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
An official reimplementation of the method described in the INTERSPEECH 2021 paper - Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

Facebook Research 253 Jan 06, 2023
Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al.

nam-pytorch Unofficial PyTorch implementation of Neural Additive Models (NAM) by Agarwal, et al. [abs, pdf] Installation You can access nam-pytorch vi

Rishabh Anand 11 Mar 14, 2022
Data and codes for ACL 2021 paper: Towards Emotional Support Dialog Systems

Emotional-Support-Conversation Copyright © 2021 CoAI Group, Tsinghua University. All rights reserved. Data and codes are for academic research use onl

126 Dec 21, 2022
A simple python stock Predictor

Python Stock Predictor A simple python stock Predictor Demo Run Locally Clone the project git clone https://github.com/yashraj-n/stock-price-predict

Yashraj narke 5 Nov 29, 2021
PyTorch implementation of Rethinking Positional Encoding in Language Pre-training

TUPE PyTorch implementation of Rethinking Positional Encoding in Language Pre-training. Quickstart Clone this repository. git clone https://github.com

Jake Tae 5 Jan 27, 2022
CVPR '21: In the light of feature distributions: Moment matching for Neural Style Transfer

In the light of feature distributions: Moment matching for Neural Style Transfer (CVPR 2021) This repository provides code to recreate results present

Nikolai Kalischek 49 Oct 13, 2022
GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images

GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-

VITA 298 Dec 12, 2022
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations The code of: Weakly Supervised Learning of Instance Segmentation with I

Jiwoon Ahn 472 Dec 29, 2022
This is the repository for paper NEEDLE: Towards Non-invertible Backdoor Attack to Deep Learning Models.

This is the repository for paper NEEDLE: Towards Non-invertible Backdoor Attack to Deep Learning Models.

1 Oct 25, 2021
FastyAPI is a Stack boilerplate optimised for heavy loads.

FastyAPI A FastAPI based Stack boilerplate for heavy loads. Explore the docs » View Demo · Report Bug · Request Feature Table of Contents About The Pr

Ali Chaayb 47 Dec 27, 2022
History Aware Multimodal Transformer for Vision-and-Language Navigation

History Aware Multimodal Transformer for Vision-and-Language Navigation This repository is the official implementation of History Aware Multimodal Tra

Shizhe Chen 46 Nov 23, 2022
Notes taking website build with Docker + Django + React.

Notes website. Try it in browser! / But how to run? Description. This is monorepository with notes website. Website provides web interface for creatin

Kirill Zhosul 2 Jul 27, 2022
Pytorch implementation of the paper "Topic Modeling Revisited: A Document Graph-based Neural Network Perspective"

Graph Neural Topic Model (GNTM) This is the pytorch implementation of the paper "Topic Modeling Revisited: A Document Graph-based Neural Network Persp

Dazhong Shen 8 Sep 14, 2022
Source code for our paper "Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures"

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures Code for the Multiplex Molecular Graph Neural Network (M

shzhang 59 Dec 10, 2022
Dynamic Capacity Networks using Tensorflow

Dynamic Capacity Networks using Tensorflow Dynamic Capacity Networks (DCN; http://arxiv.org/abs/1511.07838) implementation using Tensorflow. DCN reduc

Taeksoo Kim 8 Feb 23, 2021
YOLOX-RMPOLY

本算法为适应robomaster比赛,而改动自矩形识别的yolox算法。 基于旷视科技YOLOX,实现对不规则四边形的目标检测 TODO 修改onnx推理模型 更改/添加标注: 1.yolox/models/yolox_polyhead.py: 1.1继承yolox/models/yolo_

3 Feb 25, 2022
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Automated Side Channel Analysis of Media Software with Manifold Learning Official implementation of USENIX Security 2022 paper: Automated Side Channel

Yuanyuan Yuan 175 Jan 07, 2023
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

Bogireddy Sai Prasanna Teja Reddy 103 Dec 29, 2022
Scrutinizing XAI with linear ground-truth data

This repository contains all the experiments presented in the corresponding paper: "Scrutinizing XAI using linear ground-truth data with suppressor va

braindata lab 2 Oct 04, 2022
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.

shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t

Marco Cerliani 422 Jan 08, 2023