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Naming Convention for Unpublished Samples

Please note that the convention for sample names provided in the caNanoLab Curation "Tips" is for samples that have been published. For unpublished samples, caNanoLab encourages users to use the following convention for sample names:

Abbreviation of institution name, name of the lead PI (without middle name), "DRAFT", year of study, and sample sequential number--e.g.,   UNM-JBrownDRAFT2012-01.

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Stephanie Morris onto caNanoLab submission FAQs

caNanoLab FAQ Wiki

General questions about caNanoLab can be found on the caNanoLab FAQ Wiki. Below are a few highlighted facts pertaining to submission. This list is expected to grow as we hear back from users.

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Stephanie Morris onto caNanoLab submission FAQs

Suggested Uses for This Site

Consider posting information about your own projects here or introduce a specific area or question you wish to explore inviting others to participate. 

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Tony Dickherber onto Advanced Omic Analyses

Registering for a caNanoLab Login Account

To submit data, you must first register for a login account. Additional information on how to use caNanoLab once an account is created can be found on the caNanoLab FAQ NCI Wiki. To learn more about how to submit data, we suggest that users watch the caNanoLab demo video and review the caNanoLab User Guide.

 

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Stephanie Morris onto caNanoLab submission FAQs

caNanoLab and PubMed

caNanoLab is one of the PubMed LinkOut resources! 

Pubmed Articles with caNanoLab Data

 

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Mervi Heiskanen onto Linking to Journals

caNanoLab and Elsevier Journals

Elsevier and US National Cancer Institute Implement Reciprocal Linking Between Research Articles and Datasets

Press Release

 

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Mervi Heiskanen onto Linking to Journals

Scientific Data

Nature journal Scientific Data is interested in data descriptor articles concerning nanotechnology, and caNanoLab is one of their recommended repositories—thus, there is the potential to publish caNanoLab data submissions!

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Mervi Heiskanen onto Linking to Journals

Creating a Collaboration Group

Collaboration groups can be used to privately share data with colleagues. At this time, data shared with colleagues can be found by group members using the search function. Members must know the name of the shared caNanoLab sample, protocol, or publication.

To create a new collaboration group:

1. On the Manage Collaboration Groups page, click Add corresponding to New Collaboration Group.
The Collaboration Group Information panel opens at the bottom of the page.

2. Enter a Name and Description for the group.

3. To add a user to a group, click Add next to User Login.

4. Click Search and all users are loaded in the drop-down list.

5. When the third column is loaded, enter the user name that you want to add.
The field populates or matches the user name in the User Login field (text and third column). 

6.  From the Access to the Group selection, specify read (only) or read-update-delete access for a user.

7. Click Save, and the user is added to the group's list. 
To modify a selection, click the Edit link next to each person's login name.

8. Once you add all of the users to the group, click Submit
The group is now shown on the Manage Collaboration Groups page.

See caNanoLab User Guide for instructions how to create and mange collaboration groups.

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Mervi Heiskanen onto caNanoLab submission FAQs

National Outreach Network Fact Sheet

The National Outreach Network (NON) seeks to strengthen NCI's ability to develop and disseminate culturally appropriate, evidence-based cancer information that is tailored to the specific needs and expectations of underserved communities, working through NON community health educators (CHEs) located at NCI-Designated Cancer Centers.

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Jennifer Wiles onto Fact Sheets

CRCHD Overview Fact Sheet

Since 2001, the Center to Reduce Cancer Health Disparities (CRCHD) has served as the cornerstone of NCI’s efforts to reduce the unequal burden of cancer in our nation.

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Jennifer Wiles onto Fact Sheets

NDEx - Video Demo - June 2016

This video demo was prduced for the Annual ITCR PI Meeting hosted by The Broad Institute in June 2016 and consists of 3 parts:

  • Part 1 describes several UI improvements for network search, including examples of advanced search features.
  • Part 2 introduces the new network visualizer that has been deployed to the NDEx Public server at the beginning of July 2016.
  • Finally, in part 3, we demonstrate a network round-trip between NDEx and Cytoscape that shows how layout and graphic style information are faithfully preserved.

For more information, please visit the NDEx Website and follow us on Twitter and LinkedIn

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Rudolf T Pillich onto ITCR Training Assets

Tools to Analyze Morphology and Spatially Mapped Molecular Data

Motivation: Predict treatment outcome, select, monitor treatments; Reduce inter-observer variability in diagnosis; Computer assisted exploration of new classification schemes; Multi-scale cancer simulations

 

Analysis pipelines for multi-scale, integrative image analysis. Cell, Nuclear Segmentation & Deep Learning
Database infrastructure to manage and query images and Pathomics features.  MongoDB based FeatureDB
HPC software that targets clusters, cloud computing, and leadership scale systems. HPC Region Template infrastructure targeting NSF XSEDE and Titan
Interactive visualization tools to link Pathomics feature and image data; integrate Pathomics image and “omic” data.  caMicroscope, Feature Explorer and 3D Slicer Pathology
Dissemination:  HPC, Containers, Cancer Imaging Archive (TCIA), Linked Image/NLP analytics

 

 

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Joel Haskin Saltz onto ITCR 2016 Annual PI Meeting

QIICR: Quantitative Imaging Informatics for Cancer Research

Overview demonstration for the NCI ITCR QIICR project, http://qiicr.org presented on June 13, 2016 at the annual ITCR face-to-face meeting.

 

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Mervi Heiskanen onto ITCR Training Assets

UCSC Xena Basic Tutorial

UCSC Xena is a tool that allows researchers and bioinformaticians to easily and securely view both their own cancer genomics data and data from large consortiums, such as TCGA, TARGET and GTEx. This video shows how to use Xena.

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Mary Goldman onto ITCR Training Assets

Future-Proofing Overview

For any technology developer operating in an ever evolving field, it is important to ensure both forward and backward compatibility of your design to enhance the usability of the tool as well as extend the lifetime of its relevance. As the IMAT and ITCR programs serve to support the development of novel technologies that are in many cases complementary to each other, it becomes useful to consider that developers from either group would greatly benefit from having the opportunity to test the ability of novel data generating technologies to work with multiple data analysis tools, and vice versa. This project site is intended to facilitate broader awareness of relevant tools and to potentially catalyze collaborations between complementary groups to facilitate such interoperability testing.

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Tony Dickherber onto Future-proofing

Omic Analysis Overview

Modern biotechnologies allow for extensive molecular profiling of cancer cells across various analyte families, including genomes, transcripts, metabolites, proteins, epigenetic markers, and so on creating a substantial data analysis challenge due to the size of the data produced alone (to say nothing of the inherent complexity of any given data set). Furthermore, high-throughput capabilities across these profiling techniques are becoming more available, further exacerbating this "big data" challenge. Within any of these data sets lies clues to the molecular contributors and drivers of cancer (aka biomarkers), but sorting out this "biological signal" from the noise of entirely normal and complex biology is a substantial challenge. Most biomarker discovery approaches involve linear analytical techniques and regression analyses which may not be sufficient to overcoming the S/N challenges. Emerging approaches from ITCR investigators are being developed to specifically target such shortcomings. In parallel, IMAT-supported investigators are developing a variety of tools that seek to improve upon existing 'omic platforms and should be working with ITCR investigators to improve the analysis of their data to maximize the utility of these technologies. This project site is intended to facilitate improved awareness across relevant projects and facilitate collaborations between complementary groups.

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Tony Dickherber onto Advanced Omic Analyses

Cancer Interactome Overview

It is well appreciated that cancer is a complex disease accounted for by cellular dysfunction. It is further well appreciated that the multiple molecular pathways involved in the hundreds of conditions collectively called cancer are at the heart of this complexity. The ability to explore and understand the interaction of nucleic acids and proteins will be critical for offering patients improved diagnosis and treatment for their conditions. In order to achieve this, an integration of various molecular profiling technologies (e.g. genomic, proteomic, metabolomics, etc) and the data management and processing tools that will facilitate this will be required. The IMAT and ITCR programs support a variety of technology development projects likely to contribute to this potential. This project site is intended to facilitate broad awareness of relevant projects and catalyze collaborations between complementary groups.

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Tony Dickherber onto Cancer Interactome

Next Gen Histopathology Overview

Conventional histopathology must evolve to take advantage of our evolving understanding for the complexity of cancer. Improving upon current clinical practices will involve incorporating innovations in imaging tools, labeling reagents and strategies, data and image processing technologies and sample preparation tools. Emerging technologies that may contribute to all of these fields exist across both IMAT and ITCR projects. This project workspace is intended to facilitate improved awareness of relevant technology projects as well as facilitate collaborations between complementary tools.

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Tony Dickherber onto Next-Gen Histopathology

Standards Development Overview

The technological capacity exists to produce low quality data from low quality samples with unprecedented efficiency. Unraveling the massive matrix of misleading data is compromising progress in unprecedented ways making it difficult to differentiate artifact from biology. Garbage in means garbage out, and quality in means quality out.

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Posters

Posters

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Mervi Heiskanen onto ITCR 2016 Annual PI Meeting