Jumper Enables Discontinuous Transcript Assembly in Coronaviruses

Sashittal, Palash; Zhang, Chuanyi; Peng, Jian; El-Kebir, Mohammed

doi:10.1101/2021.02.12.431026

Cited by 2 publications

(2 citation statements)

References 53 publications

(106 reference statements)

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“…In fact, one could even add additional constraints to C to further restrict the solution space. For example, some ILP models from [15,46] handle the case when the input also contains a set of paths (subpath constraints) that must appear in at least one of the k solution paths.…”

Section: Preliminariesmentioning

confidence: 99%

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A Safety Framework for Flow Decomposition Problems via Integer Linear Programming

Dias¹,

Caceres²,

Williams³

et al. 2023

Preprint

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Many important problems in Bioinformatics (e.g., assembly or multi-assembly) admit multiple solutions, while the final objective is to report only one. A common approach to deal with this uncertainty is finding safe partial solutions (e.g., contigs) which are common to all solutions. Previous research on safety has focused on polynomially-time solvable problems, whereas many successful and natural models are NP-hard to solve, leaving a lack of "safety tools" for such problems. We propose the first method for computing all safe solutions for an NP-hard problem, minimum flow decomposition. We obtain our results by developing a "safety test" for paths based on a general Integer Linear Programming (ILP) formulation. Moreover, we provide implementations with practical optimizations aimed to reduce the total ILP time, the most efficient of these being based on a recursive group-testing procedure. Results: Experimental results on the transcriptome datasets of Shao and Kingsford (TCBB, 2017) show that all safe paths for minimum flow decompositions correctly recover up to 90% of the full RNA transcripts, which is at least 25% more than previously known safe paths, such as (Cáceres et al. TCBB, 2021), (Zheng et al., RECOMB 2021), (Khan et al., RECOMB 2022, ESA 2022. Moreover, despite the NP-hardness of the problem, we can report all safe paths for 99.8% of the over 27,000 non-trivial graphs of this dataset in only 1.5 hours. Our results suggest that, on perfect data, there is less ambiguity than thought in the notoriously hard RNA assembly problem.

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Section: Preliminariesmentioning

confidence: 99%

“…This is an NP-hard problem, even when restricted to DAGs [53,22]. Various approaches have been proposed to tackle the problem, including fixed-parameter tractable algorithms [30], approximation algorithms [36,7] and Integer Linear Programming formulations [15,46].…”

Section: Introductionmentioning

confidence: 99%

A Safety Framework for Flow Decomposition Problems via Integer Linear Programming

Dias¹,

Caceres²,

Williams³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

CORSID enables de novo identification of transcription regulatory sequences and genes in coronaviruses

Zhang

Sashittal

El-Kebir

2021

Preprint

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Genes in coronaviruses are preceded by transcription regulatory sequences (TRSs), which play a critical role in gene expression mediated by the viral RNA-dependent RNA-polymerase via the process of discontinuous transcription. In addition to being crucial for our understanding of the regulation and expression of coronavirus genes, we demonstrate for the first time how TRSs can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS AND Gene Identification (TRS-Gene-ID) problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID (CORe Sequence IDentifier), a computational tool to solve this problem. We also present CORSID-A, which solves a constrained version of the TRS-Gene-ID problem, the TRS Identification (TRS-ID) problem, identifying TRS sites in a coronavirus genome with specified gene annotations. We show that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses and that CORSID outperforms state-of-the-art gene finding methods in finding genes in coronavirus genomes. We demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronaviruses. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

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Jumper Enables Discontinuous Transcript Assembly in Coronaviruses

Cited by 2 publications

References 53 publications

A Safety Framework for Flow Decomposition Problems via Integer Linear Programming

A Safety Framework for Flow Decomposition Problems via Integer Linear Programming

CORSID enables de novo identification of transcription regulatory sequences and genes in coronaviruses

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