Efficient Minimum Flow Decomposition via Integer Linear Programming

Dias, Fernando H. C.; Williams, Lucia; Mumey, Brendan; Tomescu, Alexandru I.

doi:10.1089/cmb.2022.0257

Cited by 5 publications

(10 citation statements)

References 62 publications

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“…In case 3, we have more complex phage components where there are more than two unitigs forming branching paths, and we model them as a minimum flow decomposition (MFD) problem. The MFD problem decomposes a directed acyclic graph (DAG) into a minimum number of source-to-sink ( s − t ) paths that explain the flow values of the edges of the graph (Vatinlen et al 2008; Dias et al 2022). The most prominent applications of the MFD problem in bioinformatics include reconstructing RNA transcripts (Shao and Kingsford 2017; Tomescu et al 2013; Gatter and Stadler 2019) and viral quasispecies assembly (Baaijens, Stougie, and Schönhuth 2020).…”

Section: Methodsmentioning

confidence: 99%

“…Hence, Phables models each of the remaining phage components as an MFD problem and uses the MFD-ILP implementation from Dias et al . (Dias et al 2022). MFD-ILP finds a FD (𝒫, w ) with a set of s − t flow paths 𝒫 and associated weights w such that the number of flow paths is minimized.…”

Section: Methodsmentioning

confidence: 99%

“…minutes with less than 4 gigabytes of memory for all the datasets.Phables uses a modified version of the MFD-ILP implementation from Dias et al(Dias et al 2022) which supports inexact flow decomposition with subpath constraints. Gurobi version 10.0.2 was used as the ILP solver.…”

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confidence: 99%

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Phables: from fragmented assemblies to high-quality bacteriophage genomes

Mallawaarachchi

Roach

Papudeshi

et al. 2023

Preprint

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Microbial communities found within the human gut have a strong influence on human health. Intestinal bacteria and viruses influence gastrointestinal diseases such as inflammatory bowel disease. Viruses infecting bacteria, known as bacteriophages, play a key role in modulating bacterial communities within the human gut. However, the identification and characterisation of novel bacteriophages remain a challenge. Available tools use similarities between sequences, nucleotide composition, and the presence of viral genes/proteins. Most available tools consider individual contigs to determine whether they are of viral origin. As a result of the challenges in viral assembly, fragmentation of viral genomes can occur, leading to the need for new approaches in viral identification. We introduce Phables, a new computational method to resolve bacteriophage genomes from fragmented viral metagenomic assemblies. Phables identifies bacteriophage-like components in the assembly graph, models each component as a flow network, and uses graph algorithms and flow decomposition techniques to identify genomic paths. Experimental results of viral metagenomic samples obtained from different environments show that over 80% of the bacteriophage genomes resolved by Phables have high quality and are longer than the individual contigs identified by existing viral identification tools.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Phables: from fragmented assemblies to high-quality bacteriophage genomes

Mallawaarachchi

Roach

Papudeshi

et al. 2023

Preprint

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“…On DAGs, a FD into paths always exists ( Ahuja et al 1988 ), but in general graphs, cycles may be necessary to decompose the flow [see e.g. Dias et al (2022a) for different possible formulations of the problem].…”

Section: Methodsmentioning

confidence: 99%

A safety framework for flow decomposition problems via integer linear programming

Dias,

Cáceres,

Williams

et al. 2023

Bioinformatics

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Motivation 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. Availability https://github.com/algbio/mfd-safety Supplementary information Supplementary data are available at Bioinformatics online.

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“…Computational methods are increasingly used to identify novel isoforms to complement annotations of model species and to construct transcriptomes for non-model species, thanks to the availability of largescale reposited RNA-seq data. The process of reconstructing full-length transcripts from RNA-seq reads, known as transcript assembly, has been extensively studied, with significant progress made in advancing the theory [20,21,22] and in developing practical assemblers, including Cufflinks [23], CLASS2 [24],…”

Section: Introductionmentioning

confidence: 99%

Transcript Assembly and Annotations: Bias and Adjustment

Zhang

Shao

2023

Preprint

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Motivation: Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. Results: We investigate the impact of annotations on transcript assembly. We observe that conflicting conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.

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Efficient Minimum Flow Decomposition via Integer Linear Programming

Cited by 5 publications

References 62 publications

Phables: from fragmented assemblies to high-quality bacteriophage genomes

Phables: from fragmented assemblies to high-quality bacteriophage genomes

A safety framework for flow decomposition problems via integer linear programming

Transcript Assembly and Annotations: Bias and Adjustment

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