When dealing with Next-Generation Sequencing data for the first time, you might be a little confused when seeing all the different types of sequencing files that are out there. Although it may seem intimidating at first, a little bit of time around these files and you'll become a sequencing pro in no time!
FASTQ files are sometimes referred to as the raw sequencing reads. They are usually the format file that you receive from whatever company you have chosen to conduct the Next-Generation Sequencing of your data (or the machine itself, if you performed the sequencing.) The reason we refer to them as raw reads is because the file has all of the reads from your data, without any additional processes conducted on them. The other format files that we talk about later will have had something done to them, as to change the way we can process the data.
The image below shows an extremely simplified view of how the FASTQ file comes to be. For example, let's say you are interested in getting heart tissue sequenced for your research. You isolate the heart tissue sample and send it off to a company to get it sequenced. Due to how sequencing is currently conducted by the most popular companies, the file that you will end up getting will be chunks of your original DNA sequence in X amounts of base-pairs (anywhere between 75-200), with a quality score right below the nucleotides. The quality score will be a character that corresponds to a particular number. In our example, we have included the @ quality score, which has a value of 31.
Aligned Format Files: BAM and SAM
Raw sequencing files can give you an idea of the quality of the sequencing that was conducted and other general information about your data. But what if you wanted to find out how your heart tissue data was different than the tissue of other individuals? You would not be able to find this information out by just analyzing your raw FASTQ file. This is where genomic alignment comes into play. Genomic alignment is the process of taking your raw sequencing data and aligning it to a reference genome. (If you don't know what a reference genome, it's an assembled genome sequence that is representative of a particular species.)
The SAM file, which stands for sequence aligned mapping file, will have all the reads of your data, just like the FASTQ file had, but it will also have what the reference genome at that particular nucleotide is, right below it. So, going back to our example data, if we had aligned it to a reference genome, we may see something like this:
As you can see, all of our data matches the references, besides the bolded G. So what does this mean? It could be that at that position, our data has a single nucleotide polymorphism or it could be some sequencing error.
Variant Call Format Files: VCF
We just mentioned, that comparing our data to a reference genome is useful in finding how our data is different than what the consensus genomic sequence is. At this stage, you could use a genomic viewer, such as the Integrative Genomic Viewer and manually analyze these differences. Or, you could run something called variant-calling, and produce a list of all of the variants that are present, in a file format called a Variant Call Format File, or VCF. A VCF file will tell you the exact position of the variant present, what the allele should have been in comparison to the consensus genome (reference allele), and what the allele currently is for your individual (alternative allele.) An example VCF file is shown below:
The first column of a VCF file is chromosomal location. Depending on what reference genome was used for alignment, you may get chromosome number listed similar to the image (with the chromosome abbreviation, chr, and the number of a chromosome), or you may only get the chromosome number. The second column has the actual location, within the specified chromosome. The third column in our example has a period, but VCF files typically will have a variant identification number, denoted as a SNP id, in this column, which means that this variant has been identified and is listed within various databases. The fourth column is the reference allele that we referred to above, while the fifth column is the alternative allele. The last two columns both contain tidbits of information that we will discuss in a later article. For now, just know that the sixth column refers to a variant quality score, while the seventh column refers to whether that variant passed or failed a statistical test to remove false-positives.