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\n \n\n \n \n Dhaivat Joshi; Suhas Diggavi; Mark J P Chaisson; and Sreeram Kannan.\n\n\n \n \n \n \n \n HQAlign: aligning nanopore reads for SV detection using current-level modeling.\n \n \n \n \n\n\n \n\n\n\n Bioinformatics, 39(10). September 2023.\n \n\n\n\n
\n\n\n\n \n \n \"HQAlign: arxiv\n  \n \n \n \"HQAlign: biorxiv\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{joshi2023hqalign,\n    author = {Joshi, Dhaivat and Diggavi, Suhas and Chaisson, Mark J P and Kannan, Sreeram},\n    title = "{HQAlign: aligning nanopore reads for SV detection using current-level modeling}",\n    journal = {Bioinformatics},\n    volume = {39},\n    number = {10},\n    year = {2023},\n    month = {September},\n    abstract = "{Detection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments.We show that HQAlign captures about 4\\\\%–6\\\\% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10\\\\%–50\\\\% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35\\\\% from minimap2 85.64\\\\% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65\\\\% from 83.48\\\\% for nanopore reads alignment to GRCh37 human genome.https://github.com/joshidhaivat/HQAlign.git.}",\n    issn = {1367-4811},\n    doi = {10.1093/bioinformatics/btad580},\n    url_arxiv={https://arxiv.org/abs/2301.03834},\n    url_biorxiv={https://doi.org/10.1101/2023.01.08.523172},\n    eprint = {https://academic.oup.com/bioinformatics/article-pdf/39/10/btad580/52147189/btad580.pdf},\n    tags={journal,BioInf,NDS},\n    type={2},\n}\n\n
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\n Detection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments.We show that HQAlign captures about 4\\%–6\\% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10\\%–50\\% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35\\% from minimap2 85.64\\% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65\\% from 83.48\\% for nanopore reads alignment to GRCh37 human genome.https://github.com/joshidhaivat/HQAlign.git.\n
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\n \n\n \n \n Dhaivat Joshi; Shunfu Mao; Sreeram Kannan; and Suhas Diggavi.\n\n\n \n \n \n \n \n QAlign: Aligning nanopore reads accurately using current-level modeling.\n \n \n \n \n\n\n \n\n\n\n Bioinformatics, 37(5): 625-633. 12 2021.\n \n\n\n\n
\n\n\n\n \n \n \"QAlign: biorxiv\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{10.1093/bioinformatics/btaa875,\n author = {Joshi, Dhaivat and Mao, Shunfu and Kannan, Sreeram and Diggavi, Suhas},\n title = "{QAlign: aligning nanopore reads accurately using current-level modeling}",\n journal = {Bioinformatics},\n volume = {37},\n number = {5},\n pages = {625-633},\n year = {2020},\n month = {12},\n abstract = "{Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner.We show that QAlign is able to improve alignment rates from around 80\\\\% up to 90\\\\% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2, 2.5 and 10.8\\\\% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6\\\\% to 75.4\\\\% and 82.6\\\\% to 90\\\\% in two real datasets.https://github.com/joshidhaivat/QAlign.git.Supplementary data are available at Bioinformatics online.}",\n issn = {1367-4803},\n doi = {10.1093/bioinformatics/btaa875},\n tags = {journal,BioInf,NDS},\n title = {{QAlign: Aligning nanopore reads accurately using current-level modeling}},\n type = {2},\n url_biorxiv = {https://www.biorxiv.org/content/10.1101/862813v2},\n year = {2021}\n}\n\n
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\n Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner.We show that QAlign is able to improve alignment rates from around 80\\% up to 90\\% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2, 2.5 and 10.8\\% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6\\% to 75.4\\% and 82.6\\% to 90\\% in two real datasets.https://github.com/joshidhaivat/QAlign.git.Supplementary data are available at Bioinformatics online.\n
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\n \n\n \n \n Sundara Rajan Srinivasavaradhan; Michelle Du; Suhas Diggavi; and Christina Fragouli.\n\n\n \n \n \n \n \n Algorithms for reconstruction over single and multiple deletion channels.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Information Theory, 67(6): 3389-3410. June 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Algorithms arxiv\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{srinivasavaradhan2020algorithms,\n abstract = {Recent advances in DNA sequencing technology and DNA storage systems have rekindled the interest in deletion channels. Multiple recent works have looked at variants of sequence reconstruction over a single and over multiple deletion channels, a notoriously difficult problem due to its highly combinatorial nature. Although works in theoretical computer science have provided algorithms which guarantee perfect reconstruction with multiple independent observations from the deletion channel, they are only applicable in the large blocklength regime and more restrictively, when the number of observations is also large. Indeed, with only a few observations, perfect reconstruction of the input sequence may not even be possible in most cases. In such situations, maximum likelihood (ML) and maximum aposteriori (MAP) estimates for the deletion channels are natural questions that arise and these have remained open to the best of our knowledge. In this work, we take steps to answer the two aforementioned questions. Specifically: 1. We show that solving for the ML estimate over the single deletion channel (which can be cast as a discrete optimization problem) is equivalent to solving its relaxation, a continuous optimization problem; 2. We exactly compute the symbolwise posterior distributions (under some assumptions on the priors) for both the single as well as multiple deletion channels. As part of our contributions, we also introduce tools to visualize and analyze error events, which we believe could be useful in other related problems concerning deletion channels.},\n author = {Srinivasavaradhan, Sundara Rajan and Du, Michelle and Diggavi, Suhas and Fragouli, Christina},\n journal = {IEEE Transactions on Information Theory},\n volume={67},\n number={6},\n pages={3389-3410},\n doi={10.1109/TIT.2020.3033513},\n tags = {journal,BioInf,IT,NDS},\n title = {Algorithms for reconstruction over single and multiple deletion channels},\n type = {2},\n url_arxiv = {https://arxiv.org/abs/2005.14388},\n ISSN={1557-9654},\n month={June},\n year = {2021}\n}\n\n
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\n Recent advances in DNA sequencing technology and DNA storage systems have rekindled the interest in deletion channels. Multiple recent works have looked at variants of sequence reconstruction over a single and over multiple deletion channels, a notoriously difficult problem due to its highly combinatorial nature. Although works in theoretical computer science have provided algorithms which guarantee perfect reconstruction with multiple independent observations from the deletion channel, they are only applicable in the large blocklength regime and more restrictively, when the number of observations is also large. Indeed, with only a few observations, perfect reconstruction of the input sequence may not even be possible in most cases. In such situations, maximum likelihood (ML) and maximum aposteriori (MAP) estimates for the deletion channels are natural questions that arise and these have remained open to the best of our knowledge. In this work, we take steps to answer the two aforementioned questions. Specifically: 1. We show that solving for the ML estimate over the single deletion channel (which can be cast as a discrete optimization problem) is equivalent to solving its relaxation, a continuous optimization problem; 2. We exactly compute the symbolwise posterior distributions (under some assumptions on the priors) for both the single as well as multiple deletion channels. As part of our contributions, we also introduce tools to visualize and analyze error events, which we believe could be useful in other related problems concerning deletion channels.\n
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\n \n\n \n \n Sundara Rajan Srinivasavaradhan; Suhas Diggavi; and Christina Fragouli.\n\n\n \n \n \n \n Equivalence of ml decoding to a continuous optimization problem.\n \n \n \n\n\n \n\n\n\n In 2020 IEEE International Symposium on Information Theory (ISIT), pages 343–348, 2020. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{srinivasavaradhan2020equivalence,\n abstract = {Maximum likelihood (ML) and symbolwise maximum aposteriori (MAP) estimation for discrete input sequences play a central role in a number of applications that arise in communications, information and coding theory. Many instances of these problems are proven to be intractable, for example through reduction to NP-complete integer optimization problems. In this work, we prove that the ML estimation of a discrete input sequence (with no assumptions on the encoder/channel used) is equivalent to the solution of a continuous non-convex optimization problem, and that this formulation is closely related to the computation of symbolwise MAP estimates. This equivalence is particularly useful in situations where a function we term the expected likelihood is efficiently computable. In such situations, we give a ML heuristic and show numerics for sequence estimation over the deletion channel.},\n author = {Srinivasavaradhan, Sundara Rajan and Diggavi, Suhas and Fragouli, Christina},\n booktitle = {2020 IEEE International Symposium on Information Theory (ISIT)},\n organization = {IEEE},\n pages = {343--348},\n tags = {conf,BioInf,IT,NDS},\n title = {Equivalence of ml decoding to a continuous optimization problem},\n type = {4},\n doi = {10.1109/ISIT44484.2020.9174130},\n year = {2020}\n}\n\n
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\n Maximum likelihood (ML) and symbolwise maximum aposteriori (MAP) estimation for discrete input sequences play a central role in a number of applications that arise in communications, information and coding theory. Many instances of these problems are proven to be intractable, for example through reduction to NP-complete integer optimization problems. In this work, we prove that the ML estimation of a discrete input sequence (with no assumptions on the encoder/channel used) is equivalent to the solution of a continuous non-convex optimization problem, and that this formulation is closely related to the computation of symbolwise MAP estimates. This equivalence is particularly useful in situations where a function we term the expected likelihood is efficiently computable. In such situations, we give a ML heuristic and show numerics for sequence estimation over the deletion channel.\n
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\n \n\n \n \n Sundara R Srinivasavaradhan; Michelle Du; Suhas Diggavi; and Christina Fragouli.\n\n\n \n \n \n \n Symbolwise map for multiple deletion channels.\n \n \n \n\n\n \n\n\n\n In 2019 IEEE International Symposium on Information Theory (ISIT), pages 181–185, 2019. IEEE\n \n\n\n\n
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@inproceedings{srinivasavaradhan2019symbolwise,\n abstract = {We consider the problem of reconstructing a sequence from fixed number of deleted versions of itself (also called traces). The problem is motivated from recent developments in de novo DNA sequencing technologies. The main contribution of this work is to provide a polynomial time algorithm for symbolwise MAP decoding with multiple traces. The algorithm leverages a dynamic program on the edit graph. We also develop a heuristic with reduced time complexity using similar ideas and provide preliminary numerical evaluations.},\n author = {Srinivasavaradhan, Sundara R and Du, Michelle and Diggavi, Suhas and Fragouli, Christina},\n booktitle = {2019 IEEE International Symposium on Information Theory (ISIT)},\n organization = {IEEE},\n pages = {181--185},\n tags = {conf,BioInf,IT,NDS},\n title = {Symbolwise map for multiple deletion channels},\n type = {4},\n doi = {10.1109/ISIT.2019.8849567},\n year = {2019}\n}\n\n
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\n We consider the problem of reconstructing a sequence from fixed number of deleted versions of itself (also called traces). The problem is motivated from recent developments in de novo DNA sequencing technologies. The main contribution of this work is to provide a polynomial time algorithm for symbolwise MAP decoding with multiple traces. The algorithm leverages a dynamic program on the edit graph. We also develop a heuristic with reduced time complexity using similar ideas and provide preliminary numerical evaluations.\n
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@article{8301564,\n abstract = {Nanopore sequencing is an emerging new technology for sequencing Deoxyribonucleic acid (DNA), which can read long fragments of DNA (~50000 bases), in contrast to most current short-read sequencing technologies which can only read hundreds of bases. While nanopore sequencers can acquire long reads, the high error rates (20\\%-30\\%) pose a technical challenge. In a nanopore sequencer, a DNA is migrated through a nanopore, and current variations are measured. The DNA sequence is inferred from this observed current pattern using an algorithm called a base-caller. In this paper, we propose a mathematical model for the “channel” from the input DNA sequence to the observed current, and calculate bounds on the information extraction capacity of the nanopore sequencer. This model incorporates impairments, such as (non-linear) intersymbol interference, deletions, and random response. These information bounds have two-fold application: 1) The decoding rate with a uniform input distribution can be used to calculate the average size of the plausible list of DNA sequences given an observed current trace. This bound can be used to benchmark existing base-calling algorithms, as well as serving a performance objective to design better nanopores. 2) When the nanopore sequencer is used as a reader in a DNA storage system, the storage capacity is quantified by our bounds.},\n author = {W. {Mao} and S. N. {Diggavi} and S. {Kannan}},\n doi = {10.1109/TIT.2018.2809001},\n issn = {1557-9654},\n journal = {IEEE Transactions on Information Theory},\n keywords = {biology computing;DNA;genomics;intersymbol interference;molecular biophysics;nanobiotechnology;nanopore sequencing;sequencing Deoxyribonucleic acid;sequencing technologies;nanopore sequencer;input DNA sequences;base-calling algorithms;DNA storage system;storage capacity;DNA;Sequential analysis;Decoding;Nanobioscience;Current measurement;Reliability;Mathematical model;Deoxyribonucleic acid (DNA) sequencing;bioinformatics;base calling;channel with synchronization errors;deletion channel;finite state channels},\n month = {April},\n number = {4},\n pages = {3216-3236},\n tags = {journal,IT,BioInf,NDS},\n title = {Models and Information-Theoretic Bounds for Nanopore Sequencing},\n type = {2},\n url_arxiv = {https://arxiv.org/abs/1705.11154},\n volume = {64},\n year = {2018}\n}\n\n
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\n Nanopore sequencing is an emerging new technology for sequencing Deoxyribonucleic acid (DNA), which can read long fragments of DNA ( 50000 bases), in contrast to most current short-read sequencing technologies which can only read hundreds of bases. While nanopore sequencers can acquire long reads, the high error rates (20%-30%) pose a technical challenge. In a nanopore sequencer, a DNA is migrated through a nanopore, and current variations are measured. The DNA sequence is inferred from this observed current pattern using an algorithm called a base-caller. In this paper, we propose a mathematical model for the “channel” from the input DNA sequence to the observed current, and calculate bounds on the information extraction capacity of the nanopore sequencer. This model incorporates impairments, such as (non-linear) intersymbol interference, deletions, and random response. These information bounds have two-fold application: 1) The decoding rate with a uniform input distribution can be used to calculate the average size of the plausible list of DNA sequences given an observed current trace. This bound can be used to benchmark existing base-calling algorithms, as well as serving a performance objective to design better nanopores. 2) When the nanopore sequencer is used as a reader in a DNA storage system, the storage capacity is quantified by our bounds.\n
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\n \n\n \n \n Sundara Rajan Srinivasavaradhan; Michelle Du; Suhas Diggavi; and Christina Fragouli.\n\n\n \n \n \n \n On maximum likelihood reconstruction over multiple deletion channels.\n \n \n \n\n\n \n\n\n\n In 2018 IEEE International Symposium on Information Theory (ISIT), pages 436–440, 2018. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{srinivasavaradhan2018maximum,\n abstract = {The problem of reconstructing a sequence when observed through multiple looks over deletion channels occurs in “de novo” DNA sequencing. The DNA could be sequenced multiple times, yielding several “looks” of it, but each time the sequencer could be noisy with (independent) deletion impairments. The main goal of this paper is to develop reconstruction algorithms for a sequence observed through the lens of a fixed number of deletion channels. We use the probabilistic model of the deletion channels to develop both symbol-wise and sequence maximum likelihood decoding criteria, and algorithms motivated by them. Numerical evaluations demonstrate improvement in terms of edit distance error, over earlier algorithms.},\n author = {Srinivasavaradhan, Sundara Rajan and Du, Michelle and Diggavi, Suhas and Fragouli, Christina},\n booktitle = {2018 IEEE International Symposium on Information Theory (ISIT)},\n organization = {IEEE},\n pages = {436--440},\n tags = {conf,BioInf,IT,NDS},\n title = {On maximum likelihood reconstruction over multiple deletion channels},\n type = {4},\n doi = {10.1109/ISIT.2018.8437519},\n year = {2018}\n}\n\n
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\n The problem of reconstructing a sequence when observed through multiple looks over deletion channels occurs in “de novo” DNA sequencing. The DNA could be sequenced multiple times, yielding several “looks” of it, but each time the sequencer could be noisy with (independent) deletion impairments. The main goal of this paper is to develop reconstruction algorithms for a sequence observed through the lens of a fixed number of deletion channels. We use the probabilistic model of the deletion channels to develop both symbol-wise and sequence maximum likelihood decoding criteria, and algorithms motivated by them. Numerical evaluations demonstrate improvement in terms of edit distance error, over earlier algorithms.\n
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\n \n\n \n \n Wei Mao; Suhas Diggavi; and Sreeram Kannan.\n\n\n \n \n \n \n Models and information-theoretic bounds for nanopore sequencing.\n \n \n \n\n\n \n\n\n\n 2017.\n \n\n\n\n
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@conference{Mao-Diggavi-Kannan-nanopore-isit,\n author = {Mao, Wei and Diggavi, Suhas and Kannan, Sreeram},\n booktitle = {2017 IEEE International Symposium on Information Theory Proceedings (ISIT)},\n file = {:papers:isit_nanopore.pdf},\n tags = {conf,IT,Nanopore},\n title = {Models and information-theoretic bounds for nanopore sequencing},\n type = {4},\n year = {2017}\n}\n\n
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