cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5763837
title: Datasets for Trautmann et al. 2021

description: Data used to reproduce the analysis and figures of<strong><em> Trautmann, T., Koirala, S., Carvalhais, N., Güntner, A., & Jung, M. (2021). The importance of vegetation to understand terrestrial water storage variations. Hydrology and Earth System Sciences Discussions, 1-31</em></strong> <pre><strong>The .zip files include: </strong></pre> input.7zip forcing data and observational constraints ancillary data such as the regional classification all data is provided in <em>*.mat</em> files and adjusted to the study area and considered time period all original data is publical available, please se the references and processing as described in <em>Trautmann et al. 2021</em> model output from different experiments output from different model experiments, including simulated fluxes & states and information on model settings when unpacked, that data should be stored in a folder like follows: <em><strong>data/</strong></em> <em><strong>input/</strong></em> -&gt; the unpacked input.7zip <strong><em>model_output/ </em></strong>-&gt; the unpacked other .zip files, in 1 folder each
description: {"references": ["Trautmann, T., Koirala, S., Carvalhais, N., G\u00fcntner, A., & Jung, M. (2021). The importance of vegetation to understand terrestrial water storage variations. Hydrology and Earth System Sciences Discussions, 1-31"]}
DOI: 10.5281/zenodo.5909798
title: Impact of propofol general anesthesia on cardiovascular and cerebrovascular closed loop variability interactions

description: Dataset from Impact of propofol general anesthesia on cardiovascular and cerebrovascular closed loop variability interactions Vlasta Bari, Angela Fantinato, Emanuele Vaini, Francesca Gelpi, Beatrice Cairo, Beatrice De Maria, Valeria Pistuddi, Marco Ranucci, Alberto Porta 10.1016/j.bspc.2021.102735 Abstract The aim of the study is to assess the impact of general propofol-based anesthesia on both cardiovascular and cerebrovascular regulations. The strength and latency of cardiovascular and cerebrovascular controls were monitored by computing cross-correlation function (CCF) between heart period (HP) and systolic arterial pressure (SAP) and between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF) velocity. HP, SAP, MAP and MCBF velocity were recorded in 25 patients (63.4 ± 8.7 yrs, 2 females) undergoing coronary artery bypass grafting before (PRE) and after (POST) induction of propofol general anesthesia. Subjects breathed spontaneously during PRE, while they were mechanically ventilated during POST. CCFs were sampled at the first peak, or valley, with both negative and positive lags closest to zero according to a priori physiological notions about the CCF sign and latency of the presumed dynamic interactions. The null hypothesis of uncoupling was tested by associating pairs of signals from different subjects. Bidirectional interactions were detected, thus indicating the action of closed loop regulatory mechanisms along baroreflex and mechanical feedforward pathway in the case of HP-SAP control and along pressure-to-flow link and Cushing reflex in the case of MCBF-MAP control. Propofol anesthesia did not alter the coupling strength along baroreflex, feedforward mechanical pathway and pressure-to-flow link, while significantly decreased the strength of the Cushing reflex. Moreover, propofol anesthesia significantly shortened the baroreflex and pressure-to-flow link latencies. The proposed analysis suggests profound modifications of cardiovascular and cerebrovascular controls during general anesthesia as a likely result of autonomic function modifications induced by propofol.
DOI: 10.5281/zenodo.5909797
title: Impact of propofol general anesthesia on cardiovascular and cerebrovascular closed loop variability interactions

description: Dataset from Impact of propofol general anesthesia on cardiovascular and cerebrovascular closed loop variability interactions Vlasta Bari, Angela Fantinato, Emanuele Vaini, Francesca Gelpi, Beatrice Cairo, Beatrice De Maria, Valeria Pistuddi, Marco Ranucci, Alberto Porta 10.1016/j.bspc.2021.102735 Abstract The aim of the study is to assess the impact of general propofol-based anesthesia on both cardiovascular and cerebrovascular regulations. The strength and latency of cardiovascular and cerebrovascular controls were monitored by computing cross-correlation function (CCF) between heart period (HP) and systolic arterial pressure (SAP) and between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF) velocity. HP, SAP, MAP and MCBF velocity were recorded in 25 patients (63.4 ± 8.7 yrs, 2 females) undergoing coronary artery bypass grafting before (PRE) and after (POST) induction of propofol general anesthesia. Subjects breathed spontaneously during PRE, while they were mechanically ventilated during POST. CCFs were sampled at the first peak, or valley, with both negative and positive lags closest to zero according to a priori physiological notions about the CCF sign and latency of the presumed dynamic interactions. The null hypothesis of uncoupling was tested by associating pairs of signals from different subjects. Bidirectional interactions were detected, thus indicating the action of closed loop regulatory mechanisms along baroreflex and mechanical feedforward pathway in the case of HP-SAP control and along pressure-to-flow link and Cushing reflex in the case of MCBF-MAP control. Propofol anesthesia did not alter the coupling strength along baroreflex, feedforward mechanical pathway and pressure-to-flow link, while significantly decreased the strength of the Cushing reflex. Moreover, propofol anesthesia significantly shortened the baroreflex and pressure-to-flow link latencies. The proposed analysis suggests profound modifications of cardiovascular and cerebrovascular controls during general anesthesia as a likely result of autonomic function modifications induced by propofol.
DOI: 10.48370/ofd/vzlivk
title: Athelia alnicola (Bourdot Galzin) Jülich, 2842

description: Fungarium Macromycetes
DOI: 10.5517/ccdc.csd.cc1p869g
title: CCDC 1557015: Experimental Crystal Structure Determination

description: Related Article: El-Sayed A. El-Samanody, Ayman K. El-Sawaf, Metwally Madkour|2019|Inorg.Chim.Acta|487|307|doi:10.1016/j.ica.2018.12.027
DOI: 10.5517/ccdc.csd.cc1pjtst
title: CCDC 1565307: Experimental Crystal Structure Determination

description: Related Article: Leili Esrafili, Alireza Azhdari Tehrani, Ali Morsali, Lucia Carlucci, Davide M. Proserpio|2019|Inorg.Chim.Acta|484|386|doi:10.1016/j.ica.2018.09.025
DOI: 10.5517/ccdc.csd.cc12kpp0
title: CSD 1000112: Experimental Crystal Structure Determination

description: Related Article: Qiao Gao, Dong-Hua Hu, De-Hui Li, Ming-Hua Duan, Yong Wu|2019|Inorg.Chim.Acta|487|107|doi:10.1016/j.ica.2018.12.001
DOI: 10.5517/ccdc.csd.cc13353h
title: CCDC 1015935: Experimental Crystal Structure Determination

description: Related Article: Filip Torić, Gordana Pavlović, Damir Pajić, Tomica Hrenar, Krešo Zadro, Marina Cindrić|2019|Inorg.Chim.Acta|484|457|doi:10.1016/j.ica.2018.09.068
DOI: 10.5517/ccdc.csd.cc13354j
title: CCDC 1015936: Experimental Crystal Structure Determination

description: Related Article: Filip Torić, Gordana Pavlović, Damir Pajić, Tomica Hrenar, Krešo Zadro, Marina Cindrić|2019|Inorg.Chim.Acta|484|457|doi:10.1016/j.ica.2018.09.068
DOI: 10.5517/ccdc.csd.cc2052x6
title: CCDC 1851937: Experimental Crystal Structure Determination

description: Related Article: Michał Terlecki, Iwona Justyniak, Daniel Prochowicz, Janusz Lewiński|2020|Eur.J.Inorg.Chem.|2020|119|doi:10.1002/ejic.201901288
DOI: 10.5517/ccdc.csd.cc2052z8
title: CCDC 1851939: Experimental Crystal Structure Determination

description: Related Article: Michał Terlecki, Iwona Justyniak, Daniel Prochowicz, Janusz Lewiński|2020|Eur.J.Inorg.Chem.|2020|119|doi:10.1002/ejic.201901288
DOI: 10.5517/ccdc.csd.cc20530b
title: CCDC 1851940: Experimental Crystal Structure Determination

description: Related Article: Michał Terlecki, Iwona Justyniak, Daniel Prochowicz, Janusz Lewiński|2020|Eur.J.Inorg.Chem.|2020|119|doi:10.1002/ejic.201901288
DOI: 10.5517/ccdc.csd.cc20dnkn
title: CCDC 1859211: Experimental Crystal Structure Determination

description: Related Article: Jia Lin, Qiuxia Cheng, Jianen Zhou, Xiaoming Lin, R. Chenna Krishna Reddy, Tao Yang, Gang Zhang|2019|J.Solid State Chem.|270|339|doi:10.1016/j.jssc.2018.11.033
DOI: 10.5517/ccdc.csd.cc20dnlp
title: CCDC 1859212: Experimental Crystal Structure Determination

description: Related Article: Jia Lin, Qiuxia Cheng, Jianen Zhou, Xiaoming Lin, R. Chenna Krishna Reddy, Tao Yang, Gang Zhang|2019|J.Solid State Chem.|270|339|doi:10.1016/j.jssc.2018.11.033
DOI: 10.5517/ccdc.csd.cc20dnmq
title: CCDC 1859213: Experimental Crystal Structure Determination

description: Related Article: Jia Lin, Qiuxia Cheng, Jianen Zhou, Xiaoming Lin, R. Chenna Krishna Reddy, Tao Yang, Gang Zhang|2019|J.Solid State Chem.|270|339|doi:10.1016/j.jssc.2018.11.033
DOI: 10.5517/ccdc.csd.cc20dnnr
title: CCDC 1859214: Experimental Crystal Structure Determination

description: Related Article: Jia Lin, Qiuxia Cheng, Jianen Zhou, Xiaoming Lin, R. Chenna Krishna Reddy, Tao Yang, Gang Zhang|2019|J.Solid State Chem.|270|339|doi:10.1016/j.jssc.2018.11.033
DOI: 10.5517/ccdc.csd.cc207tjm
title: CCDC 1854560: Experimental Crystal Structure Determination

description: Related Article: Wei-Qiang Zhang, Ru-Fen Zhang, Qing-Fu Zhang, Shao-Liang Zhang, Jing Ru, Qian-Li Li, Chun-Lin Ma|2019|J.Solid State Chem.|270|360|doi:10.1016/j.jssc.2018.11.035
DOI: 10.5517/ccdc.csd.cc20dnps
title: CCDC 1859215: Experimental Crystal Structure Determination

description: Related Article: Jia Lin, Qiuxia Cheng, Jianen Zhou, Xiaoming Lin, R. Chenna Krishna Reddy, Tao Yang, Gang Zhang|2019|J.Solid State Chem.|270|339|doi:10.1016/j.jssc.2018.11.033
DOI: 10.5517/ccdc.csd.cc207tlp
title: CCDC 1854562: Experimental Crystal Structure Determination

description: Related Article: Wei-Qiang Zhang, Ru-Fen Zhang, Qing-Fu Zhang, Shao-Liang Zhang, Jing Ru, Qian-Li Li, Chun-Lin Ma|2019|J.Solid State Chem.|270|360|doi:10.1016/j.jssc.2018.11.035
DOI: 10.5517/ccdc.csd.cc207tps
title: CCDC 1854565: Experimental Crystal Structure Determination

description: Related Article: Wei-Qiang Zhang, Ru-Fen Zhang, Qing-Fu Zhang, Shao-Liang Zhang, Jing Ru, Qian-Li Li, Chun-Lin Ma|2019|J.Solid State Chem.|270|360|doi:10.1016/j.jssc.2018.11.035
DOI: 10.5517/ccdc.csd.cc207tqt
title: CCDC 1854566: Experimental Crystal Structure Determination

description: Related Article: Wei-Qiang Zhang, Ru-Fen Zhang, Qing-Fu Zhang, Shao-Liang Zhang, Jing Ru, Qian-Li Li, Chun-Lin Ma|2019|J.Solid State Chem.|270|360|doi:10.1016/j.jssc.2018.11.035
DOI: 10.5517/ccdc.csd.cc20mmdp
title: CCDC 1865902: Experimental Crystal Structure Determination

description: Related Article: Ling-Ling Ren, Ying-Ying Cui, Ai-Ling Cheng, En-Qing Gao|2019|J.Solid State Chem.|270|463|doi:10.1016/j.jssc.2018.11.041
DOI: 10.5517/ccdc.csd.cc20mmfq
title: CCDC 1865903: Experimental Crystal Structure Determination

description: Related Article: Ling-Ling Ren, Ying-Ying Cui, Ai-Ling Cheng, En-Qing Gao|2019|J.Solid State Chem.|270|463|doi:10.1016/j.jssc.2018.11.041
DOI: 10.5517/ccdc.csd.cc20nbz0
title: CCDC 1866602: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Ru-Xia Yang, Hai-Ming Lan, Duo-Zhi Wang|2019|J.Solid State Chem.|270|379|doi:10.1016/j.jssc.2018.11.039
DOI: 10.5517/ccdc.csd.cc20nc02
title: CCDC 1866603: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Ru-Xia Yang, Hai-Ming Lan, Duo-Zhi Wang|2019|J.Solid State Chem.|270|379|doi:10.1016/j.jssc.2018.11.039
DOI: 10.5517/ccdc.csd.cc20nc13
title: CCDC 1866604: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Ru-Xia Yang, Hai-Ming Lan, Duo-Zhi Wang|2019|J.Solid State Chem.|270|379|doi:10.1016/j.jssc.2018.11.039
DOI: 10.5517/ccdc.csd.cc20nc24
title: CCDC 1866605: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Ru-Xia Yang, Hai-Ming Lan, Duo-Zhi Wang|2019|J.Solid State Chem.|270|379|doi:10.1016/j.jssc.2018.11.039
DOI: 10.5517/ccdc.csd.cc20nc35
title: CCDC 1866606: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Ru-Xia Yang, Hai-Ming Lan, Duo-Zhi Wang|2019|J.Solid State Chem.|270|379|doi:10.1016/j.jssc.2018.11.039
DOI: 10.5517/ccdc.csd.cc20nc46
title: CCDC 1866607: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Ru-Xia Yang, Hai-Ming Lan, Duo-Zhi Wang|2019|J.Solid State Chem.|270|379|doi:10.1016/j.jssc.2018.11.039
DOI: 10.5517/ccdc.csd.cc20wj5m
title: CCDC 1873490: Experimental Crystal Structure Determination

description: Related Article: Dmitrii M. Roitershtein, Alexander A. Vinogradov, Konstantin A. Lyssenko, Ivan V. Ananyev, Vladimir A. Yakovlev, Natalya N. Kostitsyna, Ilya E. Nifant'ev|2019|Inorg.Chim.Acta|487|153|doi:10.1016/j.ica.2018.12.012
DOI: 10.5517/ccdc.csd.cc20wj6n
title: CCDC 1873491: Experimental Crystal Structure Determination

description: Related Article: Dmitrii M. Roitershtein, Alexander A. Vinogradov, Konstantin A. Lyssenko, Ivan V. Ananyev, Vladimir A. Yakovlev, Natalya N. Kostitsyna, Ilya E. Nifant'ev|2019|Inorg.Chim.Acta|487|153|doi:10.1016/j.ica.2018.12.012
DOI: 10.5517/ccdc.csd.cc20wj7p
title: CCDC 1873492: Experimental Crystal Structure Determination

description: Related Article: Dmitrii M. Roitershtein, Alexander A. Vinogradov, Konstantin A. Lyssenko, Ivan V. Ananyev, Vladimir A. Yakovlev, Natalya N. Kostitsyna, Ilya E. Nifant'ev|2019|Inorg.Chim.Acta|487|153|doi:10.1016/j.ica.2018.12.012
DOI: 10.5517/ccdc.csd.cc20wj8q
title: CCDC 1873493: Experimental Crystal Structure Determination

description: Related Article: Dmitrii M. Roitershtein, Alexander A. Vinogradov, Konstantin A. Lyssenko, Ivan V. Ananyev, Vladimir A. Yakovlev, Natalya N. Kostitsyna, Ilya E. Nifant'ev|2019|Inorg.Chim.Acta|487|153|doi:10.1016/j.ica.2018.12.012
DOI: 10.5517/ccdc.csd.cc20wj9r
title: CCDC 1873494: Experimental Crystal Structure Determination

description: Related Article: Dmitrii M. Roitershtein, Alexander A. Vinogradov, Konstantin A. Lyssenko, Ivan V. Ananyev, Vladimir A. Yakovlev, Natalya N. Kostitsyna, Ilya E. Nifant'ev|2019|Inorg.Chim.Acta|487|153|doi:10.1016/j.ica.2018.12.012
DOI: 10.5517/ccdc.csd.cc2052y7
title: CCDC 1851938: Experimental Crystal Structure Determination

description: Related Article: Michał Terlecki, Iwona Justyniak, Daniel Prochowicz, Janusz Lewiński|2020|Eur.J.Inorg.Chem.|2020|119|doi:10.1002/ejic.201901288
DOI: 10.5517/ccdc.csd.cc23pn2k
title: CCDC 1957218: Experimental Crystal Structure Determination

description: Related Article: Kyungseop Lee,  Yining Huang,  John F. Corrigan|2020|Eur.J.Inorg.Chem.|2020|57|doi:10.1002/ejic.201901124
DOI: 10.5517/ccdc.csd.cc23pn3l
title: CCDC 1957219: Experimental Crystal Structure Determination

description: Related Article: Kyungseop Lee,  Yining Huang,  John F. Corrigan|2020|Eur.J.Inorg.Chem.|2020|57|doi:10.1002/ejic.201901124
DOI: 10.5517/ccdc.csd.cc23pn4m
title: CCDC 1957220: Experimental Crystal Structure Determination

description: Related Article: Kyungseop Lee,  Yining Huang,  John F. Corrigan|2020|Eur.J.Inorg.Chem.|2020|57|doi:10.1002/ejic.201901124
DOI: 10.5522/04/14979990
title: Protein structures predicted using DMPfold2, plus training data

description: This dataset comprises predicted protein structures from the paper "Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins". Structures were predicted using DMPfold2.<br><br>BFD_1.3M.hdf5 contains all the models from the set of 1.3M that were generated. The models can be retrieved from this file using the provided hdf5_extract.py script and the list of IDs in bfdfold_1.3M_target_ids.csv.<br><br>Also provided are tarballs of the models and sequence alignments for the 5193 Pfam families modelled in the paper, as well as for the set of 255 Pfams with released structures used for comparisons against DMPfold1 and C-I-TASSER.<br>train_data.tar.bz2 contains the data used to train the DMPfold2 neural network. Further scripts and instructions are available on the associated GitHub page: https://github.com/psipred/DMPfold2<br><br>
DOI: 10.5522/04/14979990.v3
title: Protein structures predicted using DMPfold2, plus training data

description: This dataset comprises predicted protein structures from the paper "Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins". Structures were predicted using DMPfold2.<br><br>BFD_1.3M.hdf5 contains all the models from the set of 1.3M that were generated. The models can be retrieved from this file using the provided hdf5_extract.py script and the list of IDs in bfdfold_1.3M_target_ids.csv.<br><br>Also provided are tarballs of the models and sequence alignments for the 5193 Pfam families modelled in the paper, as well as for the set of 255 Pfams with released structures used for comparisons against DMPfold1 and C-I-TASSER.<br>train_data.tar.bz2 contains the data used to train the DMPfold2 neural network. Further scripts and instructions are available on the associated GitHub page: https://github.com/psipred/DMPfold2<br><br>
DOI: 10.5522/04/14979990.v2
title: Protein structures predicted using DMPfold2, plus training data

description: This dataset comprises predicted protein structures from the paper "Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins". Structures were predicted using DMPfold2.<br><br>BFD_1.3M.hdf5 contains all the models from the set of 1.3M that were generated. The models can be retrieved from this file using the provided hdf5_extract.py script and the list of IDs in bfdfold_1.3M_target_ids.csv.<br><br>Also provided are tarballs of the models and sequence alignments for the 5193 Pfam families modelled in the paper, as well as for the set of 255 Pfams with released structures used for comparisons against DMPfold1 and C-I-TASSER.<br>train_data.tar.bz2 contains the data used to train the DMPfold2 neural network. Further scripts and instructions are available on the associated GitHub page: https://github.com/psipred/DMPfold2<br><br>
DOI: 10.1285/i27241904n2
title: Gustavo Del Vecchio e la Nuova Collana di Economisti Stranieri e Italiani. Corrispondenza, 1930-1938
DOI: 10.1285/i27241904n2pi
title: Frontespizio e pagine iniziali
DOI: 10.1285/i27241904n2pv
title: Indice
DOI: 10.1285/i27241904n2p1
title: Presentazione
DOI: 10.1285/i27241904n2p3
title: Introduzione
DOI: 10.6084/m9.figshare.19076660
title: NIfTI_images.zip

description: Images from a healthy male subject and a male subject with penetrating right eyeball injury and right optic nerve neuropathy.
DOI: 10.6084/m9.figshare.19076660.v1
title: NIfTI_images.zip

description: Images from a healthy male subject and a male subject with penetrating right eyeball injury and right optic nerve neuropathy.
DOI: 10.1285/i27241904n2p15
title: Manifesto della Serie V della Biblioteca dell'Economista (1905)
DOI: 10.6084/m9.figshare.19076716
title: Supplementary table for belief updating task order permutations from Is depression associated with reduced optimistic belief updating?

description: Supplementary table 5
DOI: 10.6084/m9.figshare.19076716.v1
title: Supplementary table for belief updating task order permutations from Is depression associated with reduced optimistic belief updating?

description: Supplementary table 5
DOI: 10.1285/i27241904n2p19
title: Nota di edizione
DOI: 10.17632/kxgsb3bj3g.1
title: SUPPORTED RAW DATA OF "Entomopathogenic nematodes for control of the cotton cutworm Spodoptera litura in marigolds"

description: This study examines the two commercial entompathogenic nematodes (EPNs) strains available in Thailand, Steinernema siamkayai and S. carpocapsae, against different developmental stages of Spodoptera litura. The most important finding was that the S. siamkayai is highly virulent against younger larvae of first and third instar, whereas S. carpocapsae caused higher mortality in older larvae and pupae. Field test, EPNs treatments significantly reduced the percentage of damaged flowers as well as increased the Benefit-Cost Ratio (BCR) compared to insecticide treatments.
DOI: 10.1285/i27241904n2p23
title: Corrispondenza. Lettere di Celestino Arena a Gustavo Del Vecchio (1930-1938)
DOI: 10.1285/i27241904n2p191
title: Appendice
DOI: 10.48370/ofd/2dpiic
title: Peniophora incarnata (Pers.) P. Karst, 2973

description: Fungarium Makromycetes
DOI: 10.1285/i27241904n2p229
title: Indice dei nomi
DOI: 10.1285/i27241904n2p236
title: Colophon
DOI: 10.6084/m9.figshare.13359926
title: Experimental evidence that UV/yellow colouration functions as a signal in blue tit parents

description: Nest database and Nestling database. <br>The keys of all listed variables are provided in the Excel file once the file is download. <br>
DOI: 10.6084/m9.figshare.13359926.v1
title: Experimental evidence that UV/yellow colouration functions as a signal in blue tit parents

description: Nest database and Nestling database. <br>The keys of all listed variables are provided in the Excel file once the file is download. <br>
DOI: 10.48370/ofd/myvigy
title: Peniophora incarnata (Pers.) P. Karst, 3078

description: Fungarium Makromycetes
DOI: 10.5281/zenodo.5889009
title: Database

description: Database
DOI: 10.5281/zenodo.5889008
title: Database

description: Database
DOI: 10.5281/zenodo.5812228
title: Data

description: DBS
DOI: 10.5281/zenodo.5812227
title: Data

description: DBS
DOI: 10.48370/ofd/yaeufc
title: Peniophora incarnata (Pers.) P. Karst, 3133

description: Fungarium Makromycetes
DOI: 10.35081/p7f4-q871
title: DATABASE TICKS CAMEROON
DOI: 10.5281/zenodo.5909576
title: GRAND-SLAM analysis of HCMV infection of ZAP knockout cells from Gonzalez-Perez et al., mBio 2021 (https://doi.org/10.1128/mbio.02683-20)

description: This is the processed SLAM-seq data from Gonzalez-Perez et al., mBio 2021 (https://doi.org/10.1128/mbio.02683-20). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table. <br> To generate the GRAND-SLAM output yourself, first prepare the human (ensembl v90) and the HCMV genome (EF999921). Then run: gedi -e Slam -double -reads sars.cit -genomic h.ens90 HCMV_TB40_BAC4 -prefix grandslam_du/zap_bac4 -plot -progress [Zum Verschieben anwählen und ziehen] To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it. You can also start from scratch (i.e., the json file): Prepare the human genome (ensembl v90), the HCMV genome (EF999921), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence<br> Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 HCMV_TB40_BAC4<br> Modify the starindex entry in the json file to match your file system<br> Run: gedi -e Pipeline -r parallel -j zap_bac4.json rnaseq_mapping.sh report.sh grandslam.sh Software versions: gedi toolkit 1.0.2<br> GRAND-SLAM 2.0.5a<br> Trimmomatic 0.39<br> STAR version 2.5.3a