OCR Error Correction: State-of-the-Art vs an NMT-based Approach

2018 ◽  
Author(s):  
Kareem Mokhtar ◽  
Syed Saqib Bukhari ◽  
Andreas Dengel
Keyword(s):  
Error Correction ◽  
State Of The Art

scholarly journals abPOA: an SIMD-based C library for fast partial order alignment using adaptive band

2020 ◽  
Author(s):  
Yan Gao ◽  
Yongzhuang Liu ◽  
Yanmei Ma ◽  
Bo Liu ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Error Correction ◽  
Partial Order ◽  
Directed Acyclic Graph ◽  
State Of The Art ◽  
Single Instruction Multiple Data ◽  
Multiple Sequence ◽  
Software Interface ◽  
Multiple Data ◽  
Long Read ◽  
Read Error Correction

AbstractSummaryPartial order alignment, which aligns a sequence to a directed acyclic graph, is now frequently used as a key component in long-read error correction and assembly. We present abPOA (adaptive banded Partial Order Alignment), a Single Instruction Multiple Data (SIMD) based C library for fast partial order alignment using adaptive banded dynamic programming. It can work as a stand-alone multiple sequence alignment and consensus calling tool or be easily integrated into any long-read error correction and assembly workflow. Compared to a state-of-the-art tool (SPOA), abPOA is up to 15 times faster with a comparable alignment accuracy.Availability and implementationabPOA is implemented in C. A stand-alone tool and a C/Python software interface are freely available at https://github.com/yangao07/[email protected] or [email protected]

scholarly journals GraphAligner: rapid and versatile sequence-to-graph alignment

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mikko Rautiainen ◽  
Tobias Marschall
Keyword(s):  
Genetic Variation ◽  
Error Correction ◽  
Genome Assembly ◽  
State Of The Art ◽  
Source Code ◽  
The State ◽  
Graph Alignment ◽  
Link Type ◽  
Long Reads

Abstract Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pangenome graph. Yet, so far, this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to the state-of-the-art tools, GraphAligner is 13x faster and uses 3x less memory. When employing GraphAligner for error correction, we find it to be more than twice as accurate and over 12x faster than extant tools.Availability: Package manager: https://anaconda.org/bioconda/graphalignerand source code: https://github.com/maickrau/GraphAligner

scholarly journals Grammar Error Correction in Morphologically Rich Languages: The Case of Russian

2019 ◽  
Vol 7 ◽  
pp. 1-17 ◽  
Author(s):  
Alla Rozovskaya ◽  
Dan Roth
Keyword(s):  
Error Correction ◽  
State Of The Art ◽  
Training Data ◽  
English Writing ◽  
Art Methods ◽  
Morphologically Rich Languages

Until now, most of the research in grammar error correction focused on English, and the problem has hardly been explored for other languages. We address the task of correcting writing mistakes in morphologically rich languages, with a focus on Russian. We present a corrected and error-tagged corpus of Russian learner writing and develop models that make use of existing state-of-the-art methods that have been well studied for English. Although impressive results have recently been achieved for grammar error correction of non-native English writing, these results are limited to domains where plentiful training data are available. Because annotation is extremely costly, these approaches are not suitable for the majority of domains and languages. We thus focus on methods that use “minimal supervision”; that is, those that do not rely on large amounts of annotated training data, and show how existing minimal-supervision approaches extend to a highly inflectional language such as Russian. The results demonstrate that these methods are particularly useful for correcting mistakes in grammatical phenomena that involve rich morphology.

scholarly journals GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

2019 ◽  
Author(s):  
Mikko Rautiainen ◽  
Tobias Marschall
Keyword(s):  
Genetic Variation ◽  
Error Correction ◽  
Genome Assembly ◽  
State Of The Art ◽  
Source Code ◽  
Graph Alignment ◽  
Link Type ◽  
Long Reads ◽  
Reference Genomes ◽  
Genome Graph

AbstractGenome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pan-genome graph. Yet, so far this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to state-of-the-art tools, GraphAligner is 12x faster and uses 5x less memory, making it as efficient as aligning reads to linear reference genomes. When employing GraphAligner for error correction, we find it to be almost 3x more accurate and over 15x faster than extant tools.Availability Package managerhttps://anaconda.org/bioconda/graphaligner and source code: https://github.com/maickrau/GraphAligner

scholarly journals ELECTOR: Evaluator for long reads correction methods

2019 ◽  
Author(s):  
Camille Marchet ◽  
Pierre Morisse ◽  
Lolita Lecompte ◽  
Arnaud Lefebvre ◽  
Thierry Lecroq ◽  
...  
Keyword(s):  
Error Correction ◽  
State Of The Art ◽  
Error Rates ◽  
Sequencing Data ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Wide Range ◽  
Downstream Processes ◽  
Generation Sequencing

AbstractMotivationIn the last few years, the error rates of third generation sequencing data have been capped above 5%, including many insertions and deletions. Thereby, an increasing number of long reads correction methods have been proposed to reduce the noise in these sequences. Whether hybrid or self-correction methods, there exist multiple approaches to correct long reads. As the quality of the error correction has huge impacts on downstream processes, developing methods allowing to evaluate error correction tools with precise and reliable statistics is therefore a crucial need. Since error correction is often a resource bottleneck in long reads pipelines, a key feature of assessment methods is therefore to be efficient, in order to allow the fast comparison of different tools.ResultsWe propose ELECTOR, a reliable and efficient tool to evaluate long reads correction, that enables the evaluation of hybrid and self-correction methods. Our tool provides a complete and relevant set of metrics to assess the read quality improvement after correction and scales to large datasets. ELECTOR is directly compatible with a wide range of state-of-the-art error correction tools, using whether simulated or real long reads. We show that ELECTOR displays a wider range of metrics than the state-of-the-art tool, LRCstats, and additionally importantly decreases the runtime needed for assessment on all the studied datasets.AvailabilityELECTOR is available at https://github.com/kamimrcht/[email protected] or [email protected]

scholarly journals abPOA: an SIMD-based C library for fast partial order alignment using adaptive band

2020 ◽  
Author(s):  
Yan Gao ◽  
Yongzhuang Liu ◽  
Yanmei Ma ◽  
Bo Liu ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Error Correction ◽  
Partial Order ◽  
Directed Acyclic Graph ◽  
State Of The Art ◽  
Supplementary Information ◽  
Multiple Sequence ◽  
Software Interface ◽  
Multiple Data ◽  
Long Read ◽  
Read Error Correction

Abstract Summary Partial order alignment, which aligns a sequence to a directed acyclic graph, is now frequently used as a key component in long-read error correction and assembly. We present abPOA (adaptive banded Partial Order Alignment), a Single Instruction Multiple Data (SIMD)-based C library for fast partial order alignment using adaptive banded dynamic programming. It can work as a stand-alone multiple sequence alignment and consensus calling tool or be easily integrated into any long-read error correction and assembly workflow. Compared to a state-of-the-art tool (SPOA), abPOA is up to 10 times faster with a comparable alignment accuracy. Availability and implementation abPOA is implemented in C. A stand-alone tool and a C/Python software interface are freely available at https://github.com/yangao07/abPOA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Review of error correction for PUFs and evaluation on state-of-the-art FPGAs

2020 ◽  
Vol 10 (3) ◽  
pp. 229-247
Author(s):  
Matthias Hiller ◽  
Ludwig Kürzinger ◽  
Georg Sigl
Keyword(s):  
Error Correction ◽  
State Of The Art

scholarly journals High-Quality Genomes of Nanopore Sequencing by Homologous Polishing

2020 ◽  
Author(s):  
Yao-Ting Huang ◽  
Po-Yu Liu ◽  
Pei-Wen Shih
Keyword(s):  
Neural Network ◽  
Error Correction ◽  
Error Rate ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Systematic Errors ◽  
Nanopore Sequencing ◽  
High Quality ◽  
Microbial Genomes ◽  
Homologous Sequences

AbstractNanopore sequencing has been widely used for reconstruction of a variety of microbial genomes. Owing to the higher error rate, the assembled genome requires further error correction. Existing methods erase many of these errors via deep neural network trained from Nanopore reads. However, quite a few systematic errors are still left on the genome. This paper proposed a new model trained from homologous sequences extracted from closely-related genomes, which provides valuable features missed in Nanopore reads. The developed program (called Homopolish) outperforms the state-of-the-art Racon/Medaka and MarginPolish/HELEN pipelines in metagenomic and isolates of bacteria, viruses and fungi. When Homopolish is combined with Medaka or with HELEN, the genomes quality can exceed Q50 on R9.4 flowcells. The genome quality can be also improved on R10.3 flowcells (Q50-Q90). We proved that Nanopore-only sequencing can now produce high-quality genomes without the need of Illumina hybrid sequencing.

scholarly journals Practical probabilistic and graphical formulations of long-read polyploid haplotype phasing

2020 ◽  
Author(s):  
Jim Shaw ◽  
Yun William Yu
Keyword(s):  
Error Correction ◽  
State Of The Art ◽  
Simulated Data ◽  
Error Minimization ◽  
Challenging Problem ◽  
Phase Information ◽  
Minimum Error ◽  
Haplotype Phasing ◽  
Long Read ◽  
Probabilistic Error

AbstractResolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition (MSMTP) problem, which is a more flexible graphical metric compared to the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization (UPEM) model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably to state-of-the-art algorithms—up to 30 times faster with 2 times fewer switch errors on 6x ploidy simulated data.

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