Search Results

Documents authored by Shao, Mingfu

Document
Extended Abstract
DOI: 10.4230/LIPIcs.WABI.2026.31
Minimum Flow Decomposition Guided by Saturating Subflows (Extended Abstract)

Authors: Ke Chen, Abhishek Talesara, Sanchal Thakkar, and Mingfu Shao

Published in: LIPIcs, Volume 390, 26th International Conference on Algorithms for Bioinformatics (WABI 2026)

Abstract
- Introduction. The minimum flow decomposition (MFD) problem asks to decompose a directed acyclic flow network (G,f) with a unique source s and a unique sink t into the fewest weighted s-t paths whose combined contributions exactly reproduce f. MFD underlies a broad class of multi-assembly tasks in bioinformatics: reference-based RNA assembly from splice graphs [Trapnell et al., 2010; Guttman et al., 2010; Tomescu et al., 2013; Song et al., 2016; Liu et al., 2016; Pertea et al., 2015; Kovaka et al., 2019; Shao and Kingsford, 2017; Zhang et al., 2022; Tung et al., 2019], metagenomic assembly [Shaw et al., 2024], and viral quasi-species inference [Baaijens et al., 2020]. MFD is strongly NP-hard [Vatinlen et al., 2008] and hard to approximate within some fixed constant factor [Hartman et al., 2012]. Exact solvers include an FPT algorithm whose runtime grows exponentially in the solution size [Kloster et al., 2018] and a family of integer linear programming (ILP) formulations [Dias et al., 2022; Grigorjew et al., 2024] capable of handling extensions such as inexact flows [Williams et al., 2019; Dias and Tomescu, 2024], safety and subpath constraints [Williams et al., 2022; Gibney et al., 2022; Khan et al., 2022; Dias et al., 2023], and graphs with cycles [Dias et al., 2025]. However, ILP remains unscalable on large practical instances. The widely used greedy-width heuristic [Vatinlen et al., 2008] is very efficient but can be exponentially worse than optimal in the worst case [Cáceres et al., 2024]. The state-of-the-art heuristic, catfish [Shao and Kingsford, 2017], substantially improves this efficiency-performance tradeoff by identifying linear equations among edge flow values - structural constraints implied by any optimal decomposition - and resolving them via safe graph transformations. On simpler instances catfish is highly effective, but three interrelated limitations degrade its performance on complex graphs: (1) it cannot distinguish good equations (arising from a true optimal decomposition) from superficial ones that distort the graph when resolved; (2) many good equations cannot be fully resolved due to the absence of suitable closed subgraphs, so catfish discards their information entirely; and (3) when no equation is resolvable catfish falls back to greedy-width, which performs poorly on entangled graphs. - Method. We introduce catfish-LP, which augments catfish with a lightweight linear programming (LP) formulation based on saturating subflows. For each edge e ∈ E we define continuous variables {x_e(a) : a ∈ E} modeling a valid s-t subflow that saturates e; intuitively, x_e(a) represents the amount of flow on e that must passes through edge a. Five base constraints enforce saturation, symmetry, flow validity, and flow conservation. Two additional equation constraints require that the aggregate subflow through the left-hand-side edges of an equation equals that through the right-hand-side edges. The full LP is polynomial-time solvable, adding only modest overhead over catfish. The LP plays three complementary roles within a single unified framework: (1) equation filtering: if adding a candidate equation renders the LP infeasible, that equation cannot arise from any minimum decomposition and is discarded, preventing structurally invalid graph transformations; (2) safe edge merging: a feasible LP solution reveals pairs of edges that must carry identical subflow and can therefore be safely contracted; (3) informed greedy extraction: when no further simplification is possible, rather than invoking greedy-width blindly, catfish-LP extracts from the LP solution the heaviest simple path consistent with all surviving equations, deferring the error-prone greedy step as long as possible. - Experimental Results. We compare catfish-LP against greedy-width, catfish, and the optimized ILP solver [Grigorjew et al., 2024] on two benchmarks, using Gurobi [{Gurobi Optimization, 2024] as the underlying LP/ILP engine. Table 1 reports decomposition quality on four datasets of biologically derived splice graphs, with abundances estimated by Salmon [Patro et al., 2017] or simulated with the Flux-Simulator [Griebel et al., 2012]. Catfish-LP achieves the smallest excess among heuristics on the Salmon dataset and negative excess on the remaining three, matching ILP quality while being orders of magnitude faster. Figure 1 summarizes results on 1,440 simulated graphs spanning 72 complexity configurations. Catfish-LP consistently produces the smallest decompositions and recovers the most ground-truth paths among all heuristics, achieving near-ILP quality in a fraction of its runtime - including a threefold improvement over catfish on the hardest 498 instances where ILP times out on every instance. - Conclusion. Catfish-LP demonstrates that incorporating a polynomial-time LP oracle into a combinatorial heuristic yields substantial gains in decomposition quality with negligible scalability cost, addressing each of catfish’s core limitations in a unified manner. Future directions include strengthened LP formulations, domain-specific constraints for transcriptome assembly, and probabilistic interpretations of LP-guided decompositions.
Cite as

Ke Chen, Abhishek Talesara, Sanchal Thakkar, and Mingfu Shao. Minimum Flow Decomposition Guided by Saturating Subflows (Extended Abstract). In 26th International Conference on Algorithms for Bioinformatics (WABI 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 390, pp. 31:1-31:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{chen_et_al:LIPIcs.WABI.2026.31,
  author =	{Chen, Ke and Talesara, Abhishek and Thakkar, Sanchal and Shao, Mingfu},
  title =	{{Minimum Flow Decomposition Guided by Saturating Subflows}},
  booktitle =	{26th International Conference on Algorithms for Bioinformatics (WABI 2026)},
  pages =	{31:1--31:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-446-8},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{390},
  editor =	{El-Mabrouk, Nadia and Vandin, Fabio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2026.31},
  URN =		{urn:nbn:de:0030-drops-275350},
  doi =		{10.4230/LIPIcs.WABI.2026.31},
  annote =	{Keywords: flow decomposition, RNA assembly, linear programming, optimality gap}
}
Document
DOI: 10.4230/LIPIcs.WABI.2025.7
Sequence Similarity Estimation by Random Subsequence Sketching

Authors: Ke Chen, Vinamratha Pattar, and Mingfu Shao

Published in: LIPIcs, Volume 344, 25th International Conference on Algorithms for Bioinformatics (WABI 2025)

Abstract
Sequence similarity estimation is essential for many bioinformatics tasks, including functional annotation, phylogenetic analysis, and overlap graph construction. Alignment-free methods aim to solve large-scale sequence similarity estimation by mapping sequences to more easily comparable features that can approximate edit distances efficiently. Substrings or k-mers, as the dominant choice of features, face an unavoidable compromise between sensitivity and specificity when selecting the proper k-value. Recently, subsequence-based features have shown improved performance, but they are computationally demanding, and determining the ideal subsequence length remains an intricate art. In this work, we introduce SubseqSketch, a novel alignment-free scheme that maps a sequence to an integer vector, where the entries correspond to dynamic, rather than fixed, lengths of random subsequences. The cosine similarity between these vectors exhibits a strong correlation with the edit similarity between the original sequences. Through experiments on benchmark datasets, we demonstrate that SubseqSketch is both efficient and effective across various alignment-free tasks, including nearest neighbor search and phylogenetic clustering. A C++ implementation of SubseqSketch is openly available at https://github.com/Shao-Group/SubseqSketch.
Cite as

Ke Chen, Vinamratha Pattar, and Mingfu Shao. Sequence Similarity Estimation by Random Subsequence Sketching. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 7:1-7:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{chen_et_al:LIPIcs.WABI.2025.7,
  author =	{Chen, Ke and Pattar, Vinamratha and Shao, Mingfu},
  title =	{{Sequence Similarity Estimation by Random Subsequence Sketching}},
  booktitle =	{25th International Conference on Algorithms for Bioinformatics (WABI 2025)},
  pages =	{7:1--7:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-386-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{344},
  editor =	{Brejov\'{a}, Bro\v{n}a and Patro, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2025.7},
  URN =		{urn:nbn:de:0030-drops-239332},
  doi =		{10.4230/LIPIcs.WABI.2025.7},
  annote =	{Keywords: Alignment-free sequence comparison, Phylogenetic clustering, Nearest neighbor search, Edit distance embedding}
}
Document
DOI: 10.4230/LIPIcs.WABI.2024.22
Anchorage Accurately Assembles Anchor-Flanked Synthetic Long Reads

Authors: Xiaofei Carl Zang, Xiang Li, Kyle Metcalfe, Tuval Ben-Yehezkel, Ryan Kelley, and Mingfu Shao

Published in: LIPIcs, Volume 312, 24th International Workshop on Algorithms in Bioinformatics (WABI 2024)

Abstract
Modern sequencing technologies allow for the addition of short-sequence tags, known as anchors, to both ends of a captured molecule. Anchors are useful in assembling the full-length sequence of a captured molecule as they can be used to accurately determine the endpoints. One representative of such anchor-enabled technology is LoopSeq Solo, a synthetic long read (SLR) sequencing protocol. LoopSeq Solo also achieves ultra-high sequencing depth and high purity of short reads covering the entire captured molecule. Despite the availability of many assembly methods, constructing full-length sequence from these anchor-enabled, ultra-high coverage sequencing data remains challenging due to the complexity of the underlying assembly graphs and the lack of specific algorithms leveraging anchors. We present Anchorage, a novel assembler that performs anchor-guided assembly for ultra-high-depth sequencing data. Anchorage starts with a kmer-based approach for precise estimation of molecule lengths. It then formulates the assembly problem as finding an optimal path that connects the two nodes determined by anchors in the underlying compact de Bruijn graph. The optimality is defined as maximizing the weight of the smallest node while matching the estimated sequence length. Anchorage uses a modified dynamic programming algorithm to efficiently find the optimal path. Through both simulations and real data, we show that Anchorage outperforms existing assembly methods, particularly in the presence of sequencing artifacts. Anchorage fills the gap in assembling anchor-enabled data. We anticipate its broad use as anchor-enabled sequencing technologies become prevalent. Anchorage is freely available at https://github.com/Shao-Group/anchorage; the scripts and documents that can reproduce all experiments in this manuscript are available at https://github.com/Shao-Group/anchorage-test.
Cite as

Xiaofei Carl Zang, Xiang Li, Kyle Metcalfe, Tuval Ben-Yehezkel, Ryan Kelley, and Mingfu Shao. Anchorage Accurately Assembles Anchor-Flanked Synthetic Long Reads. In 24th International Workshop on Algorithms in Bioinformatics (WABI 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 312, pp. 22:1-22:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{zang_et_al:LIPIcs.WABI.2024.22,
  author =	{Zang, Xiaofei Carl and Li, Xiang and Metcalfe, Kyle and Ben-Yehezkel, Tuval and Kelley, Ryan and Shao, Mingfu},
  title =	{{Anchorage Accurately Assembles Anchor-Flanked Synthetic Long Reads}},
  booktitle =	{24th International Workshop on Algorithms in Bioinformatics (WABI 2024)},
  pages =	{22:1--22:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-340-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{312},
  editor =	{Pissis, Solon P. and Sung, Wing-Kin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2024.22},
  URN =		{urn:nbn:de:0030-drops-206660},
  doi =		{10.4230/LIPIcs.WABI.2024.22},
  annote =	{Keywords: Genome assembly, de Bruijn graph, synthetic long reads, anchor-guided assembly, LoopSeq}
}
Document
DOI: 10.4230/LIPIcs.WABI.2022.22
Locality-Sensitive Bucketing Functions for the Edit Distance

Authors: Ke Chen and Mingfu Shao

Published in: LIPIcs, Volume 242, 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)

Abstract
Many bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existing k-mer-based bucketing methods have been efficient in processing sequencing data with low error rate, but encounter much reduced sensitivity on data with high error rate. Locality-sensitive hashing (LSH) schemes are able to mitigate this issue through tolerating the edits in similar sequences, but state-of-the-art methods still have large gaps. Here we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be (d₁, d₂)-sensitive if any two sequences within an edit distance of d₁ are mapped into at least one shared bucket, and any two sequences with distance at least d₂ are mapped into disjoint subsets of buckets. We construct locality-sensitive bucketing (LSB) functions with a variety of values of (d₁,d₂) and analyze their efficiency with respect to the total number of buckets needed as well as the number of buckets that a specific sequence is mapped to. We also prove lower bounds of these two parameters in different settings and show that some of our constructed LSB functions are optimal. These results provide theoretical foundations for their practical use in analyzing sequences with high error rate while also providing insights for the hardness of designing ungapped LSH functions.
Cite as

Ke Chen and Mingfu Shao. Locality-Sensitive Bucketing Functions for the Edit Distance. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 22:1-22:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{chen_et_al:LIPIcs.WABI.2022.22,
  author =	{Chen, Ke and Shao, Mingfu},
  title =	{{Locality-Sensitive Bucketing Functions for the Edit Distance}},
  booktitle =	{22nd International Workshop on Algorithms in Bioinformatics (WABI 2022)},
  pages =	{22:1--22:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-243-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{242},
  editor =	{Boucher, Christina and Rahmann, Sven},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2022.22},
  URN =		{urn:nbn:de:0030-drops-170563},
  doi =		{10.4230/LIPIcs.WABI.2022.22},
  annote =	{Keywords: Locality-sensitive hashing, locality-sensitive bucketing, long reads, embedding}
}
Document
DOI: 10.4230/LIPIcs.WABI.2019.15
Context-Aware Seeds for Read Mapping

Authors: Hongyi Xin, Mingfu Shao, and Carl Kingsford

Published in: LIPIcs, Volume 143, 19th International Workshop on Algorithms in Bioinformatics (WABI 2019)

Abstract
Motivation: Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows (such as in long reads with high error rate), this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. Results: We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mapping but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS reduces seed frequencies by up to 20.3% when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver. Availability: https://github.com/Kingsford-Group/CAS_code
Cite as

Hongyi Xin, Mingfu Shao, and Carl Kingsford. Context-Aware Seeds for Read Mapping. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

Copy BibTex To Clipboard

@InProceedings{xin_et_al:LIPIcs.WABI.2019.15,
  author =	{Xin, Hongyi and Shao, Mingfu and Kingsford, Carl},
  title =	{{Context-Aware Seeds for Read Mapping}},
  booktitle =	{19th International Workshop on Algorithms in Bioinformatics (WABI 2019)},
  pages =	{15:1--15:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-123-8},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{143},
  editor =	{Huber, Katharina T. and Gusfield, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2019.15},
  URN =		{urn:nbn:de:0030-drops-110452},
  doi =		{10.4230/LIPIcs.WABI.2019.15},
  annote =	{Keywords: Read Mapping, Seed and Extend, Edit Distance, Suffix Trie}
}

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact