MGAPipe

Installation Instructions

To install Metagenome Analysis Pipeline, you must have a minimum of 8 GiB free disk space and minimum of 16 GiB free RAM to test run.

To provide an easier way to install, we provide a miniconda based installer. Installation also requires pre-instaled git, gcc, cpp and zlib1g-dev.

git clone https://github.com/sarangian/MGAPipe.git
cd MGAPipe
chmod 755 INSTALL.sh
./INSTALL.sh

Post Installation Instructions After successful installation, close the current terminal. In a new terminal. source the bashrc file: source ~/.bashrc

All the third party tools installed using conda are available at $HOME/MGAPipe/ [default location] or the user specified location during the installation process.

The script to run Metagenome Analysis Pipeline is metagenome.py is available inside the MGAPipe folder, that you cloned from github.

Input Files

Raw Illumina paired-end Reads

The raw ilumina reads in FASTQ format must be placed inside a folder with read permission Allowed extension for FASTQ reads: fq , fq.gz , fastq, fastq.gz

For illimina paired-end reads, sample name must be suffixed with _R1. extension and _R2. extension for forward and reverse reads respectively

      *Example*

       sample1_R1.fastq 
       sample1_R2.fastq

       sample2_R1.fastq.gz 
       sample2_R2.fastq.gz

       sample3_R1.fq
       sample3_R2.fq

       sample4_R1.fq.gz 
       sample4_R2.fq.gz

where sample1 , sample2 , sample3 , sample4 are the sample names sample_name must be suffixed with _R1.{extension} and _R2.{extension}

.. _commands:

Commands

.. toctree:: :hidden:

Commands to run Metagenome Analysis Workflow

metagenome.py - -help

.. code-block:: none

Command                      Description   

1. configureProject             Configure a Metagenome Analysis Project

2. rawReadsQC                   Raw Reads Quality Assessment using FASTQC tool 

3. cleanReads                   Process Raw Reads using BBDUK

4. metagenomeAssembly           Assemble Metagenome using MegaHIT

5. genomeBinning                Binning individual assembled genome using metabat2

6. binRefinement                Refine Bins using refineM

7. dRepBins                     de-replicate genome bins using dRep

8. annotateGenomeBins           Annotation of de-replicated bins using PROKKA

9. deaGenomeBins                Differential Enrichment Analysis of genome bins (2 different conditions) using enrichM

Prepare Project

A. Single (or) multiple samples with out any conditions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: none

STEP 1: copy the luigi.cfg template file to the present working directory

luigi.cfg Template File
---------------------------------------------------------
[core]
default-scheduler-port:
error-email=

[GlobalParameter]
projectName=
projectDir=
domain=
adapter=
pe_read_dir=
pe_read_suffix=
seq_platforms=
pac_read_dir=
pac_read_suffix=
ont_read_dir=
ont_read_suffix=
threads=
maxMemory=
-----------------------------------------------------------

STEP2: Fill the parameters as instructed

NOTE:  1. User has to provide the path for the adapter file (tasks/utility/adapters.fasta.gz)
       2. For illumina paired end reads, seq_platforms must be ``pe``
              Nanopore reads, seq_platforms must be ``ont`` 
              PacBio reads, seq_platforms must be ``pac`` 

Example: luigi.cfg
---------------------------------------------------------
[core]
default-scheduler-port:8082
[email protected]

[GlobalParameter]
projectName=metagenome_demo_analysis
projectDir=/home/sutripa/Documents/metagenome_demo_analysis/
domain=prokaryote
adapter=/home/sutripa/scriptome/tasks/utility/adapters.fasta.gz
pe_read_dir=/home/sutripa/Documents/metagenome_symlink/pe/
pe_read_suffix=fastq.gz
seq_platforms=pe
pac_read_dir=NA
pac_read_suffix=NA
ont_read_dir=NA
ont_read_suffix=NA
threads=15
maxMemory=28
---------------------------------------------------------

STEP 3: a. User has to create a project folder with exact name provided against ``projectName`` parameter
        b. User must create a folder in the name of ``config`` in the current working directory
        c. The config folder must contain a file in the name of ``pe_samples.lst`` containing the name of the samples

Example of ``pe_samples.lst``

---------------
SRS017713
SRS019327
SRS019221
SRS013506
---------------

B. Single (or) Multiple Samples with conditions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To design and perform a Metagenome Analysis experiment, a Project need to be prepaired using the configureProject command. The current working directory must contain a template of luigi.cfg file.

Conda environment must be activated before running the script.

Usage: projectConfig.py -h

.. code-block:: none

luigi.cfg Template File
---------------------------------------------------------
[core]
default-scheduler-port:
error-email=

[GlobalParameter]
projectName=
projectDir=
domain
adapter=
pe_read_dir=
pe_read_suffix=
seq_platforms=
pac_read_dir=
pac_read_suffix=
ont_read_dir=
ont_read_suffix=
threads=
maxMemory=
-----------------------------------------------------------

metagenome.py configureProject --help <arguments>
--help             Show this help message and exit

mandatory arguments         Description

--inputDir                  Path to Directory containing raw illumina paired-end reads
                            type: string
                            Example: /home/sutripa/Documents/scriptome_metaphlan/data



--domain            Organism Domain
                            type: string
                            allowed values: [prokaryote, eukaryote]


Optional arguments
------------------
--projectName               Name of the Project Directory to be created
                            Must not contain blank spaces and (or) special characters
                            type: string
                            Default: metagenome_project

--schedulerPort             Scheduler Port Number for luigi
                            type: int
                            Default: 8082

--userEmail                 Provide your email address
                            type: string
                            Default: Null

--cpus                      Number of threads to be used
                            type: int
                            Default = (total threads -1)

--maxMemory                 Maximum allowed memory in GB. 
                            type: int
                            Default = [(available memory in GB) -1)

Run Example

.. code-block:: none

mkdir MetagenomeAnalysis cd MetagenomeAnalysis Note: MetagenomeAnalysis folder must contain the luigi.cfg template file

[MetagenomeAnalysis]$ metagenome.py configureProject
--domain prokaryote
--inputDir /home/sutripa/Documents/scriptome_metaphlan/data
--projectName metagenome_demo_analysis
--symLinkDir metagenome_symlink
--userEmail [email protected]
--local-scheduler

Running the prepareProject.py script with the above parameters asks for the individual file types present inside the inputData folder.

User has to choose [pe: paired end ont: nanopore pac: pacbio ]

.. code-block:: none

Successful run of the projectConfig.py script with appropriate parameters will generate

Luigi Configuration file luigi.cfg in the parent folder

Edit the luigi.cfg file if required.

Note: It is mandatory to provide the path of the adapter file (default location: /tasks/utility/adapter.fastq.gz)
a project folder in the name of metagenome_demo_analysis will be generated
a configuration folder in the name of config containing 3 files

a. metagenome_condition.tsv b. metagenome_group.tsv b. pe_samples.lst c. samples.txt
A folder named metagenome_symlink (provided as parameter to --symLinkDir) will be created which contains the symbolic links to the read files present in the inputData (/home/sutripa/Documents/scriptome_metaphlan/data) folder

The metagenome_condition.tsv file contains the sample names with their associated environmental conditions, which will be used for condition based genome binning and differential enrichment analysis. Kindly check the generated files and modify if required

Raw reads quality assessment

| Note | Before running any of the Metagenome Analysis Workflow commands, a project must be configured using configureProject command. | The parent forlder must have the luigi.cfg file, in which the globalparameters are defined. | Running any of the Metagenome Analysis Workflow commands without generating the project folder and luigi.cfg file will give rise to luigi.parameter.MissingParameterException |

.. code-block:: none

**Steps**
1. Run Prepare Projcet with project name ``metagenome_demo_analysis`` as discussed before 
   and inspect the files generated inside config folder

2. Run rawReadsQC
   [MetagenomeAnalysis]$ metagenome.py rawReadsQC --local-scheduler

  Successful execution of rawReadsQC will generate a folder $PWD/metagenome_demo_analysis/ReadQC/PreQC
  which contains the FASTQC reports of the raw paired-end fastq files

Raw samples quality control

Quality control analysis of the raw samples can be done using command preProcessSamples | | Requirements | 1. Execution of prepareProject.py command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config. |

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py cleanReads --local-scheduler

arguments               type      Description
  

 --cleanFastq-kmer-length        int   Kmer length used for finding contaminants
                                  Examle: 13  
                                  Default: 11 

--cleanFastq-k-trim              str   Trimming protocol to remove bases matching reference
                                  kmers from reads. Choose From['f: dont trim','r: trim to right','l: trim to left] 
                                  Choices: {f, r, l} 

--cleanFastq-quality-trim       int   Trim read ends to remove bases with quality below trimq.
                                  Performed AFTER looking for kmers.  Values: 
                                      rl  (trim both ends), 
                                      f   (neither end), 
                                      r   (right end only), 
                                      l   (left end only),
                                      w   (sliding window).

                                      Default: f


--cleanFastq-trim-quality        float  Regions with average quality BELOW this will be trimmed,
                                 if qtrim is set to something other than f.  Can be a 
                                 floating-point number like 7.3  
                                 Default: 6                 

--cleanFastq-min-length        int   Minimum read length after trimming
                                  Example: 50
                                  Default:50

--cleanFastq-trim-front          int   Number of bases to be trimmed from the front of the read
                                  Example: 5
                                  Default: 0

--cleanFastq-trim-tail           int   Number of bases to be trimmed from the end of the read
                                  Example: 5
                                  Default: 0

--cleanFastq-min-average-quality int   Minimum average quality of reads.
                                  Reads with average quality (after trimming) below 
                                  this will be discarded
                                  Example: 15
                                  Default: 10

--cleanFastq-mingc              float Minimum GC content threshold
                                  Discard reads with GC content below minGC
                                  Example: 0.1 
                                  Default: 0.0

--cleanFastq-maxgc              float Maximum GC content  threshold
                                  Discard reads with GC content below minGC
                                  Example: 0.99 
                                  Default: 1.0
--local-scheduler

Example Run

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py cleanReads
--cleanFastq-min-average-quality 15
--cleanFastq-mingc 0.20
--cleanFastq-maxgc 0.70
--cleanFastq-quality-trim w
--local-scheduler

  **Output**
  /path/to/ProjectFolder/metagenome_demo_analysis/ReadQC/CleanedReads/PE-Reads --contains the processed FastQ-reads

A. Genome Resolved Metagenimics

Metagenome Assembly using megaHIT ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Assembly of individual samples can be done using command metagenomeAssembly

| | Requirements | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config folder. |
|

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the raw illumina reads required or not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--local-scheduler

| Example Run 2 | Metagenome assembly with out read quality control analysis | | | [MetagenomeAnalysis]$ metagenome.py metagenomeAssembly
| --pre-process-reads yes
| --local-scheduler | | Note: Output folder in the name of MGAssembly will be created at $PWD/metagenome_demo_analysis/MGAssembly | MGAsembly folder contains the sub-folders by the name of the samples and each sub-folder contains the resultant assembly |

Binning of assembled contigs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Binning of individual assembled samples can be done using command genomeBinning

| | Requirements | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config folder. |
|

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  genomeBinning <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the ilumina reads required or Not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--local-scheduler

| Example Run 2 | Metagenome assembly with read quality control analysis followed by genome binning | | | [MetagenomeAnalysis]$ metagenome.py genomeBinning
| --pre-process-reads yes
| --local-scheduler | | Note: Output folder in the name of binning will be created at $PWD/metagenome_demo_analysis/binning | binning folder contains the sub-folders by the name of the samples and each sub-folder contains the resultant genome bins |

Refinement of genome bins ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Refinement of individual genome bins can be done using command binRefinement

| | Requirements | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config folder. |
|

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  binRefinement <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the ilumina reads required or Not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--local-scheduler

| Example Run | Metagenome assembly with read quality control analysis followed by genome binning and bin refinement | | | [MetagenomeAnalysis]$ metagenome.py binRefinement
| --pre-process-reads yes
| --local-scheduler | | Note: Output folder in the name of binning will be created at $PWD/metagenome_demo_analysis/bin_refinement | bin_refinement folder contains the sub-folders by the name of the samples and each sub-folder contains the resultant refined genome bins. refineM tool is used for refinement of genome bins |

de-replication of genome bins ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Condition-based or condition-free de-replication of individual genome bins can be done using command dRepBins

| | Requirements | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config folder. | 3. Availability of metagenome_group.tsv file inside the config folder if user opts for condition_based de-replication |

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  dRepBins <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the ilumina reads required or Not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--checkM-method        str       choice of workflow to run checkm. choice [lineage_wf, taxonomy_wf]
                                 default: taxonomy_wf

--dRep-method          str       Mandatory Parameter. choice of de-replication method. choice [condition_based, condition_free]
                
--contamination        int       accepted percentage of contamination [default:25] 

--completeness         int       accepted percentage of genome completeness [default:75]

--min-genome-length    int       minimum accepted genome length [default: 50000] 

--local-scheduler

|
| 4.a. Metagenome assembly with read quality control analysis followed by genome binning, bin refinement and condition_free de-replication | | [MetagenomeAnalysis]$ metagenome.py dRepBins
| --pre-process-reads yes
| --dRep-method condition_free
| --checkM-method taxonomy_wf
| --contamination 10
| --completeness 80
| --local-scheduler | | Note: 1. Output folder in the name of dReplicated_bins_condition_free will be created at $PWD/metagenome_demo_analysis/dRep_bins/dReplicated_bins_condition_free/dereplicated_genomes which contains the de-replicated genomes |

|
| 4.b. Metagenome assembly with read quality control analysis followed by genome binning, bin refinement and condition_based de-replication | | [MetagenomeAnalysis]$ metagenome.py dRepBins
| --pre-process-reads yes
| --dRep-method condition_based
| --reference-condition buccal_mucosa
| --contrast-condition tongue_dorsum
| --checkM-method taxonomy_wf
| --contamination 10
| --completeness 80
| --local-scheduler | | Note: 1. Output folder in the name of dReplicated_bins_condition_free will be created at $PWD/metagenome_demo_analysis/dRep_bins/dReplicated_bins_condition_based/ which contains the de-replicated genomes |

annotate de-replicated genome bins ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Annotation of condition-based or condition-free de-replicated genome bins can be done using command annotateGenomeBins

| | Requirements | 1. annotateGenomeBins must be run after condition_based or condition_free de-replication of genome bins | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config folder. | 3. If user opted for condition_based de-replication, availability of genomes_dRep_by_condition.lst file containing the list of genome bins, inside the $PWD/metagenome_demo_analysis/dRep_bins folder | 4. If user opted for condition_based de-replication, availability of genomes_dRep_regardless_condition.lst file containing the list of genome bins inside the $PWD/metagenome_demo_analysis/dRep_bins folder |

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  dRepBins <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the ilumina reads required or Not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--min-contig-length    int       minimum contig length [default 200]

--dRep-method          str       Mandatory Parameter. choice of de-replication method. choice [condition_based, condition_free]
                
--local-scheduler

| Example Run | Metagenome annotation after condition_free de-replication of genome bins | | | [MetagenomeAnalysis]$ metagenome.py annotateGenomeBins
| --pre-process-reads yes
| --dRep-method condition_free \
| --local-scheduler | | Note: 1. Output folder in the name of prokka_condition_free will be created at $PWD/metagenome_demo_analysis/prokka_condition_free |

| Example Run | Metagenome annotation after condition_based de-replication of genome bins | | | [MetagenomeAnalysis]$ metagenome.py annotateGenomeBins
| --pre-process-reads yes
| --dRep-method condition_based \
| --local-scheduler | | Note: 1. Output folder in the name of prokka_condition_based will be created at $PWD/metagenome_demo_analysis/prokka_condition_based | |

Differential Enrichment Analysis of de-replicated genome bins ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Differential Enrichment Analysis of de-replicated genome bins can be done using command deaGenomeBins

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  deaGenomeBins <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the ilumina reads required or Not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--checkM-method        str       choice of workflow to run checkm. choice [lineage_wf, taxonomy_wf]
                                 default: taxonomy_wf

--contamination        int       accepted percentage of contamination [default:25] 

--completeness         int       accepted percentage of genome completeness [default:75]

--min-genome-length    int       minimum accepted genome length [default: 50000] 

--reference-condition  str       Mandatory Parameter: reference condition name

--contrast-condition   str       Mandatory Parameter: contrast condition name

--pvalcutoff           float     Default: 0.05

--correction           str       correction method. Choose from [Bonferroni: b, 
                                                                 FDR 2-stage Benjamini-Hochberg: fdr_tsbh
                                                                 Holm: h
                                                                 Hommel: ho  
                                                                 Holm-Sidak: hs 
                                                                 Simes-Hochberg: sh
                                                                 FDR Benjamini-Yekutieli: fdr_by
                                                                 FDR 2-stage Benjamini-Krieger-Yekutieli: fdr_tsbky]
--local-scheduler

.. code-block:: none

[RNASeq-Analysis]$ rnaseq.py alignmentFreeDEA <arguments> --local-scheduler

argument               type      Description

[required arguments]

--pre-process-reads     str       Run Quality Control Analysis of the RNASeq reads or Not
                                  [yes / no]

                                  If yes, cleanReads command will be run with default parameters.
                                  If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                  script of bbmap will be run based on paired-end or single-end reads.

--quant-method str Read quantification method [salmon / kallisto]

--dea-method str Method to be used for differential expression analysis. [deseq2 / edger]

--reference-condition str Reference biological condition. example: control

--local-scheduler

[optional arguments]

--attribute-type str Atrribute type in GTF annotation choose from {gene_id, transcript_id}

--strand-type int perform strand-specific read counting. choose from [0: unstranded, 1: stranded, 2: reversely-stranded]

--report-name str Name of the differential expression analysis report Default: DEA_Report

--factor-of-intrest str Factor of intrest column of the target file Default: conditions

--fit-type str mean-variance relationship. Choices: {local, parametric, mean}

--size-factor str method to estimate the size factors. Choices: {shorth, median}

--result-tag str Tag need to be apended to result file Default: treated_vs_control

B. Marker Gene based Taxonomy Profiling

Taxonomy profiling from metagenomic shotgun sequencing data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Taxonomy profiling of individual samples can be done using command compositionProfiling

| | Requirements | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder and pe_samples.lst inside the config folder. |
|

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  compositionProfiling <arguments> --local-scheduler

argument               type      Description

--pre-process-reads    str       Run Quality Control Analysis of the raw illumina reads required or not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

--local-scheduler

| Example | Composition profiling with read quality control analysis | | [MetagenomeAnalysis]$ metagenome.py compositionProfiling
| --pre-process-reads yes
| --local-scheduler | | | Note: 1. Output folder in the name of profiled_samples will be created at $PWD/metagenome_demo_analysis/, which contains the taxonomy profile of individual samples |
|

Generate plots using hclust, graphlan and ampvis ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | | Requirements | 1. Pre execution of configureProject command | 2. Availability of luigi.cfg file in parent folder,metagenome_group.tsv and pe_samples.lst files inside the config folder. |
|

.. code-block:: none

[MetagenomeAnalysis]$ metagenome.py  compositionProfiling <arguments> --local-scheduler

argument               type      Description

[mandatory]
--pre-process-reads    str       Run Quality Control Analysis of the raw illumina reads required or not
                                 [yes / no]

                                 If yes, cleanReads command will be run with default parameters.
                                 If no, quality control analysis will not be done, instead re-pair.sh or reformat.sh 
                                 script of bbmap will be run based on paired-end or single-end reads.

[optional]

--condition-column     str       Name of the condition column in metagenome_group.tsv file. [Default: conditions]
--local-scheduler

| Example | Composition profiling with read quality control analysis | | [MetagenomeAnalysis]$ metagenome.py profileTaxonomy
| --pre-process-reads yes
| --condition-column conditions \
| --local-scheduler | | | Note: 1. Output folder in the name of figures will be created at $PWD/metagenome_demo_analysis/, which contains the images |
|

sarangian / mgapipe Goto Github PK

mgapipe's Introduction

MGAPipe

Installation Instructions

Input Files

Raw Illumina paired-end Reads

Commands

Commands to run Metagenome Analysis Workflow

A. Genome Resolved Metagenimics

B. Marker Gene based Taxonomy Profiling

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent