To fully leverage these opportunities, bioinformaticians require specialized tools and databases. This blog highlights essential resources for mycology research, focusing on databases, tools, and platforms tailored for fungal biology.

1. Fungal Databases

1.1. MycoCosm

Website: MycoCosm
Developed by the DOE Joint Genome Institute, MycoCosm is a comprehensive portal for fungal genomics. It offers genomic and transcriptomic data for a wide range of fungi, including saprobes, pathogens, and symbionts.

• Key Features: Genome browsers, comparative genomics tools, and functional annotations.
• Best For: Large-scale studies on fungal evolution and ecology.

1.2. FungiDB

Website: FungiDB
FungiDB is an integrated genomic resource for fungal pathogens and non-pathogens. It provides access to genome sequences, transcriptomic data, and functional annotations.

• Key Features: Advanced search options, BLAST, and pathway analysis tools.
• Best For: Studying fungal pathogenesis and host-pathogen interactions.

1.3. Index Fungorum

Website: Index Fungorum
This nomenclatural database provides information on the scientific names of fungi. It’s an essential resource for taxonomists and researchers focused on fungal biodiversity.

• Key Features: Taxonomic hierarchy and synonymy tracking.
• Best For: Identifying and classifying fungal species.

1.4. UNITE

Website: UNITE
UNITE is a specialized database for fungal ITS (Internal Transcribed Spacer) sequences, often used in fungal identification and phylogenetics.

• Key Features: Curated reference datasets and community annotations.
• Best For: Environmental mycology and microbial ecology studies.

2. Analytical Tools

2.1. Funannotate

Repository: GitHub - Funannotate
Funannotate is a genome annotation tool designed for fungi. It supports tasks like gene prediction, functional annotation, and orthology analysis.

• Best For: Annotating newly sequenced fungal genomes.

2.2. BUSCO (Benchmarking Universal Single-Copy Orthologs)

Website: BUSCO
BUSCO evaluates genome assembly and annotation completeness using orthologs. It includes a fungal-specific dataset.

• Best For: Assessing the quality of fungal genome assemblies.

2.3. Pathogen-Host Interactions Database (PHI-base)

Website: PHI-base
PHI-base is a manually curated resource containing information on pathogen-host interactions, including fungal pathogens.

• Best For: Exploring virulence factors and host-pathogen relationships.

3. Visualization Platforms

3.1. Cytoscape

Website: Cytoscape
A powerful tool for visualizing molecular interaction networks, Cytoscape can be used to study protein-protein interactions, gene networks, and metabolic pathways in fungi.

• Best For: Network biology and functional genomics.

3.2. iTOL (Interactive Tree of Life)

Website: iTOL
iTOL is an interactive tool for visualizing phylogenetic trees.

• Best For: Displaying fungal phylogenies and comparing evolutionary relationships.

4. Community Resources

4.1. Mycological Society of America (MSA)

Website: MSA
The MSA promotes fungal research and provides access to resources, conferences, and publications.

• Best For: Networking with fungal researchers and accessing recent studies.

4.2. OpenFungi

Website: OpenFungi
OpenFungi is an open-source initiative providing fungal genomic and transcriptomic datasets for research and education.

• Best For: Sharing and accessing public fungal datasets.

5. Genomics Workflows

5.1. Galaxy

Website: Galaxy Project
Galaxy offers a web-based platform for reproducible bioinformatics workflows, including tools for fungal genome and transcriptome analysis.

• Best For: User-friendly analysis pipelines without requiring coding skills.

5.2. Snakemake

Repository: Snakemake
A flexible pipeline management tool that supports fungal data processing and analysis.

• Best For: Custom workflows for large-scale fungal datasets.

Conclusion

Fungal research is a rapidly growing field with vast implications for medicine, agriculture, and industry. For bioinformaticians, the availability of specialized resources—databases, tools, and community platforms—opens doors to innovative discoveries. Whether you are investigating fungal genomics, studying host-pathogen interactions, or exploring fungal biodiversity, the resources outlined above will empower your research journey.

Dive into these resources and help unravel the mysteries of the fungal kingdom!

• Bioconductor – A plethora of tools for analysis and comprehension of high-throughput genomic data, including 1500+ software packages. [ paper-2004 | web ]
• Biopython – Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the Open Bioinformatics Foundation. Contains the very useful Entrez package for API access to the NCBI databases. [ paper-2009 | web ]
• Bioconda – A channel for the conda package manager specializing in bioinformatics software. Includes a repository with 3000+ ready-to-install (with conda install) bioinformatics packages. [ paper-2018 | web ]
• BioJulia – Bioinformatics and computational biology infastructure for the Julia programming language. [ web ]
• Rust-Bio – Rust implementations of algorithms and data structures useful for bioinformatics. [ paper-2016 ]
• SeqAn – The modern C++ library for sequence analysis.

List of analysis tools developed for Oxford Nanopore data

BWA
Fast nanopore data tuned alignment tool
https://github.com/lh3/bwa

GraphMap
Mapper for long and error-prone reads
https://github.com/isovic/graphmap

LAST
Nanopore tuned alignment tool
http://last.cbrc.jp/

LINKS
Software tool for long read scaffolding
https://github.com/warrenlr/LINKS/

marginAlign
Tools to align nanopore reads to a reference
https://github.com/benedictpaten/marginAlign

minoTour
Real time analysis tools
http://minotour.nottingham.ac.uk/

nanoCORR
Error-correction tool for nanopore sequence data
https://github.com/jgurtowski/nanocorr

NanoOK
Software for nanopore data, quality and error profiles
https://documentation.tgac.ac.uk/display/NANOOK/NanoOK

Nanopolish
Nanopore analysis and genome assembly software
https://github.com/jts/nanopolish

nanopore
Variant-detection tool for nanopore sequence data
https://github.com/mitenjain/nanopore

Nanocorrect
Error-correction tool for nanopore sequence data
https://github.com/jts/nanocorrect/

npReader
Real-time conversion and analysis of nanopore reads
https://github.com/mdcao/npReader

poRe
Tool for analyzing and visualizing nanopore data
https://sourceforge.net/p/rpore/wiki/Home/

PoreSeq
Error-correction and variant-calling software
https://github.com/tszalay/poreseq

Poretools
Nanopore sequence analysis and visualization software
https://github.com/arq5x/poretools

SSPACE-LongRead
Genome scaffolding tool
http://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE-longread

SMIS
Genome scaffolding tool
https://sourceforge.net/projects/phusion2/files/smis/

List of assemblers for Oxford Nanopore MinION long reads

LQS
DALIGNER, Celera OLC Nanocorrect,
Nanopolish corrector
https://github.com/jts/nanopolish

PBcR
HGAP or BLASR, Celera OLC
PBcR corrector
http://wgs-assembler.sourceforge.net/wiki/index.php/PBcR
–
Canu
MHAP, Celera OLC
Canu corrector
https://github.com/marbl/canu

Falcon
String graph, Celera OLC
Falcon corrector
https://github.com/PacificBiosciences/falcon

Miniasm
OLC
https://github.com/lh3/miniasm

ra-integrate
OLC
https://github.com/mariokostelac/ra-integrate/

ALLPATHS-LG
de Bruijn graph
ALLPATHS-L corrector
https://www.broadinstitute.org/software/allpaths-lg/blog/?page_id=12

SPAdes
de Bruijn graph
SPAdes corrector
http://bioinf.spbau.ru/spades

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

A list of programs for genotype and SNP calling :

SOAP2 http://soap.genomics.org.cn/index.html

Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)

realSFS http://128.32.118.212/thorfinn/realSFS/

Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation

Samtools http://samtools.sourceforge.net/

Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)

GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)

Beagle http://faculty.washington.edu/browning/beagle/beagle.html

Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling

IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html

Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map

QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL

Multi-sample LD ‘Feasible’ genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The ‘feasible’ genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)

MaCH http://genome.sph.umich.edu/wiki/Thunder

Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information

Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

https://academic.oup.com/bioinformatics/article/35/12/2153/5162749

Address of the bookmark: https://github.com/arzwa/wgd

Breeding Insight is funded by the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) through Cornell University. The USDA ARS delivers scientific solutions to national and global agricultural challenges. As a global leader in agricultural discovery through scientific excellence, ARS is committed to delivering cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustaining our nation’s agroecosystems and natural resources; and ensuring the economic competitiveness and excellence of our agriculture.

Address of the bookmark: https://www.breedinginsight.org/

This also includes:

• A2Amapper: ATAC, Assembly to Assembly Comparision tool:
  • Comparative mapping between two genome assemblies (same species), or between two different genomes (cross species).

• Sim4db:
  • Spliced alignment of cDNA and genomic sequences, from the same (sim4) or related (sim4cc) species. Optimized for high-throughput batched alignment.

• LEAFF:
  • LEAFF (ahem, Let's Extract Anything From Fasta) is a utility program for working with multi-fasta files. In addition to providing random access to the base level, it includes several analysis functions.

• Meryl:
  • An out-of-core k-mer counter. The amount of sequence that can be processed for any size k depends only on the amount of free disk space.

Address of the bookmark: https://help.rc.ufl.edu/doc/Kmer

In an upset plot, each row represents a category or set, and each column represents a data point. The length of the bar for each category indicates the number of data points that belong to that category. The plot also shows the intersections between categories, represented by overlapping bars.

Upset plots are useful for visualizing complex data with multiple categories and intersections, and can help identify patterns and relationships between categories. They are often used in fields such as bioinformatics, where they can be used to analyze gene expression data or to compare the results of different experimental conditions.

https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions

Address of the bookmark: https://jokergoo.github.io/ComplexHeatmap-reference/book/upset-plot.html#example-with-the-genomic-regions