29:null 2a:["$","title",null,{"children":"WebGen | Erika Nemeth | Observable"}] 2f:T48b,html`

WebGen

Companion notebook to web4bio.github.io/webgen, Stony Brook University's Vertically Integrated Project (VIP) on WebGenomics.

About Us: This notebook was contributed to by a subteam of WebGen, consisting of the following individuals (with their associated notebooks linked):

Erika Nemeth
Carolyn Bremer, Notebook
Richard Moffitt, Ph.D., Notebook
Jonas Almeida, Ph.D., Notebook

About this Notebook: This notebook will serve as a diary which will be updated throughout the course of the WebGen project (January 2019 - May 2019) with curiosities, findings, and questions.

`30:T497,md` **End of Spring 2019 Semester Checkpoint:** - Have in fact made progress in the goals set forth at the beginning of the project - **Goals for the summer:** - Improve JavaScript skills - Further investigate exactly what others with similar ambitions (e.g., All of Us) are investigating and how they are doing this - Create **something** useful using TCGA gene expression data - Explore... - **Preliminary goals for next semester:** - Increase use of GitHub - Establish a platform for better communication amongst team members (something between text and email) - To help save class time in, for example, sharing new findings - Set up an assigned day/time for subteams to meet before class - To eliminate redundancy in work efforts - To allow us to work through things that may have been confusing from the previous week's class - Create a semester schedule for when each subteam is to formally present its current progress (at least 2-3 times per semester) - Before-class preparation: Takes notes on what the presenting subteams have prepared - To make in-class discussions more productive/constructive `31:Taf7,html`

Breakdown of the GDC API Structure:

a) Endpoint:

https://api.gdc.cancer.gov/projects

Endpoint: Essentially, the "destination" of your communication through the API; Click here to view available endpoints and their functionalities

Example endpoints:

/files: Each returned record represents one file
/cases: Each retured record represents one patient
/projects: Each returned record represents one project
/annotations: Returns annotations that may have been added to the data

b) Separator:

https://api.gdc.cancer.gov/projects?

A question mark (?) separates the website address from the query commands

c) Filters:

https://api.gdc.cancer.gov/projects?filters={"op":"=", "content": {"field":"cases.demographic.gender", "value": ["female"]}}

Designed by you to specify your query
Operator + set of "field" and "value" operands

d) Fields:

https://api.gdc.cancer.gov/projects?fields=cases.demographics.gender

Fields are separated by commas

Note: Returned records will be unordered

Also note: All fields select for METADATA

A list of all field names with accompanying descriptions

A list of field names with no descriptions, but easier to look at

e) Size:

https://api.gdc.cancer.gov/projects?fields=disease_type,name,program.name,project_id,summary.case_count&from=0&size=1000

Specifies the range/maximum number of records to return

Default size is 10

f) Sort:

https://api.gdc.cancer.gov/projects?fields=disease_type,name,program.name,project_id,summary.case_count&from=0&size=1000&sort=disease_type:asc

Sorts the returned records by a specified field

Direction of sorting is :asc (ascending) or :desc (descending)

`32:T4cf,PrintTCGA = function(projectData) { let div = DOM.element('div') // set up the main object div.innerHTML='

TCGA Project types in GDC ()

' let ol=DOM.element('ol') // set up the sub-object which will contain our data div.appendChild(ol) let caseCount=0 projectData.data.hits.forEach(function(prj,i){ // for each hit (project) if ((/TCGA/.test(prj.id))) { let li = DOM.element('li') // build up the header "li" li.innerHTML=`${prj.id}: ${prj.name} (${prj.summary.case_count})` caseCount=caseCount+prj.summary.case_count ol.appendChild(li) // append the header "li" to the larger object "ol" let ul = DOM.element('ul') // build up the bullets "ul" prj.disease_type.forEach(function(dtp){ // for each disease_type let li = DOM.element('li') li.style.fontSize='small' li.innerHTML=dtp ul.appendChild(li) // append one bullet "li" to the list of bullets "ul" }) li.appendChild(ul) // append the bullets "ul" to the header "li" } }) div.querySelector('#caseCountSpan').textContent=caseCount return div };33:T46d,{ var trace1 = { x:Object.keys(femaleDisPlot), y:Object.keys(femaleDisPlot).map(tp=>femaleDisPlot[tp]), type:'bar', name:'Female Data', marker: {color: "rgb(255,105,180)"} }; var trace2 = { x:Object.keys(malePlot), y:Object.keys(malePlot).map(tp=>malePlot[tp]), type:'bar', name:'Male Data', marker: {color: "rgb(30,154,255)"} }; var data = [trace1, trace2]; var layout = { title: 'Frequency of Disease Type', xaxis: { tickangle: 45, automargin:true, tickfont: { size: 14, color: 'rgb(107,107,107)' }}, yaxis: { title: 'Incidence', titlefont: { size: 16, color: 'rgb(107,107,107)'}, tickfont: { size: 14, color: 'rgb(107,107,107)' }}, legend: { x: 0, y: 1.0, bgcolor: 'rgba(255, 255, 255, 0)', bordercolor: 'rgba(255, 255, 255, 0)' }, barmode: 'group', bargap: 0.15, bargroupgap: 0.0, width: width }; const div = DOM.element('div'); plotly.newPlot(div, data, layout); return div; }34:T61e,html`

About TCGA

(Ref: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga)

The Cancer Genome Atlas (TCGA): A project begun by the National Cancer Institute and the National Human Genome Research Institute in 2006 to catalogue genetic mutations that occur in cancer

Phase I (2006-2009): Focus was on genomic characterization of gliblastoma multiforme, lung, and ovarian cancer
Phase II (2010-2015): Scope expanded to characterization of 20-25 different tumor types

Ultimately characterized a total of 33 tumor types

TCGA barcode: An identifier for a single tumor sample's set of results, which were generated by GCC (Genome Characterization Centers), GSC (Genome Sequencing Centers), and GDAC (Genome Data Analysis Centers)

Case: All of the data under a given project for a certain patient

TCGA "Data Types:"

Somatic mutation data
mRNA expression data
miRNA expression data
DNA methylation data
Copy number alteration data
Proteomic data

TCGA "Data Categories:"

Transcriptome Profiling
Copy Number Variation
Simple Nucleotide Variation
DNA Methylation
Clinical
Sequencing Reads
Biospecimen

`35:T40a,html`

Databases

A potential solution to the above issue may be to relocate the MAF files to a database system. Some background on databases...

Database: A set of data typically organized into tables (i.e., rows and columns) and stored on a computer

Relational database management system (RDBMS): A program which consists of one or more databases and which can be modified, managed, and queried

Structured query language (SQL): A language which is used to interact with data contained in a RDBMS; There are several variations of SQL syntax, including:

SQLite - Open-source; Used in electronic devices
MySQL - Open-source; Most popular, especially for web apps
PostgreSQL - Open-source; Also used for web apps; Slower
Oracle DB - Close-sourced
SQL Server - Close-sourced

So, how could we upload a MAF file to a database that we create? Well, it turns out that FireBrowse has already done this.

`36:[] 2d:{"id":"ee0d2fed747719e6","slug":"webgen","title":"WebGen","thumbnail":"3ffeb7fa955317ac714614bc8b2c658acadf3d685038dc018494242082ea5a12","access_level":"public","create_time":"$D2019-02-15T16:24:21.521Z","update_time":"$D2020-05-07T15:24:10.607Z","view_time":null,"first_public_time":"$D2019-02-15T16:24:30.046Z","fork":true,"stars":1,"starred":false,"owner":{"type":"team","id":"be31c14f0c46ba8e","login":"enemeth19","name":"Erika Nemeth","avatar_url":"https://avatars-next.observableusercontent.com/4b8ec923a78002d01c443682ba0a0b90eeab2fb7f884a3e506bed247453915a9","tier":"starter_2024"},"version":1278,"publish_version":1278,"role":null,"authored":false,"authors":[{"type":"individual","id":"9887bbe532a2fe8d","login":"enemeth19","name":"Erika Nemeth","avatar_url":"https://avatars-next.observableusercontent.com/4b8ec923a78002d01c443682ba0a0b90eeab2fb7f884a3e506bed247453915a9","tier":null}],"members":[{"type":"individual","id":"9887bbe532a2fe8d","login":"enemeth19","name":"Erika Nemeth","avatar_url":"https://avatars-next.observableusercontent.com/4b8ec923a78002d01c443682ba0a0b90eeab2fb7f884a3e506bed247453915a9","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"WebGen","cells":[{"id":0,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":118,"value":"html`\n\n

Project Motivation & Goals

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":64,"value":"md` **Overarching Project Motivation:** To utilize bioinformatics in order to catalogue the mutations that occur at different stages of cancer progression for the larger purposes of better understanding the biological manifestations of cancer and developing more precise therapies. We hope that this may be accomplished by making cancer genomic data more accessible to researchers and citizen scientists via web design. To this end, we will utilize JavaScript in handling of genomic data.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":66,"value":"md` **Beginning of Project Goals and Questions:**\n- Learn HTML, CSS, and JavaScript\n- Learn how to manipulate APIs\n- Become aquainted with SQL to understand how large data may be stored, organized, modified, and accessed\n- Bring insight, problems, and questions to team meetings\n- **Question: How can somatic mutation data stored in GDC be made more easily accessible to potential users?**\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":888,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":985,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":75,"value":"html`

The Genomic Data Commons (GDC)

A unified data repository sourced with cancer genomic data from a variety of projects, and created with the goal of providing accessible information for the advancement of precision medicine. This is currently the source of data that we are working with.

To view a summary of the types of data collected from tumor samples, click here.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":968,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":953,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":429,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":457,"value":"html`\n

Retrieving Project Data Via the GDC API

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":481,"value":"projectData=(await fetch('https://api.gdc.cancer.gov/projects?fields=disease_type,name,program.name,project_id,summary.case_count&from=0&size=1000&sort=disease_type:asc')).json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1122,"value":"html`\n

Await: We are waiting for the asynchronous request to finish before potentially passing the value to a subsequent function

Fetch: A GET request; i.e., We are requesting data

.json() The returned information is parsed in JSON

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1154,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":636,"value":"html`\n

Below, Jonas formatted the project data that we just retrieved (Ref: Jonas's Notebook)

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":13,"value":"{\n let div = DOM.element('div')\n div.innerHTML='

Cancer Projects Contributing to GDC ()

'\n let ol=DOM.element('ol')\n div.appendChild(ol)\n let caseCount=0\n projectData.data.hits.forEach(function(prj,i){\n let ul=DOM.element('ul')\n let li = DOM.element('li')\n li.innerHTML=`${prj.id}: ${prj.name} (${prj.summary.case_count})`\n caseCount=caseCount+prj.summary.case_count\n ol.appendChild(li)\n li.appendChild(ul)\n prj.disease_type.forEach(function(dtp){\n let li = DOM.element('li')\n li.style.fontSize='small'\n li.innerHTML=dtp\n ul.appendChild(li)\n })\n div\n //if(i==1){debugger}\n })\n div.querySelector('#caseCountSpan').textContent=caseCount\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":814,"value":"html`\n

For reference: TCGA Study Abbreviations

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":199,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":168,"value":"html`\n

Another API Call

Here, we are calling all files with field=cases.project.project_id and value=\"TCGA-PRAD\". This means that we are calling all files in the TCGA project for Prostate Adenocarcinoma.

To retrieve the files for TCGA-PRAD, a filter was needed: {\"field\":\"cases.project.project_id\",\"value\":[\"TCGA-PRAD\"]}}. This filter was pasted into a JSON Editor (https://www.url-encode-decode.com) to translate it to %encoded format. The filter in %encoded format was then pasted into the API call.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":147,"value":"prostateData=(await fetch('https://api.gdc.cancer.gov/files?filters=%7B%22op%22%3A%22%3D%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22TCGA-PRAD%22%5D%7D%7D&from=0&size=15000')).json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1222,"value":"html`\n

Next, we would like to visualize the number of files in each data category for TCGA-PRAD. The Plotly.js library will enable this visualization through a bar chart.

First, we need to call the library:

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":160,"value":"weWillUsePlotly=require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":158,"value":"prostateDataPlot={\n let array={}\n let y = prostateData.data.hits.map(x=>\n {\n if(!array[x.data_category]) \n {array[x.data_category]=0}\n array[x.data_category]+=1\n })\n return array\n}\n//Here, we have defined the two variables that will be contained in our chart","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":162,"value":"Object.keys(prostateDataPlot).map(tp=>prostateDataPlot[tp])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":166,"value":"plot= {\n let div = DOM.element('div')\n let trace={\n x:Object.keys(prostateDataPlot),\n y:Object.keys(prostateDataPlot).map(tp=>prostateDataPlot[tp]),\n type:'bar'\n }\n return weWillUsePlotly.plot(div,[trace])\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":212,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":292,"value":"html`\n

\"Import:\" A Feature of the Observable Notebook

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":640,"value":"html`\nWe can use functions defined in the notebooks of others using the following \"import\" method:\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":266,"value":"import {plotIt} from \"@cgbrem/web-genomics\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":521,"value":"import {malePlot} from \"@cgbrem/web-genomics\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":523,"value":"import {femaleDisPlot} from \"@cgbrem/web-genomics\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":525,"value":"import {plotly} from \"@cgbrem/web-genomics\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":647,"value":"html`\nFor example, after importing functions, we can create a chart using Plotly:\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":297,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":433,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":566,"value":"html`\n

What We Discovered About GDC

Richard presented the following points (Ref: Richard's Notebook):

\n
\n

I. Of all the project types, FM-AD contributes the largest number of cases to GDC (Please see the pie chart below).

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":572,"value":"import {Plotly} from \"@rmoffitt/webgen\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":578,"value":"import {caseCounts} from \"@rmoffitt/webgen\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":570,"value":"{\n let plotData = {\n labels: caseCounts.map(x=>x[\"label\"]),\n values: caseCounts.map(x=>x[\"y\"]),\n type: 'pie',\n }\n \n let layout = {\n autosize:true\n }\n const div = DOM.element('div')\n Plotly.newPlot(div, [plotData],layout);\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":606,"value":"html`\n

II.FM-AD mutation data is not open-access. However, TCGA mutation data is open-access.

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":611,"value":"html`\n

III. There are four somatic mutation (i.e., MAF) files for each tumor type in TCGA, and these files are open-acess. (See the histogram below).

So, perhaps we should focus on accessing TCGA somatic mutation data from here on out.

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":619,"value":"import {mafHistData} from \"@rmoffitt/webgen\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":612,"value":"{\n let plotData = {\n x: Object.keys(mafHistData),\n y: Object.keys(mafHistData).map(x => {\n let y = mafHistData[x]\n return(Object.keys(y).length)\n }),\n type: 'bar',\n }\n\n let layout = {\n autosize:false,\n width:800,\n height:250,\n margin:{\n l:50,\n r:200,\n b:100,\n t:25,\n pad:4\n },\n }\n\n const div = DOM.element('div')\n Plotly.newPlot(div, [plotData],layout);\n return div\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":918,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":916,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":751,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":730,"value":"html`\n

And Then We Ran Into This...

The somatic mutation files stored in GDC cannot be \"called\" as we previously called specific fields (e.g., gender, data type, etc.). Rather, the somatic mutation data must be downloaded as a MAF file in order for the user to interact with it - and this presents issues:

Once downloaded, there are issues with the formatting of the data.
The fact that such data must be privately installed rather than shared through \"the cloud\" (i.e., online) presents roadblocks to those who would like to more easily communicate through it.
It is impractical to download a MAF file every time one is searching for only a small subset of characteristics in a given dataset of large file size.

The GDC API only allows retrieval of metadata, not actual genomic data.

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":754,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":735,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":759,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":560,"value":"html`\n

FireBrowseR

Researchers at the Broad Insitute realized the issues associated with handling the actual genomic data in GDC and developed an API. The FireBrowse site

Exactly how did the creators of FireBrowse resolve some of these problems?

Representational state transfer (REST): An architectural structure that allows for consistent framework of web APIs; This assures that communication to users is not disrupted by changes to a database, and allows for more design uniformity, which is helpful for downstream data analysis

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":845,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":863,"value":"html`\n

Calling TCGA Data Through a FireBrowse API

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1258,"value":"html`\n

The FireBrowse site has interactive documentation of its API for extracting TCGA data. That is, FireBrowse will construct the Request URL for us.

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":841,"value":"firebrowseUrl = \"http://firebrowse.org/api/v1/Samples/mRNASeq?format=json&gene=BCL2&cohort=PRAD&protocol=RSEM&page=1&page_size=250&sort_by=cohort\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":849,"value":"firebrowseData =(await fetch(firebrowseUrl)).json();","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":855,"value":"html`\n

However, when we try to fetch the data via the FireBrowse API, we receive an error because the Observable Notebook requires that the content be served over HTTPS; the FireBrowse API is not secured with an SSL certificate.

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":872,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":879,"value":"html`\n

miRCancerDB

A database was created with microRNA (miR), transcription factor, and gene expression data, all from TCGA, using an R package; Github; Web interface

Something to consider alongside FireBrowse?

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":391,"value":"html`\n

GTEx

A project which seeks to explore tissue-specific gene expression and regulation

Provides public access to non-diseased tissue data (i.e., gene expression data, QTL [quantitative trait locus] data, histology images) in about 1,000 individuals

All data are from post-mortem samples

The GTEx site

Jonas found that the data could be explored through two APIs: One native to GTEx and another via Ensembl. Reference Jonas's notebook for more information

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1022,"value":"html`\n

R Packages (Bioconductor) for TCGA Data Analysis

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1026,"value":"md`\n- \"TCGAbiolinks\"\n- \"TCGAbionksGUI\"\n - Requires no knowledge of R programming\n - Buggy, not very reliable\n- \"GenomicDataCommons\"\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1037,"value":"html`\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1035,"value":"html`\n

Tools Developed to Analyze Various Large-Scale Genomics Data Efforts

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1042,"value":"html`\n

UCSC Xena Platform
More

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 2e:"$2d:body"