![]() Oasis WikipediaNext Generation Sequencing (NGS)/De novo RNA assembly. De novo RNA- seq assembly consists in assembling transcripts from RNA- seq read without the support of a reference genome. This process is done either because no genome assembly is available or to detect events which are inconsistent with the genome assembly (e. ![]() ![]() However, RNA- seq de novo assembly is arguably a step more complex than the DNA version. In particular, RNA- seq assembly must deal with extremely uneven coverage depths (across genes, isoforms, and even position along a transcript), conserved gene families which present high sequence identity, and alternative splicing. Typical workflow. Below are points which are specific to RNA- seq analysis: Choosing a protocol. Quality control and data filtering. Adapting parameters to expression levels.
Merging assemblies. Protocols. A hash table must first be generated using velveth, and then velvetg is used to assemble the nodes. Finally, Oases is used to reassemble the nodes into transcripts, transcript variants, and splice junctions. A final validation step can be performed by mapping the reads back to the assembly using mapping software capable of accounting for transcript variants, such as Tophat. The following is a sample of commands. Paired end reads can also be entered as two separate files using the option - separate./velvetg New. Directory. Name - read. This is because reads are not randomly sampled from all genes, but there will be more reads from genes that are more highly expressed. Some steps which are likely common to most assemblies. Before you start make sure you have suitable hardware, you might need > 1. GB of RAM (see below)If it is within reason and would not tamper with the biology: Try to get strand specific RNAIt may help to generate normalized c. DNA libararies. Make sure that all libraries are really ok quality- wise and that there is no major concern (Quality Control Sotware)Before submitting data to a de- novo assembler it might often a good idea to clean the data, e. ![]() Velvet-Sapelo. From Research. For transcriptomic assembly, Velvet is extended by Oases. Oases is installed at GACRC and documented at Oases. Documentation. Velvet is designed for DNA assembly. To assemble short reads from RNA, we need another program called Oases. In this tutorial, we will try assembling the single-end. As low quality bases are more likely to contain errors, these might complicate the assembly process and might lead to a higher memory consumption. That said, Trinity for example can use the ALLPATHS- LG read correction module prior to assembly. In addition, remove adapter and/or primer sequences that might still be present. More is probably even better. 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 potentially to visualize the data (Assembly Visualization)Decision Helper. In particular, the original publications introducing new tools were searched for comparisons (even though these might be often biased towards new tools introduced by the authors). In addition,data from manuscript comparing transcript assemblers were queried. If you use 4. 54 data => use a OLC based assembler, probably you will obtain very good results with Newbler. If you use Illumina data => try Trinity, Trans- Aby. SS or Velvet- Oases if you have the ressources. Which method will perform best is a function of read length, sequencing coverage, and transcriptome complexity. Please consult references for comparisons of the assemblers below. If you have a CLC pipeline and no computer experience => this is probably good enough. Software Packages. As Aby. SS distributes tasks, the amount of RAM needed per machine is smaller and thus Abyss is able to cope with large genomes. For transcriptome assemblies it is usually combined with Trans- ABy. SS. Pros. distributed interface a cluster can be used. 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 and as an effect and based on the technology extremly slow. Probably not recommended to assemble larger transcriptomes. SOAP de novo. It was used to assemble the giant panda genome. Pros. SOAP de novo uses a medium amount of RAMSOAP de novo is relatively fast (probably the fastest free assembler)SOAP de novo contains a scaffolder and a read- corrector. SOAP de novo is relatively modular (read- corrector, assembly, scaffold, gap- filler)Cons. SOAP denovo has no special extension for transcriptome assemblies. Trinity. It runs best with strand specific data. When compared by the Trinity authors to Trans- Abyss and SOAPdenovo it performed better than these in recovering full length mouse and yeast genes. Trinity recommends 1 GB RAM per 1 Million Illumina read pairs. Trinity can use the ALLPATHS- LG read corrector. However this requires ALLPATHS to be installed. Pros. Produces very good transcriptome assemblies. Cons. Takes time, inchworm the assembler (the first step) does not profit much from multithreading. Velvet- Oases. Velvet is discussed here in the forum. Pros. Oases is one of the most sensitive and accuratede novo transcriptome assemblers. Oases contains a module to merge several single- k assemblies into one. Oases users get fast answers via the Oases mailing list . It is most likely based on a kmer approach. Pros. CLC uses very little RAMCLC is very fast. Newbler. Whilst it can accommodate some limited amount of Illumina data as has been described by bioinformatician Lex Nederbragt. Interestingly Newbler v. CAP3, MIRA, Newbler, and Oases assemblers on simulated 4. Comparisons Illumina data. Zaho et al. 2. 01. SOAPdenovo, ABy. SS, Trinity and Oases on three different RNA- seq data sets analyzing the influence of merging different single- k assemblies.
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