scholarly journals Overlap graph-based generation of haplotigs for diploids and polyploids

2019 ◽  
Vol 35 (21) ◽  
pp. 4281-4289 ◽  
Author(s):  
Jasmijn A Baaijens ◽  
Alexander Schönhuth
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Iterative Scheme ◽  
State Of The Art ◽  
Simulated Data ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Specific Sequence ◽  
New Approach ◽  
Polyploid Genome

Abstract Motivation Haplotype-aware genome assembly plays an important role in genetics, medicine and various other disciplines, yet generation of haplotype-resolved de novo assemblies remains a major challenge. Beyond distinguishing between errors and true sequential variants, one needs to assign the true variants to the different genome copies. Recent work has pointed out that the enormous quantities of traditional NGS read data have been greatly underexploited in terms of haplotig computation so far, which reflects that methodology for reference independent haplotig computation has not yet reached maturity. Results We present POLYploid genome fitTEr (POLYTE) as a new approach to de novo generation of haplotigs for diploid and polyploid genomes of known ploidy. Our method follows an iterative scheme where in each iteration reads or contigs are joined, based on their interplay in terms of an underlying haplotype-aware overlap graph. Along the iterations, contigs grow while preserving their haplotype identity. Benchmarking experiments on both real and simulated data demonstrate that POLYTE establishes new standards in terms of error-free reconstruction of haplotype-specific sequence. As a consequence, POLYTE outperforms state-of-the-art approaches in various relevant aspects, where advantages become particularly distinct in polyploid settings. Availability and implementation POLYTE is freely available as part of the HaploConduct package at https://github.com/HaploConduct/HaploConduct, implemented in Python and C++. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Overlap graph-based generation of haplotigs for diploids and polyploids

2018 ◽  
Author(s):  
Jasmijn A. Baaijens ◽  
Alexander Schönhuth
Keyword(s):  
Recent Work ◽  
Genome Assembly ◽  
De Novo ◽  
Iterative Scheme ◽  
State Of The Art ◽  
Simulated Data ◽  
Specific Sequence ◽  
New Approach ◽  
Link Type ◽  
Polyploid Genome

AbstractHaplotype aware genome assembly plays an important role in genetics, medicine, and various other disciplines, yet generation of haplotype-resolved de novo assemblies remains a major challenge. Beyond distinguishing between errors and true sequential variants, one needs to assign the true variants to the different genome copies. Recent work has pointed out that the enormous quantities of traditional NGS read data have been greatly underexploited in terms of haplotig computation so far, which reflects that methodology for reference independent haplotig computation has not yet reached maturity. We present POLYTE (POLYploid genome fitTEr) as a new approach to de novo generation of haplotigs for diploid and polyploid genomes. Our method follows an iterative scheme where in each iteration reads or contigs are joined, based on their interplay in terms of an underlying haplotype-aware overlap graph. Along the iterations, contigs grow while preserving their haplotype identity. Benchmarking experiments on both real and simulated data demonstrate that POLYTE establishes new standards in terms of error-free reconstruction of haplotype-specific sequence. As a consequence, POLYTE outperforms state-of-the-art approaches in various relevant aspects, where advantages become particularly distinct in polyploid settings. POLYTE is freely available as part of the HaploConduct package at https://github.com/HaploConduct/HaploConduct, implemented in Python and C++.

scholarly journals Assembly Graph Browser: interactive visualization of assembly graphs

2019 ◽  
Vol 35 (18) ◽  
pp. 3476-3478 ◽  
Author(s):  
Alla Mikheenko ◽  
Mikhail Kolmogorov
Keyword(s):  
Genome Assembly ◽  
Open Problem ◽  
Interactive Visualization ◽  
Supplementary Information ◽  
Supplementary Data ◽  
New Approach ◽  
Repeat Analysis

Abstract Summary Currently, most genome assembly projects focus on contigs and scaffolds rather than assembly graphs that provide a more comprehensive representation of an assembly. Since interactive visualization of large assembly graphs remains an open problem, we developed an Assembly Graph Browser (AGB) tool that visualizes large assembly graphs, extending the functionality of previously developed visualization approaches. Assembly Graph Browser includes a number of novel functions including repeat analysis, construction of the contracted assembly graphs (i.e. the graphs obtained by collapsing a selected set of edges) and a new approach to visualizing large assembly graphs. Availability and implementation http://www.github.com/almiheenko/AGB. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CorGAT: a tool for the functional annotation of SARS-CoV-2 genomes

2020 ◽  
Author(s):  
Matteo Chiara ◽  
Federico Zambelli ◽  
Marco Antonio Tangaro ◽  
Pietro Mandreoli ◽  
David S Horner ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Ad Hoc ◽  
State Of The Art ◽  
Supplementary Information ◽  
Genomic Sequences ◽  
Supplementary Data ◽  
Evolutionary Patterns ◽  
Genomic Variants ◽  
Art Methods ◽  
Available Resources

Abstract Summary While over 200 000 genomic sequences are currently available through dedicated repositories, ad hoc methods for the functional annotation of SARS-CoV-2 genomes do not harness all currently available resources for the annotation of functionally relevant genomic sites. Here, we present CorGAT, a novel tool for the functional annotation of SARS-CoV-2 genomic variants. By comparisons with other state of the art methods we demonstrate that, by providing a more comprehensive and rich annotation, our method can facilitate the identification of evolutionary patterns in the genome of SARS-CoV-2. Availabilityand implementation Galaxy   http://corgat.cloud.ba.infn.it/galaxy; software: https://github.com/matteo14c/CorGAT/tree/Revision_V1; docker: https://hub.docker.com/r/laniakeacloud/galaxy_corgat. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

2018 ◽  
Author(s):  
Avantika Lal ◽  
Keli Liu ◽  
Robert Tibshirani ◽  
Arend Sidow ◽  
Daniele Ramazzotti
Keyword(s):  
Cross Validation ◽  
De Novo ◽  
State Of The Art ◽  
Point Mutations ◽  
Simulated Data ◽  
Large Datasets ◽  
Genome Sequences ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Current State

AbstractCancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates DNA replication error as a background, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using standard metrics. We then apply SparseSignatures to whole genome sequences of 147 tumors from pancreatic cancer, discovering 8 signatures in addition to the background.

scholarly journals CPS analysis: self-contained validation of biomedical data clustering

2020 ◽  
Vol 36 (11) ◽  
pp. 3516-3521 ◽  
Author(s):  
Lixiang Zhang ◽  
Lin Lin ◽  
Jia Li
Keyword(s):  
Data Clustering ◽  
State Of The Art ◽  
R Package ◽  
Research Community ◽  
Supplementary Information ◽  
Biomedical Data ◽  
Data Generation ◽  
Supplementary Data ◽  
Point Set ◽  
Class Labels

Abstract Motivation Cluster analysis is widely used to identify interesting subgroups in biomedical data. Since true class labels are unknown in the unsupervised setting, it is challenging to validate any cluster obtained computationally, an important problem barely addressed by the research community. Results We have developed a toolkit called covering point set (CPS) analysis to quantify uncertainty at the levels of individual clusters and overall partitions. Functions have been developed to effectively visualize the inherent variation in any cluster for data of high dimension, and provide more comprehensive view on potentially interesting subgroups in the data. Applying to three usage scenarios for biomedical data, we demonstrate that CPS analysis is more effective for evaluating uncertainty of clusters comparing to state-of-the-art measurements. We also showcase how to use CPS analysis to select data generation technologies or visualization methods. Availability and implementation The method is implemented in an R package called OTclust, available on CRAN. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

RMTL: an R library for multi-task learning

2018 ◽  
Vol 35 (10) ◽  
pp. 1797-1798 ◽  
Author(s):  
Han Cao ◽  
Jiayu Zhou ◽  
Emanuel Schwarz
Keyword(s):  
Biological Networks ◽  
Simulated Data ◽  
R Package ◽  
Low Rank ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Software Environment ◽  
Machine Learning Technique ◽  
Task Learning ◽  
Learning Technique

Abstract Motivation Multi-task learning (MTL) is a machine learning technique for simultaneous learning of multiple related classification or regression tasks. Despite its increasing popularity, MTL algorithms are currently not available in the widely used software environment R, creating a bottleneck for their application in biomedical research. Results We developed an efficient, easy-to-use R library for MTL (www.r-project.org) comprising 10 algorithms applicable for regression, classification, joint predictor selection, task clustering, low-rank learning and incorporation of biological networks. We demonstrate the utility of the algorithms using simulated data. Availability and implementation The RMTL package is an open source R package and is freely available at https://github.com/transbioZI/RMTL. RMTL will also be available on cran.r-project.org. Supplementary information Supplementary data are available at Bioinformatics online.

Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers

2020 ◽  
Author(s):  
Yuansheng Liu ◽  
Xiaocai Zhang ◽  
Quan Zou ◽  
Xiangxiang Zeng
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Complementary Strand ◽  
Short Reads ◽  
Sequencing Technologies ◽  
Computational Resources

Abstract Summary Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. Availability and implementation https://github.com/yuansliu/minirmd. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals HiLight-PTM: an online application to aid matching peptide pairs with isotopically labelled PTMs

2019 ◽  
Author(s):  
Harry J Whitwell ◽  
Peter DiMaggio
Keyword(s):  
De Novo ◽  
De Novo Sequencing ◽  
Mass Shift ◽  
Supplementary Information ◽  
Database Searching ◽  
Supplementary Data ◽  
Exact Match ◽  
High Confidence ◽  
Online Application ◽  
Internet Browser

Abstract Motivation Database searching of isotopically labelled PTMs can be problematic and we frequently find that only one, or neither in a heavy/light pair are assigned. In such cases, having a pair of MS/MS spectra that differ due to an isotopic label can assist in identifying the relevant m/z values that support the correct peptide annotation or can be used for de novo sequencing. Results We have developed an online application that identifies matching peaks and peaks differing by the appropriate mass shift (difference between heavy and light PTM) between two MS/MS spectra. Furthermore, the application predicts, from the exact-match peaks, the mass of their complementary ions and highlights these as high confidence matches between the two spectra. The result is a tool to visually compare two spectra, and downloadable peaks lists that can be used to support de novo sequencing. Availability and implementation HiLight-PTM is released using shinyapps.io by RStudio, and can be accessed from any internet browser at https://harrywhitwell.shinyapps.io/hilight-ptm/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Temporal network alignment via GoT-WAVE

2019 ◽  
Vol 35 (18) ◽  
pp. 3527-3529 ◽  
Author(s):  
David Aparício ◽  
Pedro Ribeiro ◽  
Tijana Milenković ◽  
Fernando Silva
Keyword(s):  
User Interface ◽  
State Of The Art ◽  
Source Code ◽  
Network Alignment ◽  
Supplementary Information ◽  
Temporal Network ◽  
Temporal Networks ◽  
Supplementary Data ◽  
Node Similarity ◽  
User Friendly

Abstract Motivation Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results On synthetic networks, GoT-WAVE improves DynaWAVE’s accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE. Availability and implementation http://www.dcc.fc.up.pt/got-wave/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PaKman: Scalable Assembly of Large Genomes on Distributed Memory Machines

2019 ◽  
Author(s):  
Priyanka Ghosh ◽  
Sriram Krishnamoorthy ◽  
Ananth Kalyanaraman
Keyword(s):  
Genome Assembly ◽  
Large Scale ◽  
Distributed Memory ◽  
High Throughput Sequencing ◽  
De Novo ◽  
State Of The Art ◽  
Fundamental Problem ◽  
Parallel Computer ◽  
Assembly Process ◽  
Data Movement

AbstractDe novo genome assembly is a fundamental problem in the field of bioinformatics, that aims to assemble the DNA sequence of an unknown genome from numerous short DNA fragments (aka reads) obtained from it. With the advent of high-throughput sequencing technologies, billions of reads can be generated in a matter of hours, necessitating efficient parallelization of the assembly process. While multiple parallel solutions have been proposed in the past, conducting a large-scale assembly at scale remains a challenging problem because of the inherent complexities associated with data movement, and irregular access footprints of memory and I/O operations. In this paper, we present a novel algorithm, called PaKman, to address the problem of performing large-scale genome assemblies on a distributed memory parallel computer. Our approach focuses on improving performance through a combination of novel data structures and algorithmic strategies for reducing the communication and I/O footprint during the assembly process. PaKman presents a solution for the two most time-consuming phases in the full genome assembly pipeline, namely, k-mer counting and contig generation.A key aspect of our algorithm is its graph data structure, which comprises fat nodes (or what we call “macro-nodes”) that reduce the communication burden during contig generation. We present an extensive performance and qualitative evaluation of our algorithm, including comparisons to other state-of-the-art parallel assemblers. Our results demonstrate the ability to achieve near-linear speedups on up to 8K cores (tested); outperform state-of-the-art distributed memory and shared memory tools in performance while delivering comparable (if not better) quality; and reduce time to solution significantly. For instance, PaKman is able to generate a high-quality set of assembled contigs for complex genomes such as the human and wheat genomes in a matter of minutes on 8K cores.

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