Are there any other ways of assessing the quality of assembled data obtained from different assemblers apart from metrics like N50 and assembly size?
I know few like,
Any other ideas are appreciated.
QUAST (QUality ASsesment Tool for Genome Assembly) can be used to assess the quality of genome assemblies (both de novo reference based):
Not to beat my own drum (too much) - but - I've written a tool that can be useful for this. The idea is that you map all raw reads back to the assembled genome and then assess what read pairs map, and, more importantly, which map, but at an unexpected distance. The tools takes a BAM input file, processes it, and then allows you to generate plots.
See: https://github.com/mfiers/hagfish
cheers Mark
What do you mean by "already mated"?
If you align paired-end reads to your assembly, the insert-size shouldn't be too large or too small, if the size is too large then there's an indication that your assembly includes regions that do not exist, if the size is too small then there's an indication that your assembly misses a region. If a region in the assembly is not bridged by paired reads then that's an indication that the region doesn't exist in reality.
I meant (pre)-assembly of the 2 reads that belong to each pair (if read/insert sizes combination allows) , like this kind of tools: http://genomics.jhu.edu/software/FLASH/index.shtml does. In that case, relying on a tool that study the mapping of the pairs would be useless.
Old topic, but this was just published. I'm curious how well it performs and will hopefully be testing it myself this week or soon (whenever time permits)
http://www.ncbi.nlm.nih.gov/pubmed/23303509
http://sc932.github.com/ALE/about.html
Abstract
MOTIVATION:
Researchers need general purpose methods for objectively evaluating the accuracy of single and metagenome assemblies and for automatically detecting any errors they may contain. Current methods do not fully meet this need because they require a reference, only consider one of the many aspects of assembly quality or lack statistical justification, and none are designed to evaluate metagenome assemblies.
RESULTS:
In this article, we present an Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.
One more to the list: Assessing genome assembly and annotation completeness with Benchmarking Universal Single-Copy Orthologs (in short BUSCO). It replaces discontinued CEGMA.
I don't have experience with any tools that estimate quality based on re-mapping reads to the de novo assembled sequence, and I'll have to check some of these out. I typically use the following metrics to compare the relative quality of my genome assemblies.
For this last one, I use the CEGMA method[[1][1]] to identify genes that are highly conserved among all eukaryotes (implementation available at http://korflab.ucdavis.edu/datasets/cegma). The more of these conserved genes CEGMA is able to identify, the more confidence I have in the quality of the assembly and my ability to accurately annotate other genes in that genome.
In addition to other mentioned, I think that an ORF prediction step can give you a strong and fast comparative insight to compare several assemblies.
One can also assess number of misassembly errors in the genome using tools like REAPR and misSEQuel. That would also give nice gauge how good is the assembled genome.
This is an old topic but here is a list of the tool I currently use:
Moreover, the very interesting paper for the assemblathon 2 (https://gigascience.biomedcentral.com/articles/10.1186/2047-217X-2-10) describes how they assessed the different assemblies.
I like the Kmer approach; as an aside it can be interesting to see how much intersect() you get in Kmer spectrum between paired ends and final assemblies. Comparing the distance between PE/assembler1 and PE/assembler2 or between contigs from assembly1 and assembly2 can be quite interesting...
I also wrote a pipeline to assess de-novo assemblies. It's not particularly strong in the plotting department, but it will use a variety of data (454, Illumina, DNAseq, RNAseq, ESTs, proteomes, etc) and calculate a bunch of numbers - in addition to internal metrics like N50 sizes and nucleotide counts - that lets you compare your candidate drafts. More info on http://blog.malde.org/posts/assembly-evaluation.html
I think I've found all main dependencies in conda repos (you can search them on anaconda.org) so it should be as simple as few conda commands. And if you don't use conda already (esp with Bioconda channel), you all should start right now :p
PS: I also found haskell in brew, but I dunno how to easily search the other packages (I don't have brew on my system to not collide PATHs with conda, so I can't just "brew search")
Also a couple of other parameters to judge on are:
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QUAST paper was published in Bioinformatics — http://bioinformatics.oxfordjournals.org/content/early/2013/02/18/bioinformatics.btt086.abstract
I also highly recommend QUAST for these tasks