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https://github.com/dn070017/quanteval

QuantEval is an analysis pipeline which evaluate the reliability of quantification tools.
https://github.com/dn070017/quanteval

connected-components quantification-evaluation-methods transcriptome-assembly

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QuantEval is an analysis pipeline which evaluate the reliability of quantification tools.

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README 

        
 # 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:

 ```shell

 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:

 ```shell

 # 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)

 ```shell

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

 ```

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

 ```shell

 # 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:

 ```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 (time consuming):

```shell

./pipelines/run_analysis.sh

```

___

 

- Output format



| column | description |

|--------|-------------|

| match_name | alignment (ref.contig.strand) |

| contig_name | target contig name |

| ref_name | target ref name |

| accuracy | accuracy of the alignment |

| recovery | recovery of the alignment |

| ***contig/ref***\_length | length of ***contig/ref*** |

| ***contig/ref***\_tr\_***transrate_score*** | ***transrate score*** of ***contig/ref*** |

| ***contig/ref***\_xprs\_***tpm/count***_***quantifier*** | quantification result of ***contig/ref*** |

| ***contig/ref***\_component | label of connected component of ***contig/ref*** |

| ***contig/ref***\_component_size | number of sequences in the connected component of ***contig/ref*** |

| ***contig/ref***\_component_contribute_xprs_***tpm/count***\_***quantifier*** | proportion of ***TPM/read count (RPEA)*** in the connected component of ***contig/ref*** |

| ***contig/ref***\_component_relative_xprs_***tpm/count***\_***quantifier*** | ***TPM/count*** of ***contig/ref*** / highest ***TPM/count*** in the same connected component |

| ***contig/ref***\_component_max_xprs_***tpm/count***\_***quantifier*** | highest ***TPM/count*** of ***contig/ref*** in the same connected component |

| ***contig/ref***\_component_avg_xprs_***tpm/count***\_***quantifier*** | average ***TPM/count*** of ***contig/ref*** in the same connected component |

| ***contig/ref***\_component_tot_xprs_***tpm/count***\_***quantifier*** | total ***TPM/count*** of ***contig/ref*** in the same connected component |

| ref_gene_contribute_xprs_***tpm/count***\_***quantifier*** | proportion of ***TPM/read count*** in the gene of ref |

| ref_gene_relative_xprs_***tpm/count***\_***quantifier*** | ***TPM/count*** of ref / highest ***TPM/count*** in the same gene |

| ref_gene_max_xprs_***tpm/count***\_***quantifier*** | highest ***TPM/count*** of ref in the same gene |

| ref_gene_avg_xprs_***tpm/count***\_***quantifier*** | average ***TPM/count*** of ref in the same gene |

| ref_gene_tot_xprs_***tpm/count***\_***quantifier*** | total ***TPM/count*** of ref in the same gene |

| length_difference | the difference of length between contig and reference |

| xprs_***tpm/count***\_error_***quantifier*** | quantificaion error for the estimated abundance of contig | 

> 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.