Author Archives: Jennifer

Video Tip of the Week: Integrative Multi-species Prediction (IMP) Network Analysis Resource

A while back Mary saw the following tweet go by & collected it as a possible topic for one of our weekly tips:

RT @moorejh: #bioinformatics #genomics RT @GreeneScientist Interactive and video tutorials for IMP are available from:

This week I have claimed Mary’s “collected” tip idea & will be featuring one of their videos as this week’s quick tip.

The Integrative Multi-species Prediction (IMP) web server is a gene-gene network analysis resource. There are several such resources (Cytoscape, IntAct, MINT, STRING, VisANT, and one of my personal favorites GeneMania) that OpenHelix has tutorials on (see our Pathway catalog listing). The IMP developers provide a nice amount of help for their users – not only do they have multiple YouTube videos (as do we on the OpenHelix YouTube channel), but they also offer two interactive tutorials that allow users to be guided through an example usage of IMP.

For today’s tip I am featuring the third YouTube video that they list on their tutorial page, because I thought it had the best sound and image quality. The other videos are also informative & are worth a viewing – enjoy!

Wong AK, Park CY, Greene CS, Bongo LA, Guan Y, & Troyanskaya OG (2012). IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Research, 40 DOI: 10.1093/nar/gks458

Video Tip of the Week: 1000 Genomes Dataset Browser from NCBI

A recent NCBI Newsletter announced the release of a new resource named the 1000 Genomes Dataset Browser, and that is the resource that I will be featuring in this tip. It is one of the tools available through the new NCBI Variation resources page, which also features resources such as dbSNP, dbVar, dbGaP and ClinVar (many of which OpenHelix has tutorials for) as well as other variation tools – Variation Reporter (pre-release version), Clinical Remap (beta version) and the Phenotype-Genotype Integrator.

Before I discuss NCBI’s 1000 Genomes Dataset Browser, I’d like to spend a bit of time on the 1000 Genomes project, in order to distinguish what is from NCBI and what is from the project itself. From the 1000 Genomes Pilot paper:

“The aim of the 1000 Genomes Project is to discover, genotype and provide accurate haplotype information on all forms of human DNA polymorphism in multiple human populations. Specifically, the goal is to characterize over 95% of variants that are in genomic regions accessible to current high-throughput sequencing technologies and that have allele frequency of 1% or higher (the classical definition of polymorphism) in each of five major population groups (populations in or with ancestry from Europe, East Asia, South Asia, West Africa and the Americas).”

You can access the full paper from the link below. The project has now moved past the pilot phase and is releasing new data all the time. You can see announcements and project details, or access that data, through the official 1000 Genomes project site, or through the official 1000 Genomes version of the Ensembl Browser. As you might imagine for a “big data” project such as this, data has been added to a variety of NCBI databases, including dbSNP, the Sequence Read Archive (SRA) and BioSample. Although you could search for this data through the universal Entrez search system, previously to view the data you would have to view individual results at each separate database. The 1000 Genomes Browser at NCBI has been created as a powerful interface for comprehensively searching for, and viewing, 1000 Genomes data contained in NCBI resources on a single page.

In the video tip I will familiarize you to the various areas of the page - the browser is created with series of widgets, each with its own function. I will not be able to cover all of the features, or demonstrate how users can upload their own variation data to the browser – I’ll leave you the fun of exploring those on your own. Because the tool is so young, bugs and suggestions/comments are still being actively requested – if you find something, check out the FAQs (which discuss bugs at various stages of being fixed) and then email the team.

Quick Links:
NCBI Newsletter announcement July 20, 2012:

NCBI Variation page:

NCBI 1000 Genomes Browser page:

1000 Genomes Project site:

The 1000 genomes project specific version of the Ensembl Browser:

The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing Nature, 467, 1061-1073 DOI: 10.1038/nature09534

Video Tip of the Week: MetaboAnalyst 2.0

In looking through the 2012 Web Server Issue of NAR, Nucleic Acids Research journal, I couldn’t help notice resource names that revealed a bit about the developers’ sense of humor, such as “TaxMan” and “XXmotif“.  There were others on the list (“MAGNET“, “GENIES” and “VIGOR“, for example) whose names made me cringe imagining someone trying to find them with the average search engine. [Our family’s favorite such resource is iHOP, or Information Hyperlinked Over Proteins - I gotta think that the developers aimed at that name in honor of the other IHOP :) and breakfasts everywhere.]

I scrolled through many such names until I found a resource to feature in today’s tip. I wanted something dealing with a current topic – they all pretty much fit that criteria – and one that I was interested in, but that was outside my “normal area of expertise”. I decided on “MetaboAnalyst 2.0“, which is the resource that I will feature in today’s tip. It is described in the article “MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis” as follows:

“MetaboAnalyst is a web-based suite for high-throughput metabolomic data analysis. It was originally released in 2009… MetaboAnalyst 2.0 now includes a variety of new modules for data processing, data QC and data normalization. It also has new tools to assist in data interpretation, new functions to support multi-group data analysis, as well as new capabilities in correlation analysis, time-series analysis and two-factor analysis. We have also updated and upgraded the graphical output to support the generation of high resolution, publication quality images.”

As I often do, I began “exploring” MetaboAnalyst 2.0 by reading their NAR article. It is well written and describes how the goal of the interface is to be user friendly and intuitive, so I headed over to MetaboAnalyst 2.0 “kick some tires”, so to speak. I found that the interface is quite easy & intuitive to use. And to really help users understand the resource before launching into uploading their own data, the developers provide a wide range of example data sets that users can play with, as well as step-by-step guides (pdf, PowerPoint, & two articles that require journal subscriptions, no videos yet). In my video I use one of their datasets & show a quick example of some analysis steps. Of course there isn’t time to fully cover MetaboAnalyst 2.0, but hopefully I show you enough to tempt you to try it out on your own.

*Please note that the developers suggest that you download results immediately because all user data is treated as private and confidential by MetaboAnalyst 2.0 will remain on the server for only 72 hours before automatically deleted.

Quick Link:

MetaboAnalyst 2.0 –

Jianguo Xia, Rupasri Manda, Igor V. Sinelnikov, David Broadhurst, & David S. Wishart (2012). MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis Nucleic Acids Research, 40 (W1) DOI: 10.1093/nar/gks374

Jianguo Xia, Nick Psychogios, Nelson Young, & David S. Wishart (2009). MetaboAnalyst: a web server for metabolomic data analysis and interpretation Nucleic Acids Research Volume 37, Issue suppl 2 Pp. W652-W660. , 37 DOI: 10.1093/nar/gkp356

Enjoying the 2012 NAR Web Server Issue & a Cup of Coffee

In hunting for something to feature for this week’s tip, I noticed that Nucleic Acids Research had released their 2012 Web Server Issue back in July. As many of you are might be aware, the Nucleic Acids Research journal is a forum where developers can present computational biology papers that describe the development of biologically relevant algorithms, novel usage of existing algorithms, or that report the development of biological databases & their usage. The web server issue is an annual special issue focused specifically on web-based software resources for analysis and visualization of molecular biology data.

This year marks their 10th web server issue & I decided to check it out. In order to devote full attention to the issue, I began by pouring myself a big cup of coffee in one of my favorite mugs, which somehow makes it taste better. Then I set out to enjoy the issue – every year I always begin by reading the opening editorial & then the article on the bioinformatics links directory. The editorial usually explains special emphasis for the issue (this year it is analysis of next-generation sequencing data), and is written by the executive editor of the issue, Gary Benson. For me, the editorial sets the tone of the issue, so to speak.

Next I consume the directory article, along with a couple of sips of my java. What interests me in the article is multifold. First is the discussion of trends that they see in the development of tools and resources, which is important for us here at OpenHelix. Figure 6 provides an interesting look at the categories and counts of resources from each annual issue – I am curious as to why all but one category decline in 2008. Table 1 also provides interesting data on tool trends.

I am also interested in the content of the list itself – it is a great list being developed by people that we have a lot of respect for. I was especially interested in this sentence from their article:

“The Bioinformatics Links Directory has also initiated active curation of its content, removing dead content and correcting content errors, which has resulted in more accurate although occasionally smaller counts for 2012.”

The emphasis is mine in the quote above. In my opinion this is a very important aspect of any list. If you remember, Mary posted on the idea of “Obituaries for bioinformatics tools.” and started a BioStar post to collect this information. The BioStar post generated significant comment & looks like it may have helped inspire the Bioinformatics Links Directory team, from the comments. But it makes sense that you need not just collect information but to continue to maintain and filter that data so that it remains relevant – I mean if the forest is cluttered with dead wood, the useful “live trees” (ok, resources) are obscured from users, right?

The problem is that keeping any list (or documentation or tutorials, etc.) up-to-date is a hard, labor intensive activity. Here at OpenHelix we also keep a list of biology-relevant resources that can be searched through for free, without registering, from our homepage. We currently have a summer intern culling through a list of over 5,000 resources and tools that we know of. She is eliminating duplicate entries in our database by finding and collecting alternative URLs – it is amazing how many resources have multiple entryways, each with their own URL. But different doors don’t make a different resource or utility so we eliminate them form our list. Then we will tackle the dead resources, the listings that just go to a tiny tool internal to a main resource, or to a pre-formatted PubMed search for something.

Creating AND maintaining a high quality list is not a trivial effort. In their paper the Bioinformatics Links Directory team describes remaining current as a “future challenge” and says:

“Although necessary to remain current and to advance the utility of the Bioinformatics Links Directory, these improvements will only prove useful if driven by the community. As a community-driven repository, everyone in the research or bioinformatics community has the opportunity to help make the collection better and more meaningful. “

I truly wish them better luck at “community curation” than many resources have had in the past, & hope they succeed. In our experience it works best with stable, sufficient funding because as they say: “you get what you pay for”.

OK, next post will be on actual resources in the web server issue, I promise! :)

Quick links:

2012 NAR Web Server Issue:

Bioinformatics Links Directory:

OpenHelix Homepage & Search Portal:

Gary Benson (2012). Editorial: NUCLEIC ACIDS RESEARCH ANNUAL WEB SERVER ISSUE IN 2012 Nucleic Acids Research, 40 (W1) DOI: 10.1093/nar/gks607

Michelle D. Brazas, David Yim, Winston Yeung, & B. F. Francis Ouellette (2012). A decade of web server updates at the bioinformatics links directory: 2003–2012 Nucleic Acids Research, 40 (W1) DOI: 10.1093/nar/gks632

Friday SNPpets

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…

Video Tip of the Week: Microbiome Resources From JGI

Just over a month ago an issue of Nature had two articles from the Human Microbiome Project Consortium – you may have seen them, or noticed the Friday SNPets items we had on them. I promised myself that I’d read the articles (which I did), and that I’d visit my old friends the IMG (Integrated Microbial Genomes) & IMG/M (IMG with Microbiome Samples) to see what is new at these powerful microbial genome resources. In today’s tip I decided to take you along on my visit with me, because I found that IMG now has a resource dedicated to the analysis of genomes related to the Human Microbiome Project called the Integrated Microbial Genomes-Human Microbiome Project, or IMG/HMP. We visit both IMG/M (briefly) and the IMG/HMP in today’s tip.

When I referred to IMG as an old friend, I really do feel that way – our tutorial on IMG* was one of my first projects for OpenHelix. I was new, and IMG was new, having been released in March of 2005, only a few months before I created our tutorial (on their June 2005 release, if I am remembering correctly). They have grown into such an extensive, powerful resource. To give you an idea of how fast they have grown & developed, our current IMG tutorial is version 12 and I’ll be working on version 13 as soon as I finish updating our SGD tutorial. When we first created our IMG/M tutorial*, metagenomes were a relatively new concept and the resource included a total of 24 microbiome samples – now it has over 1000!

But enough with the nostalgia, let’s get to the resources! :) IMG/M integrates metagenome data with isolate microbial genome sequences from the integrated microbial genome (IMG) system to enable the analysis of phylogenetic composition and functional or metabolic potential of the aggregate genomes (metagenomes) in microbial communities (microbiomes). Genomes generated as part of the Human Microbiome Project (HMP) are included into IMG/M from RefSeq via IMG. IMG/M resources allow users analyze metagenomes, genomes, genes and functions by making lists of items and then manipulating them in “analysis carts”. Metagenomes can also be analyzed using the tools provided from their ‘Metagenome Details’ page. These options are explained in much more detail than I can cover here in the IMG/M reference that I site below. I also link to the most recent IMG publication, since an understanding of it is essential to understand any IMG/M-based resource.

* OpenHelix tutorial for this resource available for individual purchase or through a subscription.

Quick Links:
Integrated Microbial Genomes (IMG):

Integrated Microbial Genomes with Microbiomes (IMG/M):

Integrated Microbial Genomes-Human Microbiome Project (IMG/HMP):

OpenHelix Introductory Tutorial on IMG:

OpenHelix Introductory Tutorial on IMG/M:

Victor M. Markowitz, I-Min A. Chen, Ken Chu, Ernest Szeto, Krishna Palaniappan, Yuri Grechkin, Anna Ratner, Biju Jacob, Amrita Pati, Marcel Huntemann, Konstantinos Liolios, Ioanna Pagani, Iain Anderson, Konstantinos Mavromatis, Natalia N. Ivanova, & Nikos C. Kyrpides (2012).
IMG/M: the integrated metagenome data management and comparative analysis system Nucl. Acids Res. , 40 DOI: 10.1093/nar/gkr975

Victor M. Markowitz1, I-Min A. Chen, Krishna Palaniappan, Ken Chu, Ernest Szeto, Yuri Grechkin, Anna Ratner, Biju Jacob, Jinghua Huang, Peter Williams, Marcel Huntemann, Iain Anderson, Konstantinos Mavromatis, Natalia N. Ivanova, & Nikos C. Kyrpides (2012). IMG: the integrated microbial genomes database and comparative analysis system Nucl. Acids Res., 40 DOI: 10.1093/nar/gkr1044

More Big Data to Consider: Bioimage Informatics

I’m not sure any more when I signed up for complementary copies of Nature Methods, but just like clockwork my copy arrives each month. If you’d like to get it too, you can apply for a subscription here (Firefox seems to work better than IE, btw). This month’s issue particularly interested me because it contains a focus on Bioimage Informatics. The focus appears to be free to read online.

I found the focus just after having read the Science News article “Blast Injuries Linked to Neurodegeneration in Veterans” by Greg Miller. In Greg’s piece there is a description of a distinctive neuropathology that has been seen in athletes and military veterans who had incurred head injuries. This same distinctive pattern is seen in a mouse model of blast injury & the image of the tangles of tau protein shown in the article struck me as so interesting that I told my husband about it over dinner one night, so I already had bioimages on my mind. I am also always interested in the field of bioinformatics, both personally and as a member of the OpenHelix team.

The commentaries, in the order that they were printed, were what I read initially. The first commentary is by Gene Myers, who was also involved in early genome bioinformatics, and it provided a very interesting perspective on both the current state of bioimage informatics and on the historic use of bioimages in systems genetics.The following quote made me grin:

The field is still in its early days, and there is no such thing as a typical bioimage informatician: they are either computer vision experts looking for new problems, classic sequence-based bioinformaticians looking for the new thing or physicists and molecular biologists whose experiments require them to bite the informatics bullet. … From my perspective, it is very reminiscent of the state of bioinformatics in the early 1980s: the exciting, somewhat chaotic free-for-all that is potentially the birth of something new.”

And the following paragraph stressing the importance of “due diligence of pilot studies” and “optimized protocols” reminded me of my days setting up a Biocore facility without enough funding for either sufficient pilot studies or optimization, which ultimately doomed the utility of the machine to my advisor and department alike. This commentary set the stage well for the rest of the articles. The other commentaries included a description of the difference in goals of the computer vision field and the bioimage informatics field, a plea for usability to be built into bioimaging software, and a historical commentary on the 25 years of NIH Image, now ImageJ.

The usability article sounded many many of the same cries that we make here at OpenHelix – if you want to have usable bioscience software that IS in fact USED, at a minimum you must 1) have funding and a mandate to maintain it over the long run, 2) have motivated developers that are responsive to their users needs and feedback, including fixing bugs and 3) (last but absolutely not least) you must provide awareness and training on your software. And in my opinion, any old training WON”T due – it has to be high quality, up-to-date, and easier to use & absorb than your average dry documentation on programming your VCR clock (OK, I’m dating myself there, but you KNOW what I mean…) I like their suggestion that funding agencies request descriptions of how the software be maintained and documented, and to be prepared to provide funding not just for development, but also for maintenance. (Why reinvent the wheel over & over, just to let each one go flat with disrepair?)

There were also reports on specific software, such as OMERO.searcher, SimuCell, PhenoRipper, Fiji, BioImageXD, and Icy, as well as on the Broad Bioimage Benchmark Collection (BBBC), a collection of microscopy image sets available for the testing and validation of new image-analysis algorithms.

The focus then concludes with a great review of bioimaging software tools, with the goal of providing a “how to” summary of using open-source imaging software for every stage of bioimage informatics. It begins with a discussion of data aquisition & continues through data storage and workflow systems. I might tweek figure one just a bit, but it does visualize that today software is required at every stage of image analysis – from automated image attainment to image retrieval and analysis. The authors also touch on the importance of image annotation and controlled vocabularies, or ontologies. Table 1 provides a nice resource listing including software names, primary function and URL – I have some new resources to check out now! :)

Overall, I’d suggest this focus on bioimage informatics to any life scientist, whether you are analyzing images today or not – I think it is provides a glimpse into an up&coming, exciting field.

Quick Links:

Broad  Broad Bioimage Benchmark Collection (BBBC):







Reference List:
Greg Miller (2012). Blast Injuries Linked to Neurodegeneration in Veterans Science, 336 (6083), 790-791 DOI: 10.1126/science.336.6083.790

Gene Myers (2012). Why bioimage informatics matters Nature Methods, 9, 659-660 DOI: 10.1038/nmeth.2024

Anne E Carpenter, Lee Kamentsky, & Kevin W Eliceiri (2012). A call for bioimaging software usability Nature Methods 9, 9, 666-670 DOI: 10.1038/nmeth.2073

Kevin W Eliceiri, Michael R Berthold, Ilya G Goldberg, Luis Ibáñez, B S Manjunath, Maryann E Martone, Robert F Murphy, Hanchuan Peng, Anne L Plant, Badrinath Roysam, Nico Stuurmann, Jason R Swedlow, Pavel Tomancak, & & Anne E Carpenter (2012). Biological imaging software tools Nature Methods, 9, 697-710 DOI: 10.1038/nmeth.2084

Video Tip of the Week: The PSI SBKB’s New Content Hubs

In today’s tip I will feature the newly organized content hubs over at the Protein Structure Initiative’s Structural Biology Knowledgebase, or PSI SBKB. We do have a free, full-length tutorial on the PSI SBKB that we are in the process of updating, but I thought I’d just touch on one of the new updates to the PSI SBKB now while it is “hot”. :) Quoting from their PSI: Biology in the Spotlight, July 2012 News:

This month, we release a new left menu which highlights our new scientific Hubs. These Hubs are designed to collect PSI and SBKB information so that our audience can find features based on their needs.

I’ve been working with these hubs over the last several months, and have gotten to watch as they have been developed. These scientific hubs make it easy for users to access specific content offered by both the SBKB and the PSI as a whole, because they are organized by content. I think the new left-hand organization is nice – the bright banner and organization will highlight these hubs to users, who will hopefully take full advantage of the resources found in each hub. Currently the scientific hubs are organized around the following content: targets; protein structures, sequences and function; membrane proteins; homology models; and methods. In the tip video I briefly visit the membrane proteins hub and the methods hub to give you an idea of the types of links and contents that you will find, but so be sure to check them out on your own to see what resources you can find for furthering your research.

For further details on the PSI SBKB, see the links below & stay tuned for our full, updated tutorial coming free to a browser near you! :)

Note: the open access papers will not cover the new hubs, but will describe many of the resources accessible from the hubs.

Quick links:
PSI Structural Biology Knowledgebase resource:

OpenHelix Introductory tutorial on the PSI SBKB (free to access):

Related resource – RCSB Protein Data Bank (RCSB PDB):

Related OpenHelix Introductory tutorial on the RCSB PDB (free to access):

PSI SBKB References:

Gifford LK, Carter LG, Gabanyi MJ, Berman HM, & Adams PD (2012). The Protein Structure Initiative Structural Biology Knowledgebase Technology Portal: a structural biology web resource. Journal of structural and functional genomics, 13 (2), 57-62 PMID: 22527514  (requires subscription)

Gabanyi MJ, Adams PD, Arnold K, Bordoli L, Carter LG, Flippen-Andersen J, Gifford L, Haas J, Kouranov A, McLaughlin WA, Micallef DI, Minor W, Shah R, Schwede T, Tao YP, Westbrook JD, Zimmerman M, & Berman HM (2011). The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods. Journal of structural and functional genomics, 12 (2), 45-54 PMID: 21472436 (open access here)

Cormier CY, Park JG, Fiacco M, Steel J, Hunter P, Kramer J, Singla R, & LaBaer J (2011). PSI:Biology-materials repository: a biologist’s resource for protein expression plasmids. Journal of structural and functional genomics, 12 (2), 55-62 PMID: 21360289 (open access here)


Video Tip of the Week: the New PubMed Filters Sidebar

In today’s tip I am linking to a YouTube video from NCBI that briefly explains the new Filters Sidebar feature that has been added to PubMed. We first saw a tweet that the change was coming back on May 2nd, just as I was completing a total update to our full PubMed tutorial*.

I struggled with whether to hold our production team for the new sidebar, or to produce our tutorial with the plan to update in the near future – it is always a struggle to know which is the best option because resource changes can occur at the speed of light, or according to geological time scales (ok, that’s an exaggeration but it feels that way when you want to release a wonderful, up-to-date project & something holds you up and causes delayed publication of our tutorial materials). With PubMed I was lucky – I saw a tweet that the sidebar feature would be added “in the next week”. I asked our voice professional to put the script on hold & I paced around PubMed waiting to see what (& when) things would occur.

True to their word, the sidebar feature showed up on PubMed results on May 10th, exactly one week since I had seen the “in the next week” announcement – my THANKS to the NCBI & PubMed Teams! :) Not only did they push out their updates in a timely manner, they made a YouTube video explaining the changes & discussing where future changes are slated to go. The video is clear, and quick, so I am using it as my tip this week. I’m not sure the feature is 100% stable, as I show in the image below, and describe later in the post, but I think the change might accomplish NCBI’s goal – for more people to notice & utilize filters for their searches.

In the video the narrator states that the filters area is gone & the two default filters are permanently selected, as indicated by the check marks that can’t be “unclicked”. I”m not seeing those check marks on either “Free full text available” link (shown) or the “Review” link, which is not in view in my image. I also see a difference as to whether I get the right filtered subsets depending on whether I am logged into My NCBI (the upper window shown in the back of the image), or not (the lower, front window). In my hands IE 9.0 & Firefox 12.0 both function similarly in these aspects.

The NCBI video doesn’t really show how results look after filters are added, but in playing with it to me it looks like all of your filters are applied to your search & you only get one set of results, not links to various subsets. Although it is now easier to add filters to searches, if that’s how filters are going to work going forward, I think I will miss the old filters – I kind of like being able to switch between various subcategories of results without having to change my filters or rerun searches. Be sure to share your thoughts & preferences with NCBI so that they can create the best resource for their users needs!

* OpenHelix tutorial for this resource available for individual purchase or through a subscription.

Quick links:

OpenHelix Introductory Tutorial on using PubMed (soon to be updated):

PubMed Resource:

PubMed Reference:
Sayers, E.W., Barrett, T., Benson, D.A., Bolton, E., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Federhen, S. & (2011). Database resources of the National Center for Biotechnology Information, Nucleic Acids Research, 40 (D1) D25. DOI: 10.1093/nar/gkr1184

Video Tip of the Week – The Cell: An Image Library

Who says social media is a waste of time? Not me – my LinkedIn updates keep including announcements of the “Image of the week” from The Cell: An Image Library. For my tip this week I decided to follow up on that & check out the images available from this resource, & I’m glad I did. The Cell Image Library is brought to you by the The American Society for Cell Biology (ASCB), and contains thousands of images, time series and groups of images, videos and animations of cells in a variety of organisms. Images are organized by Cell Process, Cell Component, Cell Type, Organism and Recently added. You can browse images or do a basic search from the homepage, or perform advanced searches. The advanced search form allows users to query with keywords, and for image attributes, specific image licensing categories, biological categories, imaging techniques, or associated anatomy terms.

To quote from their About page, the Cell Image Library:

“This library is a public and easily accessible resource database of images, videos, and animations of cells, capturing a wide diversity of organisms, cell types, and cellular processes. The purpose of this database is to advance research on cellular activity, with the ultimate goal of improving human health.”

And the library doesn’t merely allow you to access images, you can also provide your own images to be featured in the Library, as described in their “contribute” page. You contribute your raw data or minimally processed data images or videos to them and they will  be annotated by professionals with broad disciplinary expertise. Each image receives a CIL, or Cell Image Library accession number, which can be used to reference an image.

In this tip I’ll touch on the features of the image displays, and anything else that I can fit in, but I can guarantee there is more for you to explore on your own. After watching our video tip, I suggest you head over to The Cell: an Image Library & check it out yourself. If you do, be sure to share your insights with the Library’s development team by filling out their user survey. Thanks!

Quick Links:

The Cell: an Image Library  –


(On the utility of the Cell Image Library for science education) – Miller, K. (2010). Finding the key – cell biology and science education Trends in Cell Biology, 20 (12), 691-694 DOI: 10.1016/j.tcb.2010.08.008