Archiv der Kategorie: Tipp der Woche

sneakPeekGateway

Video Tipp der Woche: New UCSC Genome Browser Gateway look

sneakPeekGateway

For years now we’ve been doing training and outreach on the UCSC Genome Browser. And there’s been a lot of change over the years–so much more data, so many new tools, neue Arten. All that ENCODE information and a portal für die. But the look of the main site was largely the same. Here’s a post we did that included the UCSC site traffic in 2000, and another time we took a look at the old style interface ~2004. And there was the switch to the new blue look in 2012.

Allerdings, the main gateway page was largely the familiar look. The gateway–where you begin to do most text-based or region-based queries for a species–was mostly altered only with some additional buttons and options. And an increasingly long list of species to choose from. Aber jetzt–it’s time to look again. The gateway is very different today. You’ll have faster and easier access to get started when you go to the site, and new ways to engage with the data that you want to begin to access.

There are additional details on the UCSC landing page in the News area, including credits to the development team involved. The other key pieces include some relocations of the previous button options:

Note that a few browser utilities that were previously accessed through links and buttons on the Gateway page have been moved to the top menu bar:

*Browser reset: Genom-Browser > Reset All User Settings
*Track search: Genom-Browser > Track Search
*Add custom tracks: My Data > Custom Tracks
*Track hubs: My Data > Track-Hubs
*Konfigurieren Sie Tracks und Anzeige: Genom-Browser > Configure

The UCSC team has created a short intro video to the new look. That is our Video Tip of the Week:

Natürlich, this means we’ll need to update our slides and exercises. We like things to stabilize a bit after a rollout to be sure things are solid. But soon we’ll include the new navigation in our materials.

The underlying ways to access the particular assembly features you need for a given genome, and the data for your tracks of interest, is unchanged. So those parts of our training materials will still help you to get the most out of your searches. We’ll let you know when we’ve made the changes to the materials as well.

 

Quick-Links:

UCSC Genome Browser main landing page: http://genome.ucsc.edu

Training materials:

Intro: http://openhelix.com/ucsc

Advanced: http://openhelix.com/ucscadv

Referenz:

Sporn, M., Zweig, A., Rosenbloom, K., Raney, B., Paten, B., Nejad, P., Lee, B., Learned, K., Karolchik, D., RAMspan>, Hinrichs, AS, Hsu, F., Kober, KM, Miller, W., Pedersen, JS, Pohl, A., Raney, BJ , A., Heitner, S., Harte, R., Häußler, M., Guruvadoo, L., Fujita, P., Eisenhart, C., Diekhans, M., Clawson, H., Casper, J., Friseur, G., Haussler, D., Kuhn, R., & Kent, In. (2015). Die UCSC Genome Browser Datenbank: 2016 Update Nucleic Acids Research DOI: 10.1093/nar/gkv1275

Bekanntgabe: UCSC Genome Browser tutorials are freely available because UCSC Sponsoren us to do training and outreach on the UCSC Genome Browser.

expVIP example

Video Tipp der Woche: expVIP, an Expression, Visualisierung, and Integration Platform

Als ich letzte Woche erwähnt, I am watching a lot of farmers on twitter talk about this year’s North American growing season. To get a taste of that yourself, einen Blick auf #Plant16 + Weizen as a search. This is where the rubber of tractor tires and plant genomics hits the…gut…rows. And just coincidentally I saw a story about this new plant genomics research tool–actually in the farming media.

It’s kind of nice to see plant bioinformatics get some recognition beyond the bioinformatics nerd community. The piece “New online tool helps predict gene expression in food crops” did a pretty good job of talking about the features of the expVIP tool, and I was eager to have a look.

expVIP stands for expression Inisualisierung und InINBEZIEHUNG Platform. expVIP exampleAlthough the emphasis here is plant data, it can be used for any species. A good summary of their project is taken from their paper (unten verlinkt):

expVIP takes an input of RNA-seq reads (from single or multiple studies), quantifies expression per gene using the fast pseudoaligner kallisto (Bray et al., 2015) and creates a database containing the expression and sample information.

And it can handle polyploid species–try that on some of the tools aimed at human genomics! They illustrate this with some wheat samples from a number of different studies. And then they use the metadata about the studies, such as tissues and treatment conditions, to show how it works with some great sorting and filtering options. They created a version of this for you to interact with on the web: Wheat Expression Browser. But you can create your own data collections with their tools, aimed at your species or topics of interest.

This week’s Video Tip of the Week is their sample of how this Wheat Expression Browser works. Although you see the wheat data here, it’s just an example of how it can work with any species you’d like to examine.

I followed along and tried what they were showing in the video, and I found it to be a really slick and impressive way to explore the data. The dynamic filtering and sorting was really nice. You can customise the filtering/sorting/etc for the visualizations with the metadata that’s useful to your research. So you could set the tissue types, or treatment conditions, or whatever you want–and filter around to look at the expression with those. They go on to show that their strategies to compare genes in different situations seemed to reflect known biology in disease and abiotic stress conditions.

So their pipeline for gene matching, as well as the tools to explore and visualize RNA-Seq data, offer a great way to look at data that you might generate yourself or you could mine from existing submitted data–but that might not be well organized and available in a handy database just yet.

Quick-Links:

Wheat expression browser: www.wheat-expression.com

expVIP at GitHub: https://github.com/homonecloco/expvip-web

Referenz:

Philippa Borrill, Ricardo Ramirez-Gonzalez, & Cristobal Uauy (2016). expVIP: a customisable RNA-seq data analysis and visualisation platform Pflanzenphysiologie, 170, 2172-2186 : 10.​1104/​pp.​15.​01667

SoyBase

Video Tipp der Woche: SoyBase CMap

SoyBaseOver the years I’ve started to follow a lot of farmers on twitter. It might sound odd to folks who are immersed in human genomics and disease. But I actually find the plant and animal genomics communities to be pushing tech faster and further to the hands of end-users than a lot of the clinical applications are at this point in time. And as #Plant16 rolls out to feed us, there was a lot of soybean chatter in my twittersphere.

So when SoyBase tweeted a reminder about some of their videos, I thought the timing was great. Sie haben eine YouTube-Kanal for some videos to help users access the SoyBase data. And one of the tools they illustrate is CMap. Although we’ve touched on CMap a couple of times on the blog and in our training videos, we never featured it. It’s one of the GMOD family members that can offer you comparisions of different map coordinate data sets. But conceptually I think it’s a good idea for people to think about physical map vs sequence mapping data. And this video shows how you can examine these different representations at SoyBase.

Besides their software videos, obwohl, SoyBase also links to a lot of other videos that help people to understand more about many aspects of soybean cultivation. Check out their wide range of topics on their Video Tutorials Seite. You never see how to use a two row harvester at human genomics databases, do you?

Quick-Link:

SoyBase: http://www.soybase.org/

Hat tip:

Referenz:

Grant, D., Nelson, R., Cannon, S., & Shoemaker, R. (2009). SoyBase, the USDA-ARS soybean genetics and genomics database Nucleic Acids Research, 38 (Database) DOI: 10.1093/nar/gkp798

Pathfinder_scap

Video Tipp der Woche: Pathfinder, for exploring paths through data sets

Pathfinder_scapI didn’t expect to do another tip on the paths through experiments or data this week. But there must be something in the water cooler lately, and all of these different tools converged on my part of the bioinformatics ecosphere. As I was perusing my tweetdeck columns, a new tool from the folks who do the Caleydo projects offered more paths through data: Pathfinder, Visual Analysis of Paths in Graphs.

For years I’ve been celebrating the great visualization options from the Caleydo tools. The first time we highlighted them was 2010. But I’ve been continuing to follow their work and kick the tires when they have new ones. My most recent favorite of theirs was Verärgert–a better-than-Venn way to look at sets and subsets among your data.

This new tool offers another way to look across relationships in data sets. Finding paths through data is only getting harder with every new data set we get, but continues to become more important to pull in the characteristics of the alternate routes and yet still have the context of the overall picture. Scaling paths is hard. So the Calydo team aims at several key aspects of the problem with their new Pathfinder tool. The full details are in the paper (nachstehend genannten), but I’ll list the points for the features they deliver here:

1. Query for paths.
2. Visualize attributes.
3. Visualze group structures in paths.
4. Rank paths.
5. Visual topology context.
6. Compare paths.
7. Group paths.

In addition to clever visualization and query strategies, the team always offers an nice intro video to give you a sense of what the tool can do for you. So the new video on Pathfinder is our Video Tip of the Week.

The example used is the sets of authors on publications. But it’s easy to imagine signalling pathways, or some types of sequence variation pathways, or many other kinds of paths researchers need to represent. They have a use case example in the paper of KEGG Wege. In dem Video, there’s a quick look at a pathway that includes copy number variations and gene expression data as attributes that may be important for understanding the paths.

Probieren Sie es aus. There’s a demo site available (unten verlinkt), and start to think about how you could use Pathfinder to analyze data that you are interested for your research directions.

Hat tip to Alexander Lex for the notice of the new tool:

Quick-Links:

Pathfinder demo: http://demo.caleydo.org/pathfinder/

Pathfinder overview site: http://www.caleydo.org/publications/2016_eurovis_pathfinder/

Source code: https://github.com/Caleydo/pathfinder/

Referenz:

Christian Partl, Samuel Gratzl, Marc Streit, Anne Mai Wassermann, Hanspeter Pfister, Dieter Schmalstieg, & Alexander Lex (2016). Pathfinder: Visual Analysis of Paths in Graphs Computer Graphics Forum ((EuroVis ’16)) In press.

Branch

Video Tipp der Woche: Ast, ein web-basiertes Tool-Angebot Entscheidungsbäume für die Datenanalyse

Kürzlich habe ich einen Entscheidungsbaum-Tool markiert für experimentelle Design. EDA, oder Experimental Design Assistant, hilft Ihnen, Ihr Experiment zu planen, wählen Sie die approrpiate Gruppen und Zahlen, die Sie benötigen. Legen Sie einige Variablen, usw.. Dieses Video Woche bietet auch Entscheidungsbäume–aber sie helfen Ihnen, die Daten aus Studien von Interesse zu bewerten statt. Ast ist ein web-basiertes Tool, das Sie testen Sie Ihre Hypothesen und Entwicklung von Modellen mit Hilfe von Daten verfügbar ist in einem bestimmten Datensammlung zu helfen.

BranchEs gibt ein Papier (unten verlinkt) mit der Hintergrundgeschichte und Informationen darüber, wie das Tool funktioniert. Aber sie haben auch eine schöne Reihe von Videos gemacht, Ihnen zu zeigen, wie man mit den Werkzeugen zur Interaktion. Die erste wird in dieser Woche Video Tipp der Woche sein. Aber seien Sie sicher, auch die andere, die für zusätzliche Funktionen zu überprüfen,. Jedes Video greift verschiedene Aspekte der Funktionalität, die Sie das Beste aus Ihrer Erkundungen wird dazu beitragen, zu erhalten.

Probieren Sie es aus. Sie können vorhandene Beispiele verwenden, oder schließen Sie Ihre eigenen Daten. Sie können Ihre eigenen Daten als privat, oder machen es verfügbar mit anderen teilen. Achten Sie darauf, ihre Haftungsausschlüssen und denken Sie sorgfältig zu lesen, wenn Sie bestimmte Datensätze verwenden, die Datenschutzprobleme haben. Aber es gibt wahrscheinlich viele öffentlich zugängliche Datensätze, die Sie einige Hypothese um Ihr Thema von Interesse bekommen könnte erkunden.

Hat Spitze an den Autor, dessen Tweet schickte mich an, dies zu untersuchen,:

Quick-Link:

Niederlassung Website: http://biobranch.org/

Referenz:

Ga sein varapu, K., Babji, V, Meißner, T., Seine, A., & Gut, B. (2016). Ast: an interactive, Web-basiertes Tool für Hypothesen und Entwicklung von Vorhersagemodellen zu testen Bioinformatik DOI: 10.1093/Bioinformatik / btw117

Lior_RatVenn_sm

Video Tipp der Woche: RGDs OLGA Werkzeug, Object List Generator und Analyzer

Lior_RatVenn_smEiner der wirklich hartnäckige Probleme in der Genomik ist, wie entweder eine Liste der Dinge zu bekommen, oder eine Liste der Dinge handhaben. oder die Überlappung unter den Dingen,. Ich denke, dass eine der beliebtesten Themen war mit denen wir in den frühen Tagen der OpenHelix behandelt, aber es ist immer noch ein Problem, dass die Menschen auf verschiedene Weise behandeln müssen. Einige der interessantesten Lösungen haben verschiedene Organismus Venn-Diagramme gewesen, und die Ratte Genom ist ein Klassiker, modelliert hier von Lior Pachter. Ich bin der Notwendigkeit, bestimmte aufzulisten und Genom Features organisieren wird nicht weggehen. Also, wenn ich sah, dass die RGD Leute hatten ein anderes Werkzeug Möglichkeiten, um dies zu tun, Ich habe es richtig in meiner Liste der kommenden Tipps. Und dann wurde der Entwurf der Post unter einer Liste anderer Dinge begraben ich tun musste,. Aber ich wollte zurück, um es zu bekommen–so hier ist ihr Schritt-für-Schritt-Anleitung für das Werkzeug OLGA sie bieten, wie dieses Video Tipp der Woche Woche.

OLGA steht für: Object List Generator und Analyzer-Tool. Ihre Newsletter-Mitteilung beschreibt es in mehr Details.

OLGA ist eine einfache Liste-Builder für Ratte, Mensch und Maus-Genen oder QTLs, oder Rattenstämme, unter Verwendung eines beliebigen (oder alle) einer Vielzahl von Abfragen von Optionen. Das neue Tutorial-Video führt Sie durch den Prozess die RGD-Datenbank Abfragen mit OLGA, einschließlich

  • wie eine einfache Abfrage in OLGA auszuführen
  • wie weiter ausbauen oder filtern Sie die Ergebnismenge zusätzliche Kriterien
  • So erreichen Sie Ihre Abfrageparameter im laufenden Betrieb ändern Ergebnismenge zu verfeinern
  • Welche Möglichkeiten gibt OLGA für die Analyse Ihrer Liste, wenn Sie es haben.

Sie können eine Liste der Elemente mit verschiedenen Ontologien bekommen–Vielleicht möchten Sie eine bestimmte Art von Rezeptor, zum Beispiel, Sie können eine Liste von ihnen bekommen. Oder Sie können schnell eine Liste von Genen in einer bestimmten genomischen Spanne erstellen. Sie können die Einzelteile erhalten, die in einem QTL fallen. Oder Sie können mit einer Liste beginnen und Anmerkungen erhalten. Sie können auch nach Überschneidungen zwischen Sätzen suchen.

Das Video ist ein schöner Spaziergang Durch, wie Sie Ihre Abfrage zu erstellen, und was können Sie zugreifen. Ein wesentliches Merkmal ist, dass es nicht nur Ratte Daten ist, wie Sie bei RGD erwarten. Maus und Humandaten sind ebenfalls verfügbar.

Sie können komplexe und clevere Abfragen erstellen, und Link zu allen Arten von verwandten Daten in sehr einfachen Schritten. Werfen Sie einen Blick auf ihre Ressourcen, und ihre anderen Videos für weitere Hilfe mit den verschiedenen Aspekten ihrer Sammlungen.

Quick-Links:

RGD Hauptseite: http://rgd.mcw.edu/

OLGA direkt: http://rgd.mcw.edu/rgdweb/generator/list.html

Referenz:

Shimoyama, M., Pons, J., Hayman, G., Laulederkind, S., Liu, W., Nigam, R., Petri, V, Smith, J., hier, M., Wang, S., Worthey, E., Dwinell, M., & Jacob, H. (2014). Der Rat Genome Database 2015: genomische, phänotypischen und Umgebungsveränderungen und Krankheiten Nucleic Acids Research, 43 (D1) DOI: 10.1093/Nar / gku1026

EDA_video

Video Tipp der Woche: EDA, Experimental Design Assistant

Most of the bioinformatics tools we examine are things that come into play downstream of an experiment. People wish to analyze their data, look at genes that popped up (or dropped down), visualize relationships, usw.. So this week’s Video Tip tool is unusual–it’s software that helps people design the upstream pieces of their experiments.

Experimental Design Assistant is targeted at the proper design of animal research studies. Using animals carefully and responsibly includes well-designed experiments, because wasted experiments due to poor design is something researchers should want to avoid. It’s bad animal welfare practice, and it’s also expensive. Das EDA folks describe this very nicely on a background piece linked on their site.

Because of the way they have their Vimeo settings, I can’t embed their video here. You’ll have to click to watch it on their site: https://eda.nc3rs.org.uk/guide-tutorials

EDA_video

Das 13 minute video is a nice overview of how the workflow will guide you. They recommend that you start with some of their templates that might be similar to your research goals, and edit that. They show you how to start with a blank canvas or a template in the video. They illustrate how you can set up different groups of animals, denote some kind of pharmaceutical intervention or treatment–in the case they show it’s different light cycles. You can establish doses or other variables that are appropriate. Then you move on to a “Measurement” node. They demonstrate that only the right connections in the diagrams can be made, or you get warnings. Then an outcome node can be added. There’s a way to add numerous variables and other experimental details that need to be accounted for.

Other shorter tutorials cover other pieces–like critiquing your experiment, power calculation and randomization sequence, exporting/importing and sharing the diagrams you create.

This is a different but really useful kind of biology software tool. I think it could be great in teaching situations as well. Sie sollten check it out.

Quick-Links:

Experimental Design Assistant: http://eda.nc3rs.org.uk/

Videos page: https://eda.nc3rs.org.uk/guide-tutorials

Referenzen:

Cressey, D. (2016). Web tool aims to reduce flaws in animal studies Nature, 531 (7592), 128-128 DOI: 10.1038/531128ein

UCSC Genome Browser new feature

Video Tipp der Woche: Multi-Region Visualisierung im UCSC Genome Browser

Dieses Video-Tipp der Woche zeigt eine neue Funktion bei der UCSC Genome Browser. Ich denke, es ist eine Art ungewöhnlich, und nahm mich vom Konzept her eine kleine Weile daran zu gewöhnen, als ich begann es zu testen. Also wollte ich für Sie über die Grundlagen zu gehen, und geben Ihnen ein paar Tipps auf Dinge, die ich hatte grok, wie ich zu dieser neuen Sichtoption verwendet wurde.

Das Überschrift für die News beschreibt es als: “Kombinieren mehrerer Regionen des Genoms Browser in einer einzigen Visualisierung!” und

Haben Sie sich jemals gewünscht, Sie alle der Intron oder intergenischen Regionen aus dem Genom-Browser-Anzeige entfernen könnte? Haben Sie schon einmal davon geträumt mehrere entlegene Regionen eines Genoms zu visualisieren? Nun, jetzt können Sie mit dem neuen “Multi-Region” Option im Genome Browser!

Ich sollte wohl mit der ersten Sache beginnen, die mich verwirrt–der Name “Multi-Region”. Ich dachte, dass ich in der Lage sein vielleicht Teil einer Region auf dem Chromosom zu sehen 1, und etwas auf dem Chromosom 8, vielleicht zugleich. Aber das ist nicht, wie das funktioniert. In diesem Fall, Sie schauen auf mehrere Regionen auf dem gleichen Chromosom, mit einigen der intervenierende Sequenzen snipped out. Dies schafft eine Art von “virtuelle Chromosom” Sie interagieren mit.

In dieser Woche Video, Ich werde Ihnen zeigen, wie das aussieht das BRCA1-Gen unter Verwendung von. Zuerst zeige ich, wie Sie bei allen Exons aussehen kann zusammen–mit Introns ausgeclipst. Und dann zeige ich, wie man die Gene in der Nachbarschaft zusammen angezeigt sehen, mit den nicht-kodierenden Regionen ausgeclipst. Dies sind 2 der einzelnen Optionen für die Anzeige.

Ich die “Anzeigen” Menüoption zu illustrieren diese Funktion. Aber es gibt eine andere Möglichkeit, darauf zuzugreifen–Sie können die Verwendung “Multi-Region” Button in der Browser-Buttons Bereich.

multiregion_button

Damit das Video kurz, Ich habe nicht in jedem Detail zu diesem Werkzeug. Sie sollten überprüfen, die news- Mitteilung für sie, und die Verbindung zu den weiteren Einzelheiten in der Benutzerhandbuch Dokumentation Für weitere. Die neue Funktion wird auch kurz in der neuesten NAR Papier auf dem UCSC Genome Browser erwähnt (unten verlinkt). Und Sie sollten es ausprobieren, natürlich! Das ist der beste Weg, um wirklich zu verstehen, wie es Ihnen Regionen des Genoms zu visualisieren könnte helfen, die Sie interessiert sein könnte in.

Wie auch in den Nachrichten, Dank an das Entwicklungsteam. Ich bin immer auf der Suche nach neuen Visualisierungen, und dieser Spaß zu testen!

Wir danken Ihnen, Galt Barber, Matthew Speir, und die gesamte UCSC Genome Browser Qualitätssicherung Team für all ihre Bemühungen in diese aufregende neue Anzeige-Modi zu schaffen.

Folgen Sie UCSC auf Twitter:

Quick-Links:

UCSC Genome Browser: genome.ucsc.edu

Nachrichten Artikel auf Multi-Region: http://genome.ucsc.edu/goldenPath/newsarch.html#030816

Schulungsunterlagen auf der UCSC Genome Browser: http://openhelix.com/ucsc

Referenz:

Sporn, M., Zweig, A., Rosenbloom, K., Raney, B., Paten, B., Nejad, P., Lee, B., Learned, K., Karolchik, D., RAMspan>, Hinrichs, AS, Hsu, F., Kober, KM, Miller, W., Pedersen, JS, Pohl, A., Raney, BJ , A., Heitner, S., Harte, R., Häußler, M., Guruvadoo, L., Fujita, P., Eisenhart, C., Diekhans, M., Clawson, H., Casper, J., Friseur, G., Haussler, D., Kuhn, R., & Kent, In. (2016). Die UCSC Genome Browser Datenbank: 2016 Update Nucleic Acids Research, 44 (D1) DOI: 10.1093/nar/gkv1275

Bekanntgabe: UCSC Genome Browser tutorials are freely available because UCSC Sponsoren us to do training and outreach on the UCSC Genome Browser.

"Frictionless connection of bioinformatics tools"

Video Tipp der Woche: GenomeSpace Orientierung,en

"Frictionless connection of bioinformatics tools"

“Frictionless connection of bioinformatics tools”

The GenomeSpace site has been highlighted before in our “Tipps der Woche”. We appreciated this site that pulled together a lot of different useful types of data sources and analysis strategies. On their site they describe their ethos as “Frictionless connection of bioinformatics tools”. Since that time (2012), it’s continued to grow and provide new features. So I was delighted to see that there was a new orientation video that they offered, and that is this week’s Video Tip of the Week.

Currently there are 20 tools connnected in GenomeSpace, many more than when we first looked. These include mining, Visualisierung, and workflow tools. This intro video focuses on a couple of them, Genmuster for demonstrating workflow, und Cytoscape for visualization. But you can see how the others would help support many types of genomics analyses.

This overview talks about their “recipes” Konzept, with step-by-step analysis protocols, which can be found here: http://recipes.genomespace.org . And there’s a demo of the recipe resource. Es gibt einige, “Beamte” recipes from their team, but they definitely want to have people contributing their own as well. Towards the end of the video they describe how to do that (~28min).

The one used to illustrate the features of the recipes includes a narrative description, but also the specific steps that would be employed. This has the GenePattern and Cytoscape steps examples that they use in the demo.

About half-way through, the demo of the analysis starts (~14min). It’s a helpful walk-through of how to use the recipes effectively to reproduce an analysis. Sara Garamszegi, our guide here, completes the pieces of the work that need to be done with GenePattern, and then shows how to pull out the file you generate from GenomeSpace for Cytoscape to use on your desktop.

Es gibt auch eine separate video of the question/answer section, so if you had some unresolved issues you might check if they were covered, or you can hear about how others might be considering using the tools. I often learn as much from the questions as from the formal presentation pieces. They have transcribed the issues in their video info section as well so you could just quickly scan them.

Follow them on Twitter for more like this, and you can also follow their YouTube channel:

Quick-Link:

GenomeSpace: http://www.genomespace.org/

Referenzen:

Dass, K., Garamszegi, S., Wu, F., Thorvaldsdottir, H., Liefeld, T., Ocana, M., Borges-Rivera, D., Pochet, N., Robinson, J., Demchak, B., Hull, T., Ben-Artzi, G., Blankenberg, D., Friseur, G., Lee, B., Kuhn, R., Nekrutenko, A., Segal, E., Idek, T., Reich, M., Regev, A., Chang, H., & Mesirov, J. (2016). Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace Nature Methods, 13 (3), 245-247 DOI: 10.1038/nmeth.3732

intro_curation

Video Tipp der Woche: Introduction to Biocuration and the career path

ISB_biocuration

The ISB is a professional organization for biocurators

Am OpenHelix, we’ve long sung the praises of curators. Some of us have been curators and worked with curation and database development teams. All of us have relied on quality information in the databases for research and teaching. But I think there are a lot of people who don’t understand the value of quality curation, how it’s done, and who curators are. They are widely taken for granted.

A recent talk by Claire O’Donovan of EBI-EMBL helps to explain the roles and the importance of biocurators. So although this talk isn’t a typical software talk, I think understanding this is crucial to everyone’s appreciation of how information you rely on gets into the databases you use. And if you find yourself in situations where you are guiding students, knowing about this career is also worthwhile.

Claire O’Donovan has had a front row seat to the development of this field, and has great enthusiasm for the future. And going forward, in your doctor’s office as precision medicine and treatments become a thing–how much do you want correct information in the databases? Mining data, standardizing language for descriptions of features, and sharing this information is crucial for all of us.

Here’s what’s covered in this video, from the agenda slide:

  • Introduction to the concept of biocuration.
  • The different kinds of biocurators, and the skill set needed.
  • Our community: Biocuration Society and conference.
  • The future of biocuration and career paths.

Specific examples of what curators do are illustrated (~6:30meine). A sample UniProt entry illustrates what kind of information is captured and where it appears. She also touches on their work with Gene Ontology. And a bit about the ecosystem of curation, how teams at different resources help each other but don’t wish to duplicate work, Hilfe HGNC nomenclature as an example.

About 8min, the skill sets for biocuration are covered: data basics, curation skills, programming and database concepts, Ontologien, and usability of the data collected. This also includes data access and management, as well as dissemination and outreach. This includes user training (yay!) and the concepts of data analysis for users.

There’s no formal degree path for curation practitioners at this point, and different groups will have different needs. But the community is begining to think about this, and about professional qualifications. She also mentioned a recent report from the National Academy of Sciences press on the topic of the future workforce skills and needs (unten verlinkt). This is an alternative career route for people with science training, and it’s important to understand not only the science but computational pieces. And it should be taken seriously as a discipline. There is now a journal that reflects this (also linked below).

Claire also takes a look at the future of biocuration, mit Hilfe der Center for Target Validation (CTTV) als Beispiel. And she talks about the importance of quality information in medical records as we increasingly have genomic details in diagnosis and treatment situations. If we want precision medicine to work, we have to have the precise and correct information in the databases. So respect and value the curators. They are worth it. And if you know anyone that deserves special recognition–nominate!

Quick-Links:

Internationale Gesellschaft für Biocuration: http://biocuration.org/

Preparing the Workforce for Digital Curation: http://www.nap.edu/catalog/18590/preparing-the-workforce-for-digital-curation

Referenzen:

COMMITTEE ON FUTURE CAREER OPPORTUNITIES AND EDUCATIONAL REQUIREMENTS FOR, & DIGITAL CURATION (2015). PREPARING THE WORKFORCE FOR DIGITAL CURATION National Academies Press : 10.17226/18590

Holliday, G., Bairoch, A., Bagos, P., Chatonnet, A., Craik, D., Finne, R., Henrissat, B., Landsman, D., Manning, G., Nagano, N., O'Donovan, C., Pruitt, K., Rawlings, N., Saier, M., Sowdhamini, R., Spedding, M., Srinivasan, N., Vriend, G., Babbitt, P., & Bateman, Ein. (2015). Key challenges for the creation and maintenance of specialist protein resources Proteine: Struktur, Function, and Bioinformatics, 83 (6), 1005-1013 DOI: 10.1002/prot.24803

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