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

Cambridge Language Technology Lab 104 Dec 7, 2022
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

Wanquan Feng 205 Nov 9, 2022
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

wangtianwei 61 Nov 12, 2022
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

Jianzhu Guo 3.4k Jan 2, 2023
🧠 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

Andy Hsu 200 Dec 25, 2022
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

WangWen 79 Dec 24, 2022
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

Bran Zhu 28 Dec 11, 2022
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

DV Lab 21 Nov 28, 2022
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).

Will Thompson 166 Jan 4, 2023
Releases(v0.2.0)
Owner
Jon Palmer
Jon Palmer
GitHub Repository
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J

Zilong Huang 245 Dec 13, 2022
Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge.

KAIROS MineRL BASALT Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL B

Vinicius G. Goecks 37 Oct 30, 2022
The implementation of the algorithm in the paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020.

DS3L This is the code for paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020. Setups The code is implem

Guolz 36 Oct 19, 2022
Official implementation of "MetaSDF: Meta-learning Signed Distance Functions"

MetaSDF: Meta-learning Signed Distance Functions Project Page | Paper | Data Vincent Sitzmann*, Eric Ryan Chan*, Richard Tucker, Noah Snavely Gordon W

Vincent Sitzmann 100 Jan 01, 2023
Finetune alexnet with tensorflow - Code for finetuning AlexNet in TensorFlow >= 1.2rc0

Finetune AlexNet with Tensorflow Update 15.06.2016 I revised the entire code base to work with the new input pipeline coming with TensorFlow = versio

Frederik Kratzert 766 Jan 04, 2023
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Tom 50 Dec 16, 2022
Grounding Representation Similarity with Statistical Testing

Grounding Representation Similarity with Statistical Testing This repo contains code to replicate the results in our paper, which evaluates representa

26 Dec 02, 2022
Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

CDAN Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018) New version: https://github.com/thuml/Transfer-Learning-Library Dataset

THUML @ Tsinghua University 363 Dec 20, 2022
[CVPR'22] COAP: Learning Compositional Occupancy of People

COAP: Compositional Articulated Occupancy of People Paper | Video | Project Page This is the official implementation of the CVPR 2022 paper COAP: Lear

Marko Mihajlovic 111 Dec 11, 2022
Generating Radiology Reports via Memory-driven Transformer

R2Gen This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020. Citations If you use or extend our work,

CUHK-SZ NLP Group 101 Dec 13, 2022
Bridging Composite and Real: Towards End-to-end Deep Image Matting

Bridging Composite and Real: Towards End-to-end Deep Image Matting Please note that the official repository of the paper Bridging Composite and Real:

Jizhizi_Li 30 Oct 31, 2022
Code base for reproducing results of I.Schubert, D.Driess, O.Oguz, and M.Toussaint: Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. NeurIPS (2021)

Learning to Execute (L2E) Official code base for completely reproducing all results reported in I.Schubert, D.Driess, O.Oguz, and M.Toussaint: Learnin

3 May 18, 2022
Dynamic View Synthesis from Dynamic Monocular Video

Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus

Chen Gao 139 Dec 28, 2022
ESP32 python application to read data from a Tiltâ„¢ Hydrometer for homebrewing

TitlESP32 ESP32 MicroPython application to read and log data from a Tiltâ„¢ Hydrometer. Requirements A board with an ESP32 chip USB cable - USB A / micr

IoBeer 5 Dec 01, 2022
Informal Persian Universal Dependency Treebank

Informal Persian Universal Dependency Treebank (iPerUDT) Informal Persian Universal Dependency Treebank, consisting of 3000 sentences and 54,904 token

Roya Kabiri 0 Jan 05, 2022
Type4Py: Deep Similarity Learning-Based Type Inference for Python

Type4Py: Deep Similarity Learning-Based Type Inference for Python This repository contains the implementation of Type4Py and instructions for re-produ

Software Analytics Lab 45 Dec 15, 2022
Source code, datasets and trained models for the paper Learning Advanced Mathematical Computations from Examples (ICLR 2021), by François Charton, Amaury Hayat (ENPC-Rutgers) and Guillaume Lample

Maths from examples - Learning advanced mathematical computations from examples This is the source code and data sets relevant to the paper Learning a

Facebook Research 171 Nov 23, 2022
MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions

MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions Project Page | Paper If you find our work useful for your research, please con

96 Jan 04, 2023
[ICCV 2021] Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Amplitude-Phase Recombination (ICCV'21) Official PyTorch implementation of "Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neur

Guangyao Chen 53 Oct 05, 2022
3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks Introduction This repository contains the code and models for the follo

124 Jan 06, 2023