scholarly journals S-conLSH: Alignment-free gapped mapping of noisy long reads

2019 ◽  
Author(s):  
Angana Chakraborty ◽  
Burkhard Morgenstern ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Reference Genome ◽  
State Of The Art ◽  
Sequence Data ◽  
Downstream Processing ◽  
The State ◽  
Read Length ◽  
Alignment Free ◽  
Long Reads ◽  
Gc Bias ◽  
Target Locations

AbstractMotivationThe advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate.ResultsWe present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the state-of-the-art alignment-based methods. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing.AvailabilityThe source code of our software is freely available at https://github.com/anganachakraborty/S-conLSH

scholarly journals S-conLSH: alignment-free gapped mapping of noisy long reads

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Angana Chakraborty ◽  
Burkhard Morgenstern ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Reference Genome ◽  
Genome Mapping ◽  
Sequence Data ◽  
Downstream Processing ◽  
Read Length ◽  
Alignment Free ◽  
Spaced Seeds ◽  
Long Reads ◽  
Gc Bias ◽  
High Level

Abstract Background The advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate. Results We present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the recently developed method lordFAST. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing. Conclusions S-conLSH is one of the first alignment-free reference genome mapping tools achieving a high level of sensitivity. The spaced-context is especially suitable for extracting distant similarities. The variable-length spaced-seeds or patterns add flexibility to the proposed algorithm by introducing gapped mapping of the noisy long reads. Therefore, S-conLSH may be considered as a prominent direction towards alignment-free sequence analysis.

scholarly journals Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

2020 ◽  
Author(s):  
Andrew J. Page ◽  
Nabil-Fareed Alikhan ◽  
Michael Strinden ◽  
Thanh Le Viet ◽  
Timofey Skvortsov
Keyword(s):  
Mycobacterium Tuberculosis ◽  
State Of The Art ◽  
Sequence Data ◽  
Human Pathogen ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Long Reads ◽  
Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

scholarly journals GraphAligner: rapid and versatile sequence-to-graph alignment

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Mikko Rautiainen ◽  
Tobias Marschall
Keyword(s):  
Genetic Variation ◽  
Error Correction ◽  
Genome Assembly ◽  
State Of The Art ◽  
Source Code ◽  
The State ◽  
Graph Alignment ◽  
Link Type ◽  
Long Reads

Abstract Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pangenome graph. Yet, so far, this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to the state-of-the-art tools, GraphAligner is 13x faster and uses 3x less memory. When employing GraphAligner for error correction, we find it to be more than twice as accurate and over 12x faster than extant tools.Availability: Package manager: https://anaconda.org/bioconda/graphalignerand source code: https://github.com/maickrau/GraphAligner

scholarly journals Graphmap2 - splice-aware RNA-seq mapper for long reads

2019 ◽  
Author(s):  
Josip Marić ◽  
Ivan Sović ◽  
Krešimir Križanović ◽  
Niranjan Nagarajan ◽  
Mile Šikić
Keyword(s):  
State Of The Art ◽  
The State ◽  
Rna Seq ◽  
Link Type ◽  
Pacific Biosciences ◽  
Long Reads ◽  
Oxford Nanopore

AbstractIn this paper we present Graphmap2, a splice-aware mapper built on our previously developed DNA mapper Graphmap. Graphmap2 is tailored for long reads produced by Pacific Biosciences and Oxford Nanopore devices. It uses several newly developed algorithms which enable higher precision and recall of correctly detected transcripts and exon boundaries. We compared its performance with the state-of-the-art tools Minimap2 and Gmap. On both simulated and real datasets Graphmap2 achieves higher mappability and more correctly recognized exons and their ends. In addition we present an analysis of potential of splice aware mappers and long reads for the identification of previously unknown isoforms and even genes. The Graphmap2 tool is publicly available at https://github.com/lbcb-sci/graphmap2.

scholarly journals GraphChainer: Co-linear Chaining for Accurate Alignment of Long Reads to Variation Graphs

2022 ◽  
Author(s):  
Jun Ma ◽  
Manuel Cáceres ◽  
Leena Salmela ◽  
Veli Mäkinen ◽  
Alexandru I. Tomescu
Keyword(s):  
Edit Distance ◽  
State Of The Art ◽  
Variant Calling ◽  
Real Data ◽  
Read Length ◽  
Human Chromosomes ◽  
Distance Threshold ◽  
Nature Biotechnology ◽  
Long Reads ◽  
Standard Task

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications in e.g., improving variant calling. While the vg toolkit (Garrison et al., Nature Biotechnology, 2018) is a popular aligner of short reads, GraphAligner (Rautiainen and Marschall, Genome Biology, 2020) is the state-of-the-art aligner of long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in an acyclic variation graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of long reads to variation graphs, GraphChainer. Compared to GraphAligner, at a normalized edit distance threshold of 40%, it aligns 9% to 12% more reads, and 15% to 19% more total read length, on real PacBio reads from human chromosomes 1 and 22. On both simulated and real data, GraphChainer aligns between 97% and 99% of all reads, and of total read length. At the more stringent normalized edit distance threshold of 30%, GraphChainer aligns up to 29% more total real read length than GraphAligner. GraphChainer is freely available at https://github.com/algbio/GraphChainer

scholarly journals Scaffolding Contigs Using Multiple Reference Genomes

2020 ◽  
Author(s):  
Yi-Kung Shieh ◽  
Shu-Cheng Liu ◽  
Chin Lung Lu
Keyword(s):  
Genome Assembly ◽  
Reference Genome ◽  
State Of The Art ◽  
Draft Genome ◽  
Evolutionary Relationship ◽  
The State ◽  
Target Genome ◽  
Multiple Reference ◽  
Reference Genomes

Scaffolding is an important step of the genome assembly and its function is to order and orient the contigs in the assembly of a draft genome into larger scaffolds. Several single reference-based scaffolders have currently been proposed. However, a single reference genome may not be sufficient alone for a scaffolder to correctly scaffold a target draft genome, especially when the target genome and the reference genome have distant evolutionary relationship or some rearrangements. This motivates researchers to develop the so-called multiple reference-based scaffolders that can utilize multiple reference genomes, which may provide different but complementary types of scaffolding information, to scaffold the target draft genome. In this chapter, we will review some of the state-of-the-art multiple reference-based scaffolders, such as Ragout, MeDuSa and Multi-CAR, and give a complete introduction to Multi-CSAR, an improved extension of Multi-CAR.

scholarly journals Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning

2020 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Quan Z. Sheng ◽  
Mehmet Orgun ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Sequence Data ◽  
Multiple Choice ◽  
Prediction Performance ◽  
The State ◽  
Psychological Theories ◽  
User Intentions ◽  
Real World Datasets ◽  
User Actions

User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.

scholarly journals Squeakr: An Exact and Approximate k-mer Counting System

2017 ◽  
Author(s):  
Prashant Pandey ◽  
Michael A. Bender ◽  
Rob Johnson ◽  
Rob Patro
Keyword(s):  
Data Structure ◽  
High Throughput Sequencing ◽  
State Of The Art ◽  
Transcriptome Assembly ◽  
Downstream Processing ◽  
The State ◽  
De Bruijn Graph ◽  
Transcript Quantification ◽  
Order Of Magnitude ◽  
Simple Quotient

AbstractMotivationk-mer-based algorithms have become increasingly popular in the processing of high-throughput sequencing (HTS) data. These algorithms span the gamut of the analysis pipeline from k-mer counting (e.g., for estimating assembly parameters), to error correction, genome and transcriptome assembly, and even transcript quantification. Yet, these tasks often use very different k-mer representations and data structures. In this paper, we set forth the fundamental operations for maintaining multisets of k-mers and classify existing systems from a data-structural perspective. We then show how to build a k-mer-counting and multiset-representation system using the counting quotient filter (CQF), a feature-rich approximate membership query (AMQ) data structure. We introduce the k-mer-counting/querying system Squeakr (Simple Quotient filter-based Exact and Approximate Kmer Representation), which is based on the CQF. This off-the-shelf data structure turns out to be an efficient (approximate or exact) representation for sets or multisets of k-mers.ResultsSqueakr takes 2×−3;4.3× less time than the state-of-the-art to count and perform a random-point-query workload. Squeakr is memory-efficient, consuming 1.5X–4.3X less memory than the state-of-the-art. It offers competitive counting performance, and answers point queries (i.e. queries for the abundance of a particular k-mer) over an order-of-magnitude faster than other systems. The Squeakr representation of the k-mer multiset turns out to be immediately useful for downstream processing (e.g., de Bruijn graph traversal) because it supports fast queries and dynamic k-mer insertion, deletion, and modification.Availabilityhttps://github.com/splatlab/[email protected]

Practical Picture Processing

1974 ◽  
Vol 32 ◽  
pp. 338-339
Author(s):  
T. A. Welton
Keyword(s):  
Radiation Damage ◽  
Coherence Length ◽  
Spatial Information ◽  
State Of The Art ◽  
Coherent Radiation ◽  
The State ◽  
Energy Spread ◽  
Electron Micrograph ◽  
Picture Processing ◽  
Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

Hypothesis-Evidence Coordination: The State of the Art

2003 ◽  
Vol 48 (6) ◽  
pp. 826-829 ◽  
Author(s):  
Eric Amsel
Keyword(s):  
State Of The Art ◽  
The State
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