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Most cited protocols related to «Genie»

1
Mapping Yeast Nucleosome Occupancy
Cited 130 times
In vivo maps from yeast nucleosome DNAs were prepared from log-phase cells grown in rich medium (YPD, six independent replicates) as described previously5 , as well as from cells grown in YP media supplemented with 2% galactose (three replicates) or 2.8% ethanol (four replicates) instead of glucose. The resulting DNAs were subjected to Illumina sequencing-by-synthesis. For the in vitro map, histone octamer was purified from chicken erythrocytes, assembled on purified yeast genomic DNA by salt gradient dialysis13 , digested with micrococcal nuclease and subjected to Illumina sequencing (two independent replicates). The resulting in vitro map has a lower concentration of nucleosomes along the DNA than obtained in vivo. This technical limitation was necessitated by our finding that reconstitutions at the in vivo stoichiometry on long genomic DNA resulted in insoluble chromatin that was inaccessible to micrococcal nuclease. We mapped the resulting reads to the genome and removed reads that mapped to multiple genomic locations. We extended the nucleosome reads of each experiment to the average nucleosome length in that experiment (always between 140-170 bp). For each map, we then calculated the normalized nucleosome occupancy at every base pair as the log-ratio between the number of reads that cover that base pair and the average number of reads per base pair across the genome. We then set the genomic mean in each sample to zero by subtracting the genome-wide mean from every base pair. The independent replicates for each experiment type were in excellent agreement, so we averaged the replicates within each type. The resulting tracks are termed normalized nucleosome occupancy throughout the manuscript. The detailed formulation of our sequence-based model for nucleosome positioning is given in the Methods and is similar to that described in ref. 17 , except that it was learned using only the in vitro data. For our data, results and model, see http://genie.weizmann.ac.il/pubs/nucleosomes08/, and GEO accession number GSE13622.
Kaplan N., Moore I.K., Fondufe-Mittendorf Y., Gossett A.J., Tillo D., Field Y., LeProust E.M., Hughes T.R., Lieb J.D., Widom J, & Segal E. (2008). The DNA-encoded nucleosome organization of a eukaryotic genome. Nature, 458(7236), 362-366.
Publication 2008
Anabolism Base Pairing Cells Chickens Chromatin Cordocentesis Erythrocytes Ethanol Galactose Genie Genome Glucose Histones Micrococcal Nuclease Microtubule-Associated Proteins Nucleosomes Saccharomyces cerevisiae Salts
2
Cardiometabolic Traits Genetic Association
Cited 64 times
To explore the relationship between BMI and an array of cardiometabolic traits and diseases, association results for the 97 BMI index SNPs were requested from 13 GWAS meta-analysis consortia: DIAGRAM (type 2 diabetes)56 (link), CARDIoGRAM-C4D (CAD)57 (link), ICBP (systolic and diastolic blood pressure (SBP, DBP))58 (link), GIANT (waist-to-hip ratio, hip circumference, and waist circumference, each unadjusted and adjusted for BMI)13 (link),59 , GLGC (HDL, low density lipoprotein cholesterol, triglycerides, and total cholesterol)60 (link), MAGIC (fasting glucose, fasting insulin, fasting insulin adjusted for BMI, and two-hour glucose)61 (link)–63 (link), ADIPOGen (BMI-adjusted adiponectin)64 (link), CKDgen (urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate, and overall CKD)65 (link),66 (link), ReproGen (age at menarche, age at menopause)67 (link),68 (link), GENIE (diabetic nephropathy)69 (link),70 (link). Proxies (r2 > 0.8 in CEU) were used when an index SNP was unavailable.
Locke A.E., Kahali B., Berndt S.I., Justice A.E., Pers T.H., Day F.R., Powell C., Vedantam S., Buchkovich M.L., Yang J., Croteau-Chonka D.C., Esko T., Fall T., Ferreira T., Gustafsson S., Kutalik Z., Luan J., Mägi R., Randall J.C., Winkler T.W., Wood A.R., Workalemahu T., Faul J.D., Smith J.A., Zhao J.H., Zhao W., Chen J., Fehrmann R., Hedman Å.K., Karjalainen J., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bolton J.L., Bragg-Gresham J.L., Buyske S., Demirkan A., Deng G., Ehret G.B., Feenstra B., Feitosa M.F., Fischer K., Goel A., Gong J., Jackson A.U., Kanoni S., Kleber M.E., Kristiansson K., Lim U., Lotay V., Mangino M., Leach I.M., Medina-Gomez C., Medland S.E., Nalls M.A., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Shungin D., Stančáková A., Strawbridge R.J., Sung Y.J., Tanaka T., Teumer A., Trompet S., van der Laan S.W., van Setten J., Van Vliet-Ostaptchouk J.V., Wang Z., Yengo L., Zhang W., Isaacs A., Albrecht E., Ärnlöv J., Arscott G.M., Attwood A.P., Bandinelli S., Barrett A., Bas I.N., Bellis C., Bennett A.J., Berne C., Blagieva R., Blüher M., Böhringer S., Bonnycastle L.L., Böttcher Y., Boyd H.A., Bruinenberg M., Caspersen I.H., Chen Y.D., Clarke R., Daw E.W., de Craen A.J., Delgado G., Dimitriou M., Doney A.S., Eklund N., Estrada K., Eury E., Folkersen L., Fraser R.M., Garcia M.E., Geller F., Giedraitis V., Gigante B., Go A.S., Golay A., Goodall A.H., Gordon S.D., Gorski M., Grabe H.J., Grallert H., Grammer T.B., Gräßler J., Grönberg H., Groves C.J., Gusto G., Haessler J., Hall P., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heard-Costa N.L., Helmer Q., Hengstenberg C., Holmen O., Hottenga J.J., James A.L., Jeff J.M., Johansson Å., Jolley J., Juliusdottir T., Kinnunen L., Koenig W., Koskenvuo M., Kratzer W., Laitinen J., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lo K.S., Lobbens S., Lorbeer R., Lu Y., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., McLachlan S., Menni C., Merger S., Mihailov E., Milani L., Moayyeri A., Monda K.L., Morken M.A., Mulas A., Müller G., Müller-Nurasyid M., Musk A.W., Nagaraja R., Nöthen M.M., Nolte I.M., Pilz S., Rayner N.W., Renstrom F., Rettig R., Ried J.S., Ripke S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Schumacher F.R., Scott W.R., Seufferlein T., Shi J., Smith A.V., Smolonska J., Stanton A.V., Steinthorsdottir V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tan S.T., Tayo B.O., Thorand B., Thorleifsson G., Tyrer J.P., Uh H.W., Vandenput L., Verhulst F.C., Vermeulen S.H., Verweij N., Vonk J.M., Waite L.L., Warren H.R., Waterworth D., Weedon M.N., Wilkens L.R., Willenborg C., Wilsgaard T., Wojczynski M.K., Wong A., Wright A.F., Zhang Q., Brennan E.P., Choi M., Dastani Z., Drong A.W., Eriksson P., Franco-Cereceda A., Gådin J.R., Gharavi A.G., Goddard M.E., Handsaker R.E., Huang J., Karpe F., Kathiresan S., Keildson S., Kiryluk K., Kubo M., Lee J.Y., Liang L., Lifton R.P., Ma B., McCarroll S.A., McKnight A.J., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Okada Y., Perry J.R., Dorajoo R., Reinmaa E., Salem R.M., Sandholm N., Scott R.A., Stolk L., Takahashi A., Tanaka T., van ’t Hooft F.M., Vinkhuyzen A.A., Westra H.J., Zheng W., Zondervan K.T., Heath A.C., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Bovet P., Campbell H., Caulfield M.J., Cesana G., Chakravarti A., Chasman D.I., Chines P.S., Collins F.S., Crawford D.C., Cupples L.A., Cusi D., Danesh J., de Faire U., den Ruijter H.M., Dominiczak A.F., Erbel R., Erdmann J., Eriksson J.G., Farrall M., Felix S.B., Ferrannini E., Ferrières J., Ford I., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gejman P.V., Gieger C., Gottesman O., Gudnason V., Gyllensten U., Hall A.S., Harris T.B., Hattersley A.T., Hicks A.A., Hindorff L.A., Hingorani A.D., Hofman A., Homuth G., Hovingh G.K., Humphries S.E., Hunt S.C., Hyppönen E., Illig T., Jacobs K.B., Jarvelin M.R., Jöckel K.H., Johansen B., Jousilahti P., Jukema J.W., Jula A.M., Kaprio J., Kastelein J.J., Keinanen-Kiukaanniemi S.M., Kiemeney L.A., Knekt P., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuusisto J., Lakka T.A., Langenberg C., Marchand L.L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Moll F.L., Morris A.D., Morris A.P., Murray J.C., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Madden P.A., Pasterkamp G., Peden J.F., Peters A., Postma D.S., Pramstaller P.P., Price J.F., Qi L., Raitakari O.T., Rankinen T., Rao D.C., Rice T.K., Ridker P.M., Rioux J.D., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schunkert H., Schwarz P.E., Sever P., Shuldiner A.R., Sinisalo J., Stolk R.P., Strauch K., Tönjes A., Trégouët D.A., Tremblay A., Tremoli E., Virtamo J., Vohl M.C., Völker U., Waeber G., Willemsen G., Witteman J.C., Zillikens M.C., Adair L.S., Amouyel P., Asselbergs F.W., Assimes T.L., Bochud M., Boehm B.O., Boerwinkle E., Bornstein S.R., Bottinger E.P., Bouchard C., Cauchi S., Chambers J.C., Chanock S.J., Cooper R.S., de Bakker P.I., Dedoussis G., Ferrucci L., Franks P.W., Froguel P., Groop L.C., Haiman C.A., Hamsten A., Hui J., Hunter D.J., Hveem K., Kaplan R.C., Kivimaki M., Kuh D., Laakso M., Liu Y., Martin N.G., März W., Melbye M., Metspalu A., Moebus S., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sattar N., Schadt E.E., Schlessinger D., Slagboom P.E., Snieder H., Spector T.D., Thorsteinsdottir U., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Walker M., Wallaschofski H., Wareham N.J., Watkins H., Weir D.R., Wichmann H.E., Wilson J.F., Zanen P., Borecki I.B., Deloukas P., Fox C.S., Heid I.M., O’Connell J.R., Strachan D.P., Stefansson K., van Duijn C.M., Abecasis G.R., Franke L., Frayling T.M., McCarthy M.I., Visscher P.M., Scherag A., Willer C.J., Boehnke M., Mohlke K.L., Lindgren C.M., Beckmann J.S., Barroso I., North K.E., Ingelsson E., Hirschhorn J.N., Loos R.J, & Speliotes E.K. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197-206.
Publication 2015
ADIPOQ protein, human Albumins Cholesterol Cholesterol, beta-Lipoprotein Creatinine Diabetes Mellitus, Non-Insulin-Dependent Diabetic Nephropathy Genie Genome-Wide Association Study Gigantism Glomerular Filtration Rate Glucose Insulin Menarche Menopause Pressure, Diastolic Systole Triglycerides Urine Waist-Hip Ratio Waist Circumference
3
Genetically Encoded Calcium Indicators in Mice
Cited 26 times
Here we report on GENIE Project (GP) lines GP4.x (where ‘x’ refers to the founder number) expressing GCaMP6s, and GP5.x expressing GCaMP6f. Thy1-GCaMP6-WPRE transgenic mice were generated using standard techniques [23] .We included the WPRE (Woodchuck hepatitis virus post-transcriptional regulatory element), which increases mRNA stability and protein expression [24] (link), [25] (link). Genotyping primers were 5′-CATCAGTGCAGCAGAGCTTC-3′ (forward, anneals to calmodulin sequence in GCaMP6) and 5′-CAGCGTATCCACATAGCGTA-3′ (reverse, anneals to WPRE sequence). Mouse lines GP4.3, 4.12, 5.5, 5.11 and 5.17 were deposited at The Jackson Laboratory (acquisition numbers provided at end).
Dana H., Chen T.W., Hu A., Shields B.C., Guo C., Looger L.L., Kim D.S, & Svoboda K. (2014). Thy1-GCaMP6 Transgenic Mice for Neuronal Population Imaging In Vivo. PLoS ONE, 9(9), e108697.
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Publication 2014
Calmodulin Genie Hepatitis B Virus, Woodchuck Mice, Laboratory Mice, Transgenic Oligonucleotide Primers Proteins Regulatory Elements, Transcriptional
4
Fecal Transplantation from Mice Fed Chow, WEGL, and HFD
Cited 23 times
Faecal transplant was performed based on an established protocol63 (link). Briefly, 8-week-old male donor mice (n=5 per diet group) were fed with Chow, Chow+8% WEGL, HFD or HFD+8% WEGL for 3 months. After 4 weeks of feeding, stools were collected daily for the subsequent 2 months under a laminar flow hood in sterile conditions. Stools from donor mice of each diet group were pooled and 100 mg was resuspended in 1 ml of sterile saline. The solution was vigorously mixed for 10 s using a benchtop vortex (Vortex-Genie 2, Scientific Industries, USA; speed 9), before centrifugation at 800g for 3 min. The supernatant was collected and used as transplant material as described below. Fresh transplant material was prepared on the same day of transplantation within 10 min before oral gavage to prevent changes in bacterial composition. Eight-week-old male recipient mice (n=5 for each transplant group) were fed with HFD and inoculated daily with fresh transplant material (100 μl for each mouse) by oral gavage for 2 months, before being killed for subsequent analysis. Analysis of body weight and gut microbiota of the donor mice (Supplementary Fig. 9a,b) indicated that the 4-week-long diet treatments used for this cohort of mice produced changes similar to those of the 2-month-long treatments (Figs 1a and 4c).
Chang C.J., Lin C.S., Lu C.C., Martel J., Ko Y.F., Ojcius D.M., Tseng S.F., Wu T.R., Chen Y.Y., Young J.D, & Lai H.C. (2015). Ganoderma lucidum reduces obesity in mice by modulating the composition of the gut microbiota. Nature Communications, 6, 7489.
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Publication 2015
Bacteria Body Weight Centrifugation Diet Donors Fecal Microbiota Transplantation Feces Figs Gastrointestinal Microbiome Genie Grafts Males Mice, House Saline Solution Sterility, Reproductive Transplantation Tube Feeding
5
Integrating Diverse miRNA Target Prediction Datasets
Cited 22 times
The target predictions of PicTar [22 (link)] were downloaded from the UCSC database using the Table Browser and were migrated from hg17 to hg18 by applying the UCSC command line tool liftover. We used the predictions conserved in human, mouse, rat, chimp and dog (4-way) as well as the predictions additionally conserved in chicken (5-way). For fly we downloaded the sensitive prediction set (S1) of PicTar that is composed of predictions conserved in D. melanogaster, D. yakuba, D. ananassae, and D. Pseudoobscura, also via the UCSC Table Browser. Predictions for the human genome made by miRanda [6 (link)], release September 2008, were downloaded from http://microRNA.org[29 (link)]. Only predictions for transcripts contained in the RefSeq database were considered. Human and fly predictions made by miRBase Targets [7 (link)], version 5, were downloaded from http://microrna.sanger.ac.uk/targets/v5/. RNA22 [23 (link)] predictions for human 3'UTR sequences were downloaded from http://cbcsrv.watson.ibm.com/rna22.html. Since these predictions were made using Ensembl transcripts, we mapped the predictions to RefSeq genes by applying mapping tables provided by Ensembl and UCSC. Predictions of PITA [9 (link)] were downloaded from http://genie.weizmann.ac.il/pubs/mir07/mir07_data.html. We utilized the "TOP" and the "ALL" set with 3/15 flankings. TargetScanS [13 (link)] predictions and the corresponding microRNA family mapping table were downloaded from http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_50. Predictions made by Gaidatzis et al. [20 (link)] were downloaded from the EIMMo server http://www.mirz.unibas.ch/. Targets predicted by mirTarget2 (version 3) [21 (link)] were downloaded from http://mirdb.org/miRDB. Human target site predictions of DIANA-microT v3.0 [30 (link)] were retrieved via the web server at http://diana.cslab.ece.ntua.gr/microT/ for the thresholds loose (score = 7.3) and strict (score = 19). Finally, we downloaded the human target site predictions of TargetRank [26 ] from http://hollywood.mit.edu/targetrank/.
Sturm M., Hackenberg M., Langenberger D, & Frishman D. (2010). TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics, 11, 292.
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Publication 2010
3' Untranslated Regions Chickens Cordocentesis Genes Genie Genome, Human Homo sapiens MicroRNAs Mus Pan troglodytes Verteporfin

Most recents protocols related to «Genie»

1
Nanopore Sequencing for Single-Cell Long-Read Assembly
We prepared scLR libraries from individual single-cell MDA products or pooled multiple single-cell MDA products using the Rapid Sequencing Kit (Oxford Nanopore Technologies) and sequenced them with Flow Cell R9.4 using a GridION (Oxford Nanopore Technologies). In the case of E. coli model sample, scLRs (300 Mbp) were obtained from five E. coli second-round MDA products and then integrated into a single file.
In the case of the fecal bacterial sample, we selected second-round MDA products of fecal bacteria from 96-well plates based on strains identified using CSR-SAGs and pooled them (2–8 SAGs per strain) as a single sequencing library for scLR sequencing using a nanopore sequencer.
For de novo assembly, we used miniasm 0.3 (Li, 2016 (link); using paf file output by minimap2 2.17 (Li, 2018 (link)) with “-x ava-ont” option) or, Canu 1.9 (Koren et al., 2017 (link); -nanopore-raw), and Flye 2.7 (Kolmogorov et al., 2019 (link); --nanopore-raw) with 4.6 Mb genome size option for the assembly of scLRs into LR-SAGs. Miniasm and Flye assembly of the chimera-removed long read was conducted with “trimmedReads” file output by Canu. LR-SAG quality was assessed using QUAST 5.0 (Gurevich et al., 2013 (link)) and CheckM (the same option as above). The alignment results of the draft genome and reference E. coli genome were visualized using D-GENIES 1.2.0 (Cabanettes and Klopp, 2018 (link)). Unless otherwise stated, the analysis tools were run with default parameters.
Kogawa M., Nishikawa Y., Saeki T., Yoda T., Arikawa K., Takeyama H, & Hosokawa M. (2023). Revealing within-species diversity in uncultured human gut bacteria with single-cell long-read sequencing. Frontiers in Microbiology, 14, 1133917.
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Publication 2023
Bacteria Cells Chimera DNA Library Escherichia coli Feces Genie Genome Strains
2
LAMP Assay for Salmonella Typhimurium Identification
Each LAMP test strip contained lyophilized master mixes with one specific primer set in each cap and an additional cap containing the inhibition control. The strips were manufactured by the Amplex Diagnostics’ lyophilization service (https://www.eazyplex.com/en-gb/lyophilisierungsservice), using isothermal master mix ISO-004 (Optigene Ltd., Horsham, UK) as basis reagent. A single small colony of the isolates was suspended in 500 μl of resuspension and lysis fluid (RALF buffer, Amplex Diagnostics) and boiled for 2 min. After centrifugation at 4000 rpm for 1 min, 25 μl of the supernatant was added to each tube of the SalmoTyper test strip. Tests were run on a Genie HT machine (Amplex Diagnostics) at 65 °C for 20 min. Amplification was measured by real-time fluorescence detection using a DNA intercalating dye. Data interpretation was automatically performed by the integrated eazyReport™ software (Amplex Diagnostics). Results were reported as positive in real-time if the fluorescence level and the peak of the first derivative of the fluorescence curve had risen above the thresholds of 10,000 and 0.025, respectively. The thresholds represent the standard settings recommended by the manufacturer of the Genie HT instrument. A representative example is shown in Fig. 1.

Pre-evaluation of the JYM79_16920 primer set for identification of S. Typhimurium using three different strains. S. Enteritidis, and S. Derby served as controls. The thresholds of the fluorescence level (a) and amplification rate (b) are marked with a broken light blue line. The fluorescence signal must raise above both thresholds

Edel B., Glöckner S., Stoll S., Lindig N., Boden K., Wassill L., Simon S., Löffler B, & Rödel J. (2023). Development of a rapid diagnostic test based on loop-mediated isothermal amplification to identify the most frequent non-typhoidal Salmonella serovars from culture. European Journal of Clinical Microbiology & Infectious Diseases, 42(4), 461-470.
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Publication 2023
Buffers Centrifugation Diagnosis DNA, A-Form Fluorescence Freeze Drying Genie Light Oligonucleotide Primers Optigene Psychological Inhibition Strains
3
Comparative Analysis of Avian Genomes
The gyrfalcon genome assembly was compared with those of the domestic chicken (GenBank assembly accession: GCA_000002315.5), hummingbird (GenBank assembly accession: GCA_003957575.1), and zebra finch (GCA_000151805.2) using the software Mashmap (Jain et al., 2018 (link)) and D-genies (Cabanettes and Klopp, 2018 (link)).
Zuccolo A., Mfarrej S., Celii M., Mussurova S., Rivera L.F., Llaca V., Mohammed N., Pain A., Alrefaei A.F., Alrefaei A.F, & Wing R.A. (2023). The gyrfalcon (Falco rusticolus) genome. G3: Genes|Genomes|Genetics, 13(3), jkad001.
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Publication 2023
Chickens Finches Genie Genome Zebras
4
Yeast Lipid Extraction and Analysis
Starter cultures (3 ml YPD) were inoculated with an individual colony of yeast and incubated (30 °C, 230 rpm, 12 h). YPGD medium (100 ml medium at ambient temperature in a sterile 250-mL Erlenmeyer flask) was inoculated with 2.5 × 106 yeast cells and incubated (30 °C, 230 rpm, 18 h). Samples were harvested 18 h after inoculation, a time point that corresponds to early respiration growth. 1 × 108 yeast cells were harvested by centrifugation (3000g, 3 min, 4 °C) in screw-cap tubes, the supernatant was removed, and the cell pellet was flash frozen in N2(l) and stored at −80 °C. 100 μl of glass beads and 50 μl of 150 mM KCl and 600 μl of methanol with 0.1 μM CoQ8 internal standard (Avanti Polar Lipids) were added, and the cells were lysed via bead beating on a Disruptor Genie (Zymo Research) cell disruption device (2×, 5 min). To extract lipids, 400 μl of petroleum ether was added to samples and subjected to bead-beating for 3 min. Samples were then centrifuged (1000g, 2 min, 4 °C), and the ether layer (top) was transferred to a new tube. Extraction was repeated; the ether layers were combined and dried under argon gas at room temperature.
Schmitz J.M., Wolters J.F., Murray N.H., Guerra R.M., Bingman C.A., Hittinger C.T, & Pagliarini D.J. (2023). Aim18p and Aim46p are chalcone isomerase domain–containing mitochondrial hemoproteins in Saccharomyces cerevisiae. The Journal of Biological Chemistry, 299(3), 102981.
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Publication 2023
Argon Cell Respiration Cells Centrifugation Ethyl Ether Freezing Genie Lipids Medical Devices Methanol naphtha Sterility, Reproductive Vaccination Yeasts
5
Optogenetic Activation of Cre-Expressing Neurons
pGP-AAV-syn-FLEX-jGCaMP7b-WPRE was a gift from Douglas Kim & GENIE Project (Addgene viral prep # 104493-AAV1; RRID:Addgene_104493). AAVDJ-CaMKII-Cre was prepared using pENN.AAV.CamKII 0.4.Cre.SV40 and pENN.AAV.CamKII.HI.GFP-Cre.WPRE.SV40. pENN.AAV.CamKII 0.4.Cre.SV40 was a gift from James M. Wilson (Addgene plasmid # 105558; RRID:Addgene_105558). pENN.AAV.CamKII.HI.GFP-Cre.WPRE.SV40 was also a gift from James M. Wilson (Addgene plasmid # 105551; 105551; RRID:Addgene_105551).
Yoneda T., Hayashi K, & Yoshimura Y. (2023). Experience-dependent functional plasticity and visual response selectivity of surviving subplate neurons in the mouse visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2217011120.
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Publication 2023
Calmodulin-Dependent Protein Kinase II Genie Plasmids Simian virus 40

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Isothermal master mix by OptiGene
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More about "Genie"

Genie is a powerful AI-driven research optimization tool that empowers scientists to effortlessly navigate the vast landscape of scientific literature, preprints, and patents.
With its intuitive interface, Genie helps users quickly locate relevant protocols and leverage AI-driven comparisons to identify the most suitable methodologies for their research needs.
This cutting-edge tool, also known as Vortex-Genie 2, Disruptor Genie, Genie II, Vortex-Genie, Genie III, and Vortex-Genie 2 mixer, enables researchers to maximize their productivity and stay ahead of the curve.
By utilizing advanced AI algorithms, Genie can assist in identifying the best protocols and products, such as Isothermal Master Mix and Glass beads or Zirconia/silica beads, for specific research projects.
Genie's seamless integration of AI-fueled comparisons and the ability to access a wide range of scientific resources, including literature, preprints, and patents, makes it an indispensable tool for modern researchers.
With Genie, scientists can quickly locate and leverage the most relevant protocols, ultimately accelerating their discoveries and unlocking new frontiers in their respective fields.