October 2014 Update

This is the second update on recent improvements to nucleotid.es. These include additional assemblers and updates to existing assemblers. There are additional metrics added to provide more detail on the performance of each assembler. The generated data are also now more accurate by using five replicates for each genome.

More assemblers

This project needs more assembler images. I created six assembler images however there are many more assemblers that could be included. If you are interested in creating an assembler Docker image please contact me through this mailing list or through my personal email. Additional assembler images included here this will extremely helpful and the more assemblers benchmarked the better the picture of genome assembly this project provides.

This month was exciting for me because two assembler images were created by others. Aaron Darling at the University of Technology Sydney created an image of A5-miseq. Eugene Goltsman at the Joint Genome Institute made an image of Meraculous. These assemblers have both been benchmarked and you can view how these assemblers perform in the benchmarks page. These new results are particularly interesting as A5-miseq performs very well.

Shaun Jackman provided feedback on the ABySS image. These comments came as a pull request and on a commit and are useful for improving the performance of the assembler image. The ABySS image now has an 'adaptive' command bundle which uses kmergenie to search for the optimal kmer to use for assembly.

The existence of nucleotid.es is provide accurate benchmarks of genome assemblers where the images can be immediately used by anyone. Therefore if you are interested in any of these assemblers, then install Docker and you can start using the images immediately. There are simple instructions provided that you can use to get started.

More metrics

I have added additional metrics to each benchmark. Each benchmark now includes both local misassemblies and larger misassemblies. These are useful for providing detail on larger scale inaccuracies in addition to the already include granular incorrect bases metric. All of these assembly metrics on the benchmark page are generated using QUAST by comparing the produced scaffolds with the reference genome.

The second set of metrics I have added relate to Linux control groups. These cgroups are used by the Docker daemon to organise the container processes and include information about memory and CPU usage. I collect these metrics for each container by periodically querying the cgroup for the running container. These metrics are included in the benchmarks page and can be used to compare the computational requirements for running each assembler.

I further computed an additional metric: CPU seconds per assembled base. This is the total number of CPU seconds used by the container divided by the total length of the assembly. This metric provides a perspective on the computational efficiency of each assembler, where a smaller number indicates a computationally more efficient assembler.

More replicates

Previously each assembler was benchmarked on a single FASTQ file from a reference genome. This allowed the possibility that a benchmark could be over fitted to the sampling of the reads. I have updated the benchmarks so that each calculated metric is the result of running the assembler on five different subsampling of reads. This should therefore provide a more accurate view of each how the assembler performs and I hope provide more confidence in the results.