In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. It can be performed on the entire genome, transcriptome or proteome of an organism, and can also involve only selected segments or regions, like tandem repeats and transposable elements. Methodologies used include sequence alignment, searches against biological databases, and others. Since the development of methods of high-throughput production of gene and protein sequences, the rate of addition of new sequences to the databases increased very rapidly. Such a collection of sequences does not, by itself, increase the scientist's understanding of the biology of organisms. However, comparing these new sequences to those with known functions is a key way of understanding the biology of an organism from which the new sequence comes. Thus, sequence analysis can be used to assign function to coding and non-coding regions in a biological sequence usually by comparing sequences and studying similarities and differences. Nowadays, there are many tools and techniques that provide the sequence comparisons (sequence alignment) and analyze the alignment product to understand its biology.Sequence analysis in molecular biology includes a very wide range of processes:
The comparison of sequences to find similarity, often to infer if they are related (homologous) Identification of intrinsic features of the sequence such as active sites, post translational modification sites, gene-structures, reading frames, distributions of introns and exons and regulatory elements Identification of sequence differences and variations such as point mutations and single nucleotide polymorphism (SNP) in order to get the genetic marker. Revealing the evolution and genetic diversity of sequences and organisms Identification of molecular structure from sequence alone.
History Since the very first sequences of the insulin protein were characterized by Fred Sanger in 1951, biologists have been trying to use this knowledge to understand the function of molecules. He and his colleagues' discoveries contributed to the successful sequencing of the first DNA-based genome. The method used in this study, which is called the "Sanger method" or Sanger sequencing, was a milestone in sequencing long strand molecules such as DNA. This method was eventually used in the Human Genome Project. According to Michael Levitt, sequence analysis was born in the period from 1969 to 1977. In 1969 the analysis of sequences of transfer RNAs was used to infer residue interactions from correlated changes in the nucleotide sequences, giving rise to a model of the tRNA secondary structure. In 1970, Saul B. Needleman and Christian D. Wunsch published the first computer algorithm for aligning two sequences. Over this time, developments in obtaining nucleotide sequence improved greatly, leading to the publication of the first complete genome of a bacteriophage in 1977. Robert Holley and his team in Cornell University were believed to be the first to sequence an RNA molecule.
Overview of nucleotide sequence analysis (DNA and RNA) Nucleotide sequence analyses identify functional elements like protein binding sites, uncover genetic variations like SNPs, study gene expression patterns, and understand the genetic basis of traits. It helps to understand mechanisms that contribute to processes like replication and transcription. Some of the tasks involved are outlined below.
Quality control and preprocessing Quality control assesses the quality of sequencing reads obtained from the sequencing technology (e.g. Illumina). It is the first step in sequence analysis to limit wrong conclusions due to poor quality data. The tools used at this stage depend on the sequencing platform. For instance, FastQC checks the quality of short reads (including RNA sequences), Nanoplot or PycoQC are used for long read sequences (e.g. Nanopore sequence reads), and MultiQC aggregates the result of FastQC in a webpage format. Quality control provides information such as read lengths, GC content, presence of adapter sequences (for short reads), and a quality score, which is often expressed on a PHRED scale. If adapters or other artifacts from PCR amplification are present in the reads (particularly short reads), they are removed using software such as Trimmomatic or Cutadapt.
Read alignment At this step, sequencing reads whose quality have been improved are mapped to a reference genome using alignment tools like BWA for short DNA sequence reads, minimap for long read DNA sequences, and STAR for RNA sequence reads. The purpose of mapping is to find the origin of any given read based on the reference sequence. It is also important for detecting variations or phylogenetic studies. The output from this step, that is, the aligned reads, are stored in compatible file formats known as SAM, which contains information about the reference genome as well as individual reads. Alternatively, BAM file formats are preferred as they use much less desk or storage space. Note: This is different from sequence alignment which compares two or more whole sequences (or sequence regions) to quantify similarity or differences or to identify an unknown sequence (as discussed below). The following analyses steps are peculiar to DNA sequences:
Variant calling Identifying variants is a popular aspect of sequence analysis as variants often contain information of biological significance, such as explaining the mechanism of drug resistance in an infectious disease. These variants could be single nucleotide variants (SNVs), small insertions/deletions (indels), and large structural variants. The read alignments are sorted using SAMtools, after which variant callers such as GATK are used to identify differences compared to the reference sequence. The choice of variant calling tool depends heavily on the sequencing technology used, so GATK is often used when working with short reads, while long read sequences require tools like DeepVariant and Sniffles. Tools may also differ based on organism (prokaryotes or eukaryotes), source of sequence data (cancer vs metagenomic), and variant type of interest (SNVs or structural variants). The output of variant calling is typically in vcf format, and can be filtered using allele frequencies, quality scores, or other factors based on the research question at hand.
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