Welcome back to Applied Biological Materials Next Generation Sequencing video series, where
we discuss major concepts in NGS. In this video, we will describe some of the common
formats of NGS raw data, and software that can be used for downstream analysis. In our
previous videos we explained library preparation as well as coverage and quality control procedures.
We invite you to watch our previous videos before starting this one.
The raw output of all Illumina-based next generation sequencing machines is the .bcl
format. These files are binary files containing the base calls per cycle, as well as the quality
associated with each call. The advantage of .bcl file format is that each base is recorded
as the machine makes the call, which is the most efficient for machine operation. However,
if multiple samples were run on the sequencer, then the .bcl raw data must be sorted to separate
reads in a process called demultiplexing. This demultiplexing process converts .bcl
data into universally used fastq files via the bcl2fastq program.
The fastq format is the universally known sequencing data file in the bioinformatics
community. This format consists of four lines in each read. The first line starts with an
‘@’ character, followed by the sequencer identifier name given to the read by the sequencer.
The second line is the sequence of the read itself, the third line is simply a ‘+’
symbol, acting as a spacer, and the fourth and final line is the Phred quality scores
of the bases in the second line. The quality scores indicate the probability that the base
is called incorrectly by the sequencer. The score is expressed as a logarithmic function,
where Q is the Phred quality score, and P is the probability of an incorrect base call.
The actual probabilities are calculated by the machine by determining fluorescence peak
shape, resolution and any potential overlap at every base.
After the fastq data has been generated, there are many options for downstream analysis.
The most popular of these is alignment of the fastq data to a reference genome. Numerous
mapping programs exist, such as BWA, Bowtie2, Maq, Stampy, and Novoalign. Programs such
as Maq and Bowtie use a computational strategy called ‘indexing’, which works much like
the index at the end of a book, to speed up mapping algorithms that takes an index of
a large DNA sequence and rapidly finding shorter sequences embedded within it. Maq uses spaced
seed indexing where a read is divided into four segments of equal length called ‘seeds’.
Bowtie uses a different technique called Burrows-Wheeler transform that
can fit the entire human genome in less than two gigabytes of memory; and in contrast,
Maq’s spaced seed index approach will likely require more than fifty gigabytes of memory
to align the sequences efficiently.
Alternatively, if the organism being sequenced does not have a reference genome available,
then the reads must be aligned de novo, using programs such as ABySS and SOAPdenovo.
In this type of analysis, the reads are examined against each other to check for overlap in
order to build larger contiguous sequences called ‘contigs’s. The goal is to build
a single contig that encompasses the entire genome of the organism.
Alignment of sequenced fastq data through either reference or de novo methods will result
in the generation of a SAM file, which is the universal file format for mapped sequence
reads. This file type contains the sequence and quality scores of each read, similar to
the fastq file, but more detailed as it specifies information about the location in the genome
the read maps to and more. A BAM file is the compressed binary version of SAM, and is otherwise
identical.
After alignment to a reference genome, the next step is variant calling where a program
examines your mapped data and the reference side by side to determine the existence of
SNPs, de novo SNVs, and INDELs. SAMtools mpileup and the Genome Analysis Toolkit (GATK) are
two major variant calling programs available that use Bayesian algorithms to compare your
aligned sequence against the reference. After both of these pipelines have been completed,
visualization of the NGS data is usually one of the final steps in analysis. Visualization
can be achieved either using Integrative Genomics Viewer or the UCSC Genome Browser.
RNA-Seq analysis is slightly different than pipelines used for whole genome sequencing, since reads will only
map to the areas of the genome that code for RNA transcripts, leaving the remaining regions
of the genome uncovered. Special mapping programs have been developed such as TopHat and STAR
that include algorithms that help deal with some reads that are split as splicing junctions.
Another common type of sequencing is Exome-Seq, which is the sequencing of all the protein-coding
genes in the genome. This is accomplished by using probes which target only 1% of the
genome that codes for protein, leaving the other 99% of the genome behind. This is a
powerful approach because the majority of mutations that cause diseases are
found in the exome. The same data analysis tools for whole genome sequencing can be applied
to exome-seq data. There are additional software packages available
that can help you better understand your NGS data. FastQC is a tool that provides a quick
method of quality control on fastq files and Picard is a set of command line Java-based
tools that manipulates NGS data in SAM or BAM format. For more information on this topic
or for NGS data analysis software download links, please check out our knowledge base
by clicking on the link provided in the description below. If you enjoyed this video, check out
our other video series and subscribe to get the latest updates! Thank you for watching!