Tag Archives: genomics

From the Arxiv (What Caught My Eye Last Week)

Quantifying the impact of weak, strong, and super ties in scientific careers
Alexander Michael Petersen
PDF: http://arxiv.org/pdf/1509.01804v1.pdf
Soundbite: “We find that super ties contribute to above-average productivity and a 17% citation increase per publication, thus identifying these partnerships – the analog of life partners – as a major factor in science career development.”

Do we need another coffee house? The amenity space and the evolution of neighborhoods
César A. Hidalgo, Elisa E. Castañer
PDF: http://arxiv.org/pdf/1509.02868v1.pdf
Soundbite: “Neighborhoods populated by amenities, such as restaurants, cafes, and libraries, are considered to be a key property of desirable cities. … Finally, we use the Amenity Space to build a recommender system that identifies the amenities that are missing in a neighborhood given its current pattern of specialization.”

Liberating language research from dogmas of the 20th century
Ramon Ferrer-i-Cancho, Carlos Gómez-Rodríguez
PDF: http://arxiv.org/pdf/1509.03295v1.pdf
Soundbite: ” Those tenets can be summarized as a belief in the existence of word order constraints that cannot be explained by evolutionary processes or requirements of performance or learning, and instead require either (a) heavy assumptions that compromise the parsimony of linguistic theory as a whole or (b) explanations based on internal constraints of obscure nature.”
Interesting: “We submitted our commentary to PNAS but it was rejected. We hope that the availability of our submission helps to liberate language research from dogmas of the 20th century”

Estimating Reproducibility in Genome-Wide Association Studies
Wei Jiang, Jing-Hao Xue, Weichuan Yu
PDF: http://arxiv.org/pdf/1508.06715v1.pdf
Soundbite: “This can be used to generate a list of potentially true associations in the irreproducible findings for further scrutiny.”

Nucleosome positioning: resources and tools online
Vladimir B. Teif
PDF: http://arxiv.org/pdf/1508.06916v4.pdf
About: Gene Regulation Info
Includes: Nucleosome positioning datasets sorted by cell type

Combining exome and gene expression datasets in one graphical model of disease to empower the discovery of disease mechanisms
Aziz M. Mezlini, Fabio Fuligni, Adam Shlien, Anna Goldenberg
PDF: http://arxiv.org/pdf/1508.07527v1.pdf
Soundbite: “It is not unusual to observe a significant gene expression change in thousands of genes, the majority being a downstream, rather than the driver, effect (e.g. inflammation, drug response, etc) Additionally, and more importantly, there is a large heterogeneity in gene expression in cancer: many patients within the same subtype will appear to have an abberant expression. These variations are of unknown cause.”

Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Marco Scutari, Ian Mackay, David Balding
PDF: http://arxiv.org/pdf/1509.00415v2.pdf
Soundbite: ” In human genetics, decay curves could be used study to what extent predictions are accurate and thus to improve the performance of medical diagnostics for the general population. In plant and animal breeding, on the other hand, it is common to incorporate distantly related individuals in selection programs to maintain a sufficient level of genetic variability.”

Population genomics of intrapatient HIV-1 evolution
Fabio Zanini, Johanna Brodin, Lina Thebo, Christa Lanz, Göran Bratt, Jan Albert, Richard A. Neher
PDF: http://arxiv.org/pdf/1509.02483v1.pdf
Soundbite: “In most patients, the virus populations was initially homogeneous and diversified over the years, as expected for an infection with a single or small number of similar founder viruses (Keele et al., 2008). In two patients, p3 and p10, the first sample displayed diversity consistent with the transmission of several variants from the same donor.”
Soundbite: “Our reasoning proceeds as follows. Figure 6B indicates that diversity accumulates over a time frame of 2-4 years, i.e., about 1,000 days. Recombination at a rate of 10−5/bp/day hits a genome on average every 100 bps in 1000 days. Mutations further apart than 100bps are hence often separated by recombination and retain little linkage consistent with the observed decay length in Figure 7.”

Inadequate experimental methods and erroneous epilepsy diagnostic criteria result in confounding acquired focal epilepsy with genetic absence epilepsy
Raimondo D’Ambrosio, Clifford L. Eastman, John W. Miller
PDF: http://arxiv.org/pdf/1509.01206v1.pdf
Soundbite: “Because the authors could not induce focal seizures by FPI, they ended up comparing absence epilepsy in their controls with absence epilepsy in FPI rats, and concluded that they look similar. They also used inappropriate epilepsy diagnostic criteria that cannot distinguish between focal non-convulsive seizures and genetic absence epilepsy. Moreover, the authors failed to consider all literature conflicting with their conclusion, and surmised similarities between the absence epilepsy in their rats with the focal seizures we induce by rpFPI.”

Reduction of Alzheimer’s disease beta-amyloid pathology in the absence of gut microbiota
T. Harach, N. Marungruang, N. Dutilleul, V. Cheatham, K. D. Mc Coy, J. J. Neher, M. Jucker, F. Fåk, T., Lasser, T. Bolmont
PDF: http://arxiv.org/pdf/1509.02273v1.pdf
Soundbite: “Our results indicate a microbial involvement in the development of Alzheimer’s disease pathology, and suggest that microbiota may contribute to the development of neurodegenerative diseases.”

Fractal Fluctuations in Human Walking: Comparison of Auditory and Visually Guided Stepping
Philippe Terrier
PDF: http://arxiv.org/pdf/1509.01913v1.pdf
Soundbite: “[B]ecause it can be assumed that AC and VC mobilize the same motor pathways, they can probably be used alternatively in gait rehabilitation. The efficiency of VC to enhance walking abilities in patients with neurological gait disorders needs further studies. However, the high gait variability induced by VC might have detrimental effects, for instance, a lower dynamic balance. This should be taken into account in the development of VC rehabilitation methods.”

The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions
Sebastian Bitzer, Stefan J. Kiebel
PDF: http://arxiv.org/pdf/1509.01972v1.pdf
Soundbite: “Our analysis suggests that the brain estimates the reliability of the stimulus on a short time scale of at most a few hundred milliseconds.”

Brain Model of Information Based Exchange
James Kozloski
PDF: http://arxiv.org/pdf/1509.02580v1.pdf
Coolness: IBM Neural Tissue Simulator (about NTS | NTS slides | 1st article)

Interplay between the local information based behavioral responses and the epidemic spreading in complex networks
Can Liu, Jia-Rong Xie, Han-Shuang Chen, Hai-Feng Zhang, Ming Tang
PDF: http://arxiv.org/pdf/1509.01321v1.pdf
Soundbite: “The spreading of an infectious disease can trigger human behavior responses to the disease, which in turn plays a crucial role on the spreading of epidemic…. Our finding indicates that, with the increasing of the response rate, the epidemic threshold is enhanced and the prevalence of epidemic is reduced.”

Identification and modeling of discoverers in online social systems
Matus Medo, Manuel S. Mariani, An Zeng, Yi-Cheng Zhang
PDF: http://arxiv.org/pdf/1509.01477v1.pdf
Soundbite: “We develop an analytical time-aware framework which shows that when individuals make choices — which item to buy, for example — in online social systems, a small fraction of them is consistently successful in discovering popular items long before they actually become popular. We argue that these users, whom we refer to as discoverers, are fundamentally different from the previously known opinion leaders, influentials, and innovators.”

Time-aware Analysis and Ranking of Lurkers in Social Networks
Andrea Tagarelli, Roberto Interdonato
PDF: http://arxiv.org/pdf/1509.02030v1.pdf
Soundbite: “Our goal in this work is to push forward research in lurker mining in a twofold manner: (i) to provide an in-depth analysis of temporal aspects that aims to unveil the behavior of lurkers and their relations with other users, and (ii) to enhance existing methods for ranking lurkers by integrating different time-aware properties concerning information-production and information-consumption actions.”

Molecular Biology & Genomics SIG Meeting #mlanet15

Part 1 of a series of blogposts I wrote for the recent Annual Meeting of the Medical Library Association.


MolBio & Genomics SIG #mlanet15

I’m trying to track what’s going on with emerging technologies, new tools, new-to-me tools, and so forth. I’m not an official member of the MolBioGen SIG, but I wish I was (especially since personal genomics is one of my hobbies). I learned so much at their meeting Monday morning. The best part was the Round Table, where they each talked about who they are, what they do, what’s new at their place. Now, this was exciting! They talked about many tools they seemed to all know and take for granted, and I’ll share some of those later. They also had so many exciting and creative ideas for how to engage their target audiences, types of classes that are most effective, crowdsourcing instruction from within the audience, strategic partnerships that make a difference, strategies for point-of-care genomics, and so much more.

Here are the tools that I found most interesting.

PhenoTips
PhenoTips

Reactome
Reactome

Online Bioinformatics Resource Collection
Bioinformatics MOOCs Example

Galaxy Project
Galaxy

Open Helix
OpenHelix
(Note: These folk are in the Exhibit Hall, if you haven’t seen them yet.)

BioStars
BioStars

GenePool
GenePool

Data Carpentry
Data Carpentry

Project Hydra
Hydra

Fedora
Fedora

NCBI: Gene Expression Omnibus (GEO)
GEO (Gene Expression Omnibus)

Complete Genome: Public Genome Data
Complete Genomics Public Data

NYU Data Catalog
NYU Data Catalog

Want more? Check out the Storify!

Good Lord, People, 23andMe is NOT Dead!

Reposting from my personal genomics blog because I think it is important also for the audience of this blog.


23andMe

Good Lord, people, 23andMe is NOT dead! Or closed, or no longer taking orders, or anything like that. I hear this a lot.

“You know, I always wanted to get my genome tested. I was going to try 23andMe, but then the FDA shut them down. Oh, well, missed my chance. [sigh]”

NO! You did NOT miss your chance. Firstly, 23andMe is not closed for business. They still will take your money and your sample. They still will analyze the sample and give you results. From what I’ve been seeing in the results from folk I’ve been helping to look at their data, 23andMe seems to be running the test exactly the same way they always did, for the same SNPs.

They simply are, at this time, not offering their health reports to new customers. It isn’t the data that has changed – it iw what analysis is shared with the customer. Old fogies like me who got their tests done before the FDA folderol” still have access to our old 23andMe health reports, and they continue to improve them.

I have heard nothing to indicate that 23andMe are not working with the FDA to try to make it possible to release health reports again in the future. Issues around that get complicated and I’m going to save them for a later post. Right now, what if you wanted a test for some genetic health information? Can you do it? How long will you have to wait to find out the answers to your health questions?

You can still do it. It isn’t as easy as it was before, but it can be done. I’ve been spending a lot of time talking people through how to do this, and it is time to write it down. If nothing else, it will save me time. This will be the short short version, and I can answer more detailed questions and describe specifics, maybe give an example or two or three.

FIRST, THE DISCLAIMER

Risk is Not Just Genes

Making sense of genetic information is complicated even for experts, which most of us are not. Of course, part of the irony of looking at genetics for health conditions is that most of the time what causes the condition is not just the genetics, but genes PLUS something else. If you don’t find the genes for something, that doesn’t mean you can’t get it; if you do find the genes for something, it doesn’t mean you will get it. It is hardly ever a case of this=that.

What Does Risk Mean, Anyway?

There is also the challenge of figuring out how important the risk is, and whether or not to do something about it. So, my personal risk of celiac disease is over 4 times normal. Wow! That sounds like a lot, doesn’t it? But 4 times normal for celiac risk is still only 1 in 20 people, because normal is about 1 in a hundred. I know someone with celiac risk 17 times normal, which is 1 in 4 people. That’s getting to be pretty serious! But, while celiac is dangerous, it isn’t one of those conditions that is immediately deadly or painful. And my friend still has a 3 in 4 chance of NOT getting celiac, and that is a lifetime risk.

On the other hand, my risk of venous thromboembolism (VTE) is 1.5 times normal. That doesn’t sound like much does it? It’s higher, but only a little bit. So we don’t really need to worry about it, do we? Well, yes. VTE can kill you on the spot, and it is incredibly painful. And normal is 1 in 10 people for lifetime risk. For me, the risk is closer to 1 in 7.

Given that, according to 23andMe, my genetic risk of celiac is roughly 1/20 and my risk of VTE is 1/7, and adding in the comparative dangers of the two diseases, my docs got all excited about the VTE, and not terribly about the celiac. I hope you understand why now, and also a bit more about why genetic risk is complicated.

On Asking for Help

Last part of the disclaimer.

For both of these, celiac and VTE, 23andMe looks at SOME of the genes and SNPs known to be associated with the condition, but not ALL of them. So whatever 23andMe tells me about risk is only part of the picture. It looks at the most important genes, but is still only part of the picture. That’s why you need experts to put all the pieces together, and get more information to fill in the gaps from the 23andMe test.

Everyone always says, “Ask your doctor,” when it comes to finding something puzzling, confusing, contradictory, or worrisome in your genetic tests. I did, and found that most of my doctors didn’t have the expertise to make more out of it than I did. Some poohpoohed the 23andMe results, others made clinical decisions based on them without verifying with other tests, some asked for more medical tests to expand upon what 23andMe had, and one said, “You know more about this than I do, but I’m going to learn.” Here is a quote from an NEJM article a few months ago about the risks and benefits of trusting direct-to-consumer personal genomic services such as 23andMe.

“Clinicians will be central to helping consumer–patients use genomic information to make health decisions. Any regulatory regime must recognize this reality by doing more than simply adding the tagline on most consumer ads for prescription drugs: “Ask your physician.” That is insufficient guidance unless your physician has ready access to a clinical geneticist or genetic counselor.” Annas GJ, Elias S. 23andMe and the FDA. N Engl J Med 2014; 370:985-988. http://www.nejm.org/doi/full/10.1056/NEJMp1316367

Some of the personal genomics service offer phone-in access to genetic counselors. I tried that, and didn’t get helpful answers there, either. Even worse, one of the answers I got was blatantly wrong. It may have been just the genetic counselor who I happened to be talking with, so don’t judge the whole profession by that one person, but do be prepared to keep looking for info if needed. Where I found the most helpful information was in the 23andMe forums, BUT a lot of the info there was unreliable, and I had to sort out what was helpful and what wasn’t.

So, my recommendation is, absolutely DO ask your doctor, ask a genetic counselor if you can, but that might not be enough. You might need to do more research on your own, or find someone you trust to help you with this.

What Good Is It?

So, what good is it then? It gives you clues. Like a detective, you take the clues and look for more information, or ask for more thorough testing, or raise questions that weren’t being asked or addressed before. Some of the clues will be red herrings. Some of them may lead you to a prized solution. For me, these clues ended up dramatically improving my quality of life, and may have even saved my life.


So, now, the short short version. And PLEASE, if someone more expert than me with genomic data reads this and spots any errors, please say so!

PART ONE

1. Get your 23andMe test done.

Pic of the Day - PGenPGEN, Take 2

2. Log in at the 23andMe web site when you are notified that your results are ready.

23andMe

3. Click: Browse raw data.
23andMe: Getting to your raw data

It should look like this:

23andMe: Browse Raw Data

4. Click: Download raw data.
23andMe: Download Raw Data

5. Complete security procedure (log in again, answer security questions, etc.). It should look like this.

23andMe: Downloading Raw Data

6. Answer the question about what type of data and format you want. NOTE: I always choose ALL DNA, unless you have something else specifically in mind.

23andMe: Downloading ALL Your Raw Data Or ...

7. Find the file (which will be named something like genome_Firstname_Lastname_Full_12345678901234.txt)

PART TWO (A): Easier Way

Genetic Genie

Now you have choices. You can dig into the information the easier way, or the less easy way. Let’s start with the easier way.

1. Select a tool to do what you want with your data. There are LOTS of tools people have built to do useful things with 23andMe data files. One of my favorites is Genetic Genie, because it tells you about the MTHFR gene which has become so important in my life. I also am spending a lot of time with Promethease because it is so complete compared to most other 23andMe analysis tools. Lets start with these.

2. Go to the tool of your choice, such as:

Genetic Genie: http://geneticgenie.org/

Promethease: http://www.snpedia.com/index.php/Promethease

3. Follow the directions at the tool, but this almost always requires you to upload your 23andMe data file. Here are more details about doing this with Genetic Genie.

4. Last come what is always the tricky part — making sense of the information you get. That’s worth several posts, but for starters the main point to remember is that the 23andMe test is a place to start, not a final answer. In Genetic Genie, the code, analysis, and text are written by engaged amateurs, not by doctors or genetic counselors. They worked hard, collaborated with a lot of other people, and did a lot of research, but it isn’t going to say the same things your doctor might.

More Tools

23andMe: Tools for Everyone http://www.23andyou.com/3rdparty
NOTE: When 23andMe took out the health reports, they also edited this page to remove links to tools that provide health data from 23andMe data. So, this is interesting and useful, but not sufficient. You’ll have to look somewhere else for most tools.

23++ Chrome Extension: Get more from your data:
http://23pp.david-web.co.uk/getting-more-from-your-data/

Confessions of a Cryokid: Top 10 things to do with your FTDNA raw data (2011) http://cryokidconfessions.blogspot.com/2011/06/top-10-things-to-do-with-your-ftdna-raw.html

Genetic Genealogist: What Else Can I Do With My DNA Results: http://www.thegeneticgenealogist.com/2013/09/22/what-else-can-i-do-with-my-dna-test-results/

International Society of Genetic Genealogy: Autosomal DNA Tools: http://www.isogg.org/wiki/Autosomal_DNA_tools

Resqua: Q: What should I do after generating my Gene variance report? http://resqua.com/100005927200207/what-should-i-do-after-generating-my-gene-variance-report

Think Exponential: Get SNPd! http://thinkexponential.com/2013/01/10/why-you-should-get-snped/

PART TWO (B): Less Easy Way

Linking Disease Associations with Regulatory Information in the Human Genome

Actually, there are a LOT of different “less easy ways.” You can open the raw data file in a text editor and search manually for specific pieces of information. Or, if you code, you can write a little program to do some of the hard work for you.

Basically, it comes down to doing a lot of research, the hard way, by hand. But, believe it or not, I am doing it. I’ve had a lot of help from people who offered tips or comments in the 23andMe or MTHFR.net forums, on Facebook, on Twitter, and comments on these blogs. I am NOT an expert, but like most readers of this blog, just someone who wants or needs to know more. This is what I’ve learned and figured out on my own, offered as an example, nothing more.

Critical Background

23andMe gives SNP-based data. SNP stands for single nucleotide polymorphism. Polymorphism means something that can be itself but in different ways, our eyes are eyes whether they are blue or brown or hazel or violet or any other natural eye color. I won’t give an introduction to genetics here, but there are several online resources that explain these ideas, with one of the best resources being Genetic Home Reference from the US government. Depending on how much you want to know, you may wish to take the Coursera courses Introduction to Genetics and Evolution (Duke U) or Experimental Genome Science (U Penn).

1. What SNPs do you want to know about? Check here:

RegulomeDB (Stanford): Linking Disease Associations with Regulatory Information in the Human Genome: http://regulomedb.org/GWAS/

I have also found SNPs of interest in research articles, PUBMED, and other places, but this is a good start. The SNP identifier (what you need) will look something like this:

rs2187668

2. Find out which polymorphism is the one considered “healthy” or “normal”, and which one is the one associated with risk of disease? These maybe called “risk alleles” or
simply polymorphisms.

For example, for SNP “rs2187668” (one of the celiac risk SNPs) the risk indicator is (T), while the normal is (C).

3. Open your 23andMe raw data file in a text editor, like WordPad (Windows) or TextEdit or TextWrangler (Macintosh).

4. Search for the SNP you want to know about. The data will be in four columns:
– RSID
– Chromosome
– Position
– Genotype
You need to know about the first and last columns, RSID and Genotype. It will look a little like this.

rsid…..chromosome…..position…..genotype
… [many other rows of data] …
rs2187668…..6………32605884…..CT

So, this person (me) has for that SNP one risk allele “T,” (which I happen to know is from my dad, by comparing it to his scan) and one normal allele “C,” (which must, by default, be from my mom, since for every gene pair we have gotten one from each parent).

5. Repeat for all the other SNPs associated with the condition you are researching.

6. Search for more information and articles about those SNPs, the condition, and more. You can’t make sense of this without more information. And ask lots of questions.

More Tools

ENCODE:
About: http://www.genome.gov/encode/
Data: http://genome.ucsc.edu/encode/

ENSEMBL Genome Browser: http://useast.ensembl.org/

OpenSNP: https://opensnp.org/ OR https://opensnp.org/snps/

SNPedia: http://www.snpedia.com/

UCSC Genome Browser: http://genome.ucsc.edu/

The Neel Lecture — Hashtags of the Week (HOTW): (Week of May 12, 2014)

In the HOTW posts (Hashtag of the Week) we usually collect a bunch of tweets to illustrate topics or concepts. There are a few posts that mention Twitter tools, but not a lot. Today I’d like to talk about Storify, and am using the opportunity of having this morning livetweeted the James Neel Lecture by Richard P. Lifton. Livetweeting means to tweet about something while it is going on in real time.

To prepare for livetweeting I open web pages for the event, the speaker, and some of their articles. I make sure there is a good hashtag that isn’t likely to be misunderstood as being for something else. I check to see if it is possible to create an automatic archive of the event tweets. I also usually ask permission, if there is a chance. If there is not a chance to ask, the assumption is that events open to the public are permissible to tweet. (NOTE: If you are organizing an event, remind speakers to tell folk if and when they do NOT want things they say to be tweeted!) In this case, Dr. Lifton granted permission, with the caveat of excluding the portion of the presentation on current unpublished research. When he got to that part, he said, “Please don’t tweet this slide.” It works.

After the event finished, I was able to push all the tweets into a tool called Storify to create a kind of ‘story’ for the event. The tweet at the beginning of this post gives a link to the Storify for this event. While a Storify can be embedded in a web page, just like Youtube videos and tweets, it isn’t something that fits well in this blog, so I encourage you to go look at it there.

As you look at the Storify, you’ll notice that, as is usual with the blogged tweets, the individual tweets will show photos or certain other kinds of content. You may notice other content in addition to the tweets! There are pictures and links included, and even readable scrollable copies of entire article PDFs! Being a really academic presentation, this one was studded with research articles. Some of them are articles referenced by Dr. Lifton in his presentation, but others are simply articles on topics he mentioned. Don’t blame him for any errors in transmission – that would be my doing, probably misunderstanding something he said, since I’m not a geneticist. I hope that the overview this provides of the lecture might be useful to those who were unable to attend in person.


First posted at THL Blog: http://thlibrary.wordpress.com/2014/05/12/the-neel-lecture-hashtags-of-the-week-hotw-week-of-may-12-2014/

The Future of Genomic Medicine #FOGM14 — Hashtags of the Week (HOTW): (Week of March 21, 2014)

Lantern Slides: Heritance of Clefting

The image above is from one of the earliest studies on the genetics of clefting done here at the University of Michigan School of Dentistry. Those were the days, weren’t they? You had to track signs and symptoms across generations, for decades, trying to deduce large scale patterns. Now we spit in a tube and mail it off.

Pic of the Day - PGen

The Future of Genomic Medicine was just happening. It was being actively tweeted by a number of leading figures in healthcare and science — Eric Topol, Carl Zimmer, Dr. Khoury from the CDC, Magdalene Skipper from Nature, and (uh) Al Gore, just for starters. It was so active that the original hashtag, #FOGM14, had to be dropped because of spammers, and they group switched to #FOGM2014. It was so active that even though it happened two weeks ago, the hashtags are still active on Twitter with people continuing the conversations around the conference. Here are just a highly selected few tweets with interesting thoughts, resources, and take-aways from this important conference.


First posted at THL Blog: http://thlibrary.wordpress.com/2014/03/24/the-future-of-genomic-medicine-fogm14-hashtags-of-the-week-hotw-week-of-march-21-2014/

Hashtags of the Week (HOTW): Beyond the Genome #BTG13 (Week of October 14, 2013)

The Beyond the Genome conference happened early in October, but I am delighted to blog about it now, as the dust settles, people return home, and the meaning of the conference starts to come together in people’s minds. The conference was structured with each of the three days organized around a theme:

Day 1: Single Cell Analyses
Day 2: Plant Genomics
Day 3: Informatics

The most exciting day for me was the last, but it wouldn’t be fair to not give some sense of the other days as well. Here are some of the best tweets and information from the conference.

DAY ONE: SINGLE CELL ANALYSES

DAY TWO: PLANT GENOMICS

DAY THREE: INFORMATICS


First posted at THL Blog: http://thlibrary.wordpress.com/2013/10/18/hashtags-of-the-week-hotw-beyond-the-genome-btg13-week-of-october-14-2013/

Hashtags of the Week (HOTW): Beyond the Microbiome (Week of July 29, 2013)

I’ve been blogging elsewhere about microbiome research, and collecting a ridiculous number of links and articles about it. I’ve been lucky enough to have long conversations with some of our faculty who are publishing in this area. This week one of the faculty asked me to proofread a chapter they are writing about the microbiome, which was a great treat for me. Beautifully written, engaging and educational, I’m really looking forward to seeing it in print.

Midway through the process of writing about the microbiome, the faculty member was asked to include the virome. Oh. Well, let’s mix things up a bit, shall we? By the time I saw the draft, the mycobiome had also been added in. A brief ‘glossary’ for those not currently working in this space. Also note that because of the lack of a true glossary for some of these terms, I am intuiting definitions from a scan of the writings using the term. In other words, doing the best I can, but part of this is sort of made up, even though the terms exist and are being used*. While we don’t have enough for an alphabet book, there were enough that I felt compelled to alphabetize.


Biome = community of living things in a particular space or habitat
Exobiome = a community of living things external to the Earth’s air space
Exposome = measuring and assessing health impacts of environmental exposures external to the individual (beginning in utero)
Genome = genes of an organism
Metabolome = “small-molecule metabolites (such as metabolic intermediates, hormones and other signaling molecules” [Wikipedia]
Microbiome = genomes of a community of microbial or bacterial living things etc.
Mycobiome = genomes of the community of fungi …
Parisitome = genomes of the community of parisites …
Pathobiome = genomes of the pathological components of a microbiome; behaviors and changes in a microbiome that lead it toward a pathological state
Proteome = proteins produced by a genome
Retrovirome = genomes of the community of retroviruses …
Transcriptome = a subset of the genome comprised of the transcripts or various types of RNA fragments from a given cell
Virome = genomes of the community of viruses …
Xenome = genomes of microbiomes involved in xenografts or xenotransplants

And then there are the specific microbiomes for body regions, such as the vaginal biome, oral microbiome, aural microbiome, nasal microbiome, and the skin microbiome. I’m not aware of specialized terms for microbiomes of external locations (hospital, home, school, jungle, waterways, etc.) and other species (canine, feline, various bird species, various rodent species, etc). Most of the other Omes also are studied across species and locations. And there are more.

Here’s a tutorial for an introduction to just the genomics part.

And while this isn’t a tutorial, these are videos from the recent conference on Human Microbiome Science: Vision for the Future. That should give you an overview of that portion.

So let’s take a look via Twitter at some of the other “Ome”s and omics. As you might guess, these are BUSY topics, with formal Twitter chats discussing fine points of methods, sharing articles, conference presentations, news, and general buzz.

Personally, I find this hysterically funny.


* A proper glossary for these types of terms was just brought to my attention by Ian Bosdet.


First posted at THL Blog: http://wp.me/p1v84h-1n9