Welcome to BecA bioinformatics page
This portal serves to introduce various bioinformatics resources and tools to aid ABCF placements analyse and intepret their data.
Reference Mapping
There are numerous programs that have been developed to map reads to a reference sequence that vary in their algorithms and therefore speed (see Flicek and Birney 2009 and references therein).
Mapping refers to the process of aligning short reads to a reference sequence, whether the reference is a complete genome, transcriptome, or de novo assembly.
Having sequenced an organism of a species before, and having constructed a reference sequence, re-sequencing more organisms of the same species allows us to see the genetic differences to the reference sequence, and, by extension, to each other.
Alignments of data from these re-sequenced organisms is a relatively simple method of detecting variation in samples. There are certain instances (such as new genes in the sequenced sample that are not found in the existing reference sequence) that can not be detected by alignment alone; however, while other approaches, such as de novo assembly, are potentially more powerful, they are also much harder or, for some organisms, impossible to achieve with current sequencing methods.
Next-generation sequencing generally produces short reads or short read pairs, meaning short sequences of <~200 bases (as compared to long reads by Sanger sequencing, which cover ~1000 bases). To compare the DNA of the sequenced sample to its reference sequence, we need to find the corresponding part of that sequence for each read in our sequencing data. This is called aligning or mapping the reads against the reference sequence. Once this is done, we can look for variation (e.g. SNPs) within the sample. This poses a number of problems:
Else, choose De novo assembly approach if you do not have a reference sequence.
Mapping refers to the process of aligning short reads to a reference sequence, whether the reference is a complete genome, transcriptome, or de novo assembly.
Having sequenced an organism of a species before, and having constructed a reference sequence, re-sequencing more organisms of the same species allows us to see the genetic differences to the reference sequence, and, by extension, to each other.
Alignments of data from these re-sequenced organisms is a relatively simple method of detecting variation in samples. There are certain instances (such as new genes in the sequenced sample that are not found in the existing reference sequence) that can not be detected by alignment alone; however, while other approaches, such as de novo assembly, are potentially more powerful, they are also much harder or, for some organisms, impossible to achieve with current sequencing methods.
Next-generation sequencing generally produces short reads or short read pairs, meaning short sequences of <~200 bases (as compared to long reads by Sanger sequencing, which cover ~1000 bases). To compare the DNA of the sequenced sample to its reference sequence, we need to find the corresponding part of that sequence for each read in our sequencing data. This is called aligning or mapping the reads against the reference sequence. Once this is done, we can look for variation (e.g. SNPs) within the sample. This poses a number of problems:
- The short reads do not come with position information, that is, we do not know what part of the genome they came from; we need to use the sequence of the read itself to find the corresponding region in the reference sequence.
- The reference sequence can be quite long (~3 billion bases for human), making it a daunting task to find a matching region.
- Since our reads are short, there may be several, equally likely places in the reference sequence from which they could have been read. This is especially true for repetitive regions.
- If we were only looking for perfect matches to the reference, we would never see any variation. Therefore, we need to allow some mismatches and small structural variation (InDels) in our reads.
- Any sequencing technology produces errors. Similar to the "real" variation, we need to tolerate a low level of sequencing errors in our reads, and separate them from the "real" variation later.
- We need to do that for each of the millions of reads in our sequencing data.
Choose from the below table the appropriate aligner for the data you have.
| Choice of Aligners | ||
|---|---|---|
| Type of Analysis | Genomic DNA reference | Coding sequence reference |
| Whole genome analysis (WGS) | Unspliced | N/A |
| Transcriptome analysis (RNASeq) | Spliced | Unspliced |
Else, choose De novo assembly approach if you do not have a reference sequence.