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iFLAS: an integrated Full Length Alternative Splicing analysis tool

iFLAS is a "one-stop" alternative splicing (AS) analysis tool based on full-length transcriptome, aiming to provide a general analysis framework with diverse functions, accurate results and simple operation for the field of plant full-length AS. In order to achieve this goal, iFLAS integrates a variety of analysis methods and tools for different analysis purposes, and completes comprehensive analysis of alternative splicing through three main modules: 1) basic data processing module, 2) isoform and AS identification module, and 3) functional AS analysis module.

The basic data processing module employs optimized analysis pipelines to handle transcriptome data from different sequencing platforms, including preprocessing of raw data, sequence alignment, isoform redundancy removal, and splice site correction. The high-quality annotation of splice junctions from Illumina RNA-seq ensures the rationality of isoform structures.

The isoform and AS identification module utilizes PU learning for precise isoform identification and accurately characterizes AS events based on a hybrid-sequencing analysis strategies, which alleviates the issue of missing alternative intron commonly met in NGS analysis and provides a more reliable AS analysis results for plants.

The functional AS analysis module performs various functions, including identification of differentially spliced events, characterization of allele-specific splicing events, detection of differential 3' poly(A) tails between splice isoforms, and GO functional enrichment analysis, thereby offering multiple novel insights for plant alternative splicing analysis. Additionally, the visualization and reporting module provides a statistical summary of the analysis results, ensuring the interpretability of the findings.

iFLAS_workflow

1. Installation

1.1 Installation using conda

We recommend using conda to substantially facilitate installation of all Python dependencies. Please follow the steps here to ensure an error-free installation. The installation will be done just once, and all the dependencies will be installed automatically in a conda environment. When the environment has been entirely built, you can print the help prompt. If no errors are thrown, it indicates that iFLAS has been successfully installed.

# Create conda enviroment named iflas
conda create -y -n iflas python=2.7
conda activate iflas

# Install dependencies
conda install -y -c bioconda samtools=1.9 hisat2 bedtools=2.29.2 bamtools subread stringtie minimap2 seqkit fastp
conda install -y -c bioconda ucsc-gtftogenepred ucsc-genepredtogtf fmlrc2=0.1.4 nanopolish=0.11.1 regtools=0.5.2
conda install -y -c bioconda isoseq3=3.3 pbccs=4.2 lima pbcoretools rmats=4.0.2 bax2bam pbbam=1.0.6 pbcopper=1.3.0
conda install -y -c conda-forge r-base=3.6.3 rpy2=2.8.6 r-scales
conda install -y -c bioconda bioconductor-deseq2=1.26.0 bioconductor-clusterprofiler=3.14.0 bioconductor-gviz=1.30.0
conda install -y r-stringi=1.4.6 r-dplyr=1.0.0 r-tibble=3.0.0 r-gridBase

pip install pandas matplotlib==2.2.3 psutil biopython==1.68 pybedtools PyVCF PyPDF2 PyMuPDF-1.17.7 bx-python==0.7.3 networkx==2.2

R -e "install.packages('tidyr')"
R -e "install.packages('valr')"

# install Cupcake
git clone https://github.com/CrazyHsu/cDNA_Cupcake.git
cd cDNA_Cupcake
git checkout Py2_v8.7.x
python setup.py build && python setup.py install
cd ../

# install SpliceGrapher
git clone https://github.com/CrazyHsu/SpliceGrapher_packages.git
cd SpliceGrapher_packages
tar -xf PyML-0.7.14.tar.gz
cd PyML-0.7.14
python setup.py build && python setup.py install
cd ../
tar -xf SpliceGrapher-0.2.7.tgz
cd SpliceGrapher-0.2.7
python setup.py build && python setup.py install
cd ../
cd ../

# clone iFLAS package
git clone https://github.com/CrazyHsu/iFLAS.git
cd iFLAS
python iFLAS.py -h

1.2 Installation using Docker

Of course, it is also possible to get iFLAS using Docker image. If you have not installed Docker, you should first install Docker according to the official tutorial (https://docs.docker.com/engine/install/). Then, pull iFLAS Image from DockerHub and print the help prompt. If no errors are thrown, it indicates that iFLAS has been successfully installed.

docker pull CrazyHsu/iFLAS:lastest
docker run CrazyHsu/iFLAS:lastest iflas.py -h

2. Overview of the functions and primary tools or methods used in iFLAS

2.1 The function options of iFLAS

$ python iflas.py -h
usage: iflas.py command [options]

iFLAS: integrated Full Length Alternative Splicing analysis

optional arguments:
  -h, --help     show this help message and exit

command:
  
    preproc      Pre-process the raw PacBio/NanoPore/NGS data. When TGS and
                 NGS data both are provide, This step will use fmlrc2 to
                 correct the TGS read with the information in NGS
    mapping      Mapping the TGS/NGS reads to the reference genome with
                 minimap2
    collapse     Collapse corrected reads into high-confidence isoforms
    refine       Refine the splice junction with the information in short
                 reads
    pu_filter    Filter Low-Quality novel isoforms using a PU-learning based
                 method
    find_as      Identify alternative splicing(AS) type from high-confidence
                 isoforms. Four common AS type are included: intron retention,
                 exon skipping, alternative 3 end splicing and alternative 5
                 end splicing
    visual_as    Visualize the specific gene structure with details including
                 isoform mapping, short reads coverage and AS types identified
    asas         Identify allele-specific AS
    palen_as     Identify functional poly(A) tail length related to AS
    diff_as      Carry out differential AS ananlysis among conditions
    go           Perform GO enrichment analysis and plot results for the
                 specified gene set or multiple gene sets
    report       Automatic detect the plots generated in each step, and merge
                 them into a report file

2.2 Basic data process module

Function name Brief descriptions Primary tools or methods
preproc Raw data preprocessing and filtering ccs (v4.2.0), lima (v2.0.0), isoseq3 (v3.3.0), guppy (v3.4.5), Fastp(v0.20.1), SeqKit (v0.16.0), fmlrc2 (v0.1.4)
mapping Long and short reads alignment Minimap2 (v2.18-r1015), Hisat2 (v2.2.0), Regtools (v0.5.2)
collapse Collapsing isoforms to reduce redundancy Cupcake (vPy2_v8.7x)
refine Correction of splicing site bias due to sequencing errors and alignment algorithm preference In-house scripts

2.3 Isoform and AS identification module

Function name Brief descriptions Primary tools or methods
pu_iso Novel isoform filtration based on PU learning PU-based isoform filtration method In-house scripts
find_as Identify alternative splicing events at isoform level In-house scripts

2.4 Functional AS analysis module

Function name Brief descriptions Primary tools or methods
allele_as Determine allele-specific alternative splicing events IsoPhase (vPy2_v8.7x)
palen_as Detect isoforms with differential poly(A) tail length in a gene nanopolish (v0.11.1)
diff_as Identify differential alternative splicing events between samples rMATS (v3.1.0), DESeq2 (v1.26.0)
go GO enrichment analysis of/between candidate genes (sets) clusterProfiler (v3.14.0)

2.5 Result visualization

Function name Brief descriptions Primary tools or methods
visual_as Visualization of alternative splicing events SpliceGrapher (v0.2.7), Gviz (v1.30.0)
report Generate HTML format reports for results Yattag (v1.14.0)

3. Option details in each function of iFLAS

The operation of iFLAS is simple, and the analysis can be completed by a command similar to 'python iflas.py function -c config.cfg'. In this command, 'function' represents the name of the sub-function, such as preproc function mentioned earlier, which is used for preprocessing and correction of raw data. '-c config.cfg' specifies the configuration file to be used for the run, in which iFLAS adopts the standard format used by Python ConfigParser module, and you only need to modify the configuration file according to your needs. Of course, iFLAS also supports direct parameter setting through the command line, further enhancing the flexibility of the software.

3.1 Data preprocessing (preproc)

python iflas.py preproc -cfg config.cfg

Usage for preproc function

usage: iflas.py preproc [options]

optional arguments:
  -h, --help        show this help message and exit
  -cfg DEFAULT_CFG  The config file used for init setting.
  -merge            Merge all samples from a same strain.

3.2 Use fmlrc2 to correct sequencing errors during the mapping (mapping)

python iflas.py mapping -cfg config.cfg -c -jcs <min_junction_support_count>

Usage for mapping function

usage: iflas.py mapping [options]

optional arguments:
  -h, --help        show this help message and exit
  -cfg DEFAULT_CFG  The config file used for init setting.
  -merge            Merge all samples from a same strain.
  -c                Correct the flnc reads with fmlrc2.
  -jcs JUNCCOMBSUP  The number of junction combination supported by flnc
                    reads. Default: 2.

3.3 Isoform collapsing (collapse)

python iflas.py collapse -cfg config.cfg

Usage for collapse function

usage: iflas.py collapse [options]

optional arguments:
  -h, --help        show this help message and exit
  -cfg DEFAULT_CFG  The config file used for init setting.
  -merge            Merge all samples from a same strain.

3.4 Junction refinement using RNA-seq alignment results (refine)

python iflas.py refine -cfg config.cfg

Usage for refine function

usage: iflas.py refine [options]

optional arguments:
  -h, --help        show this help message and exit
  -cfg DEFAULT_CFG  The config file used for init setting.
  -merge            Merge all samples from a same strain.
  -adjust           Adjust the strand orient by the information of junctions.
  -refine           Refine the junction position by the reads support.

3.5 Identification of reliable new isoforms using PU learning algorithm (pu_filter)

python iflas pu_filter -cfg config.cfg 

Usage for pu_filter function

usage: iflas.py pu_filter [options]

optional arguments:
  -h, --help            show this help message and exit
  -cfg DEFAULT_CFG      The config file used for init setting.
  -merge                Merge all samples from a same strain.
  -feature_file FEATURE_FILE
                        Use user provided feature file to train PU learning 
                        model instead of learning from data.
  -filter_score FILTER_SCORE
                        The PU-score that used to filter out low quality novel
                        isoforms. Default: 0.5.
  -draw_auc             To draw the AUC plot or not.
  -pos_fl_cov POS_FL_COVERAGE
                        The minimal coverage that get the positive annotated
                        isoforms. Default: 2.
  -pos_min_junc_rpkm POS_MIN_JUNC_RPKM
                        The minimal rpkm value of the junctions needed for a
                        postive annotated isoform. Default: 0.05.
  -select_best_model    Select the best model. If not, iFLAS will use GB.
  -auto_filter_score    Auto determine pu_score when 'select_best_model' is
                        selected.

3.6 Identification of AS events (find_as)

python iflas.py find_as -cfg config.cfg

Usage for find_as function

usage: iflas.py find_as [options]

optional arguments:
  -h, --help            show this help message and exit
  -cfg DEFAULT_CFG      The config file used for init setting.
  -merge                Merge all samples from a same strain.
  -pa_rpkm PARPKM       Filter the pa cluster by RPKM(PAC). Default: 0.
  -pa_sup PA_SUPPORT    Filter the pa cluster by RPKM(PAC). Default: 5.
  -conf_pa CONFIDENTPA  The confident PA file used for filtering the results.

3.7 Identification of differential AS events(diff_as)

python iflas.py diff_as -cfg config.cfg

Usage for diff_as function

usage: iflas.py diff_as [options]

optional arguments:
  -h, --help            show this help message and exit
  -cfg DEFAULT_CFG      The config file used for init setting.
  -merge                Merge all samples from a same strain.
  -bg GENE2GOFILE       The mapping file between gene and go term used for GO
                        enrichment analysis.
  -cutoff CUTOFF        The cutoff used to filter the output. Default: 0.05
  -filterBy {pvalue,p.adjust}
                        The value used to filter. Default: p.adjust.
  -showCategory SHOWCATEGORY
                        The number of items to show off. Default: 20.
  -d COMPCOND           The condition file used to detect differential AS
                        between samples.
  -go                   Perform GO enrichment analysis for DSGs between
                        samples.
  -pu_filter            Get differentially spliced genes containing the
                        isoforms filtered by PU learning.

3.8 Identification of allele-specific AS events (asas)

python iflas.py asas -cfg config.cfg

Usage for asas function

usage: iflas.py asas [options]

optional arguments:
  -h, --help            show this help message and exit
  -cfg DEFAULT_CFG      The config file used for init setting.
  -merge                Merge all samples from a same strain.
  -bg GENE2GOFILE       The mapping file between gene and go term used for GO
                        enrichment analysis.
  -cutoff CUTOFF        The cutoff used to filter the output. Default: 0.05
  -filterBy {pvalue,p.adjust}
                        The value used to filter. Default: p.adjust.
  -showCategory SHOWCATEGORY
                        The number of items to show off. Default: 20.
  -go                   Perform GO enrichment analysis for DSGs between
                        samples.
  -ase                  Whether to Carry out ASE analysis.
  -ref_fa REFFA         The reference fasta file used to be quantified in ASE.
  -alt_fa ALTFA         The alternative fasta file used to be quantified in
                        ASE.
  -fbs                  Call the heterozygosity SNPs with freebayes in ASE.

3.9 Identification of differential poly(A) tail related AS events (palen_as)

python iflas.py palen_as -cfg config.cfg

Usage for palen_as function

usage: iflas.py palen_as [options]

optional arguments:
  -h, --help            show this help message and exit
  -cfg DEFAULT_CFG      The config file used for init setting.
  -merge                Merge all samples from a same strain.
  -bg GENE2GOFILE       The mapping file between gene and go term used for GO
                        enrichment analysis.
  -cutoff CUTOFF        The cutoff used to filter the output. Default: 0.05
  -filterBy {pvalue,p.adjust}
                        The value used to filter. Default: p.adjust.
  -showCategory SHOWCATEGORY
                        The number of items to show off. Default: 20.
  -go                   Perform GO enrichment analysis for DSGs between
                        samples.
  -pa_sup PA_SUPPORT    The pa cluster coverage supported by flnc reads.
                        Default: 10.
  -conf_pac CONFIDENTPAC
                        The confident PAC file used for filtering the results

3.10 GO enrichment analysis and visualization of candidate genes (go)

python iflas.py go -cfg config.cfg -tg <gene_list> -s <> -filterby pvalue -cutoff 0.05

Usage for go function

usage: iflas.py go [options]

optional arguments:
  -h, --help            show this help message and exit
  -cfg DEFAULT_CFG      The config file used for init setting.
  -merge                Merge all samples from a same strain.
  -bg GENE2GOFILE       The mapping file between gene and go term used for GO
                        enrichment analysis.
  -cutoff CUTOFF        The cutoff used to filter the output. Default: 0.05
  -filterBy {pvalue,p.adjust}
                        The value used to filter. Default: p.adjust.
  -showCategory SHOWCATEGORY
                        The number of items to show off. Default: 20.
  -tg TARGETGENEFILE    The target gene file or file list separated by comma
                        used for GO enrichment analysis.
  -s SAMPLENAME         The sample name used plot the track, multi-sample
                        should be separated by commma used for GO enrichment
                        analysis.
  -o OUTNAME            The prefix of the GO enrichment output file.

3.11 Visualization of AS events in interested genes (visual_as)

python iflas.py visual_as -cfg config.cfg -g <gene_id>

Usage for visual_as function

usage: iflas.py visual_as [options]

optional arguments:
  -h, --help        show this help message and exit
  -cfg DEFAULT_CFG  The config file used for init setting.
  -merge            Merge all samples from a same strain.
  -g GENES          The gene list separated by comma or a single file contain
                    genes one per line used for visualization.

3.12 Visual integration of iFLAS analysis results (report)

python iflas.py report -basic -asp -geneStruc -diff -html -cfg config.cfg

Usage for report function

usage: iflas.py report [options]

optional arguments:
  -h, --help        show this help message and exit
  -cfg DEFAULT_CFG  The config file used for init setting.
  -merge            Merge all samples from a same strain.
  -all              Generate all the plots.
  -basic            Generate basic information plots.
  -asp              Generate AS pattern plots.
  -geneStruc        Generate gene structure with AS events.
  -asas             Generate allele-specific AS events.
  -palen            Generate AS events related differential poly(A) tail
                    length.
  -diff             Generate the statistics for differential spliced events.
  -html             Generate the html report for the results have been
                    generated.

4. Testing iFLAS using maize full-length transcriptome data

4.1 Prepare testing files

We have uploaded our PacBio full-length transcriptome data for eight maize lines (B73, Chang7-2, Mo17, Huangzao4, PH207, PH4CV, PH6WC and Zheng58) into NCBI database under accession number PRJNA64316.

Here we only use the data of B73 as an example and maize AGPv4.50 annotation files as reference configuration in [refSection] of config.cfg file. The required keys include: 1) ref_genome: maize genome fasta file, and 2) ref_gtf: GTF (General Transfer Format) file describing gene model. In order to simplify the operation, we only need to run prepare.sh to prepare testing files and configurations with one click.

bash prepare.sh

4.2 Testing the functions in iFLAS

4.2.1 Basic AS identification workflow

# Pre-processing raw PacBio and Illumina data
python iflas.py preproc -cfg config.cfg

# Map the sequencing data to reference genome
python iflas.py mapping -cfg config.cfg -c -jcs 2

# Collapse isoforms
python iflas.py collapse -cfg config.cfg

# Refine splice junction bias
python iflas.py refine -cfg config.cfg -refine -adjust

# Use PU learning to filter low-confidence isoforms
# Minimal coverage and minimal junction rpkm are set to 2 and 0.05 to define positive isoforms.
python iflas pu_filter -cfg config.cfg -filter_score 0.5 -pos_fl_cov 2 -pos_min_junc_rpkm 0.05

# Find AS events
python iflas.py find_as -cfg config.cfg

4.2.2 AS event visualization

# Visualization the AS events for gene Zm00001d050245
python iflas.py visual_as -cfg config.cfg -g Zm00001d050245

Zm00001d050245

4.2.3 GO enrichment result visualization

# Visualize the GO enrichment result for the target genes
python iflas.py go -cfg config.cfg -td targetGene.lst -s B73

GO_test

4.2.4 Report all the results using HTML format

The report function can generate the visualization results with HTML files, which can be viewed in browser.

python iflas.py report -cfg config.cfg -basic -html

report

5. Contact

Please feel free to contact us: [email protected]

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