Stitch together Nanopore tiled amplicon data without polishing a reference

Related tags

Data AnalysisLilo
Overview

logo_dark_white

Stitch together Nanopore tiled amplicon data using a reference guided approach

Tiled amplicon data, like those produced from primers designed with primal scheme, are typically assembled using methods that involve aligning them to a reference and polishing the reference into a sequence that represents the reads. This works very well for obtaining a genome with SNPs and small indels representative of the reads. However in cases where the reads cannot be mapped well to the reference (e.g. genomes containing hypervariable regions between primers) or in cases where large structrual variants are expected this method may fail as polishing tools expect the reference to originate from the reads.

Lilo uses a reference only to assign reads to the amplicon they originated from and to order and orient the polished amplicons, no reference sequence is incorporated into the final assembly. Once assigned to an amplicon, a read with high average base quality of roughly median length for that amplicon is selected as a reference and polished with up to 300x coverage three times with medaka. The polished amplicons have primers removed with porechop (fork: https://github.com/sclamons/Porechop-1) and are then assembled with scaffold_builder.

Lilo has been tested on SARS-CoV-2 with artic V3 primers. It has also been tested on 7kb and 4kb amplicons with ~100-1000bp overlaps for ASFV, PRRSV-1 and PRRSV-2, schemes for which will be made available in the near future.

Requirments not covered by conda

Install Conda :)
Install this fork of porechop and make sure it is in your path: https://github.com/sclamons/Porechop-1

Installation

git clone https://github.com/amandawarr/Lilo  
cd Lilo  
conda env create --file LILO.yaml  
conda env create --file scaffold_builder.yaml

Usage

Lilo assumes your reads are in a folder called raw/ and have the suffix .fastq.gz. Multiple samples can be processed at the same time.
Lilo requires a config file detailing the location of a reference, a primer scheme (in the form of a primal scheme style bed file), and a primers.csv file (described below).

conda activate LILO
snakemake -k -s /path/to/LILO --configfile /path/to/config.file --cores N

It is recommended to run with -k so that one sample with insufficient coverage will not stop the other jobs completing.

Input specifications

  • config.file: an example config file has been provided.
  • Primer scheme: As output by primal scheme, with alt primers removed. Bed file of primer alignment locations. Columns: reference name, start, end, primer name, pool (must end with 1 or 2).
  • Primers.csv: Comma delimited, includes alt primers, with header line. Columns: amplicon_name, F_primer_name, F_primer_sequence, R_primer_name, R_primer_sequence. If there are a lot of degenerate bases in any of the primers it is recommended to expand these, the script expand.py will expand the described csv into a longer csv with IUPAC codes expanded.
  • reference.fasta Same reference used to make the scheme file.

Output

Lilo uses the names from raw/ to name the output file. For a file named "sample.fastq.gz", the final assembly will be named "sample_Scaffold.fasta", and files produced during the pipeline will be in a folder called "sample". The output will contain amplicons that had at least 40X full length coverage. Missing amplicons will be represented by Ns. Any ambiguity at overlaps will be indicated with IUPAC codes.

Note

  • Use of the wrong fork for porechop will cause the pipeline to fail.
  • Lilo is a work in progress and has been tested on a limited number of references, amplicon sizes, and overlap sizes, I recommend checking the results carefully for each new scheme.
  • The pipeline currently assumes that any structural variants are contained between the primers of an amplicon and do not change the length of the amplicon by more than 5%. If alt amplicons produce a product of a different length to the original amplicon they may not be allocated to their amplicon. Editing it to work better with alt amplicons is on my to do list.
  • Should not be used with reads produced with rapid kits, the pipeline assumes the reads are the length of the amplicons.
  • Do let me know if it destroys any cities or steals everyone's left shoe.
You might also like...
Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.
Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

Created covid data pipeline using PySpark and MySQL that collected data stream from API and do some processing and store it into MYSQL database.

Utilize data analytics skills to solve real-world business problems using Humana’s big data

Humana-Mays-2021-HealthCare-Analytics-Case-Competition- The goal of the project is to utilize data analytics skills to solve real-world business probl

Python data processing, analysis, visualization, and data operations

Python This is a Python data processing, analysis, visualization and data operations of the source code warehouse, book ISBN: 9787115527592 Descriptio

PrimaryBid - Transform application Lifecycle Data and Design and ETL pipeline architecture for ingesting data from multiple sources to redshift
PrimaryBid - Transform application Lifecycle Data and Design and ETL pipeline architecture for ingesting data from multiple sources to redshift

Transform application Lifecycle Data and Design and ETL pipeline architecture for ingesting data from multiple sources to redshift This project is composed of two parts: Part1 and Part2

Demonstrate the breadth and depth of your data science skills by earning all of the Databricks Data Scientist credentials
Demonstrate the breadth and depth of your data science skills by earning all of the Databricks Data Scientist credentials

Data Scientist Learning Plan Demonstrate the breadth and depth of your data science skills by earning all of the Databricks Data Scientist credentials

PostQF is a user-friendly Postfix queue data filter which operates on data produced by postqueue -j.

PostQF Copyright © 2022 Ralph Seichter PostQF is a user-friendly Postfix queue data filter which operates on data produced by postqueue -j. See the ma

Catalogue data - A Python Scripts to prepare catalogue data

catalogue_data Scripts to prepare catalogue data. Setup Clone this repo. Install

NumPy and Pandas interface to Big Data
NumPy and Pandas interface to Big Data

Blaze translates a subset of modified NumPy and Pandas-like syntax to databases and other computing systems. Blaze allows Python users a familiar inte

:truck: Agile Data Preparation Workflows made easy with dask, cudf, dask_cudf and pyspark
:truck: Agile Data Preparation Workflows made easy with dask, cudf, dask_cudf and pyspark

To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges: Optimus is the missing framework to prof

Comments
  • Error in rule reporechop:

    Error in rule reporechop:

    Hello, While running the sample dataset, I have encoutered the following error messages. I have made such that prochop is installed correctly and in the path.

    Any help is greatly appreciated.

    Error in rule reporechop: jobid: 2 output: FAT94769_pass_barcode02_66883b35_0/polished_trimmed.fa shell: porechop --adapter_threshold 72 --end_threshold 70 --end_size 30 --extra_end_trim 5 --min_trim_size 3 -f ASFV.primers.csv -i FAT94769_pass_barcode02_66883b35_0/polished_clipped_amplicons.fa --threads 8 --no_split -o FAT94769_pass_barcode02_66883b35_0/polished_trimmed.fa (one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

    opened by tboonf 1
  • Error while running LILO

    Error while running LILO

    Dear, I get the following error while running LILO. Any idea what could be the problem?

    /bin/bash: /home/minion/anaconda3/envs/LILO/etc/profile.d/conda.sh: No such file or directory
    
    CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.
    To initialize your shell, run
    
        $ conda init <SHELL_NAME>
    
    Currently supported shells are:
      - bash
      - fish
      - tcsh
      - xonsh
      - zsh
      - powershell
    
    See 'conda init --help' for more information and options.
    
    IMPORTANT: You may need to close and restart your shell after running 'conda init'.
    
    
    /bin/bash: line 2: scaffold_builder.py: command not found
    sed: can't read reads_24h_Scaffold.fasta: No such file or directory
    [Wed Aug 10 11:12:28 2022]
    Error in rule scaffold:
        jobid: 1
        output: reads_24h_Scaffold.fasta
        shell:
            source $CONDA_PREFIX/etc/profile.d/conda.sh
                    conda activate scaffold_builder
                    scaffold_builder.py -i 75 -t 3693 -g 80000 -r /home/minion/lilo-test/ASFV.reference.fasta -q reads_24h/polished_trimmed.fa -p reads_24h
                    sed -i '1 s/^.*$/>reads_24h_Lilo_scaffold/' reads_24h_Scaffold.fasta
            (one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)
    
    Job failed, going on with independent jobs.
    Exiting because a job execution failed. Look above for error message
    Complete log: /home/minion/lilo-test/.snakemake/log/2022-08-10T111227.425486.snakemake.log
    

    Kind regards, Elisabeth

    opened by el-mat 1
  • LILO with SLURM

    LILO with SLURM

    Hi there,

    I'm trying to run LILO on a SLURM HPC and I'm not sure what the errors are related to. Do you have an idea? It seems really environment depended, but maybe you stumbled across something similar.

    Call:

    snakemake -k -s [...]/tools/Lilo/LILO --configfile $CONFIG --profile [...]/tools/config-snippets/snake-cookies/slurm
    

    Log:

    [...]
    MissingOutputException in line 84 of [...]/tools/Lilo/LILO:
    Job Missing files after 30 seconds:
    FAR95540_pass_unclassified_7f618209_73/split/amplicon51.fastq
    This might be due to filesystem latency. If that is the case, consider to increase the wait time with --latency-wait.
    Job id: 133673 completed successfully, but some output files are missing. 133673
    Trying to restart job 133673.
    [...]
    Error in rule assign:
        jobid: 133673
        output: FAR95540_pass_unclassified_7f618209_73/split/amplicon51.fastq
        shell:
            bedtools intersect -F 0.9 -wa -wb -bed -abam FAR95540_pass_unclassified_7f618209_73/alignments/reads_to_ref.bam -b amplicons.bed  | grep amplicon51 - | awk '{print $4}' - | seqtk subseq porechop/FAR95540_pass_unclassified_7f618209_73.fastq.gz - > FAR95540_pass_unclassified_7f618209_73/split/amplicon51.fastq
            (one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)
        cluster_jobid: 210115
    
    Error executing rule assign on cluster (jobid: 133673, external: 210115, jobscript: [...]/.snakemake/tmp.cssfeg5e/snakejob.assign.133673.sh). For error details see the cluster log and the log files of the involved rule(s).
    [...]
    Traceback (most recent call last):
      File "/scratch/lataretum/miniconda3/envs/LILO/lib/python3.8/site-packages/snakemake/__init__.py", line 701, in snakemake
        success = workflow.execute(
      File "/scratch/lataretum/miniconda3/envs/LILO/lib/python3.8/site-packages/snakemake/workflow.py", line 1077, in execute
        success = self.scheduler.schedule()
      File "/scratch/lataretum/miniconda3/envs/LILO/lib/python3.8/site-packages/snakemake/scheduler.py", line 441, in schedule
        self._error_jobs()
      File "/scratch/lataretum/miniconda3/envs/LILO/lib/python3.8/site-packages/snakemake/scheduler.py", line 557, in _error_jobs
        self._handle_error(job)
      File "/scratch/lataretum/miniconda3/envs/LILO/lib/python3.8/site-packages/snakemake/scheduler.py", line 615, in _handle_error
        self.running.remove(job)
    KeyError: assign
    

    I set --latency-wait 90 it again breaks after some time at a assign rule and a KeyError: read_select from the snakemake scheduler.

    Let me know which input/config files might be interesting to solve this. :)

    opened by MarieLataretu 7
Releases(v0.2)
Owner
Amanda Warr
Amanda Warr
The official pytorch implementation of ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias Introduction | Updates | Usage | Results&Pretrained Models | Statement | Intr

104 Nov 27, 2022
Tablexplore is an application for data analysis and plotting built in Python using the PySide2/Qt toolkit.

Tablexplore is an application for data analysis and plotting built in Python using the PySide2/Qt toolkit.

Damien Farrell 81 Dec 26, 2022
PyChemia, Python Framework for Materials Discovery and Design

PyChemia, Python Framework for Materials Discovery and Design PyChemia is an open-source Python Library for materials structural search. The purpose o

Materials Discovery Group 61 Oct 02, 2022
Data pipelines built with polars

valves Warning: the project is very much work in progress. Valves is a collection of functions for your data .pipe()-lines. This project aimes to host

14 Jan 03, 2023
This is a repo documenting the best practices in PySpark.

Spark-Syntax This is a public repo documenting all of the "best practices" of writing PySpark code from what I have learnt from working with PySpark f

Eric Xiao 447 Dec 25, 2022
Efficient matrix representations for working with tabular data

Efficient matrix representations for working with tabular data

QuantCo 70 Dec 14, 2022
Python utility to extract differences between two pandas dataframes.

Python utility to extract differences between two pandas dataframes.

Jaime Valero 8 Jan 07, 2023
Tokyo 2020 Paralympics, Analytics

Tokyo 2020 Paralympics, Analytics Thanks for checking out my app! It was built entirely using matplotlib and Tokyo 2020 Paralympics data. This applica

Petro Ivaniuk 1 Nov 18, 2021
Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions.

About Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions. The tool provides rich data and a summary g

9 Nov 16, 2022
PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams

PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams Motivation When dataset freshness is critical, the annotating of high speed

4 Aug 02, 2022
Wafer Fault Detection - Wafer circleci with python

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

Avnish Yadav 14 Nov 21, 2022
Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Amundsen 3.7k Jan 03, 2023
General Assembly's 2015 Data Science course in Washington, DC

DAT8 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15). Instructor: Kevin Markham (

Kevin Markham 1.6k Jan 07, 2023
A Python package for modular causal inference analysis and model evaluations

Causal Inference 360 A Python package for inferring causal effects from observational data. Description Causal inference analysis enables estimating t

International Business Machines 506 Dec 19, 2022
VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

André Rodrigues 2 Feb 14, 2022
Geospatial data-science analysis on reasons behind delay in Grab ride-share services

Grab x Pulis Detailed analysis done to investigate possible reasons for delay in Grab services for NUS Data Analytics Competition 2022, to be found in

Keng Hwee 6 Jun 07, 2022
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8k Dec 29, 2022
Candlestick Pattern Recognition with Python and TA-Lib

Candlestick-Pattern-Recognition-with-Python-and-TA-Lib Goal Look at the S&P500 to try and get a better understanding of these candlestick patterns and

Ganesh Jainarain 11 Oct 07, 2022
MIR Cheatsheet - Survival Guidebook for MIR Researchers in the Lab

MIR Cheatsheet - Survival Guidebook for MIR Researchers in the Lab

SeungHeonDoh 3 Jul 02, 2022
Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which he recommends to buy. We will use this data to build a portfolio

Backtesting the "Cramer Effect" & Recommendations from Cramer Recommendations from Cramer: On the show Mad-Money (CNBC) Jim Cramer picks stocks which

Gábor Vecsei 12 Aug 30, 2022