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From Nanopore to Gene Prediction: a pathway

Because nanopore flow cells are expensive, it is common practice to try to sequence multiple samples at once. As part of the protocol, barcode sequences were added to the samples, which can now be used to sort the raw MINION data into individual bins, or folders, specific to the barcode, and thus the sample they’re attached to. 

We will do this a couple of times for accuracy’s sake.

The first tool we will use is deepbinner. This program will take the raw data, identify the barcode, and place it in one of thirteen folders: barcode01 through to barcode12, and unclassified, for sequences that no barcode could be identified for.

The following are **REQUIRED flags**:

in_dir [the directory that your minion data has been outputted to]

out_dir [the directory you want the program to place the folders that it will sort the reads into.]

Deepbinner is designed to be run in parallel with the sequencing, which is to say that in real time, deepbinner can take the output from nanopore and sort it. While we don’t usually do this in this lab, it’s a feature to keep in mind. 

There are a number of additional flags that can help refine the program’s behaviour to your needs.

**Model presets:**

native: preset for EXP-NBD103 read and end models. This is the default.

rapid: preset for SQK-RBK004 read start model. 

**Models:**

These are used if the presets are not being used, and can be invoked with:

s or start_model    for a model trained on the starts of reads

and

e or end_model      for a model trained on the ends of reads.

Largely we don’t have to worry about this in this lab.

**Barcoding:**

Two flags:

scan_size [value] This flag determines how much of a read’s start and end will be examined for barcode signals. **Defaults to 6144.** 

score_diff [value] This flag determines how much of a difference between the best and second best barcode scores will be allowed, in order for classification to occur. **Default is 0.5**

**two model (read start and read end) behaviour:**

Three mutually exclusive flags which determine how lenient the program is in classifying a read. They are, listed here from most lenient to most stringent:

require_either 

require_start 

require_both

The first flag will allow the program to classify a read based on the barcode call of either the start or end of the read, so long as they do not disagree.

The second flag will classify a read based on a start barcode, and having an end barcode is opitional. **This is the default behaviour**

The third flag requires the same barcode on both ends of the read in order for it to be classified.

**Performance:**

There are four flags here that govern how the program runs. You probably will have no reason to alter these from their default.

batch_size [value]	this is the neural network batch size **default is 256**

intra_op_paraelism_threads [value] 	TensorFlow’s intra_op_parallelism_threads config option. **Default is 12**

inter_op_parallelism_threads 	TensorFlow’s inter_op_parallelism_threads config option. **Default is 1**

device_count [value]	TensorFlow’s device_count config option. **Default is 1.**

omp_num_threads [value]	OMP_NUM_THREADS environment variable. **Default is 12.**

**Other:**

stop	automatically stops deepbinner when it runs out of reads to process. **By default, the program will continue to run until manually stopped.**

h or help	Shows a help message

Here is an example shell script for deepbinner. A copy can be found at /home/gseaton/public_scripts/
   #!/bin/bash
   #$ -S /bin/bash
   . /etc/profile
   #$ -cwd
   #$ -pe threaded 12
   
   source activate deepbinner
   /scratch2/software/Deepbinner-git-June-27-2019/deepbinner-runner.py realtime --in_dir [directory with the raw 
   MINION data] \
   --out_dir [the directory you’d like the stored folders placed]
   --start_model /scratch2/software/Deepbinner-git-June-27-2019/models/EXP-NBD103_read_starts \
   --end_model /scratch2/software/Deepbinner-git-June-27-2019/models/EXP-NBD103_read_ends --stop
   source deactivate



----


Nanopore technology generates a truly absurd amount of data files, which can be unwieldy to use, and for programs like Terminal to handle when trying to look into a folder with such huge amounts of data. Therefore, we will use another script to combine fast5 files into multi fast5 files. 

   #!/bin/bash
   #$ -S /bin/bash
   . /etc/profile
   #$ -cwd
   #$ -pe threaded 4
   
   source activate ont-fast5-api
   
   single_to_multi_fast5 --input_path <path to barcode folder created by deepbinner IE: barcode03> \
   --save_path <path to the directory the output should be saved as>
   --filename_base <the prefix for the files, ie Species_barcode03 --batch_size 4000 --recursive
   
   conda deactivate
   
Some notes:
 
batch_size refers to the number of fast5 the program will combine together into one single file. 4000 is a good default to work with.

recursive will run the shell on both the files in the directory you specify, as well as in any directories that are inside the directory you specified. IE /scratch3/yourname/MINION/deepbinner/barcode03/another_file_level

Additionally, there is another command, multi_to_single_fast5 that can be run using the ont-fast5-api program. As the name implies, it does the reverse process, breaking apart a single multi fast5 file into individiual fast5 files.