Chopping Text into Sequences of a Given Length

Chopping Text into Sequences of a Given Length#

Hello! Today, we’re going to take a text and chop it into sequences of a given length. This process is known in the context of DNA sequences as K-mers, but the specific application is not what we’ll focus on today.

Generating Fake DNA#

First, let’s generate some fake DNA. We can do this by taking the four bases that can appear in DNA and then indexing into them with a bunch of random numbers. For demonstration purposes, let’s use 15 random numbers.

DNA Bases#

The four different DNA bases are:

  • Adenine (A)

  • Cytosine (C)

  • Guanine (G)

  • Thymine (T)

We want some random indices for these bases, which will give us some random DNA. In APL, we can generate this random DNA as follows:

dna ← 'ACGT'[?15⍴4]

This line creates a character vector dna with 15 random bases selected from the four DNA bases.

The Problem#

The problem we are addressing is how to work with these random sequences. The solution can often be built directly using APL’s reduce command. By performing reductions over sliding windows of a specified size, we can manipulate our DNA data effectively.

For example, if we set a window size of 4, the function can concatenate adjacent characters forming subsequences. However, when using a K-mer length that exceeds the number of available characters, we might encounter issues.

Handling Maximum Boxing Levels#

For clarity, let’s say we utilize a normal alphabet, limiting ourselves to six bases. If we request subsequences of five characters from an input containing only four, we might encounter issues with inconsistency in the output format. This can be illustrated with the attempt to slice using:

Output = Select(...)       // This generates a display that may confuse the user.

In such cases, APL’s maximum boxing level changes the output’s representation, deviating from what one might expect.

The Solution#

To address this inconsistency, we can define a function to handle various lengths appropriately. The steps are as follows:

  1. Define a Length Adjustment: We can specify our lengths according to the shortest length between our input sequence and the desired K-mer length. We can do this through a maximum function which will handle cases gracefully.

  2. Padding the Input: Using the take function allows us to manage lengths dynamically and add spaces as necessary to the input.

  3. Reduction: Applying an and-wise reduction will allow us to achieve the correct results.

Here’s a simplified overview of the approach:

F ← {⍺,/⍵↑⍨(⍺-1)⌈≢⍵}

This function F can be used to extend DNA appropriately if the desired K-mer length is greater than available characters.

In cases where the K-mer length exceeds the length of the input, we can utilize another function:

G ← {⍺>≢⍵:0↑⊂⍺↑⍵ ⋄ ⍺,/⍵}

This function returns an appropriate empty vector when the DNA sequence is too short.

Alternative Method#

Another approach to coding this process, as outlined in the full problem text, is to handle cases where the requested length exceeds the length of the input by returning an empty vector.

Implementation Steps:#

  1. Initial Check: If the requested length (left argument) is greater than available data (right argument), we can return an empty vector.

  2. Handle Padding: Normalize empty arrays to create placeholders for variables; this will ensure consistent representation.

Here’s how this might look in APL:

If requested_length > data_length:
    Return vector_of(length_requested)
Else:
    Continue with the normal operation.

Conclusion#

These two methods provide a systematic approach to overcome the inherent limitations when slicing sequences, particularly when dealing with edge cases with sparse input. Thank you for joining me in this exploration of sequence manipulation using APL!

Happy coding!

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