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Documents authored by Fan, Jason

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DOI: 10.4230/LIPIcs.WABI.2023.18
Fulgor: A Fast and Compact {k-mer} Index for Large-Scale Matching and Color Queries

Authors: Jason Fan, Noor Pratap Singh, Jamshed Khan, Giulio Ermanno Pibiri, and Rob Patro

Published in: LIPIcs, Volume 273, 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)

Abstract
The problem of sequence identification or matching - determining the subset of reference sequences from a given collection that are likely to contain a short, queried nucleotide sequence - is relevant for many important tasks in Computational Biology, such as metagenomics and pan-genome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resource-efficient solution to this problem is of utmost importance. This poses the threefold challenge of representing the reference collection with a data structure that is efficient to query, has light memory usage, and scales well to large collections. To solve this problem, we describe how recent advancements in associative, order-preserving, k-mer dictionaries can be combined with a compressed inverted index to implement a fast and compact colored de Bruijn graph data structure. This index takes full advantage of the fact that unitigs in the colored de Bruijn graph are monochromatic (all k-mers in a unitig have the same set of references of origin, or "color"), leveraging the order-preserving property of its dictionary. In fact, k-mers are kept in unitig order by the dictionary, thereby allowing for the encoding of the map from k-mers to their inverted lists in as little as 1+o(1) bits per unitig. Hence, one inverted list per unitig is stored in the index with almost no space/time overhead. By combining this property with simple but effective compression methods for inverted lists, the index achieves very small space. We implement these methods in a tool called Fulgor. Compared to Themisto, the prior state of the art, Fulgor indexes a heterogeneous collection of 30,691 bacterial genomes in 3.8× less space, a collection of 150,000 Salmonella enterica genomes in approximately 2× less space, is at least twice as fast for color queries, and is 2-6 × faster to construct.
Cite as

Jason Fan, Noor Pratap Singh, Jamshed Khan, Giulio Ermanno Pibiri, and Rob Patro. Fulgor: A Fast and Compact {k-mer} Index for Large-Scale Matching and Color Queries. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 18:1-18:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{fan_et_al:LIPIcs.WABI.2023.18,
  author =	{Fan, Jason and Singh, Noor Pratap and Khan, Jamshed and Pibiri, Giulio Ermanno and Patro, Rob},
  title =	{{Fulgor: A Fast and Compact \{k-mer\} Index for Large-Scale Matching and Color Queries}},
  booktitle =	{23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)},
  pages =	{18:1--18:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-294-5},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{273},
  editor =	{Belazzougui, Djamal and Ouangraoua, A\"{i}da},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2023.18},
  URN =		{urn:nbn:de:0030-drops-186446},
  doi =		{10.4230/LIPIcs.WABI.2023.18},
  annote =	{Keywords: k-mers, Colored de Bruijn Graph, Compression, Read-mapping}
}
Document
DOI: 10.4230/LIPIcs.WABI.2021.4
Perplexity: Evaluating Transcript Abundance Estimation in the Absence of Ground Truth

Authors: Jason Fan, Skylar Chan, and Rob Patro

Published in: LIPIcs, Volume 201, 21st International Workshop on Algorithms in Bioinformatics (WABI 2021)

Abstract
There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best. Thus, we derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. To our knowledge, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.
Cite as

Jason Fan, Skylar Chan, and Rob Patro. Perplexity: Evaluating Transcript Abundance Estimation in the Absence of Ground Truth. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{fan_et_al:LIPIcs.WABI.2021.4,
  author =	{Fan, Jason and Chan, Skylar and Patro, Rob},
  title =	{{Perplexity: Evaluating Transcript Abundance Estimation in the Absence of Ground Truth}},
  booktitle =	{21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
  pages =	{4:1--4:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-200-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{201},
  editor =	{Carbone, Alessandra and El-Kebir, Mohammed},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2021.4},
  URN =		{urn:nbn:de:0030-drops-143578},
  doi =		{10.4230/LIPIcs.WABI.2021.4},
  annote =	{Keywords: RNA-seq, transcript abundance estimation, model selection}
}
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