Chapter 17 Introduction to NGS
Next-generation sequencing (NGS) is one of the fundamental technological developments of the decade in life sciences. Whole genome sequencing (WGS), RAD-Seq, RNA-Seq, Chip-Seq, and several other technologies are routinely used to investigate important biological problems. These are also called high-throughput sequencing technologies, and with good reason: they generate vast amounts of data that needs to be processed. NGS is the main reason that computational biology has become a big-data discipline. More than anything else, this is a field that requires strong bioinformatics techniques.
As this is not an introductory book, you are expected to know at least what FASTA, FASTQ, Binary Alignment Map (BAM), and Variant Call Format (VCF) files are. I will also make use of the basic genomic terminology without introducing it (such as exomes, nonsynonymous mutations, and so on). You are required to be familiar with basic Python. We will leverage this knowledge to introduce the fundamental libraries in Python to perform the NGS analysis. Here, we will follow the flow of a standard bioinformatics pipeline.
However, before we delve into real data from a real project, let’s get comfortable with accessing existing genomic databases and basic sequence processing—a simple start before the storm.
17.1 Introduction of Biology for Bioinformatics
17.1.1 What is DNA
17.1.2 What is genome
17.1.3 How to assemble a genome
17.1.4 What is gene
17.2 What is NGS
Genetic information is stored within the chemical structure of the molecule DNA. DNA can be transcribed into RNA. Then RNA can be translated into protein.
NGS (Next geneneration sequencing) is not an accurate term. An more accurate term maybe high throughput sequencing. NGS is specifically used to refer to sequencing technologies after Sanger sequencing (1st generation) and single molecular sequencing (3rd sequencing, e.g. PacBio) and nanopore based sequencing (4th generation, e.g. Oxford Nanopore).
17.3 Application of NGS
17.4
17.5 Data file formats to store the genomic information
17.5.1 fasta file
FASTA format is a text-based format for representing either nucleotide sequences or peptide sequences, in which base pairs or amino acids are represented using single-letter codes. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater-than (“>”) symbol in the first column. It is recommended that all lines of text be shorter than 80 characters in length.
A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line (defline) is distinguished from the sequence data by a greater-than (“>”) symbol at the beginning. It is recommended that all lines of text be shorter than 80 characters in length. An example sequence in FASTA format is:
>gi|186681228|ref|YP_001864424.1| phycoerythrobilin:ferredoxin oxidoreductase
MNSERSDVTLYQPFLDYAIAYMRSRLDLEPYPIPTGFESNSAVVGKGKNQEEVVTTSYAFQTAKLRQIRA
AHVQGGNSLQVLNFVIFPHLNYDLPFFGADLVTLPGGHLIALDMQPLFRDDSAYQAKYTEPILPIFHAHQ
QHLSWGGDFPEEAQPFFSPAFLWTRPQETAVVETQVFAAFKDYLKAYLDFVEQAEAVTDSQNLVAIKQAQ
LRYLRYRAEKDPARGMFKRFYGAEWTEEYIHGFLFDLERKLTVVK17.5.2 GFF file
The GFF (gene-finding format, generic feature format or general feature format) is a file format used for describing genes and other features of DNA, RNA and protein sequences. The filename extension associated with such files is .GFF and the content type associated with them is text/x-gff3. There are two versions of the GFF file format in general use. We here focus on GFF3.
are tabular files with 9 fields per line, separated by tabs. They all share the same structure for the first 7 fields, while differing in the content and format of the ninth field. The general structure is as follows:
| Position index | Position name | Description |
|---|---|---|
| 1 | sequence | The name of the sequence where the feature is located. |
| 2 | source | Keyword identifying the source of the feature, like a program (e.g. Augustus or RepeatMasker) or an organization (like TAIR). |
| 3 | feature | The feature type name, like “gene” or “exon.” In a well structured GFF file, all the children features always follow their parents in a single block (so all exons of a transcript are put after their parent “transcript” feature line and before any other parent transcript line). In GFF3, all features and their relationships should be compatible with the standards released by the Sequence Ontology Project. |
| 4 | start | Genomic start of the feature, with a 1-base offset. This is in contrast with other 0-offset half-open sequence formats, like BED files. |
| 5 | end | Genomic end of the feature, with a 1-base offset. This is the same end coordinate as it is in 0-offset half-open sequence formats, like BED files.[citation needed] |
| 6 | score | Numeric value that generally indicates the confidence of the source on the annotated feature. A value of “.” (a dot) is used to define a null value. |
| 7 | strand | Single character that indicates the Sense (molecular biology) strand of the feature; it can assume the values of “+” (positive, or 5’->3’), “-,” (negative, or 3’->5’), “.” (undetermined). |
| 8 | phase | phase of CDS features; it can be either one of 0, 1, 2 (for CDS features) or “.” (for everything else). See the section below for a detailed explanation. |
| 9 | Attributes. | All the other information pertaining to this feature. The format, structure and content of this field is the one which varies the most between the three competing file formats. |
17.5.2.1 Reference
Generic Feature Format Version 3 (GFF3): https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md
General feature format wikipage: https://en.wikipedia.org/wiki/General_feature_format