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Documents authored by Wickramarachchi, Anuradha S.

Found 2 Possible Name Variants:

Wickramarachchi, Anuradha S.

Document
DOI: 10.4230/LIPIcs.WABI.2020.8
GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs

Authors: Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin

Published in: LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)

Abstract
Metagenomic sequencing allows us to study structure, diversity and ecology in microbial communities without the necessity of obtaining pure cultures. In many metagenomics studies, the reads obtained from metagenomics sequencing are first assembled into longer contigs and these contigs are then binned into clusters of contigs where contigs in a cluster are expected to come from the same species. As different species may share common sequences in their genomes, one assembled contig may belong to multiple species. However, existing tools for contig binning only support non-overlapped binning, i.e., each contig is assigned to at most one bin (species). In this paper, we introduce GraphBin2 which refines the binning results obtained from existing tools and, more importantly, is able to assign contigs to multiple bins. GraphBin2 uses the connectivity and coverage information from assembly graphs to adjust existing binning results on contigs and to infer contigs shared by multiple species. Experimental results on both simulated and real datasets demonstrate that GraphBin2 not only improves binning results of existing tools but also supports to assign contigs to multiple bins.
Cite as

Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin. GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{mallawaarachchi_et_al:LIPIcs.WABI.2020.8,
  author =	{Mallawaarachchi, Vijini G. and Wickramarachchi, Anuradha S. and Lin, Yu},
  title =	{{GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs}},
  booktitle =	{20th International Workshop on Algorithms in Bioinformatics (WABI 2020)},
  pages =	{8:1--8:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-161-0},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{172},
  editor =	{Kingsford, Carl and Pisanti, Nadia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.8},
  URN =		{urn:nbn:de:0030-drops-127974},
  doi =		{10.4230/LIPIcs.WABI.2020.8},
  annote =	{Keywords: Metagenomics binning, contigs, assembly graphs, overlapped binning}
}

Wickramarachchi, Anuradha

Document
DOI: 10.4230/LIPIcs.WABI.2021.11
LRBinner: Binning Long Reads in Metagenomics Datasets

Authors: Anuradha Wickramarachchi and Yu Lin

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

Abstract
Advancements in metagenomics sequencing allow the study of microbial communities directly from their environments. Metagenomics binning is a key step in the species characterisation of microbial communities. Next-generation sequencing reads are usually assembled into contigs for metagenomics binning mainly due to the limited information within short reads. Third-generation sequencing provides much longer reads that have lengths similar to the contigs assembled from short reads. However, existing contig-binning tools cannot be directly applied on long reads due to the absence of coverage information and the presence of high error rates. The few existing long-read binning tools either use only composition or use composition and coverage information separately. This may ignore bins that correspond to low-abundance species or erroneously split bins that correspond to species with non-uniform coverages. Here we present a reference-free binning approach, LRBinner, that combines composition and coverage information of complete long-read datasets. LRBinner also uses a distance-histogram-based clustering algorithm to extract clusters with varying sizes. The experimental results on both simulated and real datasets show that LRBinner achieves the best binning accuracy against the baselines. Moreover, we show that binning reads using LRBinner prior to assembly reduces computational resources for assembly while attaining satisfactory assembly qualities.
Cite as

Anuradha Wickramarachchi and Yu Lin. LRBinner: Binning Long Reads in Metagenomics Datasets. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 11:1-11:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{wickramarachchi_et_al:LIPIcs.WABI.2021.11,
  author =	{Wickramarachchi, Anuradha and Lin, Yu},
  title =	{{LRBinner: Binning Long Reads in Metagenomics Datasets}},
  booktitle =	{21st International Workshop on Algorithms in Bioinformatics (WABI 2021)},
  pages =	{11:1--11:18},
  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.2021.11},
  URN =		{urn:nbn:de:0030-drops-143644},
  doi =		{10.4230/LIPIcs.WABI.2021.11},
  annote =	{Keywords: Metagenomics binning, long reads, machine learning, clustering}
}
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