Inferring species compositions of complex fungal communities from long- and short-read sequence data

Background: The kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. Results: We compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both short and long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. Conclusion: Overall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.

Download Full-text

ALSgeneScanner: a pipeline for the analysis and interpretation of DNA NGS data of ALS patients

10.1101/378158 ◽

2018 ◽

Author(s):

Alfredo Iacoangeli ◽

Ahmad Al Khleifat ◽

William Sproviero ◽

Aleksey Shatunov ◽

Ashley R Jones ◽

...

Keyword(s):

Motor Neurons ◽

Health Care Professionals ◽

Sequence Data ◽

Whole Genome Sequence ◽

Next Generation Sequencing Data ◽

Sequencing Data ◽

Whole Exome ◽

Exome Sequence Data ◽

Als Patients ◽

Ngs Data

AbstractAmyotrophic lateral sclerosis (ALS, MND) is a neurodegenerative disease of upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two years of first symptoms. Genetic factors are an important cause of ALS, with variants in more than 25 genes having strong evidence, and weaker evidence available for variants in more than 120 genes. With the increasing availability of Next-Generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyse and interpret it. Furthermore, the relevance of novel or existing variants in ALS genes is not always apparent. Here we present ALSgeneScanner, a tool that is easy to install and use, able to provide an automatic, detailed, annotated report, on a list of ALS genes from whole genome sequence data in a few hours and whole exome sequence data in about one hour on a readily available mid-range computer. This will be of value to non-specialists and aid in the interpretation of the relevance of novel and existing variants identified in DNA sequencing data.

Download Full-text

NCBI submission protocol for microbial pathogen surveillance v2

10.17504/protocols.io.bz7cp9iw ◽

2021 ◽

Author(s):

Ruth E Timme ◽

Maria Balkey ◽

Robyn Randolph ◽

Julie Haendiges ◽

Sai Laxmi Gubbala Venkata ◽

...

Keyword(s):

Active Surveillance ◽

Genome Sequence ◽

Large Volume ◽

Pathogen Detection ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Web Interface ◽

Data Submission ◽

Set Up

PURPOSE: Step-by-step instructions for submitting pathogen whole genome sequence data to NCBI and to the NCBI Pathogen Detection portal. This protocol covers the steps needed to establish a new NCBI submission environment for your laboratory, including the creation of new BioProject(s) and submission groups. Once these are step up, the protocol then walks through the process for submitting raw reads to SRA and sample metadata to BioSample through the Submission portal. SCOPE: for use by any laboratory submitting WGS data for species under active surveillance within NCBI’s Pathogen Detection. (This includes US laboratories in GenomeTrakr, NARMS, Vet-LIRN, PulseNet, and other non-US networks and submitters). For new submitters, there's quite a bit of groundwork that needs to be established before a laboratory can start its first data submission. We recommend that one person in the laboratory take a few days to get everything set up in advance of when you expect to do your first data submission. If you need a pipeline for frequent or large volume submissions, follow Step 1 to get your NCBI submission environment established, then contact [email protected] to set up an account for submitting through the API. This protocol covers submission using NCBI's Submission Portal web-interface. Version history: V5: Linking directly to the metadata template guidance instead of including duplicate copies of the files in this protocol. Updated screenshot for choosing the pathogen template to reflect changes at NCBI. V4: updated screenshots to reflect NCBI submission portal changes. Updated custom BioSample template.

Download Full-text

Phylogenomics of orchids and their mycorrhizal fungi : trees, diversity, and the pursuit of symbiosis

10.32469/10355/72205 ◽

2019 ◽

Author(s):

◽

Sarah Unruh

Keyword(s):

Mycorrhizal Fungi ◽

Phylogenetic Trees ◽

High Throughput Sequencing ◽

Genomic Sequence ◽

Sequence Data ◽

Mycorrhizal Symbiosis ◽

Sequencing Data ◽

Phylogenetic Structure ◽

University Of Missouri ◽

Fungal Symbiosis

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Phylogenetic trees show us how organisms are related and provide frameworks for studying and testing evolutionary hypotheses. To better understand the evolution of orchids and their mycorrhizal fungi, I used high-throughput sequencing data and bioinformatic analyses, to build phylogenetic hypotheses. In Chapter 2, I used transcriptome sequences to both build a phylogeny of the slipper orchid genera and to confirm the placement of a polyploidy event at the base of the orchid family. Polyploidy is hypothesized to be a strong driver of evolution and a source of unique traits so confirming this event leads us closer to explaining extant orchid diversity. The list of orthologous genes generated from this study will provide a less expensive and more powerful method for researchers examining the evolutionary relationships in Orchidaceae. In Chapter 3, I generated genomic sequence data for 32 fungal isolates that were collected from orchids across North America. I inferred the first multi-locus nuclear phylogenetic tree for these fungal clades. The phylogenetic structure of these fungi will improve the taxonomy of these clades by providing evidence for new species and for revising problematic species designations. A robust taxonomy is necessary for studying the role of fungi in the orchid mycorrhizal symbiosis. In chapter 4 I summarize my work and outline the future directions of my lab at Illinois College including addressing the remaining aims of my Community Sequencing Proposal with the Joint Genome Institute by analyzing the 15 fungal reference genomes I generated during my PhD. Together these chapters are the start of a life-long research project into the evolution and function of the orchid/fungal symbiosis.

Download Full-text

Ethnically diverse urban transmission networks of Neisseria gonorrhoeae without evidence of HIV serosorting

Sexually Transmitted Infections ◽

10.1136/sextrans-2019-054025 ◽

2019 ◽

Vol 96 (2) ◽

pp. 106-109

Author(s):

Jayshree Dave ◽

John Paul ◽

Thomas Joshua Pasvol ◽

Andy Williams ◽

Fiona Warburton ◽

...

Keyword(s):

Neisseria Gonorrhoeae ◽

Ethnic Groups ◽

Antimicrobial Susceptibility ◽

Sequence Data ◽

Small Sample ◽

Whole Genome Sequence ◽

Whole Genome ◽

Sequencing Data ◽

Transmission Networks ◽

Hiv Serosorting

ObjectiveWe aimed to characterise gonorrhoea transmission patterns in a diverse urban population by linking genomic, epidemiological and antimicrobial susceptibility data.MethodsNeisseria gonorrhoeae isolates from patients attending sexual health clinics at Barts Health NHS Trust, London, UK, during an 11-month period underwent whole-genome sequencing and antimicrobial susceptibility testing. We combined laboratory and patient data to investigate the transmission network structure.ResultsOne hundred and fifty-eight isolates from 158 patients were available with associated descriptive data. One hundred and twenty-nine (82%) patients identified as male and 25 (16%) as female; four (3%) records lacked gender information. Self-described ethnicities were: 51 (32%) English/Welsh/Scottish; 33 (21%) white, other; 23 (15%) black British/black African/black, other; 12 (8%) Caribbean; 9 (6%) South Asian; 6 (4%) mixed ethnicity; and 10 (6%) other; data were missing for 14 (9%). Self-reported sexual orientations were 82 (52%) men who have sex with men (MSM); 49 (31%) heterosexual; 2 (1%) bisexual; data were missing for 25 individuals. Twenty-two (14%) patients were HIV positive. Whole-genome sequence data were generated for 151 isolates, which linked 75 (50%) patients to at least one other case. Using sequencing data, we found no evidence of transmission networks related to specific ethnic groups (p=0.64) or of HIV serosorting (p=0.35). Of 82 MSM/bisexual patients with sequencing data, 45 (55%) belonged to clusters of ≥2 cases, compared with 16/44 (36%) heterosexuals with sequencing data (p=0.06).ConclusionWe demonstrate links between 50% of patients in transmission networks using a relatively small sample in a large cosmopolitan city. We found no evidence of HIV serosorting. Our results do not support assortative selectivity as an explanation for differences in gonorrhoea incidence between ethnic groups.

Download Full-text

MTBseq: a comprehensive pipeline for whole genome sequence analysis of Mycobacterium tuberculosis complex isolates

PeerJ ◽

10.7717/peerj.5895 ◽

2018 ◽

Vol 6 ◽

pp. e5895 ◽

Cited By ~ 35

Author(s):

Thomas Andreas Kohl ◽

Christian Utpatel ◽

Viola Schleusener ◽

Maria Rosaria De Filippo ◽

Patrick Beckert ◽

...

Keyword(s):

Antibiotic Resistance ◽

Mycobacterium Tuberculosis ◽

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome Sequencing Data ◽

Phylogenomic Analysis ◽

Whole Genome ◽

Sequencing Data ◽

Desktop Computer

Analyzing whole-genome sequencing data of Mycobacterium tuberculosis complex (MTBC) isolates in a standardized workflow enables both comprehensive antibiotic resistance profiling and outbreak surveillance with highest resolution up to the identification of recent transmission chains. Here, we present MTBseq, a bioinformatics pipeline for next-generation genome sequence data analysis of MTBC isolates. Employing a reference mapping based workflow, MTBseq reports detected variant positions annotated with known association to antibiotic resistance and performs a lineage classification based on phylogenetic single nucleotide polymorphisms (SNPs). When comparing multiple datasets, MTBseq provides a joint list of variants and a FASTA alignment of SNP positions for use in phylogenomic analysis, and identifies groups of related isolates. The pipeline is customizable, expandable and can be used on a desktop computer or laptop without any internet connection, ensuring mobile usage and data security. MTBseq and accompanying documentation is available from https://github.com/ngs-fzb/MTBseq_source.

Download Full-text

Metannot: A succinct data structure for compression of colors in dynamic de Bruijn graphs

10.1101/236711 ◽

2017 ◽

Cited By ~ 1

Author(s):

Harun Mustafa ◽

André Kahles ◽

Mikhail Karasikov ◽

Gunnar Rätsch

Keyword(s):

Data Structure ◽

High Throughput Sequencing ◽

Sequence Data ◽

Data Sets ◽

Sequencing Data ◽

Dynamic Data ◽

De Bruijn Graphs ◽

Dna And Rna ◽

Succinct Data Structure ◽

Dynamic Data Structure

AbstractMuch of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases.We propose a parallel construction method for and novel application of the wavelet trie as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data.When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes.While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes.It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices.This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.

Download Full-text

A Short Sequence Splicing Method for Genome Assembly Using a Three-Dimensional Mixing-Pool of BAC Clones and High-throughput Technology

The Open Biotechnology Journal ◽

10.2174/1874070701509010210 ◽

2015 ◽

Vol 9 (1) ◽

pp. 210-215

Author(s):

Xiaojun Kang ◽

Cheng Yang ◽

Xuguang Zhao ◽

Weiwei Chen ◽

Sifa Zhang ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Three Dimensional ◽

Whole Genome Sequence ◽

Sequencing Data ◽

Assembly Algorithm ◽

Bac Clones ◽

High Throughput Technology ◽

Sequencing Strategy ◽

The Cost

Current genome sequencing techniques are expensive, and it is still a major challenge to obtain an individual whole-genome sequence. To reduce the cost of sequencing, this paper introduced a high-throughput sequencing strategy using a three-dimensional mixing-pools based on the cube. Following the strategy, BAC clones were injected into each vertex of the cube, and sequencing of each plane provided information about multiple clones, thereby significantly reducing the cost of sequencing. In addition, Velvet was used to assemble the sequencing data. The scaffold generated from Velvet contained a number of contigs, which were orderless. Therefore, to address this problem, a scaffold assembly algorithm based on multi-way trees was used. The algorithm used a multi-way tree to build the framework of chromosomes, and subsequently, the frame was filled to complete the scaffold assembly. This algorithm alone outperformed Velvet in the assembling of a scaffold.

Download Full-text

A benchmarking of human mitochondrial DNA haplogroup classifiers from whole-genome and whole-exome sequence data

10.1101/2021.02.11.430775 ◽

2021 ◽

Author(s):

Víctor García-Olivares ◽

Adrián Muñoz-Barrera ◽

José Miguel Lorenzo-Salazar ◽

Carlos Zaragoza-Trello ◽

Luis A. Rubio-Rodríguez ◽

...

Keyword(s):

High Throughput Sequencing ◽

De Novo ◽

Sequence Data ◽

Qualitative Assessment ◽

Whole Genome ◽

Third Generation ◽

Sequencing Data ◽

Short Read ◽

Bioinformatic Tools ◽

Whole Exome

AbstractThe mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. Besides, because of its relevance, we also assess the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short-read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

Download Full-text

HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

10.1101/2020.12.16.419085 ◽

2020 ◽

Author(s):

Evangelos A. Dimopoulos ◽

Alberto Carmagnini ◽

Irina M. Velsko ◽

Christina Warinner ◽

Greger Larson ◽

...

Keyword(s):

Species Identification ◽

High Throughput Sequencing ◽

Bayesian Framework ◽

Metagenomic Data ◽

Sequencing Data ◽

Taxonomic Assignment ◽

Species Classification ◽

High Throughput Sequencing Data ◽

Reference Databases ◽

Specific Species

Identification of specific species in metagenomic samples is critical for several key applications, yet many tools available require large computational power and are often prone to false positive identifications. Here we describe High-AccuracY and Scalable Taxonomic Assignment of MetagenomiC data (HAYSTAC), which can estimate the probability that a specific taxon is present in a metagenome. HAYSTAC provides a user-friendly tool to construct databases, based on publicly available genomes, that are used for competitive reads mapping. It then uses a novel Bayesian framework to infer the abundance and statistical support for each species identification, and provide per-read species classification. Unlike other methods, HAYSTAC is specifically designed to efficiently handle both ancient and modern DNA data, as well as incomplete reference databases, making it possible to run highly accurate hypothesis-driven analyses (i.e., assessing the presence of a specific species) on variably sized reference databases while dramatically improving processing speeds. We tested the performance and accuracy of HAYSTAC using simulated Illumina libraries, both with and without ancient DNA damage, and compared the results to other currently available methods (i.e., Kraken2/Braken, MALT/HOPS, and Sigma). HAYSTAC identified fewer false positives than both Kraken2/Braken and MALT in all simulations, and fewer than Sigma in simulations of ancient data. It uses less memory than Kraken2/Braken as well as MALT both during database construction and sample analysis. Lastly, we used HAYSTAC to search for specific pathogens in two published ancient metagenomic datasets, demonstrating how it can be applied to empirical datasets. HAYSTAC is available from https://github.com/antonisdim/HAYSTAC

Download Full-text

PHARP: A pig haplotype reference panel for genotype imputation

10.1101/2021.06.03.446888 ◽

2021 ◽

Author(s):

Zhen Wang ◽

Zhenyang Zhang ◽

Zitao Chen ◽

Jiabao Sun ◽

Caiyun Cao ◽

...

Keyword(s):

Complex Traits ◽

Sequence Data ◽

Genotype Imputation ◽

Reference Panel ◽

Whole Genome Sequence ◽

Sequencing Data ◽

Large White ◽

Downstream Analysis ◽

Low Coverage ◽

Analytical Tools

Pigs not only function as a major meat source worldwide but also are commonly used as an animal model for studying human complex traits. A large haplotype reference panel has been used to facilitate efficient phasing and imputation of relatively sparse genome-wide microarray chips and low-coverage sequencing data. Using the imputed genotypes in the downstream analysis, such as GWASs, TWASs, eQTL mapping and genomic prediction (GS), is beneficial for obtaining novel findings. However, currently, there is still a lack of publicly available and high-quality pig reference panels with large sample sizes and high diversity, which greatly limits the application of genotype imputation in pigs. In response, we built the pig Haplotype Reference Panel (PHARP) database. PHARP provides a reference panel of 2,012 pig haplotypes at 34 million SNPs constructed using whole-genome sequence data from more than 49 studies of 71 pig breeds. It also provides Web-based analytical tools that allow researchers to carry out phasing and imputation consistently and efficiently. PHARP is freely accessible at http://alphaindex.zju.edu.cn/PHARP/index.php. We demonstrate its applicability for pig commercial 50K SNP arrays, by accurately imputing 2.6 billion genotypes at a concordance rate value of 0.971 in 81 Large White pigs (~ 17x sequencing coverage). We also applied our reference panel to impute the low-density SNP chip into the high-density data for three GWASs and found novel significantly associated SNPs that might be casual variants.

Download Full-text