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      Comparing de novo assemblers for 454 transcriptome data

      research-article
      1 , 1 ,
      BMC Genomics
      BioMed Central

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          Abstract

          Background

          Roche 454 pyrosequencing has become a method of choice for generating transcriptome data from non-model organisms. Once the tens to hundreds of thousands of short (250-450 base) reads have been produced, it is important to correctly assemble these to estimate the sequence of all the transcripts. Most transcriptome assembly projects use only one program for assembling 454 pyrosequencing reads, but there is no evidence that the programs used to date are optimal. We have carried out a systematic comparison of five assemblers (CAP3, MIRA, Newbler, SeqMan and CLC) to establish best practices for transcriptome assemblies, using a new dataset from the parasitic nematode Litomosoides sigmodontis.

          Results

          Although no single assembler performed best on all our criteria, Newbler 2.5 gave longer contigs, better alignments to some reference sequences, and was fast and easy to use. SeqMan assemblies performed best on the criterion of recapitulating known transcripts, and had more novel sequence than the other assemblers, but generated an excess of small, redundant contigs. The remaining assemblers all performed almost as well, with the exception of Newbler 2.3 (the version currently used by most assembly projects), which generated assemblies that had significantly lower total length. As different assemblers use different underlying algorithms to generate contigs, we also explored merging of assemblies and found that the merged datasets not only aligned better to reference sequences than individual assemblies, but were also more consistent in the number and size of contigs.

          Conclusions

          Transcriptome assemblies are smaller than genome assemblies and thus should be more computationally tractable, but are often harder because individual contigs can have highly variable read coverage. Comparing single assemblers, Newbler 2.5 performed best on our trial data set, but other assemblers were closely comparable. Combining differently optimal assemblies from different programs however gave a more credible final product, and this strategy is recommended.

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          Most cited references40

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          TIGR Gene Indices clustering tools (TGICL): a software system for fast clustering of large EST datasets.

          TGICL is a pipeline for analysis of large Expressed Sequence Tags (EST) and mRNA databases in which the sequences are first clustered based on pairwise sequence similarity, and then assembled by individual clusters (optionally with quality values) to produce longer, more complete consensus sequences. The system can run on multi-CPU architectures including SMP and PVM.
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            SSAHA: a fast search method for large DNA databases.

            We describe an algorithm, SSAHA (Sequence Search and Alignment by Hashing Algorithm), for performing fast searches on databases containing multiple gigabases of DNA. Sequences in the database are preprocessed by breaking them into consecutive k-tuples of k contiguous bases and then using a hash table to store the position of each occurrence of each k-tuple. Searching for a query sequence in the database is done by obtaining from the hash table the "hits" for each k-tuple in the query sequence and then performing a sort on the results. We discuss the effect of the tuple length k on the search speed, memory usage, and sensitivity of the algorithm and present the results of computational experiments which show that SSAHA can be three to four orders of magnitude faster than BLAST or FASTA, while requiring less memory than suffix tree methods. The SSAHA algorithm is used for high-throughput single nucleotide polymorphism (SNP) detection and very large scale sequence assembly. Also, it provides Web-based sequence search facilities for Ensembl projects.
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              Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing.

              We present a de novo assembly of a eukaryote transcriptome using 454 pyrosequencing data. The Glanville fritillary butterfly (Melitaea cinxia; Lepidoptera: Nymphalidae) is a prominent species in population biology but had no previous genomic data. Sequencing runs using two normalized complementary DNA collections from a genetically diverse pool of larvae, pupae, and adults yielded 608,053 expressed sequence tags (mean length = 110 nucleotides), which assembled into 48,354 contigs (sets of overlapping DNA segments) and 59,943 singletons. BLAST comparisons confirmed the accuracy of the sequencing and assembly, and indicated the presence of c. 9000 unique genes, along with > 6000 additional microarray-confirmed unannotated contigs. Average depth of coverage was 6.5-fold for the longest 4800 contigs (348-2849 bp in length), sufficient for detecting large numbers of single nucleotide polymorphisms. Oligonucleotide microarray probes designed from the assembled sequences showed highly repeatable hybridization intensity and revealed biological differences among individuals. We conclude that 454 sequencing, when performed to provide sufficient coverage depth, allows de novo transcriptome assembly and a fast, cost-effective, and reliable method for development of functional genomic tools for nonmodel species. This development narrows the gap between approaches based on model organisms with rich genetic resources vs. species that are most tractable for ecological and evolutionary studies.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2010
                16 October 2010
                : 11
                : 571
                Affiliations
                [1 ]Institute of Evolutionary Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
                Article
                1471-2164-11-571
                10.1186/1471-2164-11-571
                3091720
                20950480
                1f940d11-9491-4524-bbee-1a0b6e4c2321
                Copyright ©2010 Kumar and Blaxter; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 August 2010
                : 16 October 2010
                Categories
                Research Article

                Genetics
                Genetics

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