I-CONVEX is an iterative algorithm for solving "de Novo Transcriptome Recovery from long reads" problem. This algorithm starts with a set of short prefixes(4 or 5) and estimates the abundances of these prefixes based on the given noisy reads dataset. The abundance of these prefixes can be efficiently estimated by aligning them with the reads and solving a maximum likelihood estimation problem through the expectation maximization (EM) algorithm. Therefore, all the high-abundant prefixes will be extended with one base(by adding either A, C, G, or T to the end of the each one of the prefixes with size L and obtaining four new prefixes with size L+1) and the non-frequent ones will be truncated. This procedure continues until the complete recovery of all the transcripts.

It is highly recommended to install the following packages via Anaconda. Download and install Python 2.7 version of Anaconda from Anaconda, Python 2.7 Version: Then install the following packages via Conda:

• Biopython
• Matplotlib
• Igraph

For validating the similarity of the detected candidate pairs, you need to install Tensorflow GPU-version. You can follow the instructions in here to install it. It is easy to install GPU version of tensorflow via Conda:

conda install -c anaconda tensorflow-gpu 

To run the CONVEX algorithm after the pre-clustering stage, you need to install Message Passing Interface(MPI) with the following command:

sudo apt install mpich

Pre-clustering of noisy reads before running I-CONVEX algorithm has several advantages over running I-CONVEX directly on the whole dataset. First, it decreases the order complexity of the algorithm and eliminates its dependency to M, the number of transcripts (Centroids). Moreover, it enables the parallel running of I-CONVEX on different clusters.

If your input fasta file is not a large-scale one and you want to run it on your PC, follow the below instructions:

1. Move your input fasta file to the Clustering Folder and rename it to reads.fasta.
2. Run SplitFile.py to chunk the dataset:

   ClusteringReads/Clustering$ python SplitFile.py
   

3. Run the commands.sh file as follows:

   ClusteringReads/Clustering$ chmod 777 commands.sh
   ClusteringReads/Clustering$ ./commands.sh
   

At the end, the reads and their corresponding cluster identifiers will be written in MergedClusters.csv. Moreover, a folder Clusters/ which contains subfolders each of which represents a cluster will be created.

Running Pre-clustering on an HPC server makes the pre-clustering part tremendously faster; However, it has more details compared to the basic version.

In the first step, the original file is split into the chunks, each of which contains 50K reads. To run SplitFile.py make sure you change the name of the original file to the reads.fasta and put it to the Clustering folder. Then run as follows:

ClusteringReads/Clustering$ python SplitFile.py

After running, there should be a hash_functions.csv file and chunk1.fasta to chunkn.fasta files as the outputs.

To find MinHash signatures, you should run MPMH.py file. Since MPMH.py uses multiprocessing to enhance the speed, it is recommended to run it on a High-Performance Computing Server(HPC) as follows:

ClusteringReads/Clustering$ sbatch MPMH.py

If an HPC server is not available, you can run it directly with python. Note that if the number of chunks is more than 20, you need to run MPMH.py for each batch of 20 chunks separately. For example, if SplitFile.py generates 50 files chunk1.fasta to chunk50.fasta (2.5 Million reads) you need to run MPHM.py three times as follows:

ClusteringReads/Clustering$ sbatch MPMH.py
ClusteringReads/Clustering$ sbatch MPMH.py 2
ClusteringReads/Clustering$ sbatch MPMH.py 3

Using MinHash signatures as a base of a family of Locality Sensitive Hash, we can map the similar candidate pairs of noisy reads into to the same bucket. Thus, after running MultiProcessLSH.py the candidate similar pairs will be available in the batch1.csv to batchn.csv files. To obtain these files you should run the following command:

ClusteringReads/Clustering$ sbatch MultiProcessLSH.py

If you do not have access to an HPC server, you can run it as follows:

ClusteringReads/Clustering$ python MultiProcessLSH.py

In this step, you should run ./collector.sh to obtain the final candidate pairs. This script will merge all batch.csv* files and will remove the duplicate rows. The output will be the G.csv file.

ClusteringReads/Clustering$ ./collector.sh

To validate the similarity of candidate pairs, a trained Convolutional Neural Network(CNN) should be executed on the obtained candidate pairs from the previous step.

ClusteringReads/Clustering$ python FastaPreparation.py
ClusteringReads/Clustering$ python ValidatePairs.py

After running ValidatePairs.py, there should be a Net.csv file as the output.

Now, we prepare to run the clustering algorithm on the final similarity graph:

ClusteringReads/Clustering$ python Clustering.py

And then, we should merge all the mini clusters with the size less than 5:

ClusteringReads/Clustering$ python MergeClusters.py

Finally, in order to run I-CONVEX on the different clusters, we should create a folder for each cluster:

ClusteringReads/Clustering$ python CreateClusterDirectories.py

After obtaining the pre-clusters, we are ready to run I-CONVEX on each cluster. First, we need to compile the following c files with MPI library:

ClusteringReads/Clustering$ mpicc -o conv CONVEXv16.c -lm
ClusteringReads/Clustering$ mpicc -o post PostProcessingV3.c

If you do not have access to an HPC server, you can run I-CONVEX on the obtained pre-clusters from the previous stage as follows:

ClusteringReads/Clustering$ python CreateConvexScript.py 8
ClusteringReads/Clustering$ ./run_convex.sh

In the first command 8 denotes the number of cores assigned to the I-CONVEX. Make sure it is not greater than the maximum number of cores on your computer.

First, we need to run the following python script to create batches of clusters:

ClusteringReads/Clustering$ python CreateSlurmFiles.py 20

The parameter of this script is the number of clusters in each one of the batches. The default number is 20. Therefore, you should run the following script:

ClusteringReads/Clustering$ ./run_convex.sh

To collect all the obtained transcripts from the different clusters, you need to run the following script:

ClusteringReads/Clustering$ python CollectTranscripts.py

The final transcripts will be saved in both final_transcripts.txt and final_transcripts.fasta files.