9. Herramientas identificación de genes RAM y tipificación bacteriana empleando líneas de comandos

Introduction to Command Line Tools for Gene Identification and Bacterial Typification

Overview of the Module

• This module focuses on tools for gene identification and bacterial typification using command lines, building on previous knowledge about biological information extraction from complete bacterial genome sequences.

Importance of Command Line Tools

• Tools like MLST (Multi-Locus Sequence Typing) help determine the sequence type of over 100 microorganism species, while Resfinder identifies antibiotic resistance genes. These tools may seem less user-friendly but are essential in high-throughput data analysis.

• The automation capabilities of command line tools allow for efficient processing of numerous samples, overcoming limitations faced with online tools. Additionally, these software options are open-source and free to use.

Understanding Command Lines

Transition from Graphical Interfaces

• Command lines require users to input instructions as text rather than through graphical interfaces, which can be intimidating for beginners accustomed to clicking buttons in colorful environments.

• Despite the initial intimidation factor, command line interfaces save resources by enabling multiple programs to run simultaneously in a pipeline format, facilitating large-scale data analysis efficiently.

Recommended Linux Distributions

• For optimal use of these tools, a Unix/Linux distribution is necessary; Ubuntu or Debian are recommended for average or inexperienced users due to their supportive communities and accessible tutorials.

Global Context and Relevance

Laboratory Workflow Integration

• The discussed tools are integral parts of workflows in laboratories worldwide, particularly within Latin America where there is significant experience analyzing massive sequencing data related to foodborne diseases.

• The PoolNet network in Latin America aligns its procedures with international guidelines, ensuring that the presented tools yield reliable results while acknowledging room for improvement.

Module Objectives and Tool Introduction

Focus Areas

• This module aims to introduce various command line-based tools starting with Sixero for Salmonella typing and covering 12 additional tools that facilitate both MLST-based typing and gene identification methods such as SGR-Z2.

Additional Resources

• A tool called Homies will also be introduced to assist users in selecting appropriate software based on their objectives; additionally, Resist HomeTrack will be showcased for global RAM data comparison in Salmonella contexts.

Salmonella Typing Techniques

Expanding Classification Schemes

• Building upon previous discussions about MLST involving seven constitutive genes for sequence type identification correlating with bacterial serotypes, this module introduces another classification scheme specific to Salmonella based on antigen variability found in cell walls.

Antigen Recognition Methods

• Traditionally, different antigens were recognized through agglutination tests using specific sera; however, current methodologies leverage known gene associations linked to these antigens derived from sequencing data instead.

Antigen Variability and Serotyping

Key Antigens Identified

• Two main antigens associated with Salmonella include:

• Somatic Antigen: Part of the outer cell wall providing protection against external threats; variations are linked to the RFB gene.

• Flagellar Antigen (H): Integral part of flagella aiding movement; expressed in two distinct forms coded by flicC (phase 1) and fljB (phase 2).

Formula Antigenica

Analyzing Salmonella Serotypes with Tool 6.0

Overview of Antigenic Formula and Serotype Prediction

• The antigenic formula 7, e, h, e, n, z, 15 is linked to a brand Europe serotype of Salmonella, aiding in serotype prediction through the scheme c, h, h, y, h, l, m, n, z, 6, 0. This utilizes sequencing data compared against a curated database of Salmonella serotype determinants.

Command Line Usage for Tool 6.0

• Tool 6.0 can process both fast-view format reads and samples. Users must execute commands structured as shown on-screen; the first parameter varies based on sequence types (e.g., interleaved or separate files).

• The second parameter specifies the input file path. For example: 6.0.pi -m2 indicates separate reads from two files (r1 and r2), with results saved in 6.0_result.txt.

Results Interpretation

• For analyzed samples using Tool 6.0: antigen variant results include antigen H1 variant R and antigen H2 variant 1.5; it predicts the infantis serotype by identifying genes associated with antigen variability based on Coffman-White Lémino classification.

Automation Capabilities

• A command line can be executed for all FASTQ files in a folder using loops for iterative processing—this allows analysis across numerous samples efficiently without manual intervention (90 to 200 samples).

Web Version and Comparison of Results

• Tool 6.0 also offers a web version where users upload FASTQ or FASTA files; results are emailed via a link upon completion—comparable to command line outputs showing identical results for SER-Z2 detection capabilities across platforms.

Versatility and Limitations of Analysis Tools

General Utility Beyond Salmonella

• The tool is versatile for detecting genes and alleles beyond just Salmonella species; it supports any bacterial species with an MLST scheme—over 100 available currently in databases like Resfinder and Arjanot for resistance gene identification purposes.

System Requirements and Execution Environment

• Designed exclusively for Linux operating systems without graphical interface support; operates solely via command line inputs requiring specific read formats (FastQ) rather than assembled sequences as indicated by its name "vindic."

Resistance Gene Identification Process

• To search for resistance genes similar to Tool 6.0's operation: specify names for output sequences along with database location; resulting tables list identified genes alongside coverage depth parameters relevant to database comparisons (e.g., divergence metrics).

Programming Needs for Automation

• Identifying antimicrobial resistance categories requires additional tools or programming skills (e.g., Vash or Python); automation typically necessitates coding proficiency due to complexity involved in handling large datasets effectively within these frameworks.

Consistency Across Tools

Identifying Antibiotic Resistance Genes with Arriba

Overview of the Arriba Tool

• Arriba is a tool designed to identify antibiotic resistance genes from FastQ format sequence reads. It requires two input files: a multifasta file containing multiple reference sequences, which can be sourced from public databases or created custom.

• Users must also provide two FastQ files containing the sequence reads. The command line for executing an analysis with Arriba is displayed on-screen, allowing users to search using databases like Carth and others to identify RAM genes.

• The output from Arriba is typically named report.tsb, which contains a table viewable in terminal or Excel. An example demonstrates results obtained from non-Salmonella sequences where specific genes are identified.

Exploring OMIX for Bioinformatics Tools

Features and Limitations of OMIX

• OMIX serves as a user-friendly platform that helps users navigate various bioinformatics tools, providing updated information about their functionalities and system requirements.

• Despite its advantages, OMIX has limitations such as being primarily in English and having restrictions on its free version. Nonetheless, it remains a valid option due to its easy-to-use protocols and comprehensive database.

Utilizing Resist-on-tracker for Gene Analysis

Importance of Resist-on-tracker

• Resist-on-tracker is highlighted as an essential tool for exploring antibiotic resistance genes present in Salmonella genomes uploaded to NCBI's database.

• It's important to note that not all sequences come from systematic sampling programs; caution should be exercised when inferring associations between resistance determinants and sources over time.

Analyzing Trends in Antibiotic Resistance Genes

Insights Gained from Data Exploration

• The tool allows exploration of trends related to specific genes, such as extended-spectrum beta-lactamases (ESBL), which confer resistance against clinically significant third-generation cephalosporins used frequently for treating Salmonella infections.

• Users can track the geographical distribution of these genes over time through NCBI records, revealing patterns since 2010 across different regions, particularly in America.

Comparative Analysis Using Resist-on-tracker

Understanding Gene Distribution

• The comparison feature enables selection based on antibiotic category, geographic region, and year. For instance, data shows that during 2019, certain genes were predominantly found in poultry isolates.

• Notably, gene TED-G was identified across various hosts including humans and livestock. This raises questions regarding the implications of this distribution and how it relates to specific countries or animal groups.

Summary of Tools for Mass Sequencing Data Analysis

Key Takeaways on Command Line Tools

• Various tools have been presented throughout the course modules for analyzing massive sequencing data. These include command-line tools like SR-Z2 and Arriba for both identification and analysis of resistance genes.