QuantEval

About

QuantEval was released for two purposes: 1. a user can use the scripts in QuantEval to reproduce all the analyses in the following study (Hsieh et al.); 2. a user can follow the example in QuantEval to conduct the same analyses on his own study. For the first purpose, there are three modes in the QuantEval main program. (1) Reference Mode, (2) Contig Mode and (3) Match Mode. The first two modes read the quantification results and build a ambiguity cluster based on connected components for the reference transcripts and contig sequences. The match mode builds relations between contigs and reference transcripts. For the second purpose, the users are encouraged to follow the provided example to conduct new analyses on his own data.

Reference

Ping-Han Hsieh, Yen-Jen Oyang and Chien-Yu Chen. Effect of de novo transcriptome assembly on transcript quantification. Scientific Reports volume 9, Article number: 8304 (2019).

Requirement

• QuantEval main program:
  • Python3 (3.5.2)
  • Python packages: pandas (0.20.3), numpy (1.12.1)
• Generate figures and table:
  • R (3.3.0)
  • R pacakges: gridExtra, grid, stats, tidyverse, plyr, ggplot2, reshape2
• Utilities:
  • Bowtie2 (2.3.0), BLASTn (2.5.0), Flux Simulator (1.2.1), RSEM (1.2.31), Kallisto (0.43.0), rnaSPAdes (3.11.1), Salmon (0.8.2), Trans-ABySS (1.5.5), TransRate (1.0.3), Trinity (2.4.0)

Manual

• Run QuantEval individually:

  python3 ./scripts/QuantEval.py --reference --contig --match --input input.json

  The first three parameters (—reference, —contig, —match) indicate which mode to run and the input.json file specifies the input parameters for the QuantEval main program. The three modes can be run independantly, but one has to run both reference mode and contig mode before running the match mode. It is recommended to run three modes in sequential. Because the main program of QuantEval does not include a wrapper for quantification/sequence alignment/contig evaluation, which are essesntial steps for QuantEval main program, one might need to run quantification algorithms (i.e. RSEM/Kallisto/Salmon), sequence alignment (BLASTn) and contig evaluation (Transrate) by themselves in order to get similar analysis results in the reference research.

Below, we use an example dataset to explain how to use QuantEval. This example contains the following files:

• ref.fasta: the reference transcripts (In real applications, you will not have this file for the speices without reference transcripts)
• contig.fasta: the contigs assembled by short reads, e.g. read_1.fastq and read_2.fastq

• read_1.fastq read_2.fastq

  Before running QuantEval,

• Run pairwise BLASTn for reference/contig mode:

  # reference mode
  blastn -db ref.fasta -query ref.fasta -outfmt 6 -evalue 1e-5 -perc_identity 95 -out ./blastn/ref.self.tsv 
  # contig mode
  blastn -db contig.fasta -query contig.fasta -outfmt 6 -evalue 1e-5 -perc_identity 95 -out ./blastn/contig.self.tsv

• Run BLASTn for the mapping of reference and contig sequence (match mode)

  blastn -db ref.fasta -query contig.fasta -outfmt 6 -out ./blastn/contig_to_ref.tsv

• Run quantification for reference/contig mode with default parameters:

  # RSEM
  rsem-prepare-reference --bowtie2 ref.fasta ./rsem/rsem.index
  rsem-calculate-expression --paired-end --strandedness none --bowtie2 --time ref_read/read_1.fastq ref_read/read_2.fastq ./rsem/rsem.index ./rsem/rsem 
  # Kallisto
  kallisto index -i ./kallisto/kallisto.index -k 31 ref.fasta
  kallisto quant -i ./kallisto/kallisto.index -o ./kallisto ref_read/read_1.fastq ref_read/read_2.fastq
  # Salmon
  salmon index -i ./salmon/salmon.index -t ref.fasta --type quasi -k 31
  salmon quant -i ./salmon/salmon.index -l A -1 ref_read/read_1.fastq -2 ref_read/read_2.fastq -o ./salmon

• Run TransRate for reference/contig mode:

  # reference mode
  transrate --assembly ref.fasta --output ./transrate/ref --left ref_read/read_1.fastq --right ref_read/read_2.fastq
  # contig mode
  transrate --assembly contig.fasta --output ./transrate/ref --left contig_read/read_1.fastq --right contig_read/read_2.fastq

• Example of input.json:

  {
   "ref_fasta": "ref.fasta",
   "ref_blastn": "./blastn/ref.self.tsv",
   "ref_gtf": "ref.gtf",
   "ref_xprs_file": ["./answer/answer_xprs.tsv",
                     "./kallisto/ref/abundance.tsv",
                     "./rsem/ref/rsem.isoforms.results",
                     "./salmon/ref/quant.sf"],
   "ref_xprs_label": ["answer", "kallisto", "rsem", "salmon"],
   "ref_xprs_header": [true, true, true, true],
   "ref_xprs_name_col": [1, 1, 1, 1], 
   "ref_xprs_tpm_col": [2, 5, 6, 4], 
   "ref_xprs_count_col": [3, 4, 5, 5],
   "ref_transrate": "./transrate/ref/contigs.csv",
   "contig_fasta": "contig.fasta",
   "contig_blastn": "./blastn/contig.self.tsv",
   "contig_xprs_file": ["./kallisto/contig/abundance.tsv",
                        "./rsem/contig/rsem.isoforms.results",
                        "./salmon/contig/quant.sf"],
   "contig_xprs_label": ["kallisto", "rsem", "salmon"],
   "contig_xprs_header": [true, true, true],
   "contig_xprs_name_col": [1, 1, 1], 
   "contig_xprs_tpm_col": [5, 6, 4], 
   "contig_xprs_count_col": [4, 5, 5],
   "contig_transrate": "./transrate/contig/contigs.csv",
   "match_blastn": "./blastn/contig_to_ref.tsv",
   "output_dir": "./QuantEval/"
  }

• Run example:

  cd example
  python3 ../scripts/QuantEval.py --reference --contig --match --input ./example.json

One can also import the functions in utilities.py to built their own analysis pipeline.

• Construct connected component for reference only:
  ```python
  from utilities import construct_sequence, filter_blastn, intersect_match, construct_grap
  import copy

input_file = dict()
input_file[“contig_ref_file”] = [“./answer/answer_xprs.tsv”,
“./kallisto/abundance.tsv”,
“./rsem/rsem.isoforms.results”,
“./salmon/quant.sf”]
input_file[“ref_xprs_label”]: [“answer”, “kallisto”, “rsem”, “salmon”],
input_file[“ref_xprs_header”]: [true, true, true, true],
input_file[“ref_xprs_name_col”]: [1, 1, 1, 1],
input_file[“ref_xprs_tpm_col”]: [2, 5, 6, 4],
input_file[“ref_xprs_count_col”]: [3, 4, 5, 5],
ref_seq_dict = construct_sequence(‘ref.fasta’)
ref_self_blastn = filter_blastn(‘./blastn/ref.self.tsv’)
read_expression(input_file, ref_seq_dict, ‘ref’)
ref_self_match_dict = intersect_match(ref_self_blastn, ref_seq_dict, copy.deepcopy(ref_seq_dict))
ref_uf, ref_component_dict = construct_graph(ref_seq_dict, ref_self_match_dict)

print(ref_uf.component_label)
print(ref_uf.parent)

___
- Run all the analysis in the study (<b>time consuming</b>):
```shell
./pipelines/run_analysis.sh

• Output format

Note that this is the superset of the output fields (the match mode). The content of output will be different depends on reference/contig/match mode (e.g. one can only find the columns start with contig from contig mode), but one can find all the description on the table above.