It seems IonTorrent are trying to present themselves as the open democratising sequencing platform for high throughput sequencing, with their Ion Community, sample datasets and (in theory) open source software.

In my last post I looked at how the GZIP variant BGZF (Blocked GNU Zip Format, used in BAM files) allowed efficient random access to large compressed files. This time I'm looking at bzip2 (bz2) which offers better compression than GZIP, but is also block based so in theory the same random access strategy can be employed.

BAM files are compressed using a variant of GZIP (GNU ZIP), called BGZF (Blocked GNU Zip Format). Anyone who has read the SAM/BAM Specification will have seen the terms BGZF and virtual offsets , but what you may not realise is how general purpose this is for random access to any large compressed file.

I think it is time to retire the FASTQ file format in favour of storing unaligned reads in SAM/BAM format.

In my last post I talked about SAM/BAM with a gapped reference, and how this makes it much easier to work with inserted bases relative to the reference/consensus - especially for visualisation. I should point out that some viewers do actually manage to show the inserts as columns even with the traditional ungapped/unpadded reference sequence - notably Gap5, Bambino, and the text based samtools tview, as shown in these tview screenshots.

A lot of my time this week has gone into thinking and "talking" on the samtools-devel mailing list about the SAM/BAM file format and how it might be improved for ( de novo ) assemblies. SAM/BAM Anyone working with high throughput sequencing data (formerly known as Next Generation Sequencing, NGS), should be well versed with the SAM/BAM file format.

For the early part of my career in Bioinformatics I was able to avoid GFF3 files - initially I focused on finished annotated genomes from the NCBI in plain text GenBank format (which has complications of its own), but with genome sequencing becoming widespread, so too is genome assembly and annotation. And for this, you will have to learn about GFF3 files.

The BLAST chapter of the Biopython Tutorial (PDF) starts with these lines by Brad Chapman, I know what he meant - but it turns out things could be easier, especially once you start running "standalone BLAST" on your own machines, rather than using the NCBI's ever improving BLAST website. Part of the problem is setting up BLAST and its databases can be complicated (especially on a cluster), but also inevitably, BLAST has bugs.