scholarly journals Accelerating Sequence Alignment to Graphs

2019 ◽  
Author(s):  
Chirag Jain ◽  
Alexander Dilthey ◽  
Sanchit Misra ◽  
Haowen Zhang ◽  
Srinivas Aluru
Keyword(s):  
Dna Sequences ◽  
Query Sequence ◽  
Dynamic Programming Algorithm ◽  
Reference Sequence ◽  
Programming Algorithm ◽  
Peak Performance ◽  
Task Parallelism ◽  
Sequencing Data ◽  
Link Type ◽  
String Graph

AbstractAligning DNA sequences to an annotated reference is a key step for genotyping in biology. Recent scientific studies have demonstrated improved inference by aligning reads to a variation graph, i.e., a reference sequence augmented with known genetic variations. Given a variation graph in the form of a directed acyclic string graph, the sequence to graph alignment problem seeks to find the best matching path in the graph for an input query sequence. Solving this problem exactly using a sequential dynamic programming algorithm takes quadratic time in terms of the graph size and query length, making it difficult to scale to high throughput DNA sequencing data. In this work, we propose the first parallel algorithm for computing sequence to graph alignments that leverages multiple cores and single-instruction multiple-data (SIMD) operations. We take advantage of the available inter-task parallelism, and provide a novel blocked approach to compute the score matrix while ensuring high memory locality. Using a 48-core Intel Xeon Skylake processor, the proposed algorithm achieves peak performance of 317 billion cell updates per second (GCUPS), and demonstrates near linear weak and strong scaling on up to 48 cores. It delivers significant performance gains compared to existing algorithms, and results in run-time reduction from multiple days to three hours for the problem of optimally aligning high coverage long (PacBio/ONT) or short (Illumina) DNA reads to an MHC human variation graph containing 10 million vertices.AvailabilityThe implementation of our algorithm is available at https://github.com/ParBLiSS/PaSGAL. Data sets used for evaluation are accessible using https://alurulab.cc.gatech.edu/PaSGAL.

scholarly journals GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis

2019 ◽  
Author(s):  
Hasindu Gamaarachchi ◽  
Chun Wai Lam ◽  
Gihan Jayatilaka ◽  
Hiruna Samarakoon ◽  
Jared T. Simpson ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Embedded System ◽  
High Performance ◽  
Point Of Care ◽  
Dynamic Programming Algorithm ◽  
Reference Sequence ◽  
Programming Algorithm ◽  
Sequencing Data ◽  
On Chip ◽  
Gpu Architectures

AbstractNanopore sequencing has the potential to revolutionise genomics by realising portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these applications requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. For instance, comparing raw nanopore signals to a biological reference sequence is a computationally complex task despite leveraging a dynamic programming algorithm for Adaptive Banded Event Alignment (ABEA)—a commonly used approach to polish sequencing data and identify non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures. By optimising memory, compute and load balancing between CPU and GPU, we demonstrate how f5c can perform ~3-5× faster than the original implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs. Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c.

scholarly journals Effective Machine-Learning Assembly For Next-Generation Sequencing With Very Low Coverage

2018 ◽  
Author(s):  
Louis Ranjard ◽  
Thomas K. F. Wong ◽  
Allen G. Rodrigo
Keyword(s):  
Dynamic Programming Algorithm ◽  
Reference Sequence ◽  
Programming Algorithm ◽  
Original Sequence ◽  
Insertions And Deletions ◽  
Limiting Error ◽  
Grey Kangaroo ◽  
Western Grey Kangaroo ◽  
Sequence Reconstruction ◽  
Low Coverage

ABSTRACTIn short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence reconstruction from the reads can be difficult because of the occurrence of uncovered gaps. Reference guided assembly can then improve these assemblies. However, when the available reference is phylogenetically distant from the sequencing reads, the mapping rate of the reads can be extremely low. Some recent improvements in read mapping approaches aim at modifying the reference according to the reads dynamically. Such approaches can significantly improve the alignment rate of the reads onto distant references but the processing of insertions and deletions remains challenging. Here, we introduce a dynamic programming algorithm to update the reference sequence according to previously aligned reads. Substitutions, insertions and deletions are performed in the reference sequence dynamically. We evaluate this approach to assemble a western-grey kangaroo mitochondrial amplicon. Our results show that more reads can be aligned and that this method produces assemblies of length comparable to the truth while limiting error rate when classic approaches fail to recover the correct length. Our method allows us to assemble the first full mitochondrial genome for the western-grey kangaroo. Finally, we discuss how the core algorithm of this method could be improved and combined with other approaches to analyse larger genomic sequences.

scholarly journals sangeranalyseR: simple and interactive analysis of Sanger sequencing data in R

2020 ◽  
Author(s):  
Kuan-Hao Chao ◽  
Kirston Barton ◽  
Sarah Palmer ◽  
Robert Lanfear
Keyword(s):  
Sanger Sequencing ◽  
Reference Sequence ◽  
Supplementary Information ◽  
File Format ◽  
Bioconductor Package ◽  
Sequencing Data ◽  
Interactive Analysis ◽  
Link Type ◽  
Online Documentation ◽  
Wide Range

AbstractSummarysangeranalyseR is an interactive R/Bioconductor package and two associated Shiny applications designed for analysing Sanger sequencing from data from the ABIF file format in R. It allows users to go from loading reads to saving aligned contigs in a few lines of R code. sangeranalyseR provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, viewing chromatograms, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. sangeranalyseR comes with extensive online documentation, and outputs detailed interactive HTML reports.Availability and implementationsangeranalyseR is implemented in R and released under an MIT license. It is available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR) and on Github (https://github.com/roblanf/sangeranalyseR)[email protected] informationDocumentation at https://sangeranalyser.readthedocs.io/.

scholarly journals Meta-Align: A Novel HMM-based Algorithm for Pairwise Alignment of Error-Prone Sequencing Reads

2020 ◽  
Author(s):  
Kentaro Tomii ◽  
Shravan Kumar ◽  
Degui Zhi ◽  
Steven E. Brenner
Keyword(s):  
Dna Sequences ◽  
Viterbi Algorithm ◽  
Query Sequence ◽  
Pairwise Alignment ◽  
Next Generation Sequencing Data ◽  
Alignment Algorithm ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Insertion And Deletion ◽  
Protein Space

AbstractBackgroundInsertion and deletion sequencing errors are relatively common in next-generation sequencing data and produce long stretches of mistranslated sequence. These frameshifting errors can cause very serious damages to downstream data analysis of reads. However, it is possible to obtain more precise alignment of DNA sequences by taking into account both coding frame and sequencing errors estimated by quality scores.ResultsHere we designed and proposed a novel hidden Markov model (HMM)-based pairwise alignment algorithm, Meta-Align, that aligns DNA sequences in the protein space, incorporating quality scores from the DNA sequences and allowing frameshifts caused by insertions and deletions. Our model is based on both an HMM transducer of a pair HMM and profile HMMs for all possible amino acid pairs. A Viterbi algorithm over our model produces the optimal alignment of a pair of metagenomic reads taking into account all possible translating frames and gap penalties in both the protein space and the DNA space. To reduce the sheer number of states of this model, we also derived and implemented a computationally feasible model, leveraging the degeneracy of the genetic code. In a benchmark test on a diverse set of simulated reads based on BAliBASE we show that Meta-Align outperforms TBLASTX which compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database using the BLAST algorithm. We also demonstrate the effects of incorporating quality scores on Meta-Align.ConclusionsMeta-Align will be particularly effective when applied to error-prone DNA sequences. The package of our software can be downloaded at https://github.com/shravan-repos/Metaalign.

scholarly journals Optimal Control of Gene Mutation in DNA Replication

2012 ◽  
Vol 2012 ◽  
pp. 1-26
Author(s):  
Juanyi Yu ◽  
Jr-Shin Li ◽  
Tzyh-Jong Tarn
Keyword(s):  
Optimal Control ◽  
Dna Replication ◽  
Dna Sequences ◽  
Dynamic Programming Algorithm ◽  
Gene Mutations ◽  
Global Optimum ◽  
Programming Algorithm ◽  
State Variables ◽  
Level Control ◽  
Finite State

We propose a molecular-level control system view of the gene mutations in DNA replication from the finite field concept. By treating DNA sequences as state variables, chemical mutagens and radiation as control inputs, one cell cycle as a step increment, and the measurements of the resulting DNA sequence as outputs, we derive system equations for both deterministic and stochastic discrete-time, finite-state systems of different scales. Defining the cost function as a summation of the costs of applying mutagens and the off-trajectory penalty, we solve the deterministic and stochastic optimal control problems by dynamic programming algorithm. In addition, given that the system is completely controllable, we find that the global optimum of both base-to-base and codon-to-codon deterministic mutations can always be achieved within a finite number of steps.

REDUCING THE SEARCH SPACE AND TIME COMPLEXITY OF NEEDLEMAN-WUNSCH ALGORITHM (GLOBAL ALIGNMENT) AND SMITH-WATERMAN ALGORITHM (LOCAL ALIGNMENT) FOR DNA SEQUENCE ALIGNMENT

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
F. N. Muhamad ◽  
R. B. Ahmad ◽  
S. Mohd. Asi ◽  
M. N. Murad
Keyword(s):  
Dynamic Programming ◽  
Dna Sequences ◽  
Sequence Comparison ◽  
Dynamic Programming Algorithm ◽  
Search Space ◽  
Programming Algorithm ◽  
Local Alignment ◽  
Global Alignment ◽  
Main Research ◽  
Dna Sequence Alignment

The fundamental procedure of analyzing sequence content is sequence comparison. Sequence comparison can be defined as the problem of finding which parts of the sequences are similar and which parts are different, namely comparing two sequences to identify similarities and differences between them. A typical approach to solve this problem is to find a good and reasonable alignment between the two sequences. The main research in this project is to align the DNA sequences by using the Needleman-Wunsch algorithm for global alignment and Smith-Waterman algorithm for local alignment based on the Dynamic Programming algorithm. The Dynamic Programming Algorithm is guaranteed to find optimal alignment by exploring all possible alignments and choosing the best through the scoring and traceback techniques. The algorithms proposed and evaluated are to reduce the gaps in aligning sequences as well as the length of the sequences aligned without compromising the quality or correctness of results. In order to verify the accuracy and consistency of measurements obtained in Needleman-Wunsch and Smith-Waterman algorithms the data is compared with Emboss (global) and Emboss (local) with 600 strands test data.

scholarly journals Multi-rate Poisson Tree Processes for single-locus species delimitation under Maximum Likelihood and Markov Chain Monte Carlo

2016 ◽  
Author(s):  
P. Kapli ◽  
S. Lutteropp ◽  
J. Zhang ◽  
K. Kobert ◽  
P. Pavlidis ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Species Delimitation ◽  
Large Scale ◽  
Dynamic Programming Algorithm ◽  
Large Datasets ◽  
Programming Algorithm ◽  
Link Type ◽  
Similarity Threshold

ABSTRACTMotivationIn recent years, molecular species delimitation has become a routine approach for quantifying and classifying biodiversity. Barcoding methods are of particular importance in large-scale surveys as they promote fast species discovery and biodiversity estimates. Among those, distance-based methods are the most common choice as they scale well with large datasets; however, they are sensitive to similarity threshold parameters and they ignore evolutionary relationships. The recently introduced “Poisson Tree Processes” (PTP) method is a phylogeny-aware approach that does not rely on such thresholds. Yet, two weaknesses of PTP impact its accuracy and practicality when applied to large datasets; it does not account for divergent intraspecific variation and is slow for a large number of sequences.ResultsWe introduce the multi-rate PTP (mPTP), an improved method that alleviates the theoretical and technical shortcomings of PTP. It incorporates different levels of intraspecific genetic diversity deriving from differences in either the evolutionary history or sampling of each species. Results on empirical data suggest that mPTP is superior to PTP and popular distance-based methods as it, consistently, yields more accurate delimitations with respect to the taxonomy (i.e., identifies more taxonomic species, infers species numbers closer to the taxonomy). Moreover, mPTP does not require any similarity threshold as input. The novel dynamic programming algorithm attains a speedup of at least five orders of magnitude compared to PTP, allowing it to delimit species in large (meta-) barcoding data. In addition, Markov Chain Monte Carlo sampling provides a comprehensive evaluation of the inferred delimitation in just a few seconds for millions of steps, independently of tree size.AvailabilitymPTP is implemented in C and is available for download at http://github.com/Pas-Kapli/mptp under the GNU Affero 3 license. A web-service is available at http://[email protected], [email protected], [email protected]

scholarly journals Quark enables semi-reference-based compression of RNA-seq data

2016 ◽  
Author(s):  
Hirak Sarkar ◽  
Rob Patro
Keyword(s):  
State Of The Art ◽  
Reference Sequence ◽  
Rna Seq ◽  
Sequencing Data ◽  
The Past ◽  
Link Type ◽  
Exponential Increase

AbstractMotivationThe past decade has seen an exponential increase in biological sequencing capacity, and there has been a simultaneous effort to help organize and archive some of the vast quantities of sequencing data that are being generated. While these developments are tremendous from the perspective of maximizing the scientific utility of available data, they come with heavy costs. The storage and transmission of such vast amounts of sequencing data is expensive.ResultsWe present Quark, a semi-reference-based compression tool designed for RNA-seq data. Quark makes use of a reference sequence when encoding reads, but produces a representation that can be decoded independently, without the need for a reference. This allows Quark to achieve markedly better compression rates than existing reference-free schemes, while still relieving the burden of assuming a specific, shared reference sequence between the encoder and decoder. We demonstrate that Quark achieves state-of-the-art compression rates, and that, typically, only a small fraction of the reference sequence must be encoded along with the reads to allow reference-free decompression.AvailabilityQuark is implemented in C++11, and is available under a GPLv3 license at www.github.com/COMBINE-lab/[email protected]

A BIT-PARALLEL DYNAMIC PROGRAMMING ALGORITHM SUITABLE FOR DNA SEQUENCE ALIGNMENT

2012 ◽  
Vol 10 (04) ◽  
pp. 1250002 ◽  
Author(s):  
KOUICHI KIMURA ◽  
ASAKO KOIKE ◽  
KENTA NAKAI
Keyword(s):  
Dynamic Programming ◽  
Dna Sequences ◽  
Time Complexity ◽  
Reference Genome ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Short Read ◽  
Word Size ◽  
Dna Sequence Alignment ◽  
Query Length

Myers' elegant and powerful bit-parallel dynamic programming algorithm for approximate string matching has a restriction that the query length should be within the word size of the computer, typically 64. We propose a modification of Myers' algorithm, in which the modification has a restriction not on the query length but on the maximum number of mismatches (substitutions, insertions, or deletions), which should be less than half of the word size. The time complexity is O(m log |Σ|), where m is the query length and |Σ| is the size of the alphabet Σ. Thus, it is particularly suited for sequences on a small alphabet such as DNA sequences. In particular, it is useful in quickly extending a large number of seed alignments against a reference genome for high-throughput short-read data produced by next-generation DNA sequencers.

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