Analysis of Antarctica sequencing samples contaminated with SARS-CoV-2

Overview

Analysis of SARS-CoV-2 reads in sequencing of 2018-2019 Antarctica samples in PRJNA692319

The samples analyzed here are described in this preprint, which is a pre-print by Istvan Csabai and co-workers that describes SARS-CoV-2 reads in samples from Antarctica sequencing in China. I was originally alerted to the pre-print by Carl Zimmer on Dec-23-2021. Istvan Csabai and coworkers subsequently posted a second pre-print that also analyzes the host reads.

Repeating key parts of the analysis

The code in this repo independently repeats some of the analyses.

To run the analysis, build the conda environment in environment.yml and then run the analysis using Snakefile. To do this on the Hutch cluster, using run.bash:

sbatch -c 16 run.bash

The results are placed in the ./results/ subdirectory. Most of the results files are not tracked due to file-size limitations, but the following key files are tracked:

  • results/alignment_counts.csv gives the number of reads aligning to SARS-CoV-2 for each sample. This confirms that three accessions (SRR13441704, SRR13441705, and SRR13441708) have most of the SARS-CoV-2 reads, although a few other samples also have some.
  • results/variant_analysis.csv reports all variants found in the samples relative to Wuhan-Hu-1.
  • results/variant_analysis_to_outgroup.csv reports the variants found in the samples that represent mutations from Wuhan-Hu-1 towards the two closest bat coronavirus relatives, RaTG13 and BANAL-20-52. Note that some of the reads contain three key mutations relative to Wuhan-Hu-1 (C8782T, C18060T, and T28144C) that move the sequence closer to the bat coronavirus relatives. These mutations define one of the two plausible progenitors for all currently known human SARS-CoV-2 sequences (see Kumar et al (2021) and Bloom (2021)).

Archived links after initially hearing about pre-print

I archived the following links on Dec-23-2021 after hearing about the pre-print from Carl Zimmer:

Deletion of some samples from SRA

On Jan-3-2022, I received an e-mail one of the pre-print authors, Istvan Csabai, saying that three of the samples (appearing to be the ones with the most SARS-CoV-2 reads) had been removed from the SRA. He also noted that bioRxiv had refused to publish their pre-print without explanation; the file he attached indicates the submission ID was BIORXIV-2021-472446v1. I confirmed that three of the accessions had indeed been removed from the SRA as shown in the following archived links:

I also e-mailed Richard Sever at bioRxiv to ask why the pre-print was rejected, and explained I had repeated and validated the key findings. Richard Sever said he could not give details about the pre-print review process, but that in the future the authors could appeal if they thought the rejection was unfounded.

Details from Istvan Csabai

On Jan-4-2022, I chatted with Istvan Csabai. He had contacted the authors of the pre-print, and shared their reply to him. The authors had prepped the samples in early 2019, and submitted to Sangon BioTech for sequencing in December, getting the results back in early January.

Second pre-print from Csabai and restoration of deleted files

Istvan Csabai then worked on a second pre-print that analyzed host reads and made various findings, including co-contamination with African green monkey (Vero?) and human DNA. He sent me pre-print drafts on Jan-16-2022 and on Jan-24-2022, and I provided comments on both drafts and agreed to be listed in the Acknowledgments.

On Feb-3-2022, Istvan Csabai told me that the second pre-print had also been rejected from bioRxiv. Because I had previously contacted Richard Sever when I heard the first pre-print was rejected, I suggested Istvan could CC me on an e-mail to Richard Sever appealing the rejection, which he did. Unfortunately, Richard Sever declined the appeal, so instead Istvan posted the pre-print on Resarch Square.

At that point on Feb-3-2022, I also re-checked the three deletion accessions (SRR13441704, SRR13441705, and SRR13441708). To my surprise, all three were now again available by public access. Here are archived links demonstrating that they were again available:

I confirmed that the replaced accessions were identical to the deleted ones.

Inquiry to authors of PRJNA692319

On Feb-8-2022, I e-mailed the Chinese authors of the paper to ask about the sample deletion and restoration. They e-mailed back almost immediately. They confirmed what they had told Istvan: they had sequenced the samples with Sangon Biotech (Shanghai) after extracting the DNA in December 2019 from their samples. The suspect that contamination of the samples happened at Sangon Biotech. They deleted the three most contaminated samples from the Sequence Read Archive. They do not know why the samples were then "un-deleted."

You might also like...
NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"

[Official] FINE Samples for Learning with Noisy Labels This repository is the official implementation of "FINE Samples for Learning with Noisy Labels"

 Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples This repository is the official implementation of paper [Qimera: Data-free Q

The Malware Open-source Threat Intelligence Family dataset contains 3,095 disarmed PE malware samples from 454 families

MOTIF Dataset The Malware Open-source Threat Intelligence Family (MOTIF) dataset contains 3,095 disarmed PE malware samples from 454 families, labeled

Final project for machine learning (CSC 590). Detection of hepatitis C and progression through blood samples.

Hepatitis C Blood Based Detection Final project for machine learning (CSC 590). Dataset from Kaggle. Using data from previous hepatitis C blood panels

Delta Conformity Sociopatterns Analysis - Delta Conformity Sociopatterns Analysis

Delta_Conformity_Sociopatterns_Analysis ∆-Conformity is a local homophily measur

Streamlit App For Product Analysis - Streamlit App For Product Analysis

Streamlit_App_For_Product_Analysis Здравствуйте! Перед вами дашборд, позволяющий

a generic C++ library for image analysis

VIGRA Computer Vision Library Copyright 1998-2013 by Ullrich Koethe This file is part of the VIGRA computer vision library. You may use,

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

A library of extension and helper modules for Python's data analysis and machine learning libraries.
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2020 Links Doc

Comments
  • Making a new conda environment with environment.yml results in many conflicts

    Making a new conda environment with environment.yml results in many conflicts

    I'm not 100% sure I'm doing this right (the README could be more explicit), but when I make a new conda environment using the standard conda env create -f environment.yml I get a warning that conflicts are detected, followed some time later by thousands of lines of conda's obscure dump of conflicting package requirements.

    I tried removing all the version specifiers (except Python 3.9) and got the same result. I then made a new environment without using the environment.yml and started installing packages to it, according to my rough guess at importance. When I got to snakemake conda said there was a conflict and gave the thousands of lines of output again...

    In the end I just did stuff manually in my own way, running some of the commands manually (no snakemake), which of course has some benefit too (I also have alignments with bowtie2 and bwa).

    opened by terrycojones 3
  • Small Python cleanups. Everything passes flake8 now.

    Small Python cleanups. Everything passes flake8 now.

    Hi Jesse. Here are some very small suggested Python clean-ups for you, if you'd like them. They're all trivial. I don't use snakemake but I think the import snakemake is correct. I added it because it stops tools like flake8 from complaining about the unknown name and also editors from flagging it as unknown.

    opened by terrycojones 2
Owner
Jesse Bloom
I research the evolution of viruses and proteins.
Jesse Bloom
Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

CoaDTI Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2 Abstract Environment The test was conducted i

Layne_Huang 7 Nov 14, 2022
SARS-Cov-2 Recombinant Finder for fasta sequences

Sc2rf - SARS-Cov-2 Recombinant Finder Pronounced: Scarf What's this? Sc2rf can search genome sequences of SARS-CoV-2 for potential recombinants - new

Lena Schimmel 41 Oct 3, 2022
DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

DeepConsensus DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS)

Google 149 Dec 19, 2022
Code samples for my book "Neural Networks and Deep Learning"

Code samples for "Neural Networks and Deep Learning" This repository contains code samples for my book on "Neural Networks and Deep Learning". The cod

Michael Nielsen 13.9k Dec 26, 2022
Like Dirt-Samples, but cleaned up

Clean-Samples Like Dirt-Samples, but cleaned up, with clear provenance and license info (generally a permissive creative commons licence but check the

TidalCycles 39 Nov 30, 2022
PAWS 🐾 Predicting View-Assignments with Support Samples

This repo provides a PyTorch implementation of PAWS (predicting view assignments with support samples), as described in the paper Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples.

Facebook Research 437 Dec 23, 2022
Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

François Chollet 16.2k Dec 30, 2022
Learn about Spice.ai with in-depth samples

Samples Learn about Spice.ai with in-depth samples ServerOps - Learn when to run server maintainance during periods of low load Gardener - Intelligent

Spice.ai 16 Mar 23, 2022
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples This project is for the paper "Training Confidence-Calibrated Clas

null 168 Nov 29, 2022
Repo for FUZE project. I will also publish some Linux kernel LPE exploits for various real world kernel vulnerabilities here. the samples are uploaded for education purposes for red and blue teams.

Linux_kernel_exploits Some Linux kernel exploits for various real world kernel vulnerabilities here. More exploits are yet to come. This repo contains

Wei Wu 472 Dec 21, 2022