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https://github.com/ericcombiolab/LRTK

A unified and versatile toolkit for analyzing Linked-Read sequencing data
https://github.com/ericcombiolab/LRTK

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A unified and versatile toolkit for analyzing Linked-Read sequencing data

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README 

        
# LRTK: A platform agnostic toolkit for linked-read analysis of both human genomes and metagenomes

# Overview

Linked-Read ToolKit (LRTK), is a unified and versatile toolkit to process human and metagenomic linked-read sequencing data from different linked-read sequencing technologies, including 10x Genomics, single-tube long fragment read (stLFR) and transposase enzyme linked long-read sequencing (TELL-Seq). LRTK provides functions to  simulate linked-read sequencing data, perform barcode correction, barcode-aware alignment and metagenome assembly, reconstruction of virtual DNA fragments, and  barcode-assisted genomic variant detection and phasing. LRTK also has the ability to perform automatic analysis from raw sequencing data to downstream analysis and support the analysis of multiple samples in parallel. In addition, LRTK could produce publication-ready visualizations and generate reproducible reports, summarizing the key parameters at multiple checkpoints such as library preparation. 



![Main](https://github.com/ericcombiolab/LRTK/blob/main/demo_report/img/workflow.png)

# Install using Bioconda

## Bioconda install

(Please ensure channels are properly setup for bioconda before installing)

```

conda install -c bioconda lrtk=2.0

lrtk -h

```

Users could also install LRTK using the environment.yaml. 

```

conda env create -f environment.yaml

conda activate linkedreads

python /path/to/lrtk.py -h

```

## Dependencies

LRTK is mainly implemented by python3 and requires several python packages, such as numpy, pandas, pysam, scipy, sklrean, snakemake, torch, and sortedcontainers. Most of them could be installed using conda automatically, including Aquila (Zhou et al., 2021), bcftools (Danecek et al., 2021), BWA (Li and Durbin et al., 2009), fastp (Chen et al., 2018), FreeBayes (Garrison and Marth et al., 2012), HapCUT2 (Edge et al., 2017), inStrain (Olm M R et al., 2021),  SAMtools (Li et al., 2009). spades (<=3.15), jellyfish(2.3.0), WhatsHap (Patterson M et al., 2015). 

Furthermore,  LinkedSV  (Fang et al., 2019), Pangaea  (Zhang Z et al., 2022), and  VALOR2  (Karaoǧlanoǧlu et al., 2020) that are not supported by conda are needed to be installed by users. 



We have tested LRTK with Aquila==1.0.0, bcftools==1.8, bwa==0.7.17, fastp==0.23.2, freebayes==0.9.21, gatk==3.6, hapcut2==1.3.3, samtools==1.6, VALOR==2.1.5, whatshap==1.2.1.   



## Database and demo data 

The required database can be downloaded from Google Drive (https://drive.google.com/drive/folders/1XPW2avL_LZAt5yIh9tb35jZ5GfCSj7eQ).

In addition, we provide several examples to test LRTK on different linked-read sequencing technologies. We have included a human genome dataset (FQs) and a metagenomic dataset on Google Drive (https://drive.google.com/drive/folders/1XPW2avL_LZAt5yIh9tb35jZ5GfCSj7eQ).



Now the database and demo data is also available at Baidu net disk (https://pan.baidu.com/s/1B-ZRHEMGEWjS-yzTBeSIsQ?pwd=y76i).



# Running examples

## Help information

Type "lrtk -h" and the help information will be printed.

```

lrtk -h 

```

```

usage: lrtk version 2.1



Linked Reads ToolKit



positional arguments:

  {MKFQ, FQCONVER, ALIGN, RLF, SNV, SV, PHASE, ASSEMBLY, WGS, MWGS}

    MKFQ                Simulate linked-reads

    FQCONVER            Convert linked FASTQ formats

    ALIGN               Barcode aware alignment to map reads to the reference genome

    RLF                 Reconstruct the long DNA fragment

    SNV                 Detect SNVs and INDELs

    SV                  Detect structural variations

    PHASE               Phase germline variations

    ASSEMBLY            Read-cloud metagenome assembly

    WGS                 Run the human genome sequencing analysis pipeline

    MWGS                Run the metagenome sequencing analysis pipeline



optional arguments:

  -h, --help            show this help message and exit

```



## Commands for raw read and variant analysis



### function 1: linked-read simulation

```

lrtk MKFQ -CF "/path_to/diploid_config" -IT stLFR

```

* #### Required parameters



#### -CF/--config_file: 

The path to the directory containing config_files for linked-read simulation. Example data was stored on the aforementioned Google Drive.

#### -IT/--input_type:

Sequencing platform. Users can choose from 10x or stLFR.



We have prepared Two examples of config file (config1.txt and config2.txt; config1.txt is illustrated here) in the diploid_config folder.



### function 2: barcode correction

```

lrtk FQCONVER -I1 /path_to/IN_FQ1 -I2 /path_to/INF_Q2 -IT 10x -O1 /path_to/OUT_FQ1 -O2 /path_to/OUT_FQ2 -B /path_to/BARCODE_WHITELIST -T 4 

```

* #### Required parameters



#### -I1/--input_fastq1: 

Input file (uncompressed FASTQ format) for the first read of paired-end linked-read sequencing data.



#### -I2/--input_fastq2: 

Input file (uncompressed FASTQ format) for the second read of paired-end linked-read sequencing data.



#### -ID/--index_fastq: 

Input index file (uncompressed FASTQ format) for paired linked-read sequencing data. This files only contains information about barcode sequence and is required for TELL-Seq technology.



#### -IT/--input_type: 

Sequencing platforms. Users can choose from 10x,stLFR, or TELLSeq.



#### -O1/--output_fastq1: 

Output fastq file for the first read of paired-end linked-read sequencing data.



#### -O2/--output_fastq2: 

Output fastq file for the second read of paired-end linked-read sequencing data.



### function 3: barcode-aware alignment

```

lrtk ALIGN -FQ1 /path_to/IN_FQ1 -FQ2 /path_to/IN_FQ2 -R /path_to/REFERENCE -O /path_to/OUT_BAM -RG "@RG\tID:Example\tSM:Example" -P 10x -T 4

```

* #### Required parameters:



#### -FQ1/--input_fastq1:  

Input fastq file (uncompressed FASTQ format) for the first read of paired linked-read sequencing data (without barcode).



#### -FQ2/--input_fastq2:  

Input fastq file (uncompressed FASTQ format) for the second read of paired linked-read sequencing data (without barcode).



#### -RG/--read_group:  

Full read group string (e.g. '@RG\tID:foo\tSM:bar')



#### -R/--reference:  

The recommended "GRCH38/genome.fa" is the reference fasta file downloaded from Zenodo.



#### -O/--outfile:  

The output alignment file.  



#### -P/--platform: 

Sequencing platforms. Users can choose from (10x,stLFR, TELLSeq).



#### -A/--application:

Barcode-aware aligners. Users can choose from (ema, lariat). 



### function 4: Reconstruction of long DNA fragment

```

lrtk RLF -B /path_to/IN_BAM -D 200000 -O /path_to/OUTFILE

```

* #### Required parameters



#### -B/--bam: 

The alignment file (.bam) mapped using barcode aware approach.  



#### -D/--distance: 

the distance for seed extension 



#### -O/--outfile:  

The reconstructed fragments.  



### function 5: small variation calling

```

lrtk SNV -B /path_to/IN_BAM -R /path_to/REFERENCE -A "FreeBayes" -T 4 -O /path_to/OUT_VCF

```

* #### Required parameters



#### -B/--bam: 

The alignment file (.bam) obtained from ALIGN function.  



#### -R/--reference: 

Reference genome.



#### -A/--application: 

The SNV/INDEL caller (FreeBayes, Samtools or GATK).



#### -O/--outfile: 

Output VCF file.



### function 6: structural variation calling

```

lrtk SV -B /path_to/IN_BAM -R /path_to/REFERENCE -A "Aquila" -T 4 -O /path_to/OUT_VCF -V /path_to/IN_VCF -U /path_to/DATABASE_UNIQNESS

```

* #### Required parameters



#### -B/--bam: 

The alignment file (.bam) obtained from ALIGN function.  



#### -R/--reference: 

Reference genome.



#### -A/--application: 

The SV caller (Aquila, LinkedSV or VALOR).



#### -O/--outfile: 

Output VCF file.



#### -U/--uniqness: 

"Uniqness_map/" is a required database for Aquila, which can be downloaded from Google Drive.



#### -S/--sonic: 

"sonic/" is a required database for VALOR, which can be downloaded from Google Drive.



#### -V/--vcf: 

VCF file generated from ```SNV``` function.



### function 7:  variant phasing

```

lrtk PHASE -B /path_to/IN_BAM -R /path_to/REFERENCE -A "HapCUT2" -V /path_to/IN_VCF -O /path_to/OUT_VCF

```

* #### Required parameters



#### -B/--bam: 

The alignment file (.bam) obtained from ALIGN function.  



#### -R/--reference: 

Reference genome.



#### -A/--application: 

The variant phasing tool (HapCUT2, WhatsHap or SpecHap).



#### -T/--threads:

The number of threads used for variant phasing tool (default: 1). 



#### -O/--outfile: 

Output phased VCF file.



#### -V/--vcf: 

VCF with variants to phase.



### function 8:  metagenome assembly

```

lrtk ASSEMBLY -FQ1 /path_to/IN_FQ1 -FQ2 /path_to/IN_FQ2 -MS /path_to/METASPADES_CONTIG -AL /path_to/ATHENA_LOCAL_CONTIG -AH /path_to/ATHENA_HYBRID_CONTIG -LT LOW_ABD_CUT -O OUTFILE

```

* #### Required parameters



  #### -FQ1/--fq1:

  Input FASTQ1 file (uncompressed FASTQ format).

  

  #### -FQ2/--fq2:

  Input FASTQ2 file (uncompressed FASTQ format).

  

  #### -MS/--metaspades:

  assembled contigs from metaspades.

  

  #### -AL/--athena_l:

  local assembled contigs from athena.

  

  #### -AH/--athena_h:

  hybrid assembled contigs from athena.

  

  #### -LT/--low_abd_cut:

  coverage for low abundance contigs.

  

  #### -O/ --outfile:

  the final assembled contigs.



## Commands for automatic analysis pipeline

LRTK provides an easy-to-use automatic pipeline to process linked-read sequencing from single or multiple samples. Users only need to prepare FASTQ files, LRTK will run the whole pipeline and generate the final report. We show simple examples to process human and metagenome sequencing data using the automatic pipeline. 



### function 9：human genome workflow

```

lrtk WGS -SI /path_to/SAMPLE_INFO -OD /path_to/OUTDIR -DB /path_to/DATABASE -RG "@RG\tID:Example\tSM:Example" 

```

* #### Required parameters

#### -OD/--outdir: 

The output directory.



#### -DB/--database: 

The ```database``` contains reference genome and barcode whitelist, which can be downloaded from Zenodo.



#### -RG/--read_group: 

Read group (e.g. '@RG\tID:foo\tSM:bar').



#### -SI/--sample_info: 

The path to Sinfo (sample information file).



The Sinfo (tab-separated) should be prepared as: 

```

#Barcode	FQ1	FQ2	INDEXFQ	Linked-read_tech

Example_10x	/path_to/Example.large.10x.R1.fq	/path_to/Example.large.10x.R2.fq	-	10x

```

### function 10: metagenome workflow

```

lrtk MWGS -SI /path_to/SingleSample_info -MI /path_to/MultipleSample_info -OD /path_to/OUTDIR -DB /path_to/DATABASE -RG "@RG\tID:foo\tSM:bar"

```

* #### Required parameters



#### -OD/--outdir: 

The output directory.



#### -DB/--database: 

The ```database``` contains reference genome and barcode whitelist, which can be downloaded from Google Drive.



#### -RG/--read_group: 

Read group (e.g. '@RG\tID:foo\tSM:bar').



#### -SI/--sample_info: 

The path to Sinfo (sample information file).



#### -MI/--multi_info: 

The path to Sinfo (sample information file).



The Sinfo (tab-separated) should be prepared as:

```

#Barcode	FQ1	FQ2	INDEXFQ	Linked-read_tech

Example_10x	/path_to/Example.large.10x.R1.fq	/path_to/Example.large.10x.R2.fq	-	10x

Example_stLFR	/path_to/Example.large.stLFR.R1.fq	/path_to/Example.large.stLFR.R2.fq	-	stLFR

Example_TELLSeq	/path_to/Example.large.TellSeq.R1.fq	/path_to/Example.large.TellSeq.R2.fq /path_to/Example.large.TellSeq.index.fq TELLSeq

```

The Minfo (optional) should be prepared as:

```

#single sample

S1=S1

S2=S2

S3=S3

S4=S4

S5=S5

#pairwise comparison

P1=S4_vs_S5

#group comparison

G1=S4,S5

G2=S1,S2,S3

```



### demo report: 

LRTK produces reproducible reports during the processing of raw sequencing data and the multiple downstream analysis.

Here is the demo report from LRTK:



human genome 



metagenome 



# Troubleshooting

If any question, please feel free to contact with me or my supervisor Dr.Eric Zhang.

(Chao Yang, email: [email protected])

(Lu Zhang, email: [email protected])