Gapmm2: gapped alignment using minimap2 (align transcripts to genome)

Related tags

Deep Learninggapmm2
Overview

Latest Github release Conda

gapmm2: gapped alignment using minimap2

This tool is a wrapper for minimap2 to run spliced/gapped alignment, ie aligning transcripts to a genome. You are probably saying, yes minimap2 runs this with -x splice --cs option (you are correct). However, there are instances where the terminal exons from stock minimap2 alignments are missing. This tool detects those alignments that have unaligned terminal eons and uses edlib to find the terminal exon positions. The tool then updates the PAF output file with the updated information.

Rationale

We can pull out a gene model in GFF3 format that has a short 5' terminal exon:

scaffold_9	funannotate	gene	408904	409621	.	-	.	ID=OPO1_006919;
scaffold_9	funannotate	mRNA	408904	409621	.	-	.	ID=OPO1_006919-T1;Parent=OPO1_006919;product=hypothetical protein;
scaffold_9	funannotate	exon	409609	409621	.	-	.	ID=OPO1_006919-T1.exon1;Parent=OPO1_006919-T1;
scaffold_9	funannotate	exon	409320	409554	.	-	.	ID=OPO1_006919-T1.exon2;Parent=OPO1_006919-T1;
scaffold_9	funannotate	exon	409090	409255	.	-	.	ID=OPO1_006919-T1.exon3;Parent=OPO1_006919-T1;
scaffold_9	funannotate	exon	408904	409032	.	-	.	ID=OPO1_006919-T1.exon4;Parent=OPO1_006919-T1;
scaffold_9	funannotate	CDS	409609	409621	.	-	0	ID=OPO1_006919-T1.cds;Parent=OPO1_006919-T1;
scaffold_9	funannotate	CDS	409320	409554	.	-	2	ID=OPO1_006919-T1.cds;Parent=OPO1_006919-T1;
scaffold_9	funannotate	CDS	409090	409255	.	-	1	ID=OPO1_006919-T1.cds;Parent=OPO1_006919-T1;
scaffold_9	funannotate	CDS	408904	409032	.	-	0	ID=OPO1_006919-T1.cds;Parent=OPO1_006919-T1;

If we then map this transcript against the genome, we get the following PAF alignment.

$ minimap2 -x splice --cs genome.fasta cds-transcripts.fa | grep 'OPO1_006919'
OPO1_006919-T1	543	13	543	-	scaffold_9	658044	408903	409554	530	530	60	NM:i:0	ms:i:530	AS:i:466	nn:i:0	ts:A:+	tp:A:P	cm:i:167	s1:i:510	s2:i:0	de:f:0	rl:i:0	cs:Z::129~ct57ac:166~ct64ac:235

The --cs flag in minimap2 can be used to parse the coordinates (below) and you can see we are missing the 5' exon.

>>> cs2coords(408903, 13, 543, '-', ':129~ct57ac:166~ct64ac:235')
([(409320, 409554), (409090, 409255), (408904, 409032)],

So if we run this same alignment with gapmm2 we are able to properly align the 5' terminal exon.

$ gapmm2 genome.fa cds-transcripts.fa | grep 'OPO1_006919'
OPO1_006919-T1	543	0	543	-	scaffold_9	658044	408903	409621	543	543	60	tp:A:P	ts:A:+	NM:i:0	cs:Z::129~ct57ac:166~ct64ac:235~ct54ac:13
>>> cs2coords(408903, 0, 543, '-', ':129~ct57ac:166~ct64ac:235~ct54ac:13')
([(409609, 409621), (409320, 409554), (409090, 409255), (408904, 409032)]

Usage:

gapmm2 can be run as a command line script:

$ gapmm2
usage: gapmm2 [-o] [-t] [-m] [-d] [-h] [--version] reference query

gapmm2: gapped alignment with minimap2. Performs minimap2/mappy alignment with splice options and refines terminal alignments with edlib. Output is PAF format.

Positional arguments:
  reference         reference genome (FASTA)
  query             transcipts in FASTA or FASTQ

Optional arguments:
  -o , --out        output in PAF format (default: stdout)
  -t , --threads    number of threads to use with minimap2 (default: 3)
  -m , --min-mapq   minimum map quality value (default: 1)
  -d, --debug       write some debug info to stderr (default: False)

Help:
  -h, --help        Show this help message and exit
  --version         Show program's version number and exit

It can also be run as a python module. The splice_aligner function will return a list of lists containing PAF formatted data for each alignment and a dictionary of simple stats.

>>> from gapmm2.align import splice_aligner
>>> results, stats = splice_aligner('genome.fa', 'transcripts.fa')
>>> stats
{'n': 6926, 'low-mapq': 0, 'refine-left': 409, 'refine-right': 63}
>>> len(results)
6926
>>> results[0]
['OPO1_000001-T1', 2184, 0, 2184, '+', 'scaffold_1', 1803704, 887, 3127, 2184, 2184, 60, 'tp:A:P', 'ts:A:+', 'NM:i:0', 'cs:Z::958~gt56ag:1226']
>>> 

To install the python package, you can do this with pip:

python -m pip install gapmm2

To install the most updated code in master you can run:

python -m pip install git+https://github.com/nextgenusfs/gapmm2.git
You might also like...
[NAACL & ACL 2021] SapBERT: Self-alignment pretraining for BERT.
[NAACL & ACL 2021] SapBERT: Self-alignment pretraining for BERT.

SapBERT: Self-alignment pretraining for BERT This repo holds code for the SapBERT model presented in our NAACL 2021 paper: Self-Alignment Pretraining

the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)
the code of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021)

RMA-Net This repo is the implementation of the paper: Recurrent Multi-view Alignment Network for Unsupervised Surface Registration (CVPR 2021). Paper

Pytorch implementation for
Pytorch implementation for "Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter".

Implicit Feature Alignment: Learn to Convert Text Recognizer to Text Spotter This is a pytorch-based implementation for paper Implicit Feature Alignme

The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.
The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

Face Alignment in Full Pose Range: A 3D Total Solution By Jianzhu Guo. [Updates] 2020.8.30: The pre-trained model and code of ECCV-20 are made public

🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016
🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Deep CORAL A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016' Deep CORAL can learn

An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
An official implementation of the paper Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

Sequence Feature Alignment (SFA) By Wen Wang, Yang Cao, Jing Zhang, Fengxiang He, Zheng-jun Zha, Yonggang Wen, and Dacheng Tao This repository is an o

Collective Multi-type Entity Alignment Between Knowledge Graphs (WWW'20)

CG-MuAlign A reference implementation for "Collective Multi-type Entity Alignment Between Knowledge Graphs", published in WWW 2020. If you find our pa

Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment (ICCV2021)
Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment (ICCV2021)

Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment This is a pytorch project for the paper Seeing Dynamic Scene i

The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).
The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

Releases(v0.2.0)
Owner
Jon Palmer
Jon Palmer
CC-GENERATOR - A python script for generating CC

CC-GENERATOR A python script for generating CC NOTE: This tool is for Educationa

Lêkzï 6 Oct 14, 2022
Experiments with differentiable stacks and queues in PyTorch

Please use stacknn-core instead! StackNN This project implements differentiable stacks and queues in PyTorch. The data structures are implemented in s

Will Merrill 141 Oct 06, 2022
Multi-tool reverse engineering collaboration solution.

CollaRE v0.3 Intorduction CollareRE is a tool for collaborative reverse engineering that aims to allow teams that do need to use more then one tool du

105 Nov 27, 2022
LineBoard - Python+React+MySQL-白板即時系統改善人群行為

LineBoard-白板即時系統改善人群行為 即時顯示實驗室的使用狀況,並遠端預約排隊,以此來改善人們的工作效率 程式架構 運作流程 使用者先至該實驗室網站預約

Bo-Jyun Huang 1 Feb 22, 2022
Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

NOTE: This is still being developed! Setup notes This document uses Jeff Sackmann's tennis data. You can obtain it as follows: git clone https://githu

Martin Ingram 1 Jan 20, 2022
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Pytorch当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和

Bubbliiiing 102 Dec 30, 2022
A modular domain adaptation library written in PyTorch.

A modular domain adaptation library written in PyTorch.

Kevin Musgrave 225 Dec 29, 2022
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

Stock Trading Market OpenAI Gym Environment with Deep Reinforcement Learning using Keras Overview This project provides a general environment for stoc

Kim, Ki Hyun 769 Dec 25, 2022
NLMpy - A Python package to create neutral landscape models

NLMpy is a Python package for the creation of neutral landscape models that are widely used by landscape ecologists to model ecological patterns

Manaaki Whenua – Landcare Research 1 Oct 08, 2022
AOT (Associating Objects with Transformers) in PyTorch

An efficient modular implementation of Associating Objects with Transformers for Video Object Segmentation in PyTorch

162 Dec 14, 2022
In Search of Probeable Generalization Measures

In Search of Probeable Generalization Measures Exciting News! In Search of Probeable Generalization Measures has been accepted to the International Co

Mahdi S. Hosseini 6 Sep 11, 2022
City-seeds - A random generator of cultural characteristics intended to spark ideas and help draw threads

City Seeds This is a random generator of cultural characteristics intended to sp

Aydin O'Leary 2 Mar 12, 2022
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM) Introduction The average lifetime of the $D^{0}$ me

Son Gyo Jung 1 Dec 17, 2021
Synthetic structured data generators

Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati

YData 850 Jan 07, 2023
The Agriculture Domain of ERPNext comes with features to record crops and land

Agriculture The Agriculture Domain of ERPNext comes with features to record crops and land, track plant, soil, water, weather analytics, and even trac

Frappe 21 Jan 02, 2023
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

2.4k Jan 08, 2023
Breaking the Dilemma of Medical Image-to-image Translation

Breaking the Dilemma of Medical Image-to-image Translation Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field

Kid Liet 86 Dec 21, 2022
PyTorch implementation of our paper How robust are discriminatively trained zero-shot learning models?

How robust are discriminatively trained zero-shot learning models? This repository contains the PyTorch implementation of our paper How robust are dis

Mehmet Kerim Yucel 5 Feb 04, 2022
Automatically download the cwru data set, and then divide it into training data set and test data set

Automatically download the cwru data set, and then divide it into training data set and test data set.自动下载cwru数据集,然后分训练数据集和测试数据集

6 Jun 27, 2022
Semi-Supervised Learning for Fine-Grained Classification

Semi-Supervised Learning for Fine-Grained Classification This repo contains the code of: A Realistic Evaluation of Semi-Supervised Learning for Fine-G

25 Nov 08, 2022