Constructing Compressed Indexes for Biological Sequences
Publicly Offered Research
| Project Area | Creation and Organization of Innovative Algorithmic Foundations for Leading Social Innovations |
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| Project/Area Number |
23H04378
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| Research Category |
Grant-in-Aid for Transformative Research Areas (A)
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| Allocation Type | Single-year Grants |
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| Review Section |
Transformative Research Areas, Section (IV)
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| Research Institution | University of Yamanashi |
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Principal Investigator |
Koeppl Dominik 山梨大学, 大学院総合研究部, 特任准教授 (50897395)
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| Project Period (FY) |
2023-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2024: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2023: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | text indexing / data compression / pattern matching / index construction / string algorithm / resource constraints / matching statistics / compressed indexes / positional BWT / LZ78 factorization / Wheeler DFAs / compressed indices / construction algorithms / r-index / compression algorithms / lossless compression |
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| Outline of Research at the Start |
Major breakthroughs in sequencing techniques facilitate the collection of large amounts of biological data. For these to be of value, we need means to store and analyze them. Here, compressed indices are prospective candidates for answering biologically meaningful queries while keeping the data in a maintainably-small compressed format. Nonetheless, even the construction of those indices is not well studied. In this project, we want to shed light on efficient ways in how to construct such indices and how to use them for the aforementioned queries.
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| Outline of Annual Research Achievements |
During fiscal year 2023, we worked on variations of the Burrows-Wheeler transform, one built on a grammar, the positional one, and the Wheeler graph.
Firstly, by storing additionally to the FM-index the index of our DCC'22 paper, we made use of both to accelerate counting queries for all pattern lengths at the expense of more space compared to the FM-index. While the FM-index matches a pattern character-wise, we can switch to the DCC'22-version matching blocks of characters of the pattern defined by the grammar.
Second, we proposed space-efficient data structures that augment the positional Burrows-Wheeler transform for efficiently finding set-maximal exact matches, and compare these with the baseline approach, which uses the plain divergence array.
Thirdly, we worked on matching statistics on Wheeler DFAs, which allows us to match a pattern with multiple reference genome represented by a de-Brujin graph efficiently. Known results use a plain longest common prefix array, which takes space linear to the number of states. We proposed a space-efficient representation that requires a linear number in bits with logarithmic access time. We also give matching statistics computation as an application, which we now can do with a time-space trade-off.
As a side-result, we worked on the substring compression problem for derivates of the LZ78 factorization, which seem to be practically relevant. Here, we used the suffix tree as an index to quickly compute an LZ78-kind factorization of a queried substring range quickly.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We conducted the research for the fiscal year 2023 as planned,
and can continue with the research for the fiscal year 2024 as highlighted in the research plan.
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| Strategy for Future Research Activity |
We continue our investigation in processing and managing vast amounts of data arising in bioinformatics, thanks to the proliferation of low-cost sequencing technology. Having established some theoretical background for compression techniques during the previous fiscal year, and introduced practical applications of the positional Burrows-Wheeler transform within the realm of bioinformatics, we are now delving deeper into the findings we shared at DCC'24 and exploring variations of our problem settings. Our primary focus remains on constructing indexes utilizing the Burrows-Wheeler transform and data compression techniques within compressed spaces. Our main target is the efficient indexing of the data in bioinformatics, which aligns with the goals of our research project. In particular, we are striving towards publishing our WABI'23 paper in a journal, which introduced an FM-index capable of faster pattern matching by incorporating insights from our DCC'22 paper. In this endeavor, we are replacing grammar compression with prefix-free parsing (PFP). Presently, our implementation relies on a plain Burrows-Wheeler transform (BWT), resulting in a larger memory footprint compared to a standard FM-index, albeit with quicker query times. Switching to run-length compression and fine-tuning the parameters of PFP should lead to significant improvements in memory utilization. Additionally, we are expanding upon our findings from DCC'24 concerning the computation of LZ78 derivatives from suffix trees to compressed indexes.
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Report
(1 results)
Research Products
(10 results)
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[Journal Article] Data Structures for SMEM-Finding in the PBWT2023
Author(s)
Paola Bonizzoni and Christina Boucher and Davide Cozzi and Travis Gagie and Dominik Koeppl and Massimiliano Rossi
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Journal Title
Proceedings of SPIRE
Volume: 14240
Pages: 89-101
DOI
ISBN
9783031439797, 9783031439803
Related Report
Peer Reviewed / Int'l Joint Research
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