/Trans-NanoSim

Oxford nanopore transcriptome read simulator

Primary LanguagePython

Trans-NanoSim

Oxford nanopore transcriptome read simulator

Trans-NanoSim is a fast and scalable read simulator that captures the technology-specific features of ONT transcriptome reads (cDNA / directRNA), and allows for adjustments upon improvement of nanopore sequencing technology.

Dependencies

minimap2 (Tested with version 2.10)
LAST (Tested with version 581 and 916)
Python (2.7 or >= 3.4)
Python packages:

  • six
  • numpy (Tested with version 1.10.1 or above)
  • HTSeq
  • scipy (Tested with verson 1.0.0)

Usage

Trans-NanoSim is implemented using Python for error model fitting, read length analysis, and simulation. The first step of Trans-NanoSim is read characterization, which provides a comprehensive alignment-based analysis, and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation tool uses the model built in the previous step to produce in silico reads for a given reference transcriptome. It also outputs a list of introduced errors, consisting of the position on each read, error type and reference bases.

1. Characterization stage

Characterization stage takes a reference transcriptome / reference genome and a training read set in FASTA format as input and aligns these reads to the references using minimap2 (default) or LAST aligner. User can also provide their own alignment file in SAM or MAF formats.

Usage:

./read_analysis.py <options>  
    [options]:  
    -h : print usage message  
    -i : training ONT reads (cDNA / directRNA), must be fasta files  
    -rg : reference genome of the training reads  
    -rt : reference transcriptome for the training reads
    -annot : annotation file in ensembl GTF/GFF3 formats (required for Intron Retention modeling)  
    -a : Aligner to be used: minimap2 or LAST, default = 'minimap2' 
    -ga : genome alignment file in sam or maf format (optional)
    -ta : transcriptome alignment file in sam or maf format (optional)  
    -o : The prefix of output files, default = 'training'  
    --no_model_fit : disable the modeling fitting step  
    --no_intron_retention : disable the Intron Retention modeling step  
    --detect_IR : detect Intron Retention events and outputs the markov model for it  
    --quantify : quantify transcripts expression profile
    -

2. Simulation stage

Simulation stage takes reference genome and read profiles as input and outputs simulated reads in FASTA fomat.

Usage:

./simulator.py [command] <options>  
   [command]:  
    circular | linear  
    # Do not choose 'circular' when there is more than one sequence in the reference  
    <options>:  
    -h : print usage message
    -rt : reference transcriptome
    -rg : reference genome
    -e : expression profile in the specified format  
    -c : the prefix and address of training set profiles, same as the output prefix in read_analysis.py, default = training  
    -o : The prefix and address of output files, default = 'simulated'  
    -n : Number of generated reads, default = 20,000 reads  
    --perfect: Output perfect reads, no mutations, default = False  
    --KmerBias: prohibits homopolymers with length >= 6 bases in output reads, can be omitted

Explaination of output files

1. Characterization stage

  1. training_aligned_length_ecdf Length distribution of aligned regions on aligned reads
  2. training_aligned_reads_ecdf Length distribution of aligned reads
  3. training_align_ratio Empirical distribution of align ratio of each read
  4. training_besthit.maf The best alignment of each read based on length
  5. training_match.hist/training_mis.hist/training_del.hist/training_ins.hist Histogram of match, mismatch, and indels
  6. training_first_match.hist Histogram of the first match length of each alignment
  7. training_error_markov_model Markov model of error types
  8. training_ht_ratio Empirical distribution of the head region vs total unaligned region
  9. training.maf The output of LAST, alignment file in MAF format
  10. training_match_markov_model Markov model of the length of matches (stretches of correct base calls)
  11. training_model_profile Fitted model for errors
  12. training_processed.maf A re-formatted MAF file for user-provided alignment file
  13. training_unaligned_length_ecdf Length distribution of unaligned reads
  14. training_error_rate.tsv Mismatch rate, insertion rate and deletion rate

2. Simulation stage

  1. simulated.log
    Log file for simulation process

  2. simulated_reads.fasta
    FASTA file of simulated reads. Each reads has "unaligned", "aligned", or "perfect" in the header determining their error rate. "unaligned" means that the reads have an error rate over 90% and cannot be aligned. "aligned" reads have the same error rate as training reads. "perfect" reads have no errors.

  3. simulated_error_profile
    Contains all the information of errors introduced into each reads, including error type, position, original bases and current bases.