eamag/protocols-with-steps huggingface dataset:
TITLE: Hybridization and Immobilization
AUTHORS: Chia-Hsien  Shih

[DESCRIPTION]
This protocol is to assemble GotCha with circular probe, immobilization probe and magnetic beads.

[STEPS]
SECTION: Preparation
1. Dilute immobilization probe into 10µM
SECTION: Protocol of Hybridization
2. Add 4.5 µL of 1µM circular probe 

SECTION: Protocol of Hybridization
3. Add 9 µLof 10X pH 7.4 PBS buffer

SECTION: Protocol of Hybridization
4. Add 0.9 µL of 10µM immobilization probe

SECTION: Protocol of Hybridization
5. Add 75.6 µL RNase-free water 

SECTION: Protocol of Hybridization
6. Vortex slightly and spin down
SECTION: Protocol of Hybridization
7. Heated to 90 °C for 30 s then cool down to 90 Room temperature and keep for 30 min

SECTION: Protocol of Immobilization
8. Take 22.5 µL of 10mg/ml magnetic beads into a eppendorf

SECTION: Protocol of Immobilization
9. Vortex then spin down in Centrifuge 15000 rpm, 2 min 

SECTION: Protocol of Immobilization
10. Carefully take the eppendorf onto the DynaMag and wait for 1 min to collect the beads

SECTION: Protocol of Immobilization
11. Wash beads with 1X PBS for three times. Make sure that eppendorf should put on DynaMag when removing supernatant.
SECTION: Protocol of Immobilization
12. Add 90 µL of hybridization solution into the eppendorf with dry beads

SECTION: Protocol of Immobilization
13. Incubate at 90 Room temperature for 30 min, then spindown in Centrifuge15000 rpm, 2 min

SECTION: Protocol of Immobilization
14. Carefully take the eppendorf onto the DynaMag and wait for 1 min to collect the beads

SECTION: Protocol of Immobilization
15. Wash beads with 1X PBS for three times. Make sure that eppendorf should put on DynaMag when removing supernatant.
SECTION: Protocol of Immobilization
16. Preserve the functional beads in 90 µL 1X PBS
TITLE: Vector Digestion and Purification
AUTHORS: Carolina Lopez

[DESCRIPTION]
Protocol for plasmid digestion and purification

[STEPS]
SECTION: Isolate Digested Vector:
1. 1. Digest Vector with New England Biolabs Restriction Enzymes:
 
2. Incubate for 120 min at 37 °C.
3. Add 10 µL of 6x loading buffer to reaction and vortex briefly to mix.
4. Make 1% low melt-agarose gel.
        a) Mix 1 g of Agar with 100 mL of TAE Buffer.
        b) Microwave to boil agarose and let cool until you can touch bottle, but gel is not solid.
        c) Add 1.5 µL of EtBr to agarose and pour into DNA gel mold with 10 well comb.
        d) Let gel solidify.
5. Load 60 µL of reaction into well of gel
6. Run gel for 45 min at 120V.
7. Visualize band with UV light and cut out section of gel with band with new razor blade and place in 1.5 mLtube. 
8. Purify Band from gel with Promega Wizard SV Gel and PCR Purification Kit (A9282)
        a) https://www.promega.com/products/nucleic-acid-extraction/clean-up-and-concentration/wizard-sv-gel-and-pcr-clean-up-system/?catNum=A9281
        b) Weigh DNA gel fragment and add 10 µL of Membrane Binding Solution per 10 mg of gel slice. 
        c) Incubate mixture at 65 °C for 10 min or until gel is completely melted.
        d) Add melted gel mixture to SV minicolumn in Collection Tube and incubate at room temperature for 1 min.
        e) Centrifuge at max speed for 1 min. Discard flowthrough and reinsert column into tube.
        f) Add 700 µL of Membrane Wash Solution. Centrifuge at max speed for 1 min. Discard flowthrough and reinsert column into tube.
        g) Add 500 µL of membrane wash solution. Centrifuge at max speed for 1 min. Discard flowthrough and reinsert column into tube.
        h) Spin empty column for 1 min at max speed to remove excess ethanol.
        i) Transfer column to labelled 1.5 mL tube and add 35 µL of NF H20. Incubate for 1 min at room temperature. 
        j) Centrifuge at max speed for 1 min. Keep eluate and store at -20 °C.
TITLE: No Title
AUTHORS: 

[DESCRIPTION]
Pacific Halibut (Hippoglossus stenolepis) support culturally and economically important fisheries in the Gulf of Alaska, though recent decreases in mean size-at-age have substantially reduced fishery yields, generating concerns among stakeholders and resource managers. Among the prevailing hypotheses for reduced size-at-age is intensified competition with Arrowtooth Flounder (Atheresthes stomias), a groundfish predator that exhibited nearly five-fold increases in biomass between the 1960s and mid-2010s. To assess the potential for competition between Pacific Halibut and Arrowtooth Flounder, we evaluated their degree of spatiotemporal and dietary overlap in the Gulf of Alaska using bottom trawl survey and food habits data provided by the Alaska Fisheries Science Center (NOAA; 1990 to 2017). We restricted analyses to fish measuring 30 to 69 cm fork length and used a delta modeling approach to quantify species-specific presence-absence and catch-per-unit-effort as a function of survey year, tow location, depth, and bottom temperature. We then calculated an index of spatial overlap across a uniform grid by multiplying standardized predictions of species’ abundance. Dietary overlap was calculated across the same uniform grid using Schoener’s similarity index. Finally, we assessed the relationship between spatial and dietary overlap as a measure of resource partitioning. We found increases in spatial overlap, moving from east to west in the Gulf of Alaska (eastern: 0.13 ± 0.20; central: 0.21 ± 0.11; western: 0.31 ± 0.13 SD). Dietary overlap was low throughout the study area (0.13 ± 0.20 SD). There was no correlation between spatial and dietary overlap, suggesting an absence of resource partitioning along the niche dimensions examined. This finding provides little indication that competition with Arrowtooth Flounder was responsible for changes in Pacific Halibut alHHsize-at-age in the Gulf of Alaska; however, it does not rule out competitive interactions that may have affected resource use prior to standardized data collection or at different spatiotemporal scales.

[STEPS]
SECTION: Spatial Analyses - R Script File
?. 
SECTION: Dietary Analyses - R Script File
?. 
SECTION: Resource Partitioning - R Script File
?.
TITLE: Killing wolves to prevent predation on livestock may protect one farm but harm neighbors: Variables and Sample STATA code for survival analytics
AUTHORS: Francisco  Santiago-Ávila, Adrian Treves

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block">Here, we make available basic sample code for the statistical survival analyses employed in our manuscript. This code should allow for replication of analyses at all spatial scales for all datasets involved.</div><div class = "text-block">This sample code does not include code lines for dropping or adding observations, which we did for the alternate datasets used. However, this should only involve basic coding, and descriptions of observations included or dropped for each dataset can be found in the study.</div></div>

[STEPS]
?. [VARIABLES USED (and description):]
ID_TRS_yr --> identifier for each area-year combination (please see study for more information)Interv_dich --> intervention type (lethal, non-lethal)wolves_killed --> number of wolves killedInterv_dummy --> dummy variable for intervention type (0 if ‘non-lethal’, 1 if ‘lethal’)Delay_in_yrstop_time --> time to recurrent event (in days) or censoringstatus_event --> failure binary variable (1 if recurrent event; 0 if censored)start_time --> start time variable (all obs ‘0’)yr_stratum --> order of recurrent events within a particular area-yr†year --> year variable (1998-2014)
‘Area’ refers to the section, neighborhood or neighborhood of township area scales.
?. [SAMPLE .DO FILE (Section scale)‡]
‡ A similar procedure was used for the township and neighborhood of township scales, using their respective spatial unit identifiers.§ This section was used for our main dataset. We removed this section of code for our alternate ‘traps-placed’ dataset, to include as ‘lethal’ those cases were attempts were made to trap wolves (please see study).** May need to break ties in Delay_in_yrstop_time variable to retain observations (please see stset help in Stata)†† A separate dataset was created including only those area-year records with strata (yr_stratum) values with enough observations to be properly modelled (please see study).
TITLE: Miltenyi MACS Bead Isolation
AUTHORS: Dannielle Moore, Andre G Loxton

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block">This protocol includes the step-by-step used for isolation of a cell popultation of interest using MACS bead technology. The kits used for this protocol were obtained from Miltenyi. Miltenyi isolation kits are based on a simple process, in which biotin conjugated antibody-labelled mononuclear cells are separated from unlabeled cells via a column in the presence of a magnetic field.</div></div>

[STEPS]
?. Isolated PBMCs washed in 10mL 1x  at 400xg for  at
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[(Room Temp)]
?. PBMCs resuspended in 1x Reccommended volume per 1x107 cells according to kit specifications
?. MACS isolation labelling reagents inadded according to the manufacturers instruction (volume and incubation time). During this step, samples are incubated at
?. Following incubation, 1x is added to cell suspension
1 ml
?. Centrifuge cell suspension at 300xg for  at
?. removed from storage and set up in hood.Placed in MACS seperator and primed with 3x
1 ml
?. Cells resuspended in 1x up to total cell count of 1x108
500 µl
?. Cell suspension applied to primed Note: Tube in which cells stored rinsed 3x with and this suspension added to the LS column (wash 1)
1 ml
?. LS Column topped up twice with  (Wash 2 and 3)
3 ml
?. Flow through collected (isolated cell population of interest during negative selection process)
?. In seperate tube, plunge LS Column with   (isolated cell population of interest during positive selection process)
3 ml
?. Centrifuge cell suspension (containing cell population of interest) at 350xg for  at
?. Resuspend Isolated cells in -
500 µl
[(depending on cell type)]
?. Dilute cells in 1:10 ratio with  and Count the cells using haemocytometer and microscope. Record observed cell number and cell viability
?. Aliquot cell suspension for sample purity check.
10 µl
TITLE: No Title
AUTHORS: 

[DESCRIPTION]
This protocol is for the separation of free virus particles from sediments. It is from:<br /><br />Danovaro, R., and M. Middelboe. 2010. Separation of free virus particles from sediments in aquatic systems, p. 74–81. In S. W. Wilhelm, M. G. Weinbauer, and C. A. Suttle [eds.], Manual of Aquatic Viral Ecology. ASLO.<br /><br />Please see the <a href="http://www.aslo.org/books/mave/MAVE_074.pdf" target="_blank">full chapter</a> for additional details.

[GUIDELINES]
The optimized protocol for the separation of free virus particles from sediments is shown in Figure 1.<br /><br /><br />
<div style="text-align: left;"><img id="s-mce-img" class="s-mce-img" style="display: block; margin-left: auto; margin-right: auto;" src="https://s3.amazonaws.com/pr-journal/2nc2w6.jpg" alt="" data-src="https://s3.amazonaws.com/pr-journal/2mc2w6.jpg" data-ofn="Figure 1.jpg" /><strong><br />Figure 1:</strong> Protocol illustrating the steps required for the separation of viruses from the sediment particle and subsequent counting. <br /><br /><em><strong>References</strong></em><br /><br />Danovaro, R., A. Dell’Anno, A. Trucco, and S. Vannucci. 2001. Determination of virus abundance in marine sediments. Appl. Environ. Microbiol. 67:1384-1387.<br /><br />Duhamel, S., and S. Jacquet. 2006. Flow cytometric analysis of bacteria and virus-like particles in lake sediments. J. Microbiol. Met. 64:316-322<br /><br />Fischer, U. R., A. K. T. Kirschner, and B. Velimirov. 2005. Optimization of extraction and estimation of viruses in silty freshwater sediments. Aquat. Microb. Ecol. 40:207-216.<br /><br />Hewson, I. and J. A. Fuhrman. 2003. Viriobenthos production and virioplankton sorptive scavenging by suspended sediment particles in coastal and pelagic waters. Microb. Ecol. 46:337-347.<br /><br />Middelboe, M., R. N. Glud, and K. Finster. 2003. Distribution of viruses and bacteria in relation to diagenetic activity in an estuarine sediment. Limnol. Oceanogr. 48:1447-1456.<br /><br />Middelboe, M.,  R. N. Glud 2006. Viral activity along a trophic gradient in continental margin sediments off central Chile. Mar. Biol. Res. 2:41-51.<br /><br />Middelboe, M.,  R. N. Glud., F. Wenzhöfer, K. Oguri, and H. Kitazato. 2006. Spatial distribution and activity of viruses in the deep-sea sediments of Sagami Bay, Japan. Deep-Sea Res. 53:1-13.<br /><br />Noble, R. T., and J. A. Fuhrman. 1998. Use of SYBR Green I for rapid epifluorescence counts of marine viruses and bacteria. Aquat. Microb. Ecol. 14:113-118.<br /><br />Patel, A., R. T. Noble, J. A. Steele, M. S. Schwalbach, I. Hewson, and J. A. Fuhrman. 2007. Virus and prokaryote enumeration from planktonic aquatic environments by epifluorescence microscopy with SYBR Green I. Nature Protocols 2:269-276.<br /><br />Suttle, C. A. and J. A. Fuhrman. 2010. Enumeration of virus particles in aquatic or sediment samples by epifluorescence microscopy, p. 145-153. In S. W. Wilhelm, M. G. Weinbauer, and C. A. Suttle [eds.], Manual of Aquatic Viral Ecology. ASLO.</div>

[STEPS]
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TITLE: No Title
AUTHORS: 

[GUIDELINES]
Original protocol written by Yasmine Graf.

[STEPS]
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TITLE: No Title
AUTHORS: 

[DESCRIPTION]
This protocols outlines our definitions of core operational protein families (OPFs), potential auxiliary metabolic genes (AMGs), sharing of genes across anatomical sites, and Bray-Curtis dissimilarity of the OPFs by anatomical site. Based on methods from the following publication:<br /><br />Hannigan, Geoffrey D., et al. "The Human Skin Double-Stranded DNA Virome: Topographical and Temporal Diversity, Genetic Enrichment, and Dynamic Associations with the Host Microbiome." <em>mBio</em> 6.5 (2015): e01578-15.<br /><br /><br />

[BEFORE_START]
Supplemental information available at:<br /><br /><a href="https://figshare.com/articles/The_Human_Skin_dsDNA_Virome_Topographical_and_Temporal_Diversity_Genetic_Enrichment_and_Dynamic_Associations_with_the_Host_Microbiome/1281248" target="_blank">https://figshare.com/articles/The_Human_Skin_dsDNA_Virome_Topographical_and_Temporal_Diversity_Genetic_Enrichment_and_Dynamic_Associations_with_the_Host_Microbiome/1281248</a>

[GUIDELINES]
<span class="identifier">sessionInfo</span><span class="paren">(</span><span class="paren">)<br /><br />## R version 3.2.0 (2015-04-16) <br />## Platform: x86_64-apple-darwin13.4.0 (64-bit) <br />## Running under: OS X 10.10.4 (Yosemite) <br />## ## locale: <br />## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 <br />## <br />## attached base packages: <br />## [1] stats graphics grDevices utils datasets methods base <br />## <br />## loaded via a namespace (and not attached): <br />## [1] magrittr_1.5    formatR_1.2    tools_3.2.0    htmltools_0.2.6 <br />## [5] yaml_2.1.13    stringi_0.4-1    rmarkdown_0.7    knitr_1.10.5 <br />## [9] stringr_1.0.0    digest_0.6.8    evaluate_0.7</span>

[STEPS]
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TITLE: UC Davis - HDL Protocol
AUTHORS: Peter Havel

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block"><span style = "font-weight:bold;">Summary:  </span></div><div class = "text-block">LDL and VLDL are separated from HDL using a precipitation reagent. Then the HDL fraction is measured for either TC or TG using the same reagents for total cholesterol or triglyceride.</div></div>

[STEPS]
?. Add 25µl 2X precipitation buffer to 25µl of sample using a positive displacement pipet.
?. Vortex and let sit at RT for 10 minutes.
?. Centrifuge at 2000×g for 10 minutes at 4°C.
?. Pipet supernatant into new tube, this is the HDL fraction.
?. Add 5 µl of calibrator and sample to each well.IMPORTANT: Make sure not to add any bubbles to the wells when dispensing reagents, this will interfere with reading in the platereader.
?. Add 300 µl of  TC or TG reagent to each well. Incubate at 37°C for 5 minutes. Read at 540 nm.IMPORTANT: If samples are hemolyzed,pipet a blank well with 5µl sample and 300µl PBS
?. Subtract blank readings from final readings. The assay will be linear so the unknown samples can be calculated as (Sample Absorbance ÷ Calibrator Absorbance) × Calibrator Concentration.
?. HDL samples are diluted ½ so multiply these by 2 to get the final value. Subtract this from the total triglyceride or cholesterol value to get the LDL/VLDL value.
TITLE: SM buffer for making phage dilutions
AUTHORS: HANNAH ZHU

[DESCRIPTION]
How to make sm buffer for phage dilutions

[STEPS]
1. Dissolve following components in 300 mL of MilliQ water:



 

2. Adjust pH to 7.4 – 7.5 with HCl

3. Top up the volume to 400 mL with MilliQ water

4. Autoclave at 121 °C for 15 min

5. Cool down to room temperature
6. Filter-sterilise through 0.22 µm membrane
7. Store at room temperature
TITLE: Data storage and security
AUTHORS: Kajiru Gad Kilonzo, Stefanie J. Krauth, Jo Halliday, Clive Kelly, Stefan Siebert, Gloria Temu, Christopher Bunn, Nateiya M Yongolo, Sally Wyke, Emma McIntosh, Blandina Mmbaga

[DESCRIPTION]
This protocol details data storage and security.

[STEPS]
SECTION: Data storage and security
1. The database will be hosted on a web-based application designed to allow researchers to upload, view and manage data.
SECTION: Data storage and security
2. An Open Data Kit (ODK) platform(32) will be deployed for this purpose. Community and hospital survey data will be collected on ODK-programmed tablets and uploaded via secure connection to servers at KCRI.
SECTION: Data storage and security
3. The ODK platform will be used to produce a study-specific application and shall provide interoperability functions, such as exporting data to excel spreadsheet and other statistical packages.
SECTION: Data storage and security
4. This database will be developed and approved by the Data Management team for the study before utilisation. The data will be stored on 3 servers: primary, mirror and backup.
SECTION: Data storage and security
5. The primary server will be used to process incoming ODK, before being backed up on mirror and backup servers. All servers are behind firewalls and locked in secure cabinets.
SECTION: Data storage and security
6. All quantitative data will be initially stored within KCRI servers in the ODK platform. On completion of the study, or as part of routine data monitoring, the data will be extracted from ODK for analysis using statistical packages such as STATA, SPSS, R or SAS.
SECTION: Data storage and security
7. All content analysis data will be stored as Excel spreadsheet files and transcripts stored as Microsoft Word files. Final versions of all datasets and documents will also be exported to, and made available, as ASCII and/or CSV data files, with accompanying command/syntax files, so future users will still be able to access the data.
SECTION: Data storage and security
8. The audio data collected during qualitative data collection will be saved separately with no participant identification information.
SECTION: Data storage and security
9. Each data file will be catalogued in a single database, with accompanying metadata (e.g., filename, author, abstract, producer, geographic coverage, temporal period of collection, response rate, etc.) using Data Documentation (DDI) Initiative standards.
TITLE: Reconditioning PCR for removal of PCR bubbles in Illumina librarys
AUTHORS: Dominik Buchner

[DESCRIPTION]
This protocol describes how to remove partly single-stranded PCR products ("PCR bubbles") from Illumina libraries. For more information about this phenomenon please see this explanation from Illumina. For metabarcoding libraries, it can be hard to estimate the optimal input template or the perfect amount of PCR cycles and therefore overamplification happens frequently. PCR bubbles cannot be quantified reliably with fluorometric-based methods and may look different on different capillary electrophoresis devices. 

PCR bubbles can lead to failed sequencing runs due to over- or underloading the flowcell.

[GUIDELINES]
Follow general lab etiquette. Wear gloves to prevent contamination of samples. Clean the workspace before starting and after finishing with 80% EtOH.

[STEPS]
SECTION: Quality control
1. Perform a quality control of your library. PCR bubbles may look different depending on the method used for quality control. See below and example of the Fragment Analyzer, Bioanalyzer and an agarose gel.


 

 

 


SECTION: Library concentration (optional)
2. Concentrate your library by reducing the volume down to 100 µL . We usually do this with a spin-column based protocol, although this can be performed with magnetic beads as well.

 


SECTION: Reconditioning PCR
3. Fill the concentration of your library and the project name into the Excel spreadsheet. The suggested master mix for the reconditioning PCR will be calculated accordingly. We usually go for 1250 ng of template input, however, this can be adjusted if necessary. master mix

 

SECTION: Reconditioning PCR
4. Perform the PCR with 4 reactions of 50 µL .

SECTION: PCR clean-up
5. Pool the 4 PCR reactions.
SECTION: PCR clean-up
6. Perform a column-based PCR clean-up to exchange the buffer. This can also be done with magnetic beads. Elute the DNA in 100 µL .


 


SECTION: Size-selection
7. Perform a size selection with a ratio of 0.7x to remove residual primer dimers. Elute the final library in 50 µL .

 

SECTION: Perform final quality control
8. Perform a final quality control. Quantify the library concentration with a fluorometric-based method and perform quality control via electrophoresis. The shoulder should be gone and the library should be good for sequencing.
TITLE: Prepare Tris Buffer for Meat identification Protocol
AUTHORS: openbioscience Adrian

[DESCRIPTION]
Buffer protocol for meat identification experiment

[BEFORE_START]
Make sure you adjust the pH to the same temperature for the reaction in which the buffer is used.

[STEPS]
SECTION: Prepare Tris Buffer for Meat identification Protocol. Prepare (50 ml) of 0.04 mol/L Tris HCl (pH 7.75)
2. Measure and add 0.24 g of Tris

Calculation for     weight:
MW for TRIS Hydrochloride (C4H11NO3 ·HCl) 121.14 mol/Liter, (Hydrochloric HCl Acid, MW: 36.46 g/mol)
0.04 M TRIS TRIS Hydrochloride: m=CMV =0.04mol/L*121.14g/mol *50 ml=0.24 g of TRIS
https://www.cytographica.com/lab/HHTris.html


SECTION: Prepare Tris Buffer for Meat identification Protocol. Prepare (50 ml) of 0.04 mol/L Tris HCl (pH 7.75)
1. Add 25 ml deionized DNAse free, dd water to a 50 ml falcon tube. Bring to to final experiment desired Celsius temperature (22 Celsius for example) and maintain. Can use an incubator for example.
SECTION: Prepare Tris Buffer for Meat identification Protocol. Prepare (50 ml) of 0.04 mol/L Tris HCl (pH 7.75)
3. 
Add 100 μl HCl 12.1M  
  

iteratively measure pH and add 1 to 2μl HCL(12M) until pH reaches 7.75. Calculator indicates 116.
https://www.cytographica.com/lab/HHTris.html 
SECTION: Prepare Tris Buffer for Meat identification Protocol. Prepare (50 ml) of 0.04 mol/L Tris HCl (pH 7.75)
5. Store in the fridge for max 4 months
SECTION: Prepare Tris Buffer for Meat identification Protocol. Prepare (50 ml) of 0.04 mol/L Tris HCl (pH 7.75)
4. Add water to 50 ml