: 89d3865b483ca694fbb6efece0f3c6f3 Functional genomic annotation was performed using three tools, for increased robustness, as being the three most used tools available for functional annotation: i) eggNOG-mapper v.5.0 (Jensen et al. 2008); ii) kofamKOALA v. 2022-04-03 (Aramaki et al. 2020); iii) KAAS-KEEG Automat...
05_CAZYmes.rar Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome RAR Archive - 1.6 MB - MD5: b45a83c6a10236349ca433765fff1c80 Gene function predictions were also accomplished by assessing the Carbohydrate-Active EnZymes (CAZymes) database (Cantarel et al. 2009), using dbCAN2 software (Zhang et al. 2018), testing three different annotation tools to increase robustness, HMMER, eCAMI and DIAMOND, and compi...
Machine Learning Texture Optimization Mar 3, 2023 - Lab Física Computacional Marques, Luis, 2023, "Machine Learning Texture Optimization", https://doi.org/10.34622/datarepositorium/MUVOJD, Repositório de Dados da Universidade do Minho, V1, UNF:6:PYIYvLMs2mywboc9nuNvdw== [fileUNF] This data set contains the data used for training, validation and test the deep neural network used for solving a texture optimization problem for the lubricated contacts between surfaces.
00_README.txt Mar 3, 2023 - Machine Learning Texture Optimization Plain Text - 1.0 KB - MD5: 389bfc2f1a11e6e5d5229657a35eafb9
inputs.tab Mar 3, 2023 - Machine Learning Texture Optimization Tab-Delimited - 14.1 MB - MD5: ba4bc1b2c58220469b88aa09f1397912
inputs_test_set.csv Mar 3, 2023 - Machine Learning Texture Optimization Comma Separated Values - 403.7 KB - MD5: 4d27879770d10ce970acac13c54f02ff
script_DNN_training.ipynb Mar 3, 2023 - Machine Learning Texture Optimization Jupyter Notebook - 10.0 KB - MD5: 780a3e6bdf582afd7db34856f5d00922

01_De-novo_assemblies_from_SRA.rar

Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome

RAR Archive - 299.1 MB - MD5: ae7edef5e9e9fd788d95cbcd7cb30374

For the genomes downloaded from SRA database (raw data; without assembled version available), assembly was performed using SPAdes Genome Assembler software v.3.15.4 (Bankevich et al. 2012), using default parameters.

02_Genome_Annotation_Files.rar

Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome

RAR Archive - 1.2 GB - MD5: 7cd4ab27f7ed6e9c56b22b118fa8cb37

The 661 assembled genomes were annotated using AUGUSTUS software v.3.4.0 (Stanke and Morgenstern 2005), considering 16 different pre-trained models, chosen as belonging to the Ascomycota phyla (11) or the Basidiomycota phyla (5): Ascomycota – S. cerevisiae S288c, C. albicans, Mey...

03_Proteomes.rar

Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome

RAR Archive - 889.4 MB - MD5: 90a5a5170220edf89555cc8702e0ee97

A consensus proteome database was prepared by considering the 530 complete genomes (from 134 species) that passed the quality control.

04_Consensus_KOs.rar

Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome

RAR Archive - 12.2 MB - MD5: 89d3865b483ca694fbb6efece0f3c6f3

Functional genomic annotation was performed using three tools, for increased robustness, as being the three most used tools available for functional annotation: i) eggNOG-mapper v.5.0 (Jensen et al. 2008); ii) kofamKOALA v. 2022-04-03 (Aramaki et al. 2020); iii) KAAS-KEEG Automat...

05_CAZYmes.rar

Feb 1, 2024 - Phylogenomics and functional annotation of 530 non-Saccharomyces yeasts from winemaking environments reveals their Fermentome and Flavorome

RAR Archive - 1.6 MB - MD5: b45a83c6a10236349ca433765fff1c80

Gene function predictions were also accomplished by assessing the Carbohydrate-Active EnZymes (CAZymes) database (Cantarel et al. 2009), using dbCAN2 software (Zhang et al. 2018), testing three different annotation tools to increase robustness, HMMER, eCAMI and DIAMOND, and compi...

Machine Learning Texture Optimization

Mar 3, 2023 - Lab Física Computacional

Marques, Luis, 2023, "Machine Learning Texture Optimization", https://doi.org/10.34622/datarepositorium/MUVOJD, Repositório de Dados da Universidade do Minho, V1, UNF:6:PYIYvLMs2mywboc9nuNvdw== [fileUNF]

This data set contains the data used for training, validation and test the deep neural network used for solving a texture optimization problem for the lubricated contacts between surfaces.

00_README.txt

Mar 3, 2023 - Machine Learning Texture Optimization

Plain Text - 1.0 KB - MD5: 389bfc2f1a11e6e5d5229657a35eafb9

inputs.tab

Mar 3, 2023 - Machine Learning Texture Optimization

Tab-Delimited - 14.1 MB - MD5: ba4bc1b2c58220469b88aa09f1397912

inputs_test_set.csv

Mar 3, 2023 - Machine Learning Texture Optimization

Comma Separated Values - 403.7 KB - MD5: 4d27879770d10ce970acac13c54f02ff

script_DNN_training.ipynb

Mar 3, 2023 - Machine Learning Texture Optimization

Jupyter Notebook - 10.0 KB - MD5: 780a3e6bdf582afd7db34856f5d00922

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