Next Generation Sequencing (NGS)/De novo assembly. De novo assembly[edit]The generation of short reads by next generation sequencers has lead to an increased need to be able to assemble the vast amount of short reads that are generated. This is no trivial problem, as the sheer number of reads makes it near impossible to use, for example, the overlap layout consensus (OLC) approach that had been used with longer reads. Therefore, most of the available assemblers that can cope with typical data generated by Illumina use a de Bruijn graph based k- mer based approach. A clear distinction has to be made by the size of the genome to be assembled. Megabases)medium (e.
Megabases)large (e. Gigabases)All de- novo assemblers will be able to cope with small genomes, and given decent sequencing libraries will produce relatively good results. Even for medium sized genomes, most de- novo assemblers mentioned here and many others will likely fare well and produce a decent assembly. That said, OLC based assemblers might take weeks to assemble a typical genome. Large genomes are still difficult to assemble when having only short reads (such as those provided by Illumina reads). Assembling such a genome with Illumina reads will probably will require using a machine that has about 2. GB and potentially even 5.
GB RAM, unless one is willing to use a small cluster (ABy. SS, Ray, Contrail), or invest into commercial software (CLCbio_Genomics_Workbench). Typical workflow[edit].
Overview of the denovo assembly process for WGSA genome assembly project, whatever its size, can generally be divided into stages: Experiment design. Sample collection. Sample preparation. Sequencing. Pre- processing. Assembly. Post- assembly analysis.
Experiment design[edit]Like any project, a good de novo assembly starts with proper experimental design. Biological, experimental, technical and computational issues have to be considered: Biological issues: What is known about the genome? How big is it? Obviously, bigger genomes will require more material. How frequent, how long and how conserved are repeat copies? More repetitive genomes will possibly require longer reads or long distance mate- pairs to resolve structure.
How AT rich/poor is it? Genomes which have a strong AT/GC imbalance (either way) are said to have low information content. In other words, spurious sequence similarities will be more frequent.
Is is haploid, diploid, or polyploid? Currently genome assemblers deal best with haploid samples, and some provide a haploid assembly with annotated heterozygous sites. Polyploid genomes (e. Experimental issues: What sample material is available? Is it possible to extract a lot of DNA? If you have only little material, you might have to amplify the sample (e.
MDA), thus introducing biases. Does that DNA come from a single cell, a clonal population, or a heterogeneous collection of cells? Diversity in the sample can create more or less noise, which different assemblers handle differently. Technical issues: What sequencing technologies to use? How much does each cost? What is the sequence quality?
Abyss - de novo genome sequence assembler. bcgsc / abyss. Code. Issues 25. Pull requests 3. sudo apt-get install abyss. Laboratory 2: bash basics, installing software bash commands There are many bash command to learn. The following is an abbreviated list of the most useful.
The greater the noise, the more coverage depth you will need to correct for errors. How long are the reads? The longer the reads, the more useful they will be to disambiguate repetitive sequence. Can paired reads be produced cost- effectively and reliably?
If so, what is the fragment length? As with long reads, reliable long distance paired can help disambiguate repeats and scaffold the assembly. Can you use a hybrid approach? E. g. short and cheap reads mixed with long expensive ones. Computational issues: What software to run?
Install ABySS on Debian or Ubuntu. ABySS: a parallel assembler for short read sequence data. Genome research 19, no. 6 (2009): 1117-1123. Trans-ABySS. ABySS README. ABySS - assemble short reads into contigs Compiling ABySS. Compiling ABySS should be as easy as./configure && make To install ABySS in a specified. Next Generation Sequencing (NGS)/De novo assembly. How easy are they to install and. ABySS. ABySS is a de-novo assembler which can run on multiple nodes.
How much memory do they require? This criteria can be final, because if a computer does not have enough memory, it will either crash, or slow down tremendously as it swaps data on and off the hard drive. How fast are they? This criteria is generally less stringent, since the assembly time is generally minor within a complete genome assembly and annotation project. However, some scale better than other. Do they require specific hardware?
How robust are they? Are they prone to crash? Are they well supported? How easy are they to install and run?
Do they require a special protocol? Can they handle the chosen sequencing technology? Some steps which are likely common to most assemblies: If it is within reason and would not tamper with the biology: Try to get DNA from haploid or at least mostly homozygous individuals. Make sure that all libraries are really ok quality- wise and that there is no major concern (e. Fast. QC)For paired end data you might also want to estimate the insert size based on draft assemblies or assemblies which you have made already. Before submitting data to a de- novo assembler it might often be a good idea to clean the data, e.
As low quality bases are more likely to contain errors, these might complicate the assembly process and might lead to a higher memory consumption. More is not always better) That said, several general purpose short read assemblers such as SOAP de- novo and ALLPATHS- LG can perform read correction prior to assembly. Before running any large assembly, double and triple check the parameters you feed the assembler. Post assembly it is often advisable to check how well your read data really agrees with the assembly and if there are any problematic regions. If you run de Bruijn graph based assemblies you will want to try different k- mer sizes. Whilst there is no rule of thumb for any individual assembly, smaller k- mers would lead to a more tangled graph if the reads were error free.
Larger k- mer sizes would yield a less tangled graph, given error free reads. However, a lower k- mer size would likely be more resistant to sequencing errors.
And a too large k might not yield enough edges in the graph and would therefore result in small contigs. Data pre- processing[edit]For a more detailed discussion, see the chapter dedicated to pre- processing. Data pre- processing consists in filtering the data to remove errors, thus facilitating the work of the assembler. Although most assemblers have integrated error correction routines, filtering the reads will generally greatly reduce the time and memory overhead required for assembly, and probably improve results too.
Genome assembly[edit]Genome assembly consists in taking a collection of sequencing reads, which are much shorter than the actual genome, and creating a genome sequence which is a likely source of all these fragments. What defines a likely genome depends generally on heuristics and the data available.
Firstly, by parsimony, the genome must be as short as possible. One could take all the reads and simply produce the concatenation of all their sequences, but this wold not be parsimonious. Secondly, the genome must include as much of the input data as possible. Finally, the genome must satisfy as many of the experimental data as possibly. Typically, paired- end reads are expected to map onto the genome with a given respective orientation and a given distance from each other. The output of an assembler is generally decomposed into contigs, or contiguous regions of the genome which are nearly completely resolved, and scaffolds, or sets of contigs which are approximately placed and oriented with respect to each other.
There are many assemblers available (See the Wikipedia page on sequence assembly for more details). Tutorials on how to use some of them are below.
Techniques for comparing assemblies[edit]Once several genome assemblies are generated, they need to be evaluated.[1][2][3] Current methods include: N5. Post- assembly analysis[edit]Once a genome has been obtained, a number of analyses are possible, if not necessary: Quality control. Comparison to other assemblies. Variant detection. Annotation. Creating a dataset[edit]Free Software[edit]ABy. SS is a de- novo assembler which can run on multiple nodes where it uses the message parsing interface (MPI) interface for communication.
As ABy. SS distributes tasks, the amount of RAM needed per machine is smaller and thus Abyss is able to cope with large genomes. See here for a tutorial. Pros. distributed interface a cluster can be useda large genome can be assembled with relatively little RAM per compute node.
A human genome was assembled on 2. GB RAM each. Allpaths- LG[edit]Allpath- LG is a novel assembler requiring specialized libraries. The authors of the software benchmarked ALLPATH- LG against SOAP- denovo and ALLPATH- LG reported superior performance. However it must be noted that they might not have used the SOAP- denovo gap filling module for one of the data set due to time constraints. This would probably have improved the SOAP assembly contiguous sequence length. In our own hand (usadellab) we have seen similar good N5.
N5. 0 values for ALLPATHS- LG Arabidopsis assemblies. Similarly ALLPATHS- LG was named as well performing in the Assemblathon. Pros. relatively fast runtime (slower than SOAP)good scaffold length (likely better than SOAP)can use long reads (e.
PAC Bio) but only for small genomes. Cons. specially tailored libraries are necessarylarge genomes (mammalian size) need a lot of RAM. The publications estimates about 5.
GB would be sufficient thoughslower than SOAPEuler SR USR[edit]EULER is an assembler that includes an error correction module. Pros. Has an error correction module.
Cons. MIRA is a general purpose assembler that can integrate various platform data and perform true hybrid assemblies. Pros. very well documented and many switchescan combine different sequencing technologieslikely relatively good quality data. Cons. Only partly multithreaded thus and due to the technology slow. Probably not recommended to assemble larger genomes. Ray is a distributed scalable assembler tailored for bacterial genomes, metagenomes and virus genomes.
Tutorial available here. Pros. scalability (uses MPI)correctnessusabilitywell documentedresponsive mailing listcan combine different sequencing technologiesde Bruijn- based. SOAP de novo[edit]SOAPdenovo is an all purpose genome assembler.