Oct 06, 2020
Introduction to Bioinformatic Tools
This document is a draft, published without a DOI.
- 1UCSC
- UCSC BME 22L
Document Citation: Ikenna Anigbogu 2020. Introduction to Bioinformatic Tools. protocols.io https://protocols.io/view/introduction-to-bioinformatic-tools-bmfmk3k6
License: This is an open access document distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Created: September 16, 2020
Last Modified: October 06, 2020
Document Integer ID: 42189
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Introduction to Bioinformatic Tools
Goals
The goal of this lab is to get students well acquainted and familiar with commonly used tools necessary for sequence analysis.
Lesson Plan
Students will learn and perform:
- How to navigate the UCSC Genome Browser
- How to utilize NCBI Blast
Safety
NO PPE IS REQUIRED FOR THIS LAB
For this lab, there will be no need for safety requirements because students will be asked to only use their laptops.
Tips and Hazards
- Highlight important regions on Genome Browser tracks; it helps to better visualize.
Background
In the current age of molecular biology, it is almost essential that people are up to speed and familiar with the bioinformatics tools at their disposal. Although it is good to know how to do actual molecular biology lab techniques, they can almost seem useless without having the bioinformatics skills necessary to interpret and analyze data. This lesson plan has been designed with the intention of introducing some bioinformatics skills and tools so you are capable of analyzing your own data computationally.
With the advancement of computer science and sequencing technologies, comes along the emergence of the field known as bioinformatics. This interdisciplinary field encompasses an array of sciences to ultimately help scientists interpret and analyze biological data. Depending on the field these tools can fit their needs of research and analysis. For instance, a clinical geneticist might use bioinformatic tools to identify commonly known SNPs (single nucleotide polymorphisms) in a patient’s genome in order to find diseases associated with the variant.
The ability to analyze nucleic acid and amino acid sequences efficiently is one of the biggest attractions in the field of computational biology. There are several tools bioinformaticians use to get specific and accurate sequence information from databases and resources online. Tools such as BLAST (Basic Local Alignment Search Tool), Geneious Prime, NCBI, and the UCSC Genome Browser provide researchers with their desired genetic information and allow analysis computationally. For this lab, we will be looking at a couple of tools that will be used throughout the remainder of this course.
BLAST Introduction
BLAST (Basic Local Alignment Search Tool) is one of the most widely used tools to gain sequence information. Finding similarity between DNA and protein sequences against a database is one of the first things people do when trying to get immediate information about a sequence of interest. Doing these searches allows scientists to gain knowledge about that particular gene’s function. BLAST finds regions of similarity between the input sequence and sequences found in its databases. The program compares nucleotide or protein sequences to sequence databases and then calculates the statistical significance of matches. Doing this search allows scientists to infer functional and evolutionary relationships between sequences and helps identify members of the gene family. BLAST makes use of heuristics to help provide the user with the sequence information quickly. This process occurs through a “speed-read” over similar nucleotides in the respective database. How specific these searches are can be adjusted to the user's desires.
There are different versions of BLAST that can be used for different reasons depending on what sequence you have. Here are the various forms of BLAST and the reasons why each form may be advantageous given the scenario:
| Program | Database | Query | Typical Uses | |
| BLASTN | Nucleotide | Nucleotide | Mapping oligonucleotides, cDNAs, and PCR products to a genome; screening repetitive elements; cross-species sequence exploration; annotating genomic DNA; clustering sequencing reads; vector clipping | |
| BLASTP | Protein | Protein | Identifying common regions between proteins; collecting related proteins for phylogenetic analyses | |
| BLASTX | Protein | Nucleotide translated into protein | Finding protein-coding genes in genomic DNA; determining if a cDNA corresponds to a known protein | |
| TBLASTN | Nucleotide translated into protein | Protein | Identifying transcripts, potentially from multiple organisms, similar to a given protein; mapping a protein to genomic DNA | |
| TBLASTX | Nucleotide translated into protein | Nucleotide translated into protein | Cross-species gene prediction at the genome or transcript level; searching for genes missed by traditional methods or not yet in protein databases |
Overview of How it Works (BLAST)
BLAST makes use of entry sequences called “queries” and compares them to nucleotide and protein sequences called “subject sequences” in a database. Each character in the sequence then gets indexed by their starting position in the sequence. The “wordsize” option is used by the user to configure how long the length of the string they are going to the index will be. The default values for word size for protein BLAST are 3 and the default size for nucleotide BLAST is 11. The query gets accepted as a FASTA and every nucleotide or amino acid is paired to or aligned to a letter or gap of the subject sequence. The overall alignment score is determined by summing up the scores of each nucleotide over the length of the entire sequence. Nucleotide BLAST scores nucleotides by giving +2 for aligned pairs of identical letters and a -3 for every nonidentical aligned pair. For the protein BLAST, scores for every amino acid pair are provided in a substitution matrix. Likely protein pairs are given a positive score whereas unlikely pairs are given a negative score.
Search results give a list of hits; where the most similar result appears at the start of the list. These hits can also be known as alignments. Each alignment is assigned a statistical value known as an “e-value”. The e-value is the number of times that alignment as good or better than the one found on BLAST would be expected to occur given the size of the database that was searched. The smaller the e-value the better the match. The user can set the threshold for the e-value and this determines which alignments will appear. A higher “Expect Value” threshold is less stringent and the BLAST default of “10” is designed to ensure that no biologically significant alignment is missed. However, “Expect Values” in the range of 0.001 to 0.0000001 are commonly used to restrict the alignments shown to those of high quality.
UCSC Genome Browser Introduction
The UCSC Genome Browser is an online application that establishes the reference genomes for many species, including humans. Scientists use the genome browser as a reference tool in many different disciplinary fields. It can be used in bioinformatics, clinical genetics, genomic research, pharmaceutical development, and many others. Scientists can navigate the entire human genome, as well as other species, base pair by base pair. The genome browser application provides a rapid and reliable display of any requested portion of genomes at any scale, together with dozens of aligned annotation tracks. Tracks can be added to the display of the genome browser and serve as an additional tool for more information on specific parts of the genome. The website itself has multiple reference species outside of the human genome, including SARS Covid-19, and are considered model organisms. A Model organism is a non-human species that is extensively studied to understand particular biological phenomena, with the expectation that discoveries made in the model organism will provide insight into the workings of other organisms [wiki].
Overview of How it Works (UCSC Genome Browser)
To open a track, there must be a specific species genome to look at. For the purpose of this course, we will look at the GRCh37/hg19 version which is a version of the human genome assembled in 2009. Once the version is selected, input a specific region to look at. An input region can be any chromosomal position (ex. chr11:108,093,559-108,239,826) or specific gene/transcription (ex. ATM). The default display shows the region of interest with associated nucleotide sequences, genes, and other tracks.
The regions of interest can be altered directly on the display screen using the zoom in or out buttons or with the move buttons. The default display depicts the reference nucleotides in the leading strand and can be indicated by the arrow on the first track, left side of the screen. However, the display can be switched to depict the lagging strand by clicking on the arrow.
These tracks are annotated tools that serve a specific purpose such as displaying common SNPs (single nucleotide polymorphism) or protein domains (Uniprot). These tracks can be moved on the display by dragging and dropping the grey bars on the left-hand column. These tracks can also be added or removed from the display. All possible tracks are displayed below the tracks and are given in multiple categories; such as Mapping and Sequencing, Genes and Gene Predictions, and others. Add tracks by changing the status from ‘hide’ to any other option; preferred for this course would be ‘pack’. Descriptions on tracks are given if the name of the track is clicked.
Resources
Here are some resources that can be of use when first getting started with using these bioinformatics tools or working with Unix:
Disclaimer:
The information provided on this document is intended for the educational purposes of the BME 22L laboratory course. It is worth noting that the information listed on this document is subject to change and is not finalized. Therefore, the information on this document should not be used outside of this course.