Molecular Microbiology

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Molecular Microbiology by Mind Map: Molecular Microbiology

1. Microbiology in the Omics Era

1.1. BLAST

1.1.1. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.

1.1.2. Uses of BLAST

1.1.2.1. Identifying species

1.1.2.2. Locating domains

1.1.2.3. Establishing phylogeny

1.1.2.4. DNA mapping

1.1.2.5. Comparison

2. Microbial diversity and evolution

2.1. ClustalW

2.1.1. ClustalW is used for aligning multiple nucleotide or protein sequences in an efficient manner. It uses progressive alignment methods, which align the most similar sequences first and work their way down to the least similar sequences until a global alignment is created.

2.1.2. Uses of ClustalW

2.1.2.1. Computing a rough distance matrix between each pair of sequences, also known as pairwise sequence alignment.

2.1.2.2. A neighbor-joining method that uses midpoint rooting to create an overall guide tree.

2.1.2.3. The guide tree is then used as a rough template to generate a global alignment.

2.2. Clustal Omega

2.2.1. Clustal Omega is used to align protein sequences only (though nucleotide sequences are likely to be introduced in time).

2.2.2. Uses of Clustal Omega

2.2.2.1. The first is producing a pairwise alignment using the k-tuple method, also known as the word method.

2.2.2.2. The sequences are clustered using the modified mBed method.

2.2.2.3. The mBed method calculates pairwise distance using sequence embedding.

2.2.2.4. the guide tree is constructed using the UPGMA method.

2.2.2.5. the multiple sequence alignment is produced using HHAlign package from the HH-Suite.

2.3. Mega X

2.3.1. MEGA is computer software for conducting statistical analysis of molecular evolution and for constructing phylogenetic trees. It includes many sophisticated methods and tools for phylogenomics and phylomedicine.

2.3.2. Uses of Mega X

2.3.2.1. Computing a rough distance matrix between each pair of sequences, also known as pairwise sequence alignment.

2.3.2.2. Sequence alignment construction

2.3.2.3. The guide tree is then used as a rough template to generate a global alignment.

2.3.2.4. Data handling

2.3.2.5. MCL-based estimation of nucleotide substitution patterns

2.3.2.6. Substitution pattern homogeneity test

2.3.2.7. Distance estimation methods

2.3.2.8. Tests of selection

2.3.2.9. Molecular clock test

2.3.2.10. Tree-making methods

2.3.2.11. Tree explorers

3. Microbiome

3.1. Mothur

3.1.1. Mothur is a bioinformatics data processing used in the analysis of DNA from uncultured microbes. Mothur is capable of processing data generated from several DNA sequencing methods including 454 pyrosequencing, Illumina HiSeq and MiSeq, Sanger, PacBio, and IonTorrent.

3.1.2. Uses of Mothur

3.1.2.1. Provides conversions utilities to create other more-efficient formats, like the shared file for an OTU table.

3.1.2.2. Provides a utility to create a biom-format file that is independent of OTU clustering platform.

3.2. QIIME2

3.2.1. Quantitative Insights Into Microbial Ecology (QIIME2) software pipeline for analysis of marker gene-based microbiome sequencing data.

3.2.2. Uses of QIIME2

3.2.2.1. Build a phylogenetic tree

3.2.2.2. Calculate and explore diversity metrics

3.2.2.3. Assign taxonomy

3.2.2.4. Converting QIIME objects to a phyloseq object

3.2.2.5. Longitudinal Analysis

4. Molecular pathogenicity of pathogens

4.1. VFDB

4.1.1. The virulence factor database (VFDB) is an integrated and comprehensive online resource for curating information about virulence factors of bacterial pathogens.

4.1.2. Uses of VFDB

4.1.2.1. To form a database collectively presenting the virulence factors (VFs) of various medical significant bacterial pathogens.

4.1.2.2. Rapidly access to current knowledge about VFs from various bacterial pathogens.

4.2. DBETH

4.2.1. DBETH is the Database of Bacterial Exotoxins for Human. The aim of this database is to assemble information on the toxins responsible for causing bacterial pathogenesis in humans.

4.2.2. Uses of DBETH

4.2.2.1. Identify the potential toxin sequences

4.2.2.2. Homology based toxin identification

5. Antimicrobial resistance genes

5.1. ResFinder

5.1.1. ResFinder contains four databases including AMR genes (ResFinder), chromosomal gene mutations mediating AMR (PointFinder), translation of genotypes into phenotypes and species-specific panels for in silico antibiograms.

5.1.2. Uses of ResFinder

5.1.2.1. Identifies acquired genes

5.1.2.2. Finds chromosomal mutations mediating antimicrobial resistance in total or partial DNA sequence of bacteria.

5.2. Hmmscan

5.2.1. Hmmscan is used to search sequences against collections of profiles.

5.2.2. Use of Hmmscan

5.2.2.1. Search a protein sequence against a protein profile HMM database.

5.3. CARD

5.3.1. The Comprehensive Antibiotic Resistance Database. The CARD is a rigorously curated collection of characterized, peer-reviewed resistance determinants and associated antibiotics.

5.3.2. Ues of CARD

5.3.2.1. The Resistance Gene Identifier to predict resistome(s) from protein, genome, or metagenomics data based on homology and SNP models.

5.4. ICEberg

5.4.1. An website updated database of bacterial integrative and conjugative elements.

5.4.2. Uses of ICEberg

5.4.2.1. Detection of ICEs/IMEs of bacterial genomes

5.4.2.2. Some SPARQL query usecases for ICEO (Integrative and Conjugative Element Ontology)

5.4.2.3. Search against ICE-encoding proteins in ICEberg

5.4.2.4. Detect virulence or antibiotic resistance genes and extended mobilome-related gene clusters, including ICEs

6. Molecular identification of bacteriocins

6.1. BAGEL4

6.1.1. BAGEL enables researchers to mine bacterial (meta-)genomic DNA for bacteriocins and RiPPs.

6.1.2. Uses of BAGEL4

6.1.2.1. Improved core peptide (web) BLAST

6.1.2.2. Six frame translation of input DNA for improved speed

6.1.2.3. Integration of RNA-Seq data

6.1.2.4. Integrated promoter and terminator prediction

6.1.2.5. Improved and adaptable graphical report

6.2. BACTIBASE

6.2.1. BACTIBASE is an integrated open-access database designed for the characterization of bacterial antimicrobial peptides, commonly known as bacteriocins.

6.2.2. Uses of BACTIBASE

6.2.2.1. Calculated or predicted physicochemical properties of bacteriocins

6.2.2.2. Taxonomy explorer

6.2.2.3. References sub-database

6.2.2.4. Bacteriocin structural analysis tool set

6.2.2.5. Linking to the BACTIBASE database

7. Next-generation sequencing

7.1. RAST

7.1.1. The RAST server provides high-quality genome annotations for prokaryotes across the whole phylogenetic tree.The genome annotation provided does include a mapping of genes to subsystems and a metabolic reconstruction.

7.1.2. Uses of RAST

7.1.2.1. Identifies protein-encoding, rRNA and tRNA genes

7.1.2.2. Assigns functions to the genes

7.1.2.3. Predicts which subsystems are represented in the genome

7.2. GeneMarkS

7.2.1. A self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

7.2.2. Uses of GeneMarkS

7.2.2.1. Gene prediction in transcripts

7.2.2.2. Assembled eukaryotic transcripts

7.3. RNAmmer

7.3.1. The RNAmmer server predicts 5s/8s, 16s/18s and 23s/28s ribosomal RNA in full genome sequences. It has been tested on all published genomes and gives accurate predictions of rRNAs.

7.3.2. Uses of RNAmmer

7.3.2.1. localization of rRNA sequences in genomic DNA

7.3.2.2. Producing results that are comparable between genomes

7.4. tRNAscan-SE

7.4.1. A program for improved detection of transfer RNA genes in genomic sequence.

7.4.2. Uses of tRNAscan-SE

7.4.2.1. Identifying and annotating tRNA genes in genomes

7.4.2.2. Prediction of whole-genome transfer RNA gene

7.4.2.3. Application of RNA covariance models, which are general, probabilistic secondary structure

8. DNA amplification technology

8.1. LAMP

8.1.1. LAMP is an isothermal amplification method that uses multiple primers designed to create continuous loop structures during DNA amplification.

8.1.2. Use of LAMP

8.1.2.1. Simple screening assay in the field

9. Bacteria and bacteriophages coevolution

9.1. CRISPR-CAS

9.1.1. CRISPR technology is a simple yet powerful tool for editing genomes. It allows researchers to easily alter DNA sequences and modify gene function.

9.1.2. Uses of CRISPR-CAS

9.1.2.1. Cas9 could be directed to cut any region of DNA

9.1.2.2. The CRISPR/Cas system participates in a constant evolutionary battle between phages and bacteria.

9.2. PHASTER

9.2.1. PHASTER is a web server for the rapid identification and annotation of prophage sequences within bacterial genomes and plasmids.

9.2.2. Uses of PHASTER

9.2.2.1. Analyzing a typical bacterial genome

9.2.2.2. Analyzing bacteriophage sequencing data sets

9.2.2.3. Phage identification and annotation