scholarly journals multiPhATE: bioinformatics pipeline for functional annotation of phage isolates

2019 ◽  
Vol 35 (21) ◽  
pp. 4402-4404 ◽  
Author(s):  
Carol L Ecale Zhou ◽  
Stephanie Malfatti ◽  
Jeffrey Kimbrel ◽  
Casandra Philipson ◽  
Katelyn McNair ◽  
...  
Keyword(s):  
De Novo ◽  
Third Party ◽  
Supplementary Information ◽  
Modular Construction ◽  
Bioinformatics Pipeline ◽  
Annotation Pipeline ◽  
Phage Gene ◽  
Software Documentation ◽  
Multiple Processors ◽  
Calling Algorithm

Abstract Summary To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE’s modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system. Availability and implementation multiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals multiPhATE: bioinformatics pipeline for functional annotation of phage isolates

2019 ◽  
Author(s):  
Carol L. Ecale Zhou ◽  
Stephanie Malfatti ◽  
Jeffrey Kimbrel ◽  
Casandra Philipson ◽  
Katelyn McNair ◽  
...  
Keyword(s):  
De Novo ◽  
Third Party ◽  
Supplementary Information ◽  
Modular Construction ◽  
Bioinformatics Pipeline ◽  
Annotation Pipeline ◽  
Phage Gene ◽  
Software Documentation ◽  
Link Type ◽  
Multiple Processors

ABSTRACTSummaryTo address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene-calling algorithm and assigns putative functions to gene calls using protein-, virus-, and phage-centric databases. multiPhATE’s modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system.Availability and implementationmultiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE [email protected] or [email protected] informationData generated during the current study are included as supplementary files available for download at https://github.com/carolzhou/PhATE_docs.

scholarly journals KAT: A K-mer Analysis Toolkit to quality control NGS datasets and genome assemblies

2016 ◽  
Author(s):  
Daniel Mapleson ◽  
Gonzalo Garcia Accinelli ◽  
George Kettleborough ◽  
Jonathan Wright ◽  
Bernardo J. Clavijo
Keyword(s):  
Quality Control ◽  
De Novo ◽  
Pairwise Comparison ◽  
Supplementary Information ◽  
Assembly Process ◽  
High Coverage ◽  
Software Documentation ◽  
Link Type ◽  
Genome Assemblies ◽  
Ngs Data

ABSTRACTMotivationDe novo assembly of whole genome shotgun (WGS) next-generation sequencing (NGS) data benefits from high-quality input with high coverage. However, in practice, determining the quality and quantity of useful reads quickly and in a reference-free manner is not trivial. Gaining a better understanding of the WGS data, and how that data is utilised by assemblers, provides useful insights that can inform the assembly process and result in better assemblies.ResultsWe present the K-mer Analysis Toolkit (KAT): a multi-purpose software toolkit for reference-free quality control (QC) of WGS reads and de novo genome assemblies, primarily via their k-mer frequencies and GC composition. KAT enables users to assess levels of errors, bias and contamination at various stages of the assembly process. In this paper we highlight KAT’s ability to provide valuable insights into assembly composition and quality of genome assemblies through pairwise comparison of k-mers present in both input reads and the assemblies.AvailabilityKAT is available under the GPLv3 license at: https://github.com/TGAC/[email protected] InformationSupplementary Information (SI) is available at Bioinformatics online. In addition, the software documentation is available online at: http://kat.readthedocs.io/en/latest/.

scholarly journals CSMD: a computational subtraction-based microbiome discovery pipeline for species-level characterization of clinical metagenomic samples

2019 ◽  
Author(s):  
Yu Liu ◽  
Paul W Bible ◽  
Bin Zou ◽  
Qiaoxing Liang ◽  
Cong Dong ◽  
...  
Keyword(s):  
Species Level ◽  
Supplementary Information ◽  
Clinical Samples ◽  
Plasma Dna ◽  
Bioinformatics Pipeline ◽  
Microbial Identification ◽  
Reference Databases ◽  
Microbial Dna ◽  
Higher Sensitivity

Abstract Motivation Microbiome analyses of clinical samples with low microbial biomass are challenging because of the very small quantities of microbial DNA relative to the human host, ubiquitous contaminating DNA in sequencing experiments and the large and rapidly growing microbial reference databases. Results We present computational subtraction-based microbiome discovery (CSMD), a bioinformatics pipeline specifically developed to generate accurate species-level microbiome profiles for clinical samples with low microbial loads. CSMD applies strategies for the maximal elimination of host sequences with minimal loss of microbial signal and effectively detects microorganisms present in the sample with minimal false positives using a stepwise convergent solution. CSMD was benchmarked in a comparative evaluation with other classic tools on previously published well-characterized datasets. It showed higher sensitivity and specificity in host sequence removal and higher specificity in microbial identification, which led to more accurate abundance estimation. All these features are integrated into a free and easy-to-use tool. Additionally, CSMD applied to cell-free plasma DNA showed that microbial diversity within these samples is substantially broader than previously believed. Availability and implementation CSMD is freely available at https://github.com/liuyu8721/csmd. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MUM&Co: accurate detection of all SV types through whole-genome alignment

2020 ◽  
Vol 36 (10) ◽  
pp. 3242-3243 ◽  
Author(s):  
Samuel O’Donnell ◽  
Gilles Fischer
Keyword(s):  
De Novo ◽  
Supplementary Information ◽  
Genome Alignment ◽  
Whole Genome ◽  
Structural Variations ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Human Genomes ◽  
Whole Genome Alignment ◽  
Primary Output

Abstract Summary MUM&Co is a single bash script to detect structural variations (SVs) utilizing whole-genome alignment (WGA). Using MUMmer’s nucmer alignment, MUM&Co can detect insertions, deletions, tandem duplications, inversions and translocations greater than 50 bp. Its versatility depends upon the WGA and therefore benefits from contiguous de-novo assemblies generated by third generation sequencing technologies. Benchmarked against five WGA SV-calling tools, MUM&Co outperforms all tools on simulated SVs in yeast, plant and human genomes and performs similarly in two real human datasets. Additionally, MUM&Co is particularly unique in its ability to find inversions in both simulated and real datasets. Lastly, MUM&Co’s primary output is an intuitive tabulated file containing a list of SVs with only necessary genomic details. Availability and implementation https://github.com/SAMtoBAM/MUMandCo. Supplementary information Supplementary data are available at Bioinformatics online.

AStrap: identification of alternative splicing from transcript sequences without a reference genome

2018 ◽  
Vol 35 (15) ◽  
pp. 2654-2656 ◽  
Author(s):  
Guoli Ji ◽  
Wenbin Ye ◽  
Yaru Su ◽  
Moliang Chen ◽  
Guangzao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Single Molecule ◽  
Reference Genome ◽  
De Novo ◽  
Supplementary Information ◽  
Model Organisms ◽  
Sequencing Data ◽  
Extensive Evaluation ◽  
Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

Third‐party vessel allografts in kidney and pancreas transplantation: Utilization, de novo DSAs, and outcomes

2020 ◽  
Vol 20 (12) ◽  
pp. 3443-3450 ◽  
Author(s):  
Neetika Garg ◽  
Luis G. Hidalgo ◽  
Tom M. Ellis ◽  
Robert R. Redfield ◽  
Sandesh Parajuli ◽  
...  
Keyword(s):  
Pancreas Transplantation ◽  
De Novo ◽  
Third Party

scholarly journals A parallel computational framework for ultra-large-scale sequence clustering analysis

2018 ◽  
Vol 35 (3) ◽  
pp. 380-388 ◽  
Author(s):  
Wei Zheng ◽  
Qi Mao ◽  
Robert J Genco ◽  
Jean Wactawski-Wende ◽  
Michael Buck ◽  
...  
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Large Scale ◽  
De Novo ◽  
Rapid Development ◽  
Operational Taxonomic Unit ◽  
Supplementary Information ◽  
Computational Framework ◽  
Speed Up ◽  
Scale Sequence

Abstract Motivation The rapid development of sequencing technology has led to an explosive accumulation of genomic data. Clustering is often the first step to be performed in sequence analysis. However, existing methods scale poorly with respect to the unprecedented growth of input data size. As high-performance computing systems are becoming widely accessible, it is highly desired that a clustering method can easily scale to handle large-scale sequence datasets by leveraging the power of parallel computing. Results In this paper, we introduce SLAD (Separation via Landmark-based Active Divisive clustering), a generic computational framework that can be used to parallelize various de novo operational taxonomic unit (OTU) picking methods and comes with theoretical guarantees on both accuracy and efficiency. The proposed framework was implemented on Apache Spark, which allows for easy and efficient utilization of parallel computing resources. Experiments performed on various datasets demonstrated that SLAD can significantly speed up a number of popular de novo OTU picking methods and meanwhile maintains the same level of accuracy. In particular, the experiment on the Earth Microbiome Project dataset (∼2.2B reads, 437 GB) demonstrated the excellent scalability of the proposed method. Availability and implementation Open-source software for the proposed method is freely available at https://www.acsu.buffalo.edu/~yijunsun/lab/SLAD.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework

2019 ◽  
Vol 35 (14) ◽  
pp. i183-i190 ◽  
Author(s):  
Hao Yang ◽  
Hao Chi ◽  
Wen-Feng Zeng ◽  
Wen-Jing Zhou ◽  
Si-Min He
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Learning To Rank ◽  
Peptide Sequencing ◽  
De Novo Sequencing ◽  
Supplementary Information ◽  
Theoretical Spectrum ◽  
Tandem Mass ◽  
De Novo Peptide Sequencing ◽  
De Novo Peptide

AbstractMotivationDe novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.ResultsIn order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.Availability and implementationpNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Geometric potentials from deep learning improve prediction of CDR H3 loop structures

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i268-i275 ◽  
Author(s):  
Jeffrey A Ruffolo ◽  
Carlos Guerra ◽  
Sai Pooja Mahajan ◽  
Jeremias Sulam ◽  
Jeffrey J Gray
Keyword(s):  
Deep Neural Networks ◽  
De Novo ◽  
Probability Distributions ◽  
Supplementary Information ◽  
Model Parameters ◽  
Loop Structure ◽  
Variable Loops ◽  
Chain Sequence ◽  
Antibody Structure ◽  
Complementarity Determining Region

Abstract Motivation Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. Results When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark. Availability and Implementation DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. 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.

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