2 Search Results for "Shah, Nidhi"

Document
Abstract
DOI: 10.4230/LIPIcs.WABI.2023.3
BATCH-SCAMPP: Scaling Phylogenetic Placement Methods to Place Many Sequences (Abstract)

Authors: Eleanor Wedell, Chengze Shen, and Tandy Warnow

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

Abstract
Phylogenetic placement is the problem of placing one or more query sequences into a phylogenetic "backbone" tree, which may be a maximum likelihood tree on a multiple sequence alignment for a single gene, a taxonomy with leaves labeled by sequences for a single gene [Nidhi Shah et al., 2021], or a species tree [Jiang et al., 2023]. When the backbone tree is a tree estimated on a single gene, the most accurate techniques for phylogenetic placement are likelihood-based, and can be computationally intensive when the backbone trees are large [Chu and Warnow, 2023]. Phylogenetic placement into gene trees occurs when updating existing gene trees with newly observed sequences, but can also be applied in the "bulk" context, where many sequences are placed at the same time into the backbone tree. For example, phylogenetic placement can be used to taxonomically characterize shotgun sequencing reads generated for an environmental sample in metagenomic analysis [Nidhi Shah et al., 2021; Barbera et al., 2019]. The two most well known maximum likelihood phylogenetic placement methods are pplacer [Chu and Warnow, 2023] and EPA-ng [Barbera et al., 2019]. Of these two, EPA-ng is optimized for scaling the number of query sequences and is capable of placing millions of sequences into phylogenetic trees of up to a few thousand sequences [Barbera et al., 2019], and achieves sublinear runtime in the number of query sequences (see Figure 2 from [Balaban et al., 2022]). Previously we introduced the SCAMPP framework [Wedell et al., 2022] to enable both pplacer and EPA-ng to perform phylogenetic placement into ultra-large backbone trees, and we demonstrated its utility for placing into backbone trees with up to 200,000 sequences. By using maximum likelihood methods pplacer or EPA-ng within the SCAMPP framework, the resulting placements are more accurate than with APPLES-2 [Balaban et al., 2022], with the most notable accuracy improvement for fragmentary sequences, and are computationally similar for single query sequence placement [Wedell et al., 2022]. However, SCAMPP was designed to incrementally update a large tree, one query sequence at a time, and was not optimized for the other uses of phylogenetic placement, where batch placement of many sequencing reads is required. Here we introduce BATCH-SCAMPP, a technique that improves scalability in both dimensions: the number of query sequences being placed into the backbone tree and the size of the backbone tree. Furthermore, BATCH-SCAMPP is specifically designed to improve EPA-ng’s scalability to large backbone trees. Although BATCH-SCAMPP is based on SCAMPP, it uses a substantially modified design in order to be able to take advantage of EPA-ng’s ability to place many query sequences efficiently. The BATCH-SCAMPP method operates by allowing the input set of query sequences to suggest and then vote on placement subtrees, thus enabling many query sequences to select the same placement subtree. We pair BATCH-SCAMPP with EPA-ng to explore the capability of this approach for scaling to many query sequences. We show that this combination of techniques (which we call BSCAMPP+EPA-ng, or BSCAMPP(e)) not only provides high accuracy and scalability to large backbone trees, matching that of SCAMPP used with EPA-ng (i.e., SCAMPP(e)), but also achieves the goal of scaling sublinearly in the number of query sequences. Moreover, it is much more scalable than EPA-ng and faster than SCAMPP+EPA-ng: when placing 10,000 sequences into a backbone tree of 50,000 leaves, EPA-ng is unable to run due to memory issues, SCAMPP+EPA-ng requires 1421 minutes, and BSCAMPP(e) places all sequences in 7 minutes (all given the same computational resources. Figure 1 gives an example of this performance advantage on the nt78 [Chu and Warnow, 2023] simulated dataset.
Cite as

Eleanor Wedell, Chengze Shen, and Tandy Warnow. BATCH-SCAMPP: Scaling Phylogenetic Placement Methods to Place Many Sequences (Abstract). In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 3:1-3:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{wedell_et_al:LIPIcs.WABI.2023.3,
  author =	{Wedell, Eleanor and Shen, Chengze and Warnow, Tandy},
  title =	{{BATCH-SCAMPP: Scaling Phylogenetic Placement Methods to Place Many Sequences}},
  booktitle =	{23rd International Workshop on Algorithms in Bioinformatics (WABI 2023)},
  pages =	{3:1--3:2},
  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.2023.3},
  URN =		{urn:nbn:de:0030-drops-186296},
  doi =		{10.4230/LIPIcs.WABI.2023.3},
  annote =	{Keywords: Phylogenetic Placement, EPA-ng, Phylogenetics}
}
Document
DOI: 10.4230/LIPIcs.WABI.2017.23
Outlier Detection in BLAST Hits

Authors: Nidhi Shah, Stephen F. Altschul, and Mihai Pop

Published in: LIPIcs, Volume 88, 17th International Workshop on Algorithms in Bioinformatics (WABI 2017)

Abstract
An important task in a metagenomic analysis is the assignment of taxonomic labels to sequences in a sample. Most widely used methods for taxonomy assignment compare a sequence in the sample to a database of known sequences. Many approaches use the best BLAST hit(s) to assign the taxonomic label. However, it is known that the best BLAST hit may not always correspond to the best taxonomic match. An alternative approach involves phylogenetic methods which take into account alignments and a model of evolution in order to more accurately define the taxonomic origin of sequences. The similarity-search based methods typically run faster than phylogenetic methods and work well when the organisms in the sample are well represented in the database. On the other hand, phylogenetic methods have the capability to identify new organisms in a sample but are computationally quite expensive. We propose a two-step approach for metagenomic taxon identification; i.e., use a rapid method that accurately classifies sequences using a reference database (this is a filtering step) and then use a more complex phylogenetic method for the sequences that were unclassified in the previous step. In this work, we explore whether and when using top BLAST hit(s) yields a correct taxonomic label. We develop a method to detect outliers among BLAST hits in order to separate the phylogenetically most closely related matches from matches to sequences from more distantly related organisms. We used modified BILD (Bayesian Integral Log Odds) scores, a multiple-alignment scoring function, to define the outliers within a subset of top BLAST hits and assign taxonomic labels. We compared the accuracy of our method to the RDP classifier and show that our method yields fewer misclassifications while properly classifying organisms that are not present in the database. Finally, we evaluated the use of our method as a pre-processing step before more expensive phylogenetic analyses (in our case TIPP) in the context of real 16S rRNA datasets. Our experiments demonstrate the potential of our method to be a filtering step before using phylogenetic methods.
Cite as

Nidhi Shah, Stephen F. Altschul, and Mihai Pop. Outlier Detection in BLAST Hits. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 23:1-23:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{shah_et_al:LIPIcs.WABI.2017.23,
  author =	{Shah, Nidhi and Altschul, Stephen F. and Pop, Mihai},
  title =	{{Outlier Detection in BLAST Hits}},
  booktitle =	{17th International Workshop on Algorithms in Bioinformatics (WABI 2017)},
  pages =	{23:1--23:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-050-7},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{88},
  editor =	{Schwartz, Russell and Reinert, Knut},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2017.23},
  URN =		{urn:nbn:de:0030-drops-76512},
  doi =		{10.4230/LIPIcs.WABI.2017.23},
  annote =	{Keywords: Taxonomy classification, Metagenomics, Sequence alignment, Outlier detection}
}
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