Raw Data Tab

The Raw Data Tab allows users to load raw current traces from nanopore experiments, apply filters before analysis, determine the baseline, and detect events.

Step 1: Loading Your Data

1. Click the ➕ beside READER to load a data file.

2. A dropdown menu will appear listing supported reader types. These correspond to different file formats and decoding standards. Supported readers include:

   • TCossaLabABFReader and LegacyElementsReader (for .abf files)

   • BinaryReader1X and SingleBinaryDecoder (for .bin-like formats)

   • ChimeraReader20240101, ChimeraReader20240501, ChimeraReaderVC100 (for Chimera system .log files or variations; ChimeraReader20240101 is deprecated and will be removed in a future release)

Note

Make sure the reader you choose matches your file type, otherwise loading will fail.

Note

Don’t have a file to try this with?

The dataset used in the YouTube tutorial series is archived on the Federated Research Data Repository (FRDR), and its .log files work with ChimeraReader20240501:

DOI: 10.20383/103.01695

3. A plugin settings dialog will prompt you to:

   • Enter a name for the reader instance (e.g., TCossaLabABFReader_1).

   • Select the input file using the Select Input File button.

4. After loading the file, select a channel from the Channel dropdown menu.

5. In the time-range field, enter a start-end range in seconds (e.g., 0-5 or 1.5-6.3).

6. Click the Update Trace button to visualize the raw signal.

7. (Optional) To analyze noise across frequencies, click the Update PSD button.

Step 2: Applying a Filter

1. Click the ➕ beside FILTER to apply a preprocessing filter.

2. A dropdown will appear listing available filters:

   • BesselFilter – features a smooth response to signal transients, ideal for reducing high-frequency noise.

   • WaveletFilter – preserves pulse shape and SNR while denoising using a wavelet transform.

3. A plugin settings dialog will appear, depending on the selected filter:

For ``BesselFilter``:

• Name: Identifier for the filter instance (e.g., BesselFilter_1).

• Cutoff (Hz): Frequency above which signals are attenuated.

• Samplerate (Hz): Sampling rate of the signal.

• Poles: Number of poles in the filter; higher values give a steeper roll-off.

For ``WaveletFilter``:

• Name: Identifier for the filter instance (e.g., WaveletFilter_1).

• Wavelet: Wavelet basis, bior1.3 or bior1.5 (the default).

Note

Filters help improve event detection by removing baseline drift or noise. Choose the one that best suits your data characteristics.

4. After configuration, confirm by clicking OK.

5. (Optional) Adjust the Time Range using the ⏱ Timer button to limit the region of interest.

Step 3: Finding Events

1. Click the ➕ beside EVENTFINDER to load an event detection algorithm.

2. A dropdown will appear listing available event finders:

   • ClassicBlockageFinder — detects events based on a current threshold.

   • ThresholdBlockageFinder — the same, with the threshold given in baseline standard deviations.

   • BoundedBlockageFinder — detects events constrained within user-defined amplitude and baseline bounds.

Note

Choose the event finder based on the expected characteristics of your signal. All three share the parameters below but differ in flexibility.

3. A settings window will appear with the following configurable parameters:

Shared Parameters:

• Threshold: How far below the fitted baseline the signal must fall for an event to start - in pA for ClassicBlockageFinder and BoundedBlockageFinder, in σ for ThresholdBlockageFinder.

• Min Duration (µs): Minimum allowed duration for an event.

• Max Duration (µs): Maximum allowed duration for an event.

• Min Separation (µs): Minimum time between consecutive events to treat them as distinct.

BoundedBlockageFinder-only:

• Min Baseline / Max Baseline (pA), required: The window the baseline fit is restricted to; a fitted baseline outside it is refused.

Note

For ClassicBlockageFinder, only a fixed threshold is required. For BoundedBlockageFinder, both the threshold and the baseline limits are needed.

4. Click the Find Events button to run the detection process. With No Filter selected you are asked to confirm first, since finding events on unfiltered data is rarely intended.

5. (Optional) Adjust the Time Range using the ⏱ Timer button to limit the detection to specific intervals.

Note

A range like 0-0 means “start to end” of the loaded signal. You can also specify ranges such as 0-4, 6-7, or open-ended intervals like 9-0 (9 seconds to the end). An omitted end has the same meaning as an end of 0, so 3.0- and 3.0-0 both run from 3 seconds to the end of the signal.

6. Once events are detected (you will see confirmation in the right-hand panel), enter the event indices you wish to inspect.

7. Click the Plot Events button to visualize those events.

8. Use the ◀ and ▶ arrows to browse between plotted events.

Step 4: Writing Events

1. Click the ➕ beside WRITER to select a module for saving your detected events.

2. A configuration dialog will appear. If you’re using SQLiteEventWriter, you’ll be prompted to fill in the following fields:

   • Output File: Path where the SQLite .db file will be saved.

   • Experiment Name: A short label to identify this experiment in the database.

   • Voltage (mV): The applied transmembrane potential during the experiment.

   • Membrane Thickness (nm): Thickness of the nanopore membrane.

   • Conductivity (S/m): Conductivity of the solution during the experiment.

Note

The results generated during the eventfinding process are stored in a SQLite database, allowing for efficient access, export, and integration with downstream tools.

3. After confirming the settings, click the Commit Events button to write the selected events to the database. If the database already holds events for a channel, you are asked before they are replaced, and the question warns you when those events were committed under a different experiment name. Answering No keeps that channel’s events and skips only it; the other channels are still committed.

4. (Optional) You can click the Export Plot Data button at any time to save the data behind the current graph as a .csv file.