Uploading your data to iFinch

iFinch is a scaled down version of our V2 Finch system for genetic analysis. 

Unlike our larger, industrial strength systems, iFinch is designed for individual researchers, small labs, or teachers who want a trouble-free system for managing and working with genetic data.  Currently, students and teachers are using iFinch as part of the Bio-Rad Explorer Cloning and Sequencing kit.

I call iFinch "bioinformatics in a box." I've used iFinch in two bioinformatics courses and it's been pretty helpful. iFinch and FinchTV play nicely together and the combination works well for students.

You don't even have to get a computer for storing data or learn how to manage a database. We do all that for you and you use the system through the web. It's nice and painless.

**********NOTE***********

If you received an iFinch account from Bio-Rad, you will need to turn on your data processor before you begin uploading data.

Checking and starting your Finch data processor

1.  Log into your iFinch account.

2.  Find and select the Data Processor link in the System menu.

3. Look at the Data processor status.

4. If the Data processor has stopped, you will need to Restart it by selecting the Restart button.  If you are a student, you will need to have an instructor log in and do this.

Once your data processor has been started, you can go ahead and upload your data as shown in the movie below.

Uploading your data
The first thing we do with iFinch is to put our data into the iFinch database. In the movie, you can see how we upload chromatograms through the web interface.



iFinch can store any kind of file, but it really shines when it comes to working with chromatograms or genotyping data.

If you have lots of files (more than a few 96 well plates), we do have other systems for uploading data. But, that's another post and another movie.

Questions from our mailbag: How do I cite FinchTV?

One of the questions that appears in our mailbox from time to time concerns citing FinchTV or other Geospiza products. A quick search with Google Scholar for "FinchTV" finds 42 examples where FinchTV was cited in research publications. Most of the citations seem to follow the same conventions.

We recommend citing FinchTV as you would any other experimental software tool, instrument, or reagent. The citation should include the version of the program, the company, the location, and the web site. Other Geospiza products (FinchLab, Finch Suite, and iFinch) may be cited in similar manner.

In our case, a citation would most likely read:

FinchTV 1.4.0 (Geospiza, Inc.; Seattle, WA, USA; http://www.geospiza.com)

If you're not sure which version of FinchTV you're using, open the About menu. The version number will appear on the page.

It would also be a good idea to check with the journal where you plan to submit the article. Most journals have a set of instructions for authors where they provide example citations.

Using the Finder

FinchLab, iFinch, Finch Suite, and FinchLab Next Gen Edition all use a tool that we call the Finder to help you locate data by selected criteria.

This video shows some quick tips on using the Finder with iFinch as an example.


Using the finder from Sandra Porter on Vimeo.

Entering information in iFinch via FinchTV, part I

Teaching is a hard habit to break so I teach short courses now and then.

This year, I've been having my students use FinchTV to enter their blast results into iFinch. This also works with FinchLab and other Finch systems, too.

This has been pretty helpful. The data get stored for each chromatogram and we can all view the results (I'll address this part in a later post.)

How does this work?

1. Log in to your Finch account. Open a chromatogram in FinchTV either by clicking the FinchTV icon or the link from the Chromat Read page that says Open in FinchTV.

2. When you're ready to enter information, click the Commit button (outlined below).



3. You'll see a message appear asking if you're sure. Say "yes."

4. Enter the information that you want to store. Since we were using FinchTV to connect to NCBI blast and identify our bacteria, I'm entering the conclusion from my blast results.

5. Then I click the "OK" button.



6. If I refresh my web browser page, I can see that the version number for my read is now at "2", and I can see that my information has been stored in the database.



In a later post, I'll show how we get that information out.

Stay tuned...

iFinch in education: metagenomics with JHU, part I.

iFinch is the perfect bioinformatics tool to accompany a class. I used it Fall quarter in a class that I teach at Shoreline Community College (Washington) and I'm using it right now in an on-line class that I teach at Austin Community College (Texas).

We cover several different topics in the class, but I have a fondness for long projects where we can use multiple techniques and tie everything to a common theme.

This semester we're working with bacterial sequences that were obtained from students at John Hopkins University. I've been collaborating with an instructor there for several years and now we have four years of data to dig our teeth into.

This video describes the first part of the project that we're working on.



JHU bacterial metagenomics project from Sandra Porter on Vimeo.

Using the Finch Q >20 plots to evaluate your data

All of the Finch systems: Solutions Finch, FinchLab, and iFinch; have a folder report with visual snapshots that summarize the quality of data in that folder. The Q20 histogram plot is one of those tools and in these next two posts, I'll describe what we can learn from these plots.


First, we'll talk about the values on the x axis. When we use the term "Q> 20 bases," we're referring to the number of bases in a read that have a quality value greater than 20. If a base has a quality value of 20, there is a 1 in 100 chance that the base has been misidentified. We use the Q20 value to mark a threshold point where a base has an acceptable quality value.

Histogram plots work by consolidating data that fit into a certain range. In the graph above, you can see that on the x axis, we show groups of reads. The first group contains reads that have less than 50 good (Q > 20) bases. The next group contains reads that have between 50 and 99 good bases, next 100 to 149, and so on.

On the y axis, we show the number of reads that fall into each group. In this graph, we have almost 30 reads that have over 950 good quality bases.

Uhmm, uhmm, uhhmmm, good sequence data, just the stuff I like to see.