SARS-Cov-2 Recombinant Finder for fasta sequences

Overview

Sc2rf - SARS-Cov-2 Recombinant Finder

Pronounced: Scarf

What's this?

Sc2rf can search genome sequences of SARS-CoV-2 for potential recombinants - new virus lineages that have (partial) genes from more than one parent lineage.

Is it already usable?

This is a very young project, started on March 5th, 2022. As such, proceed with care. Results may be wrong or misleading, and with every update, anything can still change a lot.

Anyway, I'm happy that scientists are already seeing benefits from Sc2rf and using it to prepare lineage proposals for cov-lineages/pango-designation.

Though I already have a lot of ideas and plans for Sc2rf (see at the bottom of this document), I'm very open for suggestions and feature requests. Please write an issue, start a discussion or get in touch via mail or twitter!

Example output

Screenshot of the terminal output of Sc2rf

Requirements and Installation

You need at least Python 3.6 and you need to install the requirements first. You might use something like python3 -m pip install -r requirements.txt to do that. There's a setup.py which you should probably ignore, since it's work in progress and does not work as intented yet.

Also, you need a terminal which supports ANSI control sequences to display colored text. On Linux, MacOS, etc. it should probably work.

On Windows, color support is tricky. On a recent version of Windows 10, it should work, but if it doesn't, install Windows Terminal from GitHub or Microsoft Store and run it from there.

Basic Usage

Start with a .fasta file with one or more sequences which might contain recombinants. Your sequences have to be aligned to the reference.fasta. If they are not, you will get an error message like:

Sequence hCoV-19/Phantasialand/EFWEFWD not properly aligned, length is 29718 instead of 29903.

(For historical reasons, I always used Nextclade to get aligned sequences, but you might also use Nextalign or any other tool. Installing them is easy on Linux or MacOS, but not on Windows. You can also use a web-based tool like MAFFT.)

Then call:

sc2rf.py <your_filename.fasta>

If you just need some fasta files for testing, you can search the pango-lineage proposals for recombinant issues with fasta-files, or take some files from my shared-sequences repository, which might not contain any actual recombinants, but hundreds of sequences that look like they were!

No output / some sequences not shown

By default, a lot filters are active to show only the likely recombinants, so that you can input 10000s of sequences and just get output for the interesting ones. If you want, you can disable all filters like that, which is only recommended for small input files with less than 100 sequences:

sc2rf.py --parents 1-35 --breakpoints 0-100 \
--unique 1 --max-ambiguous 10000 <your_filename.fasta>

or even

sc2rf.py --parents 1-35 --breakpoints 0-100 \
--unique 1 --max-ambiguous 10000 --force-all-parents \
--clades all <your_filename.fasta>

The meaning of these parameters is described below.

Advanced Usage

You can execute sc2rf.py -h to get excactly this help message:

usage: sc2rf.py [-h] [--primers [PRIMER ...]]
                [--primer-intervals [INTERVAL ...]]
                [--parents INTERVAL] [--breakpoints INTERVAL]
                [--clades [CLADES ...]] [--unique NUM]
                [--max-intermission-length NUM]
                [--max-intermission-count NUM]
                [--max-name-length NUM] [--max-ambiguous NUM]
                [--force-all-parents]
                [--select-sequences INTERVAL]
                [--enable-deletions] [--show-private-mutations]
                [--rebuild-examples] [--mutation-threshold NUM]
                [--add-spaces [NUM]] [--sort-by-id [NUM]]
                [--verbose] [--ansi] [--hide-progress]
                [--csvfile CSVFILE]
                [input ...]

Analyse SARS-CoV-2 sequences for potential, unknown recombinant
variants.

positional arguments:
  input                 input sequence(s) to test, as aligned
                        .fasta file(s) (default: None)

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

  --primers [PRIMER ...]
                        Filenames of primer set(s) to visualize.
                        The .bed formats for ARTIC and EasySeq
                        are recognized and supported. (default:
                        None)

  --primer-intervals [INTERVAL ...]
                        Coordinate intervals in which to
                        visualize primers. (default: None)

  --parents INTERVAL, -p INTERVAL
                        Allowed number of potential parents of a
                        recombinant. (default: 2-4)

  --breakpoints INTERVAL, -b INTERVAL
                        Allowed number of breakpoints in a
                        recombinant. (default: 1-4)

  --clades [CLADES ...], -c [CLADES ...]
                        List of variants which are considered as
                        potential parents. Use Nextstrain clades
                        (like "21B"), or Pango Lineages (like
                        "B.1.617.1") or both. Also accepts "all".
                        (default: ['20I', '20H', '20J', '21I',
                        '21J', 'BA.1', 'BA.2', 'BA.3'])

  --unique NUM, -u NUM  Minimum of substitutions in a sample
                        which are unique to a potential parent
                        clade, so that the clade will be
                        considered. (default: 2)

  --max-intermission-length NUM, -l NUM
                        The maximum length of an intermission in
                        consecutive substitutions. Intermissions
                        are stretches to be ignored when counting
                        breakpoints. (default: 2)

  --max-intermission-count NUM, -i NUM
                        The maximum number of intermissions which
                        will be ignored. Surplus intermissions
                        count towards the number of breakpoints.
                        (default: 8)

  --max-name-length NUM, -n NUM
                        Only show up to NUM characters of sample
                        names. (default: 30)

  --max-ambiguous NUM, -a NUM
                        Maximum number of ambiguous nucs in a
                        sample before it gets ignored. (default:
                        50)

  --force-all-parents, -f
                        Force to consider all clades as potential
                        parents for all sequences. Only useful
                        for debugging.

  --select-sequences INTERVAL, -s INTERVAL
                        Use only a specific range of input
                        sequences. DOES NOT YET WORK WITH
                        MULTIPLE INPUT FILES. (default: 0-999999)

  --enable-deletions, -d
                        Include deletions in lineage comparision.

  --show-private-mutations
                        Display mutations which are not in any of
                        the potential parental clades.

  --rebuild-examples, -r
                        Rebuild the mutations in examples by
                        querying cov-spectrum.org.

  --mutation-threshold NUM, -t NUM
                        Consider mutations with a prevalence of
                        at least NUM as mandatory for a clade
                        (range 0.05 - 1.0, default: 0.75).

  --add-spaces [NUM]    Add spaces between every N colums, which
                        makes it easier to keep your eye at a
                        fixed place. (default without flag: 0,
                        default with flag: 5)

  --sort-by-id [NUM]    Sort the input sequences by the ID. If
                        you provide NUM, only the first NUM
                        characters are considered. Useful if this
                        correlates with meaning full meta
                        information, e.g. the sequencing lab.
                        (default without flag: 0, default with
                        flag: 999)

  --verbose, -v         Print some more information, mostly
                        useful for debugging.

  --ansi                Use only ASCII characters to be
                        compatible with ansilove.

  --hide-progress       Don't show progress bars during long
                        task.

  --csvfile CSVFILE     Path to write results in CSV format.
                        (default: None)

An Interval can be a single number ("3"), a closed interval
("2-5" ) or an open one ("4-" or "-7"). The limits are inclusive.
Only positive numbers are supported.

Interpreting the output

To be written...

There already is a short Twitter thread which explains the basics.

Source material attribution

  • virus_properties.json contains data from LAPIS / cov-spectrum which uses data from NCBI GenBank, prepared and hosted by Nextstrain, see blog post.
  • reference.fasta is taken from Nextstrain's nextclade_data, see NCBI for attribution.
  • mapping.csv is a modified version of the table on the covariants homepage by Nextstrain.
  • Example output / screenshot based on Sequences published by the German Robert-Koch-Institut.
  • Primers:
    • ARTIC primers CC-BY-4.0 by the ARTICnetwork project
    • EasySeq primers by Coolen, J. P., Wolters, F., Tostmann, A., van Groningen, L. F., Bleeker-Rovers, C. P., Tan, E. C., ... & Melchers, W. J. Removed until I understand the format if the .bed file. There will be an issue soon.
    • midnight primers CC-BY-4.0 by Silander, Olin K, Massey University

The initial version of this program was written in cooperation with @flauschzelle.

TODO / IDEAS / PLANS

  • Move these TODOs into actual issues
  • add disclaimer and link to pango-designation
  • provide a sample file (maybe both .fasta and .csv, as long as the csv step is still needed)
  • accept aligned fasta
    • as input file
    • as piped stream
  • If we still accept csv/ssv input, autodetect the delimiter either by file name or by analysing the first line
  • find a way to handle already designated recombinant lineages
  • Output structured results
    • csv
    • html?
    • fasta of all sequences that match the criteria, which enables efficient multi-pass strategies
  • filter sequences
    • by ID
    • by metadata
  • take metadata csv
  • document the output in README
  • check / fix --enabled-deletions
  • adjustable threshold for mutation prevalence
  • new color mode (with background color and monochrome text on top)
  • new bar mode (with colored lines beneath each sequence, one for each example sequence, and "intermissions" shown in the color of the "surrounding" lineage, but not as bright)
  • interactive mode, for filtering, reordering, etc.
  • sort sequences within each block
  • re-think this whole "intermission" concept
  • select a single sequence and let the tool refine the choice of parental sequences, not just focusing on commonly known lineages (going up and down in the tree)
  • use more common terms to describe things (needs feedback from people with actual experience in the field)
Owner
Lena Schimmel
Lena Schimmel
Light-Head R-CNN

Light-head R-CNN Introduction We release code for Light-Head R-CNN. This is my best practice for my research. This repo is organized as follows: light

jemmy li 835 Dec 06, 2022
Unsupervised Learning of Video Representations using LSTMs

Unsupervised Learning of Video Representations using LSTMs Code for paper Unsupervised Learning of Video Representations using LSTMs by Nitish Srivast

Elman Mansimov 341 Dec 20, 2022
验证码识别 深度学习 tensorflow 神经网络

captcha_tf2 验证码识别 深度学习 tensorflow 神经网络 使用卷积神经网络,对字符,数字类型验证码进行识别,tensorflow使用2.0以上 目前项目还在更新中,诸多bug,欢迎提出issue和PR, 希望和你一起共同完善项目。 实例demo 训练过程 优化器选择: Adam

5 Apr 28, 2022
Evaluating deep transfer learning for whole-brain cognitive decoding

Evaluating deep transfer learning for whole-brain cognitive decoding This README file contains the following sections: Project description Repository

Armin Thomas 5 Oct 31, 2022
Official Implementation of DE-CondDETR and DELA-CondDETR in "Towards Data-Efficient Detection Transformers"

DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-CondDETR and DELA-Cond

Wen Wang 41 Dec 12, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 09, 2022
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation Where we are ? 12.27 目前和原论文仍有1%左右得差距,但已经力压很多SOTA了 ckpt__448_epoch_25.pth mIoU

zichengsaber 60 Dec 11, 2022
[ICCV2021] 3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds

3DVG-Transformer This repository is for the ICCV 2021 paper "3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds" Our method "3DV

22 Dec 11, 2022
A video scene detection algorithm is designed to detect a variety of different scenes within a video

Scene-Change-Detection - A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logical

1 Jan 04, 2022
Individual Tree Crown classification on WorldView-2 Images using Autoencoder -- Group 9 Weak learners - Final Project (Machine Learning 2020 Course)

Created by Olga Sutyrina, Sarah Elemili, Abduragim Shtanchaev and Artur Bille Individual Tree Crown classification on WorldView-2 Images using Autoenc

2 Dec 08, 2022
Zsseg.baseline - Zero-Shot Semantic Segmentation

This repo is for our paper A Simple Baseline for Zero-shot Semantic Segmentation

98 Dec 20, 2022
Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).

Self-supervised Graph-level Representation Learning with Local and Global Structure Introduction This project is an implementation of ``Self-supervise

MilaGraph 50 Dec 09, 2022
A supplementary code for Editable Neural Networks, an ICLR 2020 submission.

Editable neural networks A supplementary code for Editable Neural Networks, an ICLR 2020 submission by Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitry Py

Anton Sinitsin 32 Nov 29, 2022
MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift

MemStream Implementation of MemStream: Memory-Based Anomaly Detection in Multi-Aspect Streams with Concept Drift . Siddharth Bhatia, Arjit Jain, Shivi

Stream-AD 61 Dec 02, 2022
Space Time Recurrent Memory Network - Pytorch

Space Time Recurrent Memory Network - Pytorch (wip) Implementation of Space Time Recurrent Memory Network, recurrent network competitive with attentio

Phil Wang 50 Nov 07, 2021
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

MoCoPnet: Exploring Local Motion and Contrast Priors for Infrared Small Target Super-Resolution Pytorch implementation of local motion and contrast pr

Xinyi Ying 28 Dec 15, 2022
This repository contains the code for the paper 'PARM: Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval' published at ECIR'22.

Paragraph Aggregation Retrieval Model (PARM) for Dense Document-to-Document Retrieval This repository contains the code for the paper PARM: A Paragrap

Sophia Althammer 33 Aug 26, 2022
Code for "Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search"

Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search This is an implementation for our paper Contextual Non-Loca

Tencent YouTu Research 50 Dec 03, 2022
Website for D2C paper

D2C This is the repository that contains source code for the D2C Website. If you find D2C useful for your work please cite: @article{sinha2021d2c au

1 Oct 21, 2021
Capstone-Project-2 - A game program written in the Python language

Capstone-Project-2 My Pygame Game Information: Description This Pygame project i

Nhlakanipho Khulekani Hlophe 1 Jan 04, 2022