RNA Transcript Assembly Using Inexact Flows

Williams, Lucia; Reynolds, Gillian; Mumey, Brendan

doi:10.1109/bibm47256.2019.8983180

Cited by 12 publications

(23 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One such method uses a minimum-cost flow approach for this adjustment [2]. Another approach [8] models the input as an inexact flow network in which edge flows belong to intervals, that are estimated from the data. In all cases, we seek a path decomposition for the flow network that minimizes the number of paths.…”

Section: Biological Settingmentioning

confidence: 99%

“…As in previous studies on flow decomposition methods for RNA-Seq assembly [9], [18], [8], we use a simulated RNA-Seq dataset from [18] where each instance is a flow network generated by simulating RNA transcripts and their abundances with Flux-Simulator [32]. The original dataset includes human, mouse, and zebrafish genes, but we restrict our attention to instances in the human dataset, which contains 100 independently generated transcriptomes.…”

Section: Datasetsmentioning

confidence: 99%

“…The original dataset includes human, mouse, and zebrafish genes, but we restrict our attention to instances in the human dataset, which contains 100 independently generated transcriptomes. As in [9], [8], we use only instances with at least two ground truth paths (since a single ground truth path is trivial to decompose). We also restrict the dataset to instances with 10 ground truth paths or fewer, yielding a total of 528,544 instances.…”

Section: Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

Flow Decomposition With Subpath Constraints

Williams

Tomescu

Mumey

2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Self Cite

View full text Add to dashboard Cite

Flow network decomposition is a natural model for problems where we are given a flow network arising from superimposing a set of weighted paths and would like to recover the underlying data, i.e., decompose the flow into the original paths and their weights. Thus, variations on flow decomposition are often used as subroutines in multiassembly problems such as RNA transcript assembly. In practice, we frequently have access to information beyond flow values in the form of subpaths, and many tools incorporate these heuristically. But despite acknowledging their utility in practice, previous work has not formally addressed the effect of subpath constraints on the accuracy of flow network decomposition approaches. We formalize the flow decomposition with subpath constraints problem, give the first algorithms for it, and study its usefulness for recovering ground truth decompositions. For finding a minimum decomposition, we propose both a heuristic and an FPT algorithm. Experiments on RNA transcript datasets show that for instances with larger solution path sets, the addition of subpath constraints finds 13% more ground truth solutions when minimal decompositions are found exactly, and 30% more ground truth solutions when minimal decompositions are found heuristically.

show abstract

Section: Biological Settingmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Flow Decomposition With Subpath Constraints

Williams

Tomescu

Mumey

2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, we are looking for an inexact flow decomposition, namely one such that the superposition of its weights belongs to the given interval of each edge. This model was studied in [58] and is used in the practical RNA assembler SSP [40], which seeks a set of transcripts explaining the read coverage within some user-defined error tolerance (i.e., interval around the observed weights) on all edges. The problem is formally stated as follows.…”

Section: Inexact Flowmentioning

confidence: 99%

Fast, Flexible, and Exact Minimum Flow Decompositions via ILP

Dias,

Williams,

Mumey

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Minimum flow decomposition (MFD) -the problem of finding a minimum set of paths that perfectly decomposes a flow -is a classical problem in Computer Science, and variants of it are powerful models in multiassembly problems in Bioinformatics (e.g. RNA assembly). However, because this problem and its variants are NP-hard, practical multiassembly tools either use heuristics or solve simpler, polynomial-time solvable versions of the problem, which may yield solutions that are not minimal or do not perfectly decompose the flow. Many RNA assemblers also use integer linear programming (ILP) formulations of such practical variants, having the major limitation they need to encode all the potentially exponentially many solution paths. Moreover, the only exact solver for MFD does not scale to large instances, and cannot be efficiently generalized to practical MFD variants.In this work, we provide the first practical ILP formulation for MFD (and thus the first fast and exact solver for MFD), based on encoding all of the exponentially many solution paths using only a quadratic number of variables. On both simulated and real flow graphs, our approach solves any instance in under 13 seconds. We also show that our ILP formulation can be easily and efficiently adapted for many practical variants, such as incorporating longer or paired-end reads, or minimizing flow errors.We hope that our results can remove the current tradeoff between the complexity of a multiassembly model and its tractability, and can lie at the core of future practical RNA assembly tools. Our implementations are freely available at github.com/algbio/MFD-ILP.

show abstract

“…[24,25]), or in the more recent and prominent application of reconstructing biological sequences (RNA transcripts, see e.g. [26,31,14,6,30,36], or viral quasi-species genomes, see e.g. [3,2]).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Safe Flow Decompositions in DAGs

Khan¹,

Tomescu²

2021

Preprint

View full text Add to dashboard Cite

Network flows are some of the most studied combinatorial optimization problems with innumerable applications. Any flow on a directed acyclic graph (DAG) G having n vertices and m edges can be decomposed into a set of O(m) paths, with applications ranging from network routing to the assembly of biological sequences. In some of these applications, the flow decomposition corresponds to some particular data that need to be reconstructed from the flow. Thus, such applications require finding paths (or subpaths) appearing in all possible flow decompositions, referred to as safe paths.Recently, Ma, Zheng, and Kingsford [WABI 2020] addressed a related problem in a probabilistic framework. In a follow-up work, they gave a quadratic-time algorithm based on a global criterion, for a generalized version (AND-Quant) of the corresponding verification problem, i.e., reporting if a given flow path is safe. Our contributions are as follows:1. A simple characterization for the safety of a given path based on a local criterion, which can be directly adapted to give an optimal linear time verification algorithm. A simple enumeration algorithm that reports all maximal safe paths on a flow network in O(mn)time. The algorithm reports all safe paths using a compact representation of the solution (called Pc), which is Ω(mn) in the worst case, but merely O(m + n) in the best case. 3. An improved enumeration algorithm where all safe paths ending at every vertex are represented as funnels using O(n 2 + |Pc|) space. These can be computed and used to report all maximal safe paths, using time linear in the total space required by funnels, with an extra logarithmic factor.Overall we present a simple characterization for the problem leading to an optimal verification algorithm and a simple enumeration algorithm. The enumeration algorithm is improved using the funnel structures for safe paths, which may be of independent interest.

show abstract

RNA Transcript Assembly Using Inexact Flows

Cited by 12 publications

References 25 publications

Flow Decomposition With Subpath Constraints

Flow Decomposition With Subpath Constraints

Fast, Flexible, and Exact Minimum Flow Decompositions via ILP

Optimizing Safe Flow Decompositions in DAGs

Contact Info

Product

Resources

About