Loading presentation...

Present Remotely

Send the link below via email or IM


Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.


Copy of Coffalyser.Net workflow

This prezi gives an overview of the workflow of Coffalyser.Net, including installation, setup, analysis and data interpretation.

Coffalyser MLPA

on 7 August 2013

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Copy of Coffalyser.Net workflow

Fragment analysis
FA consist of all sample independent steps, from the import of raw fragment traces, to signal detection and allelic designation. Final results can be evaluated by simple quality scores or more extensive through the fragment sample explorer.
Data analysis setup
Before we can start analysing data, the software needs to prepared in a few steps that are crucial to your data analysis experience and success.
Creating a single sensible empirically optimised data analysis strategy for all the experiments in your project may greatly enhance your success rate.
Comparative analysis
Each sample will be compared to each reference sample in order to bring these data on a common scale. To do this several normalisation methods are applied, finishing with a statistical distribution comparison of each sample to the available sample group types.
Experiment explorer
Explore the results over the complete experiment by using visualization of all results in heat map grids or special charts. All data is specially sorted making results interpretation easier.
Sample explorer
The sample chart explorer allows users to make a statistical evaluation of results to a great level of detail. Results can be sorted according to their genomic location to increase confidence when interpreting the results. All results can be summarised in comprehensive PDF reports.
MLPA data analysis outline
Quality & reporting
Coffalyser.Net at a glance
Obtaining / installing a copy
Data sorting and region analysis
Reliable calling by mixing arbitrary calling with distribution statistics
Simple troubleshooting and quality control
(cc) photo by theaucitron on Flickr
High-quality or green, this data can be accepted without reviewing.
Low-quality or red, this data can be rejected without reviewing; recommendations can reviewed in Coffalyser.NET and used for troubleshooting.
Intermediate-quality or orange The related data and additional recommendations can be reviewed in Coffalyser.NET and used to optimize the obtained results.
User friendly workflow
Versatile data import
Direct connection to MRC-Holland
The software has a server-client setup, allowing users to share data within and even beyond their institutions. A direct link to the MRC-Holland allows download of all relevant MLPA probemix content information.
Coffalyser.NET is free MLPA data analysis software created by MRC-Holland that supports the entire MLPA workflow, from experimental setup to results interpretation requiring only minimal user input.
The analysis can be done in 2 simple steps:

1) DNA fragment analysis
2) comparative analysis of samples.
Coffalyser.NET supports import of raw data files coming directly from all capillary systems.
Applied Biosystems
CEQ by Beckman Coulter
Megabace systems by Amersham
Quality scores substantially minimise the need to review data and allow users to easily pinpoint any problems or relevant results
Analysed results can be interpreted using advanced statistics and reporting tools helping users gain confidence on their calls
Overview of the entire experiment by heat map grids
Sample explorer allows users to make an evaluation of results to a great level of detail
Special conditional formats reveal results that are not only aberrant on an arbitrary level, but also include multiple checks on the quality of the normalization and the intrinsic significance of the found results
Results can be sorted according to their genomic location allowing users to get greater confidence when interpreting the results
All results may finally be reported in a PDF report that summarises all relevant probe information, ratio results, statistics and quality control checks
Coffalyser.Net costs 0$! Get your own free copy by registering at our website: http://wiki.coffalyser.net/ or http://www.mlpa.com
Each database may contain several organisations
Each organisation can contain an unlimited number of projects
Each project can contain an unlimited number of experiments
Users in the same organisations may share project data and settings
Organising and sharing your data
Creating a work sheet
Always download the last updates first
Create a copy from an original MRC-Holland sheet or create a custom sheet
Check if all information is correct and applicable
Setting up your fragment analysis parameters
Coffalyser.Net contains default profiles optimised for each machine type and filter
FA parameters should be optimised according to your specific data
Creating a new experiment
Multiple experiments can be created in each project

MLPA data analysis is always within an experiment

3 type of MLPA experiments exist
Experiment settings
Each Channel (dye) can be set as a size marker channel or a probe channel

Contents should match with your chemistry / filter set
Importing files
For ABI-devices, ABIF files from all series can be imported (*.*fsa extensions)

For CEQ-devices (Beckman) data from the CEQ-2000, CEQ8000 and CEQ8000 can be imported (*.*SCF or *.*esd extensions)

For Megabace-devices data of all series can be imported (*.*rsd extensions)
Setting the correct sample types
Samples or test samples (“key = s”)
Reference samples (“key = r”)
Positive reference samples (“key = p”)
No DNA or blank controls (“key = n”)
Digested samples (“key = d”)
Fragment analysis settings
Default peak detection settings usually suffice

Probe recognition method determines how peaks are related to probes, Coffalyser.Net has 3 methods:
Autobin design lengths
Autobin Coffalyser lengths
Manual filtering
Adjusting FA settings
Prevents need for manual examination of all profiles

FRSS checks fragment separation quality

FMRS checks MLPA reaction quality

Probe found vs expected
Quality control
Video 1: Adjusting CE device settings
Video 2: create a manual binset for data filtering
Analysis setup
Data interpretation
Comparative analysis notes
Prevent using low quality reference samples

Only do slope correction if needed

Always visually examine slope corrected data
Sample selection
Select samples based on the FMRS score

Select sample on their gender
MS-MLPA only
The normalisation for methylation percentages occurs after the copy number analysis

Each digested samples is normalised directly against the undigested counter part
Match digested / undigested samples
Comparative analysis settings
Match samples automatically by keeping the cut / uncut samples names ~equal to each other

(e.g. sample01-Cut & sample01-Uncut)

Manual matching is also possible but is more laborious
Basic settings

Slope correction settings

Iteration settings
Creating an experiment & Fragment analysis
Statistical overview result grid
Statistical overview chart
Sample overview and report view
Sample ratio chart
Electropherogram views and comparing profiles
Sample reporting tools
Not all expected probes found?

Adjust CE device peak detection settings if visually confirmed peaks are not recognised

Adjust CE device peak detection filter settings if the peaks are detected but not recognised as probes

Create a manual bin set if autobin failed
Automatic matching
Troubleshooting with comparative analysis settings
Quality control
General settings
Slope correction settings
Iteration settings
PSLP checks signal sloping before correction
FSLP checks signal sloping after correction
RSQ checks reproducibility of all probes in the reference samples
RPQ checks the performance of the reference probes
CAS combines all quality check in a single score
Sortable overview grid with special conditional formats
Compeletely customisable
Exported to XLS, PDF, TXT, JPG...
Customise grid or export data using right click context menu
Click here to open lower levels
Samples of the same type are grouped together
Adjust conditional format
PDF overview report of all data
Change how probe are grouped together
access sample results
After openening target info, all data can be resorted by clicking on the applicable headers
Rows with the same color indicate probes that are part of the same region (default = chromosome arm)
Asterix * after the probe name indicate probes that were used as reference probes
Statistical analysis of all probes over all samples of the same type
Average, median and standard deviation used to create confidence ranges
Easily recognise variable probes
Ratio overview grid DNA/MS
Copy number and methylation percentage in a single view
Significant results have special formats
Normal methylation percentage for males and females
Probes with a HHA1 site will be noted accordingly in the probe names. The cells related to those probes will become brown by selecting them.
Digestion controls are noted with (Dig). Zero signal is expected for these probes in the digested reaction.
This sample has 2 copies for SNRPN, which are all methylated (100%).
This sample has a single copy for SNRPN, which is unmethylated (0%).
Descriptive boxplot makes it easy to spot the most distinctive results
Indicate probes with the most aberrations
Indicate variable probes by the reference sample collection
Region analysis shown by vertical stripes
Descriptive Boxplot
Box = 50%
Whiskers = 95%
Black line = median
Yellow cross = average
Red triangle = minimum
Blue diamond = maximum
Using the right mouse context menu to customise the chart
Reporting experiment overview
Single PDF file with all relevant results
Special formats to support black white printing
Complete sample list with success of analysis
Boxplots allow quick examining of one or more sets of data graphically
Increase confidence on call by comparing sample results to the set arbitrary borders and distribution statistics
Sort results on map view locations and group probes together in regions
Combined view copy number & methylation status percentage
Confirmation of found results is often not only desirable but also imperative in order to get an indisputable assessment.
Create stacked views by sample locking mechanism
Simple overview of all important quality scores
Detailed probe target information and probe signal evaluation
Export data to pdf, xml, csv, HTML
Recommended method for reporting samples
Single page report containing all relevant data on one printable page
Dual page report containing larger charts and specific region analysis data and information
Contains special warnings and notifications from MRC-Holland
Select all samples based on FMRS score
Select samples on FMRS and gender
Select all samples that can be analysed
Match undigested sample to its digested counter part
Manual matching
Ratio overview result grid
Result interpetation
Wiki at
Other support
(cc) photo by theaucitron on Flickr
Comparison values are the result of a comparison of each probe ratio and it's estimated standard deviation to sets of samples of the same sample type and the set arbitrary borders. The comparison results are displayed both as symbols but also as specific colors in both the grids and charts.
Wiki pages (manuals & registration page)

YouTube Channel

Publications on Coffalyser
Full transcript