• RT @KevinADavies: The Last First Base — my final editorial @bioitworld — the highs and lows, 1s and 0s of the past 11 years! http://t.co/nFmyR3ngtk
• RT @BBC_Future: Craig Venter says we only know around 1% of what we’ll ultimately know about our genome. http://t.co/ayxMV4mSyC (nonUK)
• RT @sethmnookin: “Earlier headline suggested humans have same genome as western painted turtle. That is incorrect.” http://t.co/ouZQzIcr2L ht @edyong209
• RT @dgmacarthur: Flatley: rejected someone who wanted to be sequenced to show they had alien DNA. “We don’t have a good reference genome.” #UYG13
• RT @em_underwood: Powerful story by Ginny Hughes for Nat Geo: Could DNA Databases Curb Human Trafficking& http://t.co/Z7eXbJQjuJ  via @ngphenomena
• RT @BioInfo: TrioVis: a visualisation approach for filtering genomic variants of parent-child trios. http://t.co/5uogKAfNXy #bioinformatics
• RT @MishaAngrist: The next controversy in genetic testing: clinical data as trade secrets? http://t.co/svGOVHjD8S
• RT @hemedoc: Why are medical students still dissecting cadavers but not learning the tools to dissect genomic data? – @EricTopol
• Via G+: Drew Endy originally shared to Synthetic Biology (Discussion):Congrats to Linda for publishing her first survey of what tools people are actually using in synthetic biology. http://www.jbioleng.org/content/7/1/13/abstract
• RT @_inundata: The #xckd R package is finally out.  http://t.co/NoadYvECGp
  let the nerdiness ensue. #rstats http://t.co/sAeQNcbaeA
• RT @notSoJunkDNA: cc @denizkural RT “@vsbuffalo: Great article: “Short read alignment: an introduction” http://t.co/PpisZNclei HT: @jnfass”

Is this the future of genomics ? twitter.com/ElementoLab/st…

— Olivier Elemento (@ElementoLab) 4. Mai 2013

Heh. Ok, so it’s not all glamour–this discussion made me laugh. Posed in the forum:

Forum: What do you waste your time on

Like in everything else we do, the 80%-20% rule works great in bioinformatics. What is your 80% of the time wasted on? I can suggest: 1. converting accession numbers between databases 2. parsing output The bottom line of the post is how to avoid these time consuming tasks.

–Asaf

Converting accession numbers and file formats was pretty popular. But the pointing-to-documentation one made me chuckle too. Go see the full spectrum of bioinformatics glory duties.

But researchers are working all the time to understand, characterize, and evaluate viral sequences. They will access a number of different tools to do so. We talked last month about GISAID and EpiFlu as our Tip of the Week, and how the special access agreement they have developed has provided some otherwise reluctant governments to share the newest sequence data. So if you want the most current sequences–you would turn to EpiFlu.

There are other virus resources that you should investigate too. Another important site is the IRD, or Influenza Research Database. They have developed an extensive repository of many flu sequences, and have provided a wide range of tools to help researchers investigate and evaluate the data. In addition, they have incorporated some tools that provide novel analyses of the underlying data. This includes predictions of cytotoxic T-cell epitopes, and a sequence feature variant type analysis that they mention in their recent paper.

For this week’s video tip, I include their first of eight videos that will help you to understand their organization and tools. But be sure to keep going for the other 7 that they offer at their YouTube pages.

Be sure to also read the paper that they recently published–it has a nice overview of their tools, their strategies, and also provides a nice use case example of how to flow through a typical evaluation. And if you want other virus data besides flu check out the companion site Virus Pathogen Database and Analysis Resource. You’ll see a similar organization but with a wider range of sequences available. They have a separate thread of videos for the ViPR tools as well.

Quick links:

IRD, Influenza Research Database: http://www.fludb.org/

Other viruses not just flu are in the Virus Pathogen Resource (ViPR) that offers a similar structure and tools: http://viprbrc.org

References:
Squires, R., Noronha, J., Hunt, V., García-Sastre, A., Macken, C., Baumgarth, N., Suarez, D., Pickett, B., Zhang, Y., Larsen, C., Ramsey, A., Zhou, L., Zaremba, S., Kumar, S., Deitrich, J., Klem, E., & Scheuermann, R. (2012). Influenza Research Database: an integrated bioinformatics resource for influenza research and surveillance Influenza and Other Respiratory Viruses, 6 (6), 404-416 DOI: 10.1111/j.1750-2659.2011.00331.x

Pickett, B., Sadat, E., Zhang, Y., Noronha, J., Squires, R., Hunt, V., Liu, M., Kumar, S., Zaremba, S., Gu, Z., Zhou, L., Larson, C., Dietrich, J., Klem, E., & Scheuermann, R. (2011). ViPR: an open bioinformatics database and analysis resource for virology research Nucleic Acids Research, 40 (D1) DOI: 10.1093/nar/gkr859

I know there’s controversy over the patents, but you have to acknowledge that the underlying science was really important. And I’m rather pleased to see a woman scientist in film. Looking forward to seeing it somewhere.

Here’s the film website: http://decodingannieparker.com/

++++++++++++++++

Hat tip David Bachinsky via twitter:

In this amazing true story, the lives of cancer-stricken Annie Parker and UW geneticist Mary-Claire King… fb.me/zYQEngPB

— DecodingAnnieParker (@decodingannie) 10. Mai 2013

++++++++++++++++

EDIT: I just wanted to add some information that’s breaking now about Angelina Jolie’s recent announcement of her double mastectomy due to her BRCA1 testing. And here’s a good piece about some context for that: A Cautionary Perspective On Angelina Jolie’s Double Mastectomy

• RT @atulbutte: Get raw deidentified clinical trial #bigdata here & now! RT @ImmPortDB: NIAID release data 1400 subjects, 11 studies http://t.co/RnzMBHM89G
• RT @genetics_blog: BioBlend: automating pipeline analyses w/ Galaxy & CloudMan http://t.co/P394y0Nipm doc: http://t.co/iudgyRNaPT #bioinformatics #usegalaxy
• RT @erlichya: Sex (chromosomes), Lies, and GWAS catalog. http://t.co/xBi2oB3O5V interesting paper by Manolio and friends #AJHG $paywall
• RT @genomicslawyer: Reporting Fail: The Reidentification of Personal Genome Project Participants: http://t.co/KZ2DAf5bx4 HT @madprime
• RT @girlscientist: Hmmm. But nice post/summary. RT @ElementoLab: Vogelstein v. Lander, Round 1 http://t.co/sq5kikueHx
• RT @dgmacarthur: Robert Green (@genomes2people) responds to criticism of his @TheACMG incidental finding recs: http://t.co/HQwZq8ifvK
• RT @MedStu21: Great: BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biol… http://t.co/af2UHC2OlC

Ha, ha, too funny! RT @ryneches Inter-laboratory politics at the UC Davis Genome Center. twitter.com/ryneches/statu…

— Suganthi (@suganthibala) 9. Mai 2013

This week’s highlighted question got a lot of votes! Sadly, it’s far too common. And probably most people have run into this problem in bioinformatics and genomics at some point. Sometimes it’s data, sometimes it’s software.

Question: What if authors do not share their published data ?

Dear biostars,

I am wondering what do you do if any author do not share his/her published data with you ? Usually I send an email request after checking their supplemental and public database (ex: GEO) links or others in their paper carefully. Most of the time they do respond to my requests. However sometimes I never get any response from either first or corresponding author regrading sharing their data even after multiple requests. It is really frustrating as the guidelines of that journal (Cell, Nature) clearly state that the data should be public. Do you have any similar experience ? If you write any complaint to the editor, does it work ?

Thanx in advance
Sorry that it is not really a bioinformatics question

–repinementer

Zev Kronenberg gives an excellent stepwise answer. Be sure to look at step zero! But there are a couple of other items you can read through as well. Go have a look at the answers.

But there’s no excuse for this now. With FigShare, GitHub, and other existing options for various types of things that need to be made available, it needs to improve. Reviewers need to demand this, and editors have to insist on this. And those who don’t deliver will likely be subject to some public shaming–because that’s the last step when all else fails (see Zev’s step 7).

BioBase has a series of video tutorials for both TransFac and HGMD (and more for the other databases such as Proteome, Genome Trax and ExPlain). For this weeks tip of the week, we’ve embedded two video tutorials.

This first explains MATCH, an analysis tool in TransFac to predict binding sites for Transcription Factors in a particular DNA sequence.

The second video tip is a quick tutorial on how to get started with searching HGMD

If you are interested in advanced searching of these two databases, or Genome Trax, Proteome or ExPlain, check out the video tutorials from BioBase.

Related Tutorials:DGV: Database of Genomic Variants, DBTSS

• RT @BioinfoTools: Some thoughts on science journal Nature offering statisticians to their authors: http://t.co/5jhJnEbGbS
• Ha ha ha! RT @dgmacarthur: @genomesunzipped @NeuroMinded Yes: every base is sacred, every base is great. When a base gets wasted Ewan gets quite irate.
• RT @carlzimmer: Here’s a paper title I’d like to see: “The Genome of the Silver Maple Is Like the Genomes of Every Other Frigging Maple, Except More Boring”
• But then this: RT @VinJLynch: @carlzimmer I’d like to see “White Oak Genome Provides Unique Insights into Bourbon”…
• RT @ensembl: Nice balanced article on the #ENCODE controversy. http://t.co/4uYc7QClO0
• RT @phidias51: Just figured out how @utopiadocs connects you w/other researchers. Click Altmetrics, see who tweeted the article, follow them.
• Snorf (about the 1000bullgenomes.com project) RT @phylogenomics: OMG @atulbutte we need to do all the microbiomes from these bulls – it would be the 1000bullshits project
• RT @marc_rr: The strategies which a class of students uses to annotate bacterial genomes with #bioinformatics and #biocuration: http://t.co/9yrk7vdBIm
• RT @phylogenomics: The Tree of Life:  The need for a phylogeny driven genomic encyclopedia of eukaryotes http://t.co/9offu8LA9M
• RT @_inundata: Dear every journal,
  1. Open a @GitHub org account.
  2. If authors have a repo for a paper, fork it after acceptance & link it to it.

This week’s question highlights the cancer data issues, and since I did a cancer database as “Tip of the Week” that got some decent interest, I thought I’d keep with the theme. Maybe some researchers who are more familiar with the cancer data sets will have some insights.

Question: cBio portal vs. Oncomine: Difference in samples and expression data

I was looking for expression levels for two genes involved in serous ovarian cancer data from TCGA via CBio portal. Based on a Z-score threshold of 2.0, I found the following percentage of samples (cases) have expression levels affected (UP or Down)

Case Set: Tumors with mRNA data (Agilent microarray): All samples with mRNA expression data (489 samples) CCNE1 – 11% CDK12 – 7%

For same dataset in Oncomine (I am using the free version):

TCGA Ovarian (517 samples <- this number is higher than what is reported in TCGA ovarian cancer publication) expression data is provided as log-2 median intensity and the in Oncomine shows that higher expression level of CCNE1 and CDK12 expression level is correlated with different grades – for example Grade 3 tumor (Grade 3 (431 samples) have higher expression level of both genes.

I have also noticed that the dataset 517 samples were assigned as No Associated Paper 2011/03/24. I am wondering if the data is reffering to this paper on TCGA ovarian cancer dataset. http://www.nature.com/nature/journal/v474/n7353/full/nature10166.html

I am wondering why such a discrepancy or am I missing something here.

PS. I have posted this question on both Oncomine and cBio list, but did not receive any responses yet. I am wondering if anyone here with experience on one of the platform could provide insight to this.

The other issue that interested me was the support problem. This is a skilled super-user trying hard to do it right–contacting the support teams of the sites, and getting no response. I think that’s one of the most frustrating things about this arena. Some projects are well resourced for user support. Some are not. But if smart users can’t figure out what’s going on with your site’s data, your resource isn’t as useful as you think it is. I wish support was valued more. But if you know what’s up–go over and offer an answer.

The MyCancerGenome site aims to serve that actionable end of the data spectrum. It has been developing for a while, but the recent story in the New York Times reminded me of it: Variations on a Gene, and Tools to Find Them. So for this week’s Video Tip of the Week, I bring you a look at the My Cancer Genome resources. They have a nice intro video that I will include here. It highlights features that I wouldn’t have been able to access–the part that links patient records + mutations + the curated detailed pages about the mutations and relevant studies. The public has access to that last part, but you wouldn’t be able to see the electronic health record part from the public side.

Papers are coming out that describe the deposition of information into the MyCancerGenome site. You can learn more about the philosophy and strategy about cataloging the somatic mutations that are clinically relevant in the recent paper about the DIRECT (DNA mutation Inventory to Refine and Enhance Cancer Treatment) project. A tab at that site shows you the initial data associated with that, from non-small cell lung cancer (NSCLC) mutations in the Epidermal Growth Factor Receptor (EFGR). And as more of this data comes along we’ll see it grow, of course. Seems a good step in translational medicine. So have a look at the useful and evidence-based information about specific cancer-related variations they are collecting.

Another feature is a search option to find clinical trials–by disease or by gene. I don’t think I’ve seen a gene-specific search for this kind of information before. This could be useful for people who need access to new treatment options if they have specific mutation data about their own tumors.

Have a look at My Cancer Genome, and think about where we are going with this data. I hope that the new cancer genomics data will really help drive appropriate and effective treatment strategies.

Quick link:

My Cancer Genome site: http://www.mycancergenome.org/

NYT article: Variations on a Gene, and Tools to Find Them

References:

Swanton, C. (2012). My Cancer Genome: a unified genomics and clinical trial portal The Lancet Oncology, 13 (7), 668-669 DOI: 10.1016/S1470-2045(12)70312-1

Yeh, P., Chen, H., Andrews, J., Naser, R., Pao, W., & Horn, L. (2013). DNA-Mutation Inventory to Refine and Enhance Cancer Treatment (DIRECT): A Catalog of Clinically Relevant Cancer Mutations to Enable Genome-Directed Anticancer Therapy Clinical Cancer Research, 19 (7), 1894-1901 DOI: 10.1158/1078-0432.CCR-12-1894

My Cancer Genome. 2013. http://www.mycancergenome.org (Accessed 4/30/2013).