Getting Started with Bioinformatics Tools: Top 5 software & database you should know

Bioinformatics is the science of analyzing and interpreting biological data, such as DNA, RNA and protein sequences, genomic information, and other types of biological data.

Majority of your career in bioinformatics will be spent using bioinformatics tools and applying statistical methods to analyze & make sense of complex biological systems data.

This data include information about the function of genes, proteins, and metabolic pathways, as well as the interactions between various biological entities.

Bioinformatics tools are important for researchers to make sense of the growing amount of genomic and proteomic data. We will provide a beginner-friendly guide to the most commonly used bioinformatics software and databases in this article, including links to where to download them.

Top Five (5) bioinformatics software and databases:

1. FastQC:

FastQC is widely used in bioinformatics pipelines as a first step in quality control, allowing researchers to identify and correct quality issues before proceeding with downstream analysis.

FastQC is a high-throughput sequencing data quality control tool. It provides a straightforward method for evaluating the quality of raw sequencing data, including both base-calling quality and sequence-level features like adapter contamination, per-base GC content, and overrepresented sequences.

FastQC accepts FASTQ files as input, which are standard file formats for storing high-throughput sequencing data. Following that, it generates a series of quality control reports, which include summary statistics, per-base quality plots, and detailed information on specific quality issues such as low quality bases or overrepresented sequences.

Download link:

Installation using Conda package manager: Conda install -c bioconda fastQC

2. Samtools:

SAMtools (Sequence Alignment/Map Tools) software suite is widely used for manipulating and analyzing next-generation sequencing data.

The suite includes tools for reading, manipulating, and analyzing SAM (Sequence Alignment/Map) and BAM (Binary Alignment/Map) file formats, which are commonly used to store the results of aligning sequencing reads to a reference genome.

The following are some of the most common tasks performed with SAMtools:

  • Indexing and querying BAM files: SAMtools includes tools for creating and utilizing index files in order to gain efficient access to alignment information in BAM files.
  • SAMtools provides tools for calling genetic variants from next-generation sequencing data, such as single nucleotide polymorphisms (SNPs) and insertions/deletions (indels).
  • Filtering and sorting of alignments: SAMtools provides tools for filtering alignments based on criteria such as mapping quality, and sorting alignments based on various criteria, including chromosomal location and read name.

Download link:

Installation using Conda package manager: Conda install -c bioconda samtools

3. BLAST (Basic Local Alignment Search Tool):

BLAST (Basic Local Alignment Search Tool) is a popular software tool for comparing a query sequence to a database of sequences in order to find homologous regions. BLAST is used in bioinformatics for a variety of tasks such as sequence alignment, gene discovery, and functional annotation.

BLAST searches for regions of similarity between the query sequence and the database sequences, then reports the most similar matches. The matches are ranked according to a number of criteria, including the length of the aligned region, the number of mismatches, and the degree of sequence similarity.

Link to the tool:

4. R Statistical and visualization software

R is a statistical computing and graphics programming language and software environment. It is widely used for data analysis, visualization, and modeling in many fields, including bioinformatics.

R has a large user community and a large package library, making it a powerful and versatile platform for bioinformatics analysis.

Some of the key features of R for bioinformatics include:

  • Statistical analysis: R provides a wide range of statistical analysis functions, such as regression, hypothesis testing, and clustering, making it a valuable tool for exploring complex biological data.
  • Data visualization: R includes a number of plotting functions, such as scatter plots, histograms, and heatmaps, which make it simple to visualize large amounts of data.
  • Reproducibility: R provides a platform for reproducible research, making it possible to document, share, and reproduce analysis workflows and results.

Download Link:

5. DESeq2.

The bioinformatics tool DESeq2 is used to analyze gene expression data from RNA-seq experiments. RNA-seq experiments measure the amount of a specific RNA molecule (representing a gene) in a sample, which can provide information on how much the gene is being used or “expressed”.

DESeq2 compares gene expression data from different groups of samples (e.g., treated vs untreated, or diseased vs. healthy) to identify genes that are expressed differently in these groups.

It employs statistical methods to determine which genes are significantly different and aids in the correction of analysis errors.

DESeq2 produces a list of genes that are differentially expressed, as well as a measure of their significance. This information can assist researchers in understanding how different genes are involved in biological processes and disease, as well as provide insights into potential new therapeutic targets.

Download link:

Common bioinformatics databases:

1. The SRA database (Sequence Read Archive database)

The SRA database (Sequence Read Archive database) is a publicly accessible repository of next-generation sequencing data, such as DNA and RNA sequencing data, generated by various research projects. The SRA database is managed by the National Center for Biotechnology Information (NCBI), which is part of the National Institutes of Health (NIH) in the United States.

The SRA database contains a massive amount of genetic data from various organisms and tissues, including human, animal, plant, and microbial genomes. The data can be searched and downloaded using a variety of tools, including the NCBI SRA toolkit, which provides a command-line interface to the database, and the SRA Run Selector, a web-based tool for searching and downloading data.

Link to database:

2. ArrayExpress database

The ArrayExpress database is a freely accessible repository of functional genomics data, including gene expression, protein expression, and other high-throughput functional genomics data. The European Bioinformatics Institute (EBI) manages the ArrayExpress database, which is part of the larger Bioinformatics Infrastructure for Life Sciences (ELIXIR) project.

The ArrayExpress database contains data submitted by researchers from all over the world from various organisms, tissues, and experimental conditions. Various tools, such as the ArrayExpress search engine, which provides a web-based interface to access the database, can be used to search and download the data.

The ArrayExpress database is a valuable resource for functional genomics researchers because it allows them to access and analyze large amounts of functional genomics data generated by various experiments without having to generate the data themselves. This has aided in the discovery of new genes and pathways involved in various biological processes and diseases, as well as advancements in fields such as genomics, molecular biology, and bioinformatics.

Link to database:

3. Ensembl database

Ensembl is a large and freely accessible database of genomic information that focuses on vertebrate genomes but also includes some data from other organisms.

The European Molecular Biology Laboratory (EMBL) and the Wellcome Trust Sanger Institute created Ensembl, which is now maintained by the Ensembl consortium.

Ensembl offers a wide range of genomic data and tools, including:

  • Annotated genome assemblies: Ensembl provides high-quality genome assemblies for over 100 species, as well as extensive gene and transcript annotations.
  • Data on genetic variation: Ensembl provides data on genetic variation in the form of SNPs, insertions/deletions (indels), and structural variants, as well as information on how these variants are associated with diseases and phenotypes.
  • Functional analysis: Ensembl provides a range of tools for functional analysis of genomic data, including gene set enrichment analysis and pathway analysis.

Link to the database:

4. KEGG database

The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a biological database that contains a wealth of information about gene and protein functions in the context of cellular pathways and interactions.

The KEGG database includes data on metabolic pathways, genetic information, protein-protein interactions, and other topics.

It is widely used for large-scale genomics and proteomics data integration and understanding the relationships between genes, proteins, and metabolic pathways.

The database is also useful for drug discovery because it enables researchers to identify potential targets for new drugs based on their involvement in specific biological pathways.

The database is open to the public and is updated on a regular basis with new information to ensure that it remains a valuable resource for the scientific community.

Link to database:

5. GEO database (Gene Expression Omnibus database)

The GEO database (Gene Expression Omnibus database) is a publicly accessible repository of gene expression data, including microarray and RNA sequencing data, as well as other high-throughput genomic data. The National Center for Biotechnology Information (NCBI), which is part of the National Institutes of Health (NIH) in the United States, maintains the GEO database.

The GEO database contains a large amount of gene expression data from various organisms, tissues, and experimental conditions that researchers from around the world have submitted. The data can be searched and downloaded using a variety of tools, including the GEO DataSets tool, which provides a web-based interface to the database, and the GEOquery R package, which allows users to download and analyze data in R directly.

Link to the database:

In summary, bioinformatics tools are critical for making sense of the massive amounts of data generated in genomics and proteomics research.

The tools and databases discussed in this article are some of the most commonly used in the field, and they are easy to download and use. We hope that this user-friendly guide will assist you in getting started with these tools for research.